Merge branch 'dev' of http://git.fast-fun.cn:92/lmstack/data_analyze_platform into dev

LIB/MIDDLE/AlgoDemo/Demo/V_1_0_0/Main/main.py

@@ -54,18 +54,30 @@ if __name__ == '__main__':
     logger.info("{}, 算法开始".format(str(os.getpid())))
     
     try:    
-        # 连接数据库的两种方式
-        # 方式一：新dataframe写入数据库时，采用该方式可以不需要写sql语句；
-        #    该方式无法对数据库进行修改；
+        # 创建数据库连接池
         
         db_engine_1 = create_engine(
             "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
                 user1, password1, host1, port1, db1
             ))
+        Session = sessionmaker(bind=db_engine)
+        # 取数示例
+        sql = "select * from table"
+        df_diag_ram = pd.read_sql(sql, db_engine)
         
-        # 方式二：该方式可以通过写update SQL语句，对数据库中的数据进行修改
-        conn = pymysql.connect(host=host2, port=port2, user=user2, password=password2, database=db2)
-        cursor = conn.cursor()
+        # 修改数据库示例
+        # session = Session()
+        # sql = 'insert into table (data) values (:data)'
+        # params = {'data':1}
+        # session.execute(sql,params=params)
+        
+        sql = "update all_fault_info_copy set update_time=:time,end_time=:end_time where product_id=:sn and end_time=:end_time and code=:code"
+        params = {"time":datetime.datetime.now(), "end_time":end_time, "sn":sn, "code":'C493'}
+        session.execute(sql,params=params)
+        session.commit()
+        session.close()
+
+
 
         # 准备算法数据
         
@@ -120,9 +132,7 @@ if __name__ == '__main__':
                     
                 else:   
                 # 修改
-                    sql = '''update test_tb set data={} where sn='{}' '''.format(df_res['data'].values[0], (df_res['sn'].values[0]))
-                    cursor.execute(sql)
-                    conn.commit()
+                    pass
                 logger.info("{} done".format(sn))
             except Exception as e :
                 logger.error(traceback.format_exc)

USER/SPF/alibaba/01BatSoh/deploy_soh.py

@@ -0,0 +1,203 @@
+
+import os
+import time
+
+import pandas as pd
+from sqlalchemy import create_engine
+import logging.handlers
+import traceback
+import re
+import CBMSBatSoh
+import datetime
+import dateutil.relativedelta
+from urllib import parse
+from apscheduler.schedulers.blocking import BlockingScheduler
+import warnings
+from multiprocessing import Process
+
+
+def fun(host, port, db, user, password, runenv, logger, period_second):
+    logger.info("pid is {}".format(os.getpid()))
+    try:
+        db_engine = create_engine(
+            "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                user, parse.quote_plus(password), host, port, db
+            ))
+    except:
+        logger.error(u"数据库连接错误", exc_info=True)
+        logger.error(traceback.format_exc)
+        return
+
+    # 获取配置信息
+    df_confs = pd.read_sql('select * from conf where status=1 order by factory,device_name', db_engine)
+    # db_engine.dispose()
+
+
+
+
+    now_time = datetime.datetime.now()
+    pre_time = now_time + dateutil.relativedelta.relativedelta(seconds=-period_second-6*3600)  #两周前
+    end_time = datetime.datetime.strftime(now_time, "%Y-%m-%d %H:%M:%S")
+    start_time = datetime.datetime.strftime(pre_time, "%Y-%m-%d %H:%M:%S")
+    logger.info("time range from {} to {} ".format(start_time, end_time))
+    for i in range(len(df_confs)):
+        try:
+            factory = df_confs.loc[i, 'factory']
+            sn = df_confs.loc[i, 'device_name']
+            if '康普盾' in factory:
+                celltype = 1
+                cell_volt_count = 120
+                cell_temp_count = 40
+            elif '华霆' in factory:
+                celltype = 2
+                cell_volt_count = 204
+                cell_temp_count = 40
+            elif '力神' in factory:
+                celltype = 99
+                cell_volt_count = 72
+                cell_temp_count = 32
+
+            logger.info("pid-{} FACTORY:{} - SN: {} START!".format(os.getpid(), factory, sn))
+            sql = "select * from bms_data where factory ='{}' and sn = '{}' and time > '{}' and time <= '{}'".format(factory, sn, start_time, end_time)
+            logger.info(sql)
+            df_bms = pd.read_sql(sql, db_engine)
+            if df_bms.empty:
+                continue
+            df_bms.columns = ['sn', 'factory', 'add_time', 'update_time', 'time', 'BMSStat', 'PackVolt', 'PackCrnt',
+                              'PackSOC', 'PackSOH', 'alarm1', 'alarm2', 'alarm3', 'CellTemp',
+                              'CellVolt', 'id']
+            # 单体数据拆分
+            cell_temp = df_bms['CellTemp']
+            cell_volt = df_bms['CellVolt']
+            cell_temps = []
+            [cell_temps.append(list(map(float,x.split(',')))) for x in cell_temp]
+            cell_volts = []
+            [cell_volts.append(list(map(float,x.split(',')))) for x in cell_volt]
+            del_indexes = []
+            for i, cell_volt in enumerate(cell_volts):
+                if len(cell_volt) != cell_volt_count:
+                    del_indexes.append(i)
+            for i, cell_temp in enumerate(cell_temps):
+                if len(cell_temp) != cell_temp_count:
+                    del_indexes.append(i)
+            del_indexes = list(set(del_indexes))
+            for i, del_index in enumerate(del_indexes):
+                del_index = del_index - i
+                cell_volts.pop(del_index)
+                cell_temps.pop(del_index)
+            df_bms = df_bms.drop(index=del_indexes, axis=1)
+            cellvolt_name = ['CellVolt' + str(x) for x in range(1, cell_volt_count + 1)]
+            celltemp_name = ['CellTemp' + str(x) for x in range(1, cell_temp_count + 1)]
+            df_bms[cellvolt_name] = cell_volts
+            df_bms[celltemp_name] = cell_temps
+            df_bms = df_bms.drop(columns='CellVolt', axis=0)
+            df_bms = df_bms.drop(columns='CellTemp', axis=0)
+            df_bms = df_bms.reset_index(drop=True)
+            logger.info("pid-{} FACTORY:{} - SN: {} 去除单体异常行{}数据!".format(os.getpid(), factory, sn, str(del_indexes)))
+
+            # 算法执行
+            df_soh = pd.read_sql(
+                "select time_st,time_sp,sn,method,soh,cellsoh,packsoh from soh_result where factory ='{}' and sn = '{}' order by time_st desc limit 1".format(factory, sn),
+                db_engine);
+            BatSoh = CBMSBatSoh.BatSoh(sn, celltype, df_bms, df_soh)
+            df_res = BatSoh.batsoh()
+
+            # 如果以开发环境运行，则写入测试结果!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+            if (runenv == 'dev'):
+                logger.info("pid-{} FACTORY:{} - SN: {} 测试!".format(os.getpid(), factory, sn))
+                df_res = pd.DataFrame({'time_st': ["2021-01-01 00:00:00"], 'time_sp': ["2021-01-02 00:00:00"],
+                                       'sn': [sn], 'method': [1], 'bmssoh': [99], 'packsoh': [100], 'soh': [99],"cellsohmin":[10],"cellsohmax":[20],"sohmin_num":[30], 'sohmax_num':[10],
+                                       'cellsoh_diff': [1], 'cellsoh': ["98,97"]})
+            if not df_res.empty:
+                df_res.columns = ['time_st', 'time_sp', 'sn', 'method', 'bmssoh', 'packsoh', 'soh','cellsohmin', 'cellsohmax', 'sohmin_num', 'sohmax_num', 'cellsoh_diff',
+                                  'cellsoh']
+                df_res['factory'] = factory
+                df_res['add_time'] = datetime.datetime.now()
+                df_res.to_sql("soh_result", con=db_engine, if_exists="append", index=False)
+                logger.info("pid-{} FACTORY:{} - SN: {} 结果入库成功!".format(os.getpid(), factory, sn))
+
+            logger.info("pid-{} FACTORY:{} - SN: {} DONE!".format(os.getpid(), factory, sn))
+        except:
+            logger.error(u"pid-{} FACTORY:{} - SN: {} ERROR!\n".format(os.getpid(), factory, sn), exc_info=True)
+            logger.error(traceback.format_exc)
+    db_engine.dispose()
+    logger.info("time range from {} to {} done!!!!!!!!! ".format(start_time, end_time))
+
+
+def heart_beat(host, port, db, user, password, log):
+    while True:
+        try:
+            db_engine = create_engine(
+                "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                    user, parse.quote_plus(password), host, port, db
+                ))
+            while True:
+                now = datetime.datetime.now()
+                db_engine.execute("update status set algo_soh='{}'".format(now))
+                log.info("soh心跳信号" + now.strftime("%Y-%m-%d %H:%M:%S"))
+                time.sleep(5)
+        except:
+            log.error(u"soh心跳错误", exc_info=True)
+            log.error(traceback.format_exc)
+            time.sleep(5)
+        finally:
+            db_engine.dispose()
+
+if __name__ == '__main__':
+    warnings.filterwarnings("ignore")
+    env_dist = os.environ
+    # 配置信息信息
+    # host = env_dist.get("ALI_HOST", '120.25.223.1')
+    # port = env_dist.get("ALI_PORT", '4901')
+    # db = env_dist.get("ALI_DB", 'ali')
+    # user = env_dist.get("ALI_ROOT", 'root')
+    # password = parse.quote_plus(env_dist.get("ALI_PASSWORD", '123456'))
+    host = env_dist.get("ALI_HOST", '192.168.31.141')
+    port = env_dist.get("ALI_PORT", '3306')
+    db = env_dist.get("ALI_DB", 'ali')
+    user = env_dist.get("ALI_ROOT", 'root')
+    password = env_dist.get("ALI_PASSWORD", 'Ali@123456')
+    runenv = env_dist.get("ALI_RUNENV", 'dev')
+    period_second = int(env_dist.get("ALI_PERIOD_SECOND", '604800'))
+
+
+    algo_name = 'soh'
+    # 日志配置
+    log_path = './log' + '/' + algo_name
+    if not os.path.exists(log_path):
+        os.makedirs(log_path)
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/info.log'.format(log_path), maxBytes=1024 * 1024 * 1024,
+                                              backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.suffix = "%Y-%m-%d_%H-%M.log"
+    fh.extMatch = re.compile(r"^\d{4}-\d{2}-\d{2}_\d{2}-\d{2}.log$")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.DEBUG)
+    logger.addHandler(fh)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/error.log'.format(log_path), maxBytes=1024 * 1024 * 1024,
+                                              backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.ERROR)
+    logger.addHandler(fh)
+
+    logger.info("算法初始化完毕，开始周期运行！！！！！！！！！！！！！！！！！！！！！")
+    # 开启定时任务
+    p = Process(target=heart_beat, args=(host, port, db, user, password, logger,))
+    p.start()
+    scheduler = BlockingScheduler()
+    # heart_beat(host, port, db, user, password, logger)
+    # scheduler.add_job(func=heart_beat, args=(host, port, db, user, password, logger), trigger='interval', seconds=5,max_instances=1, coalesce=True)
+    fun(host, port, db, user, password, runenv, logger, period_second)
+    scheduler.add_job(func=fun, args=(host, port, db, user, password, runenv, logger, period_second), trigger='interval', seconds=period_second
+                      ,max_instances=1, coalesce=True)
+
+    try:
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        logger.error(str(e))

USER/SPF/alibaba/04BatSoc/deploy_soc.py

@@ -0,0 +1,236 @@
+
+import os
+import time
+
+import pandas as pd
+from sqlalchemy import create_engine
+import logging
+import logging.handlers
+import traceback
+import re
+import CBMSBatSoc
+import datetime
+import dateutil.relativedelta
+from urllib import parse
+from apscheduler.schedulers.blocking import BlockingScheduler
+import warnings
+from multiprocessing import Process
+
+
+
+def fun(host, port, db, user, password, runenv, logger, period_second):
+    global df_ram
+    try:
+        db_engine = create_engine(
+            "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                user, parse.quote_plus(password), host, port, db
+            ))
+    except:
+        logger.error(u"数据库连接错误", exc_info=True)
+        logger.error(traceback.format_exc)
+        return
+
+
+# 获取配置信息
+    df_confs = pd.read_sql('select * from conf where status=1 order by factory,device_name', db_engine)
+    # db_engine.dispose()
+
+
+    logger.info("pid is {}".format(os.getpid()))
+
+    now_time = datetime.datetime.now()
+    pre_time = now_time + dateutil.relativedelta.relativedelta(seconds=-period_second-1)  #
+    end_time = datetime.datetime.strftime(now_time, "%Y-%m-%d %H:%M:%S")
+    start_time = datetime.datetime.strftime(pre_time, "%Y-%m-%d %H:%M:%S")
+    logger.info("time range from {} to {} ".format(start_time, end_time))
+    for i in range(len(df_confs)):
+        try:
+            factory = df_confs.loc[i, 'factory']
+            sn = df_confs.loc[i, 'device_name']
+            if '康普盾' in factory:
+                celltype = 1
+                cell_volt_count = 120
+                cell_temp_count = 40
+            elif '华霆' in factory:
+                celltype = 2
+                cell_volt_count = 204
+                cell_temp_count = 40
+            elif '力神' in factory:
+                celltype = 99
+                cell_volt_count = 72
+                cell_temp_count = 32
+
+            logger.info("pid-{} FACTORY:{} - SN: {} START!".format(os.getpid(), factory, sn))
+            sql = "select * from bms_data where factory ='{}' and sn = '{}' and time > '{}' and time <= '{}'".format(factory, sn, start_time, end_time)
+            logger.info(sql)
+            df_bms = pd.read_sql(sql, db_engine)
+            if df_bms.empty:
+                continue
+            df_bms.columns = ['sn', 'factory', 'add_time', 'update_time', 'time', 'BMSStat', 'PackVolt', 'PackCrnt',
+                              'PackSOC', 'PackSOH', 'alarm1', 'alarm2', 'alarm3', 'CellTemp',
+                              'CellVolt', 'id']
+            # 单体数据拆分
+            cell_temp = df_bms['CellTemp']
+            cell_volt = df_bms['CellVolt']
+            cell_temps = []
+            [cell_temps.append(list(map(float,x.split(',')))) for x in cell_temp]
+            cell_volts = []
+            [cell_volts.append(list(map(float,x.split(',')))) for x in cell_volt]
+            del_indexes = []
+            for i, cell_volt in enumerate(cell_volts):
+                if len(cell_volt) != cell_volt_count:
+                    del_indexes.append(i)
+            for i, cell_temp in enumerate(cell_temps):
+                if len(cell_temp) != cell_temp_count:
+                    del_indexes.append(i)
+            del_indexes = list(set(del_indexes))
+            for i, del_index in enumerate(del_indexes):
+                del_index = del_index - i
+                cell_volts.pop(del_index)
+                cell_temps.pop(del_index)
+            df_bms = df_bms.drop(index=del_indexes, axis=1)
+            cellvolt_name = ['CellVolt' + str(x) for x in range(1, cell_volt_count + 1)]
+            celltemp_name = ['CellTemp' + str(x) for x in range(1, cell_temp_count + 1)]
+            df_bms[cellvolt_name] = cell_volts
+            df_bms[celltemp_name] = cell_temps
+            df_bms = df_bms.drop(columns='CellVolt', axis=0)
+            df_bms = df_bms.drop(columns='CellTemp', axis=0)
+            df_bms = df_bms.reset_index(drop=True)
+            logger.info("pid-{} FACTORY:{} - SN: {} 去除单体异常行{}数据!".format(os.getpid(), factory, sn, str(del_indexes)))
+
+            # 算法执行
+            df_soh = pd.read_sql(
+                "select time_st,time_sp,sn,method,soh,cellsoh from soh_result where factory ='{}' and sn = '{}' order by time_st desc limit 1".format(factory, sn),
+                db_engine);
+
+            if celltype>50:
+                df_socdiff = pd.read_sql(
+                    "select time,sn,cellsoc_diff from uniform_result where factory ='{}' and sn = '{}' order by time desc limit 1".format(factory, sn),
+                    db_engine);
+            else:
+                df_socdiff = pd.DataFrame()
+
+            df_ram_sn=df_ram[df_ram['sn']==sn]
+            BatSoc= CBMSBatSoc.BatSoc(sn, celltype, df_bms, df_soh, df_ram_sn, df_socdiff)
+            df_res, df_ram_sn=BatSoc.batsoc()
+            if not df_ram_sn.empty:
+                sn_index=df_ram.loc[df_ram['sn']==sn].index
+                df_ram=df_ram.drop(index=sn_index)
+                df_ram=df_ram.append(df_ram_sn)
+                df_ram.reset_index(inplace=True,drop=True)
+
+            # 如果以开发环境运行，则写入测试结果!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+            if (runenv == 'dev'):
+                logger.info("pid-{} FACTORY:{} - SN: {} 测试!".format(os.getpid(), factory, sn))
+                df_res = pd.DataFrame({'time': ["2021-01-01 00:00:00"],
+                                       'sn': [sn], 'bms_soc': [10],'packsoc': [1], 'socdsp':[100], 'cellsocmin': [99], 'cellsocmax': [100], 'socmin_num':[10], 'socmax_num':[20],
+                                       'cellsoc_diff':['12,22'], 'cellsoc':[10,20], 'ocvweight':[1], 'socstep':[2]})
+            if not df_res.empty:
+                df_res.columns = ['time', 'sn', 'bms_soc', 'packsoc', 'socdsp', 'cellsocmin', 'cellsocmax', 'socmin_num', 'socmax_num', 'cellsoc_diff', 'cellsoc', 'ocvweight', 'socstep']
+                df_res['factory'] = factory
+                df_res['add_time'] = datetime.datetime.now()
+
+                df_res.to_sql("soc_result", con=db_engine, if_exists="append", index=False)
+                logger.info("pid-{} FACTORY:{} - SN: {} 结果入库成功!".format(os.getpid(), factory, sn))
+
+            logger.info("pid-{} FACTORY:{} - SN: {} DONE!".format(os.getpid(), factory, sn))
+        except:
+            logger.error(u"pid-{} FACTORY:{} - SN: {} ERROR!\n".format(os.getpid(), factory, sn), exc_info=True)
+            logger.error(traceback.format_exc)
+    db_engine.dispose()
+    logger.info("time range from {} to {} done!!!!!!!!! ".format(start_time, end_time))
+
+
+# 多进程运行
+# def mainprocess(host, port, db, user, password, runenv, logger, period_second, process):
+#
+#     process = int(process)
+#     pool = multiprocessing.Pool(processes = process)
+#
+#     for i in range(process):
+#         sn_list = SNnums[i]
+#         pool.apply_async(fun, (host, port, db, user, password, runenv, logger, period_second, ))
+#
+#     pool.close()
+#     pool.join()
+
+def heart_beat(host, port, db, user, password, log):
+    while True:
+        try:
+            db_engine = create_engine(
+                "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                    user, parse.quote_plus(password), host, port, db
+                ))
+            while True:
+                now = datetime.datetime.now()
+                db_engine.execute("update status set algo_soc='{}'".format(now))
+                log.info("soc心跳信号" + now.strftime("%Y-%m-%d %H:%M:%S"))
+                time.sleep(5)
+        except:
+            log.error(u"soc心跳错误", exc_info=True)
+            log.error(traceback.format_exc)
+            time.sleep(5)
+        finally:
+            db_engine.dispose()
+
+
+
+
+
+# 开启定时任务
+if __name__ == '__main__':
+    warnings.filterwarnings("ignore")
+    env_dist = os.environ
+    # 配置信息信息
+    host = env_dist.get("ALI_HOST", '120.25.223.1')
+    port = env_dist.get("ALI_PORT", '4901')
+    db = env_dist.get("ALI_DB", 'ali')
+    user = env_dist.get("ALI_ROOT", 'root')
+    password = env_dist.get("ALI_PASSWORD", '123456')
+    runenv = env_dist.get("ALI_RUNENV", 'pro')
+    process = env_dist.get("ALI_PROCESS", '10')
+    period_second = int(env_dist.get("ALI_PERIOD_SECOND", '10'))
+
+    algo_name = 'soc'
+    # 日志配置
+    log_path = './log' + '/' + algo_name
+    if not os.path.exists(log_path):
+        os.makedirs(log_path)
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/info.log'.format(log_path), maxBytes=1024 * 1024 * 1024,
+                                              backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.suffix = "%Y-%m-%d_%H-%M.log"
+    fh.extMatch = re.compile(r"^\d{4}-\d{2}-\d{2}_\d{2}-\d{2}.log$")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.DEBUG)
+    logger.addHandler(fh)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/error.log'.format(log_path), maxBytes=1024 * 1024 * 1024,
+                                              backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.ERROR)
+    logger.addHandler(fh)
+
+    logger.info("算法初始化完毕，开始周期运行！！！！！！！！！！！！！！！！！！！！！")
+    # 算法参数初始化
+    column_name=['time', 'sn', 'bms_soc', 'soc','cellsoc','standingtime','rampackcrnt','ramcellvolt','kocellvoltmin','kocellvoltmax','ocvweight','as_accum','socstep']
+    df_ram=pd.DataFrame(columns=column_name)
+
+    p = Process(target=heart_beat, args=(host, port, db, user, password, logger,))
+    p.start()
+    scheduler = BlockingScheduler()
+    # heart_beat(host, port, db, user, password, logger)
+    # scheduler.add_job(func=heart_beat, args=(host, port, db, user, password, logger), trigger='interval', seconds=5,max_instances=1, coalesce=True)
+    fun(host, port, db, user, password, runenv, logger, period_second)
+    scheduler.add_job(func=fun, args=(host, port, db, user, password, runenv, logger, period_second), trigger='interval', seconds=period_second
+                      ,max_instances=1, coalesce=True)
+
+    try:
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        logger.error(str(e))

USER/SPF/alibaba/05BatDiag/deploy_diag.py

@@ -0,0 +1,246 @@
+
+import os
+import time
+
+import pandas as pd
+from sqlalchemy import create_engine
+import logging
+import logging.handlers
+import traceback
+import re
+import CBMSBatDiag
+import datetime
+import dateutil.relativedelta
+from urllib import parse
+from apscheduler.schedulers.blocking import BlockingScheduler
+import warnings
+import pymysql
+from urllib import parse
+from multiprocessing import Process
+
+
+def diag_cal(host, port, db, user, password, runenv, logger, period_second):
+    logger.info("pid is {}".format(os.getpid()))
+    try:
+        db_engine = create_engine(
+            "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                user, parse.quote_plus(password), host, port, db
+            ))
+        conn = pymysql.connect(host=host, port=int(port), user=user, password=password, database=db)
+        cursor = conn.cursor()
+    except:
+        logger.error(u"数据库连接错误", exc_info=True)
+        logger.error(traceback.format_exc)
+        return
+
+    # 获取历史报警数据
+    df_diag_ram = pd.read_sql("select time_st,time_sp,sn,faultcode,faultlv,faultinfo,faultadvice from fault_result where time_sp='0000-00-00 00:00:00'",db_engine);
+    # 获取配置信息
+    df_confs = pd.read_sql('select * from conf where status=1 order by factory,device_name', db_engine)
+    # db_engine.dispose()
+
+
+
+
+    now_time = datetime.datetime.now()
+    pre_time = now_time + dateutil.relativedelta.relativedelta(seconds=-period_second-5)  #
+    end_time = datetime.datetime.strftime(now_time, "%Y-%m-%d %H:%M:%S")
+    start_time = datetime.datetime.strftime(pre_time, "%Y-%m-%d %H:%M:%S")
+    logger.info("time range from {} to {} ".format(start_time, end_time))
+    for i in range(len(df_confs)):
+        try:
+            factory = df_confs.loc[i, 'factory']
+            sn = df_confs.loc[i, 'device_name']
+            if '康普盾' in factory:
+                celltype = 1
+                cell_volt_count = 120
+                cell_temp_count = 40
+            elif '华霆' in factory:
+                celltype = 2
+                cell_volt_count = 68
+                cell_temp_count = 40
+            elif '力神' in factory:
+                celltype = 99
+                cell_volt_count = 72
+                cell_temp_count = 32
+
+            logger.info("pid-{} FACTORY:{} - SN: {} START!".format(os.getpid(), factory, sn))
+            sql = "select * from bms_data where factory ='{}' and sn = '{}' and time > '{}' and time <= '{}'".format(factory, sn, start_time, end_time)
+            logger.info(sql)
+            df_bms = pd.read_sql(sql, db_engine)
+            if df_bms.empty:
+                continue
+            df_bms.columns = ['sn', 'factory', 'add_time', 'update_time', 'time', 'BMSStat', 'PackVolt', 'PackCrnt',
+                              'PackSOC', 'PackSOH', 'alarm1', 'alarm2', 'alarm3', 'CellTemp',
+                              'CellVolt', 'id']
+            # 单体数据拆分
+            cell_temp = df_bms['CellTemp']
+            cell_volt = df_bms['CellVolt']
+            cell_temps = []
+            [cell_temps.append(list(map(float,x.split(',')))) for x in cell_temp]
+            cell_volts = []
+            [cell_volts.append(list(map(float,x.split(',')))) for x in cell_volt]
+            del_indexes = []
+            for i, cell_volt in enumerate(cell_volts):
+                if len(cell_volt) != cell_volt_count:
+                    del_indexes.append(i)
+            for i, cell_temp in enumerate(cell_temps):
+                if len(cell_temp) != cell_temp_count:
+                    del_indexes.append(i)
+
+            for i, del_index in enumerate(del_indexes):
+                del_index = del_index - i
+                cell_volts.pop(del_index)
+                cell_temps.pop(del_index)
+            df_bms = df_bms.drop(index=del_indexes, axis=1)
+            cellvolt_name = ['CellVolt' + str(x) for x in range(1, cell_volt_count + 1)]
+            celltemp_name = ['CellTemp' + str(x) for x in range(1, cell_temp_count + 1)]
+            df_bms[cellvolt_name] = cell_volts
+            df_bms[celltemp_name] = cell_temps
+            df_bms = df_bms.drop(columns='CellVolt', axis=0)
+            df_bms = df_bms.drop(columns='CellTemp', axis=0)
+            df_bms = df_bms.reset_index(drop=True)
+            logger.info("pid-{} FACTORY:{} - SN: {} 去除单体异常行{}数据!".format(os.getpid(), factory, sn, str(del_indexes)))
+
+            # 算法执行
+            df_soh = pd.read_sql(
+                "select time_st,sn,soh,cellsoh from soh_result where factory ='{}' and sn = '{}' order by time_st desc limit 1".format(factory, sn),
+                db_engine);
+            df_uniform = pd.read_sql(
+                "select time,sn,cellsoc_diff,cellmin_num,cellmax_num from uniform_result where factory ='{}' and sn = '{}' order by time desc limit 1".format(factory, sn),
+                db_engine);
+            df_soc = pd.read_sql(
+                "select time,sn,packsoc from soc_result where factory ='{}' and sn = '{}' order by add_time desc limit 1".format(factory, sn),
+                db_engine);
+            #电池诊断................................................................................................................................................................
+            if not df_bms.empty:
+                df_diag_ram_sn=df_diag_ram[df_diag_ram['sn']==sn]
+                df_diag_ram_sn.reset_index(inplace=True,drop=True)
+
+                batdiag=CBMSBatDiag.BatDiag(sn,celltype,df_bms,df_soh,df_soc,df_uniform,df_diag_ram_sn)
+                df_diag_res, df_health_res=batdiag.diag()   #获取电池故障结果和电池评分结果
+
+                #电池评分写入数据库
+                if not df_health_res.empty:   #变为历史故障更改数据库
+                    df_health_res['add_time'] = datetime.datetime.now()
+                    df_health_res['factory'] = factory
+                    df_health_res.to_sql("health_result",con=db_engine, if_exists="append",index=False)
+                    logger.info(u"{} health_result 写入成功！！！\n".format(sn), exc_info=True)
+                #历史故障筛选并更改数据库故障结束时间.........................................................
+                if not df_diag_res.empty:
+                    df_diag_now=df_diag_res[df_diag_res['time_sp'] == '0000-00-00 00:00:00']   #去除历史故障
+                    df_diag_new = pd.concat([df_diag_res,df_diag_ram_sn,df_diag_ram_sn]).drop_duplicates(subset=['time_st','faultcode'],keep=False)#此次判断中新增故障
+                    df_diag_end=pd.concat([df_diag_res,df_diag_new,df_diag_new]).drop_duplicates(subset=['time_st','faultcode'],keep=False)#此次判断中新增故障
+                    df_diag_end=df_diag_end[df_diag_end['time_sp'] != '0000-00-00 00:00:00']
+                    df_diag_end.reset_index(inplace=True,drop=True)  #重置索引
+                    if not df_diag_end.empty:   #变为历史故障更改数据库
+                        try:
+                            for i in range(0,len(df_diag_end)):
+                                sql = '''update fault_result set update_time='{}', time_sp='{}' where sn='{}' and faultcode={} and time_st='{}'
+                                            '''.format(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), df_diag_end.loc[i,'time_sp'], sn, df_diag_end.loc[i,'faultcode'],
+                                                       df_diag_end.loc[i,'time_st'])
+                                cursor.execute(sql)
+                                conn.commit()
+                            logger.info(u"{} fault_result更新成功\n".format(sn), exc_info=True)
+                        except:
+                            logger.error(traceback.format_exc)
+                            logger.error(u"{} ：{},{} 任务运行错误\n".format(sn,start_time,end_time), exc_info=True)
+                    #新增故障筛选并存入数据库.....................................................................
+                    if not df_diag_new.empty:
+                        df_diag_new['add_time'] = datetime.datetime.now()
+                        df_diag_new['factory'] = factory
+                        df_diag_new.to_sql("fault_result",con=db_engine, if_exists="append",index=False)
+                        logger.info(u"{} fault_result 写入成功！！！\n".format(sn), exc_info=True)
+                    #更新diag的Ram数据
+                    df_diag_ram=df_diag_ram.drop(df_diag_ram[df_diag_ram.sn==sn].index)
+                    df_diag_ram=df_diag_ram.append(df_diag_now, ignore_index=True)
+                    df_diag_ram.reset_index(inplace=True,drop=True)  #重置索引
+
+            logger.info("pid-{} FACTORY:{} - SN: {} DONE!".format(os.getpid(), factory, sn))
+        except:
+            logger.error(u"pid-{} FACTORY:{} - SN: {} ERROR!\n".format(os.getpid(), factory, sn), exc_info=True)
+            logger.error(traceback.format_exc)
+    db_engine.dispose()
+    cursor.close()
+    conn.close()
+    logger.info("time range from {} to {} done!!!!!!!!! ".format(start_time, end_time))
+
+
+def heart_beat(host, port, db, user, password, log):
+    while True:
+        try:
+            db_engine = create_engine(
+                "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                    user, parse.quote_plus(password), host, port, db
+                ))
+            while True:
+                now = datetime.datetime.now()
+                db_engine.execute("update status set algo_diag='{}'".format(now))
+                log.info("diag心跳信号" + now.strftime("%Y-%m-%d %H:%M:%S"))
+                time.sleep(5)
+        except:
+            log.error(u"diag心跳错误", exc_info=True)
+            log.error(traceback.format_exc)
+            time.sleep(5)
+        finally:
+            db_engine.dispose()
+
+# 开启定时任务
+if __name__ == '__main__':
+    warnings.filterwarnings("ignore")
+    env_dist = os.environ
+    # 配置信息信息
+    # host = env_dist.get("ALI_HOST", '120.25.223.1')
+    # port = env_dist.get("ALI_PORT", '4901')
+    # db = env_dist.get("ALI_DB", 'ali')
+    # user = env_dist.get("ALI_ROOT", 'root')
+    # password =
+    host = env_dist.get("ALI_HOST", '192.168.31.141')
+    port = env_dist.get("ALI_PORT", '3306')
+    db = env_dist.get("ALI_DB", 'ali')
+    user = env_dist.get("ALI_ROOT", 'root')
+    password = env_dist.get("ALI_PASSWORD", 'Ali@123456')
+    runenv = env_dist.get("ALI_RUNENV", 'pro')
+    period_second = int(env_dist.get("ALI_PERIOD_SECOND", '60'))
+
+
+    algo_name = 'diag'
+    # 日志配置
+    log_path = './log' + '/' + algo_name
+    if not os.path.exists(log_path):
+        os.makedirs(log_path)
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/info.log'.format(log_path), maxBytes=1024 * 1024 * 1024,
+                                              backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.suffix = "%Y-%m-%d_%H-%M.log"
+    fh.extMatch = re.compile(r"^\d{4}-\d{2}-\d{2}_\d{2}-\d{2}.log$")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.DEBUG)
+    logger.addHandler(fh)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/error.log'.format(log_path), maxBytes=1024 * 1024 * 1024,
+                                              backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.ERROR)
+    logger.addHandler(fh)
+
+    logger.info("算法初始化完毕，开始周期运行！！！！！！！！！！！！！！！！！！！！！")
+    # 调度
+    p = Process(target=heart_beat, args=(host, port, db, user, password, logger,))
+    p.start()
+    scheduler = BlockingScheduler()
+    # heart_beat(host, port, db, user, password, logger)
+    # scheduler.add_job(func=heart_beat, args=(host, port, db, user, password, logger), trigger='interval', seconds=5,max_instances=1, coalesce=True)
+    diag_cal(host, port, db, user, password, runenv, logger, period_second)
+    scheduler.add_job(func=diag_cal, args=(host, port, db, user, password, runenv, logger, period_second), trigger='interval', seconds=period_second
+                      ,max_instances=1, coalesce=True)
+
+    try:
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        logger.error(str(e))

USER/SPF/alibaba/06BatSafetyAlarm/deploy_safetyalarm.py

@@ -0,0 +1,236 @@
+
+import os
+import time
+
+import pandas as pd
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+import logging
+import logging.handlers
+import traceback
+import re
+import CBMSSafetyAlarm
+import datetime
+import dateutil.relativedelta
+from urllib import parse
+from apscheduler.schedulers.blocking import BlockingScheduler
+import warnings
+import pymysql
+from multiprocessing import Process
+
+
+
+
+def fun(host, port, db, user, password, runenv, logger, period_second):
+    logger.info("pid is {}".format(os.getpid()))
+    global df_bms_ram, df_alarm_ram
+    try:
+        db_engine = create_engine(
+            "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                user, parse.quote_plus(password), host, port, db
+            ))
+
+        conn = pymysql.connect(host=host, port=int(port), user=user, password=password, database=db)
+        cursor = conn.cursor()
+        # 获取配置信息
+        df_confs = pd.read_sql('select * from conf where status=1 order by factory,device_name', db_engine)
+        # db_engine.dispose()
+    except:
+        logger.error(u"数据库连接错误", exc_info=True)
+        logger.error(traceback.format_exc)
+        return
+
+    # 获取历史报警数据
+
+    df_diag_ram = pd.read_sql("select time_st,time_sp,sn,faultcode,faultlv,faultinfo,faultadvice from fault_result where faultcode = 119 and time_sp='0000-00-00 00:00:00'",db_engine);
+
+
+
+    now_time = datetime.datetime.now()
+    pre_time = now_time + dateutil.relativedelta.relativedelta(seconds=-period_second)  #1min
+    end_time = datetime.datetime.strftime(now_time, "%Y-%m-%d %H:%M:%S")
+    start_time = datetime.datetime.strftime(pre_time, "%Y-%m-%d %H:%M:%S")
+
+    logger.info("time range from {} to {} ".format(start_time, end_time))
+    for i in range(len(df_confs)):
+        try:
+            factory = df_confs.loc[i, 'factory']
+            sn = df_confs.loc[i, 'device_name']
+            if '康普盾' in factory:
+                celltype = 1
+                cell_volt_count = 120
+                cell_temp_count = 40
+            elif '华霆' in factory:
+                celltype = 2
+                cell_volt_count = 68
+                cell_temp_count = 40
+            elif '力神' in factory:
+                celltype = 99
+                cell_volt_count = 72
+                cell_temp_count = 32
+
+            logger.info("pid-{} FACTORY:{} - SN: {} START!".format(os.getpid(), factory, sn))
+
+            sql = "select * from bms_data where factory ='{}' and sn = '{}' and time > '{}' and time <= '{}'".format(factory, sn, start_time, end_time)
+            logger.info(sql)
+            df_bms = pd.read_sql(sql, db_engine)
+            if df_bms.empty:
+                continue
+            df_bms.columns = ['sn', 'factory', 'add_time', 'update_time', 'time', 'BMSStat', 'PackVolt', 'PackCrnt',
+                              'PackSOC', 'PackSOH', 'alarm1', 'alarm2', 'alarm3', 'CellTemp',
+                              'CellVolt', 'id']
+            # 单体数据拆分
+            cell_temp = df_bms['CellTemp']
+            cell_volt = df_bms['CellVolt']
+            cell_temps = []
+            [cell_temps.append(list(map(float,x.split(',')))) for x in cell_temp]
+            cell_volts = []
+            [cell_volts.append(list(map(float,x.split(',')))) for x in cell_volt]
+            del_indexes = []
+            for i, cell_volt in enumerate(cell_volts):
+                if len(cell_volt) != cell_volt_count:
+                    del_indexes.append(i)
+            for i, cell_temp in enumerate(cell_temps):
+                if len(cell_temp) != cell_temp_count:
+                    del_indexes.append(i)
+
+            del_indexes = list(set(del_indexes))
+            for i, del_index in enumerate(del_indexes):
+                del_index = del_index - i
+                cell_volts.pop(del_index)
+                cell_temps.pop(del_index)
+            df_bms = df_bms.drop(index=del_indexes, axis=1)
+            cellvolt_name = ['CellVolt' + str(x) for x in range(1, cell_volt_count + 1)]
+            celltemp_name = ['CellTemp' + str(x) for x in range(1, cell_temp_count + 1)]
+            df_bms[cellvolt_name] = cell_volts
+            df_bms[celltemp_name] = cell_temps
+            df_bms = df_bms.drop(columns='CellVolt', axis=0)
+            df_bms = df_bms.drop(columns='CellTemp', axis=0)
+            df_bms = df_bms.reset_index(drop=True)
+            logger.info("pid-{} FACTORY:{} - SN: {} 去除单体异常行{}数据!".format(os.getpid(), factory, sn, str(del_indexes)))
+
+            # 算法执行
+            #电池诊断................................................................................................................................................................
+            df_diag_ram_sn=df_diag_ram[df_diag_ram['sn']==sn]
+            df_bms_ram_sn=df_bms_ram[df_bms_ram['sn']==sn]
+            df_alarm_ram_sn=df_alarm_ram[df_alarm_ram['sn']==sn]
+            if df_diag_ram_sn.empty:
+
+                SafetyAlarm=CBMSSafetyAlarm.SafetyAlarm(sn, celltype, df_bms, df_diag_ram_sn, df_bms_ram_sn, df_alarm_ram_sn)
+                df_diag_res, df_bms_res, df_ram_res=SafetyAlarm.safety_alarm_diag()
+
+                #更新bms的ram数据
+                sn_index=df_bms_ram.loc[df_bms_ram['sn']==sn].index
+                df_bms_ram=df_bms_ram.drop(index=sn_index)
+                df_bms_ram=df_bms_ram.append(df_bms_res)
+
+                sn_index=df_alarm_ram.loc[df_alarm_ram['sn']==sn].index
+                df_alarm_ram=df_alarm_ram.drop(index=sn_index)
+                df_alarm_ram=df_alarm_ram.append(df_ram_res)
+
+                #当前热失控故障写入数据库
+                if not df_diag_res.empty:
+                    df_diag_res['add_time'] = datetime.datetime.now()
+                    df_diag_res['factory'] = factory
+                    df_diag_res.to_sql("fault_result",con=db_engine, if_exists="append",index=False)
+                    logger.info(u"{} 写入成功！！！\n".format(sn), exc_info=True)
+
+            #当前热失控已超过一天变为历史故障并更改数据库
+            else:
+                fault_time=df_diag_ram_sn.iloc[-1]['time_st']
+                if (now_time-fault_time).total_seconds()>24 * 3600:
+                    df_diag_ram_sn['time_sp']=end_time
+                    try:
+
+                        cursor.execute('''
+                                    update fault_result set update_time='{}',time_sp='{}' where sn='{}' and time_sp='0000-00-00 00:00:00' and faultcode={}
+                                    '''.format(datetime.datetime.now(), end_time,sn, 119))
+                        conn.commit()
+                        logger.info(u"{} 更新成功\n".format(sn), exc_info=True)
+
+                    except:
+                        logger.error(traceback.format_exc)
+                        logger.error(u"{} ：{},{} 任务运行错误\n".format(sn,start_time,end_time), exc_info=True)
+
+            logger.info("pid-{} FACTORY:{} - SN: {} DONE!".format(os.getpid(), factory, sn))
+        except:
+            logger.error(u"pid-{} FACTORY:{} - SN: {} ERROR!\n".format(os.getpid(), factory, sn), exc_info=True)
+            logger.error(traceback.format_exc)
+    db_engine.dispose()
+    logger.info("time range from {} to {} done!!!!!!!!! ".format(start_time, end_time))
+
+def heart_beat(host, port, db, user, password, log):
+    while True:
+        try:
+            db_engine = create_engine(
+                "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                    user, parse.quote_plus(password), host, port, db
+                ))
+            while True:
+                now = datetime.datetime.now()
+                db_engine.execute("update status set algo_safetyalarm='{}'".format(now))
+                log.info("safetyalarm心跳信号" + now.strftime("%Y-%m-%d %H:%M:%S"))
+                time.sleep(5)
+        except:
+            log.error(u"safetyalarm心跳错误", exc_info=True)
+            log.error(traceback.format_exc)
+            time.sleep(5)
+        finally:
+            db_engine.dispose()
+
+if __name__ == '__main__':
+    warnings.filterwarnings("ignore")
+    env_dist = os.environ
+    # 配置信息信息
+    host = env_dist.get("ALI_HOST", '120.25.223.1')
+    port = env_dist.get("ALI_PORT", '4901')
+    db = env_dist.get("ALI_DB", 'ali')
+    user = env_dist.get("ALI_ROOT", 'root')
+    password = env_dist.get("ALI_PASSWORD", '123456')
+    runenv = env_dist.get("ALI_RUNENV", 'dev')
+    period_second = int(env_dist.get("ALI_PERIOD_SECOND", '60'))
+
+
+    algo_name = 'safetyalarm'
+    # 日志配置
+    log_path = './log' + '/' + algo_name
+    if not os.path.exists(log_path):
+        os.makedirs(log_path)
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/info.log'.format(log_path), maxBytes=1024 * 1024 * 1024, backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.suffix = "%Y-%m-%d_%H-%M.log"
+    fh.extMatch = re.compile(r"^\d{4}-\d{2}-\d{2}_\d{2}-\d{2}.log$")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.DEBUG)
+    logger.addHandler(fh)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/error.log'.format(log_path), maxBytes=1024 * 1024 * 1024, backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.ERROR)
+    logger.addHandler(fh)
+
+    #参数初始化
+    df_bms_ram=pd.DataFrame(columns=['time', 'sn', 'packvolt', 'cellvolt', 'celltemp', 'packsoc'])
+    df_alarm_ram=pd.DataFrame(columns=['sn','time','safetywarning1','safetywarning2'])
+
+    logger.info("算法初始化完毕，开始周期运行！！！！！！！！！！！！！！！！！！！！！")
+    # 开启定时任务
+    p = Process(target=heart_beat, args=(host, port, db, user, password, logger,))
+    p.start()
+    scheduler = BlockingScheduler()
+    # heart_beat(host, port, db, user, password, logger)
+    # scheduler.add_job(func=heart_beat, args=(host, port, db, user, password, logger), trigger='interval', seconds=5,max_instances=1, coalesce=True)
+    fun(host, port, db, user, password, runenv, logger, period_second)
+    scheduler.add_job(func=fun, args=(host, port, db, user, password, runenv, logger, period_second), trigger='interval', seconds=period_second
+                      ,max_instances=1, coalesce=True)
+
+
+    try:
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        logger.error(str(e))

USER/SPF/alibaba/07BatSafetyWarning/deploy_safetywarning.py

@@ -0,0 +1,332 @@
+
+import os
+import pandas as pd
+from sqlalchemy import create_engine
+import logging.handlers
+import traceback
+import re
+import CBMSBatInterShort
+import CBMSBatUniform
+import VoltStray
+import CBMSSafetyWarning
+import datetime
+import dateutil.relativedelta
+from urllib import parse
+from apscheduler.schedulers.blocking import BlockingScheduler
+import warnings
+import pymysql
+from multiprocessing import Process
+import time
+
+
+#电池热安全预警核心算法函数
+def fun(host, port, db, user, password, runenv, logger, period_second):
+    logger.info("pid is {}".format(os.getpid()))
+    global df_warning_ram
+    global df_warning_ram1
+    global df_warning_ram2
+    global df_warning_ram3
+    global df_lfp_ram
+    global df_lfp_ram1
+    try:
+        db_engine = create_engine(
+            "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                user, parse.quote_plus(password), host, port, db
+            ))
+
+        conn = pymysql.connect(host=host, port=int(port), user=user, password=password, database=db)
+        cursor = conn.cursor()
+    except:
+        logger.error(u"数据库连接错误", exc_info=True)
+        logger.error(traceback.format_exc)
+        return
+
+    now_time=datetime.datetime.now()
+    start_time=now_time-datetime.timedelta(seconds=6*3600) #6*3600
+    start_time1=now_time-datetime.timedelta(seconds=7*24*3600) #7*24*3600
+    start_time2=now_time-datetime.timedelta(seconds=3*24*3600) #3*24*3600
+    start_time3=now_time-datetime.timedelta(seconds=1*24*3600) #1*24*3600
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    start_time1=start_time1.strftime('%Y-%m-%d %H:%M:%S')
+    start_time2=start_time2.strftime('%Y-%m-%d %H:%M:%S')
+    start_time3=start_time3.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=now_time.strftime('%Y-%m-%d %H:%M:%S')
+
+
+    # now_time = datetime.datetime.now()
+    # pre_time = now_time + dateutil.relativedelta.relativedelta(seconds=-period_second)  #1min
+    # end_time = datetime.datetime.strftime(now_time, "%Y-%m-%d %H:%M:%S")
+    # start_time = datetime.datetime.strftime(pre_time, "%Y-%m-%d %H:%M:%S")
+    # 获取配置信息
+    df_confs = pd.read_sql('select * from conf where status=1 order by factory,device_name', db_engine)
+    # db_engine.dispose()
+
+    # 获取历史报警数据
+    df_fault_ram = pd.read_sql("select time_st,time_sp,sn,faultcode,faultlv,faultinfo,faultadvice from fault_result where faultcode = 110 and time_sp='0000-00-00 00:00:00'",db_engine);
+
+
+
+
+    logger.info("time range from {} to {} ".format(start_time, end_time))
+    for i in range(len(df_confs)):
+        try:
+            factory = df_confs.loc[i, 'factory']
+            sn = df_confs.loc[i, 'device_name']
+            if '康普盾' in factory:
+                celltype = 1
+                cell_volt_count = 120
+                cell_temp_count = 40
+            elif '华霆' in factory:
+                celltype = 2
+                cell_volt_count = 68
+                cell_temp_count = 40
+            elif '力神' in factory:
+                celltype = 99
+                cell_volt_count = 72
+                cell_temp_count = 32
+
+            logger.info("pid-{} FACTORY:{} - SN: {} START!".format(os.getpid(), factory, sn))
+            sql = "select * from bms_data where factory ='{}' and sn = '{}' and time > '{}' and time <= '{}'".format(factory, sn, start_time, end_time)
+            logger.info(sql)
+            df_bms = pd.read_sql(sql, db_engine)
+            if df_bms.empty:
+                continue
+            df_bms.columns = ['sn', 'factory', 'add_time', 'update_time', 'time', 'BMSStat', 'PackVolt', 'PackCrnt',
+                              'PackSOC', 'PackSOH', 'alarm1', 'alarm2', 'alarm3', 'CellTemp',
+                              'CellVolt', 'id']
+            # 单体数据拆分
+            cell_temp = df_bms['CellTemp']
+            cell_volt = df_bms['CellVolt']
+            cell_temps = []
+            [cell_temps.append(list(map(float,x.split(',')))) for x in cell_temp]
+            cell_volts = []
+            [cell_volts.append(list(map(float,x.split(',')))) for x in cell_volt]
+            del_indexes = []
+            for i, cell_volt in enumerate(cell_volts):
+                if len(cell_volt) != cell_volt_count:
+                    del_indexes.append(i)
+            for i, cell_temp in enumerate(cell_temps):
+                if len(cell_temp) != cell_temp_count:
+                    del_indexes.append(i)
+
+            del_indexes = list(set(del_indexes))
+            for i, del_index in enumerate(del_indexes):
+                del_index = del_index - i
+                cell_volts.pop(del_index)
+                cell_temps.pop(del_index)
+            df_bms = df_bms.drop(index=del_indexes, axis=1)
+            cellvolt_name = ['CellVolt' + str(x) for x in range(1, cell_volt_count + 1)]
+            celltemp_name = ['CellTemp' + str(x) for x in range(1, cell_temp_count + 1)]
+            df_bms[cellvolt_name] = cell_volts
+            df_bms[celltemp_name] = cell_temps
+            df_bms = df_bms.drop(columns='CellVolt', axis=0)
+            df_bms = df_bms.drop(columns='CellTemp', axis=0)
+            df_bms = df_bms.reset_index(drop=True)
+            logger.info("pid-{} FACTORY:{} - SN: {} 去除单体异常行{}数据!".format(os.getpid(), factory, sn, str(del_indexes)))
+
+            # 算法执行
+            df_soh = pd.read_sql(
+                "select time_st,sn,soh,cellsoh from soh_result where factory ='{}' and sn = '{}' order by time_st desc limit 1".format(factory, sn),
+                db_engine);
+            df_uniform = pd.read_sql(
+                "select time,sn,cellsoc_diff,cellmin_num,cellmax_num,cellvolt_rank from uniform_result where factory ='{}' and sn = '{}' order by time desc limit 1".format(factory, sn),
+                db_engine);
+
+            if not df_bms.empty:
+                #ram处理...............................................................................................................
+                df_warning_ram_sn=df_warning_ram[df_warning_ram['sn']==sn]
+                df_warning_ram_sn1=df_warning_ram1[df_warning_ram1['sn']==sn]
+                df_warning_ram_sn2=df_warning_ram2[df_warning_ram2['sn']==sn]
+                df_warning_ram_sn3=df_warning_ram3[df_warning_ram3['sn']==sn]
+                df_warning_ram_sn.reset_index(inplace=True,drop=True)     #重置索引
+                df_warning_ram_sn1.reset_index(inplace=True,drop=True)     #重置索引
+                df_warning_ram_sn2.reset_index(inplace=True,drop=True)     #重置索引
+                df_warning_ram_sn3.reset_index(inplace=True,drop=True)     #重置索引
+                if celltype>50 and (not df_lfp_ram.empty):
+                    df_lfp_ram_sn=df_lfp_ram[df_lfp_ram['sn']==sn]
+                    df_lfp_ram_sn.reset_index(inplace=True,drop=True)     #重置索引
+                else:
+                    df_lfp_ram_sn=pd.DataFrame()
+                    df_lfp_ram=pd.DataFrame(columns=df_bms.columns.tolist())
+                if celltype>50 and (not df_lfp_ram1.empty):
+                    df_lfp_ram_sn1=df_lfp_ram1[df_lfp_ram1['sn']==sn]
+                    df_lfp_ram_sn1.reset_index(inplace=True,drop=True)     #重置索引
+                else:
+                    df_lfp_ram_sn1=pd.DataFrame()
+                    df_lfp_ram1=pd.DataFrame(columns=df_bms.columns.tolist())
+
+                #内短路计算..................................................................................................................................................
+                BatShort=CBMSBatInterShort.BatInterShort(sn,celltype,df_bms,df_soh,df_warning_ram_sn,df_warning_ram_sn1,df_warning_ram_sn2,df_warning_ram_sn3,df_lfp_ram_sn)
+                df_short_res, df_ram_res, df_ram_res1, df_ram_res2, df_ram_res3, df_ram_res4=BatShort.intershort()
+                if not df_short_res.empty:
+                    df_short_res['add_time'] = datetime.datetime.now()
+                    df_short_res['factory'] = factory
+                    df_short_res.to_sql("intershort_result",con=db_engine, if_exists="append",index=False)
+                    logger.info(u"{} intershort_result写入成功！！！\n".format(sn), exc_info=True)
+
+                #!!!!!!!!!!!!!!往下还未进行部署修改
+
+                #静置电压排名..................................................................................................................................................
+                BatUniform=CBMSBatUniform.BatUniform(sn,celltype,df_bms,df_uniform,df_ram_res3,df_lfp_ram_sn1)
+                df_rank_res, df_ram_res3, df_ram_res5=BatUniform.batuniform()
+                if not df_rank_res.empty:
+                    df_rank_res['add_time'] = datetime.datetime.now()
+                    df_rank_res['factory'] = factory
+                    df_rank_res.to_sql("uniform_result",con=db_engine, if_exists="append",index=False)
+                    logger.info(u"{} uniform_result写入成功！！！\n".format(sn), exc_info=True)
+
+                #电压离群.....................................................................................................................................................
+                df_voltsigma=VoltStray.main(sn,df_bms,celltype)
+                if not df_voltsigma.empty:
+                    df_voltsigma['add_time'] = datetime.datetime.now()
+                    df_voltsigma['factory'] = factory
+                    df_voltsigma.to_sql("outlier_voltchangeratio_result",con=db_engine, if_exists="append",index=False)
+                    logger.info(u"{} outlier_voltchangeratio_result写入成功！！！\n".format(sn), exc_info=True)
+
+                #ram处理................................................................................................................
+                df_warning_ram=df_warning_ram.drop(df_warning_ram[df_warning_ram.sn==sn].index)
+                df_warning_ram1=df_warning_ram1.drop(df_warning_ram1[df_warning_ram1.sn==sn].index)
+                df_warning_ram2=df_warning_ram2.drop(df_warning_ram2[df_warning_ram2.sn==sn].index)
+                df_warning_ram3=df_warning_ram3.drop(df_warning_ram3[df_warning_ram3.sn==sn].index)
+
+                df_warning_ram=pd.concat([df_warning_ram,df_ram_res],ignore_index=True)
+                df_warning_ram1=pd.concat([df_warning_ram1,df_ram_res1],ignore_index=True)
+                df_warning_ram2=pd.concat([df_warning_ram2,df_ram_res2],ignore_index=True)
+                df_warning_ram3=pd.concat([df_warning_ram3,df_ram_res3],ignore_index=True)
+
+                if celltype>50:
+                    df_lfp_ram=df_lfp_ram.drop(df_lfp_ram[df_lfp_ram.sn==sn].index)
+                    df_lfp_ram=pd.concat([df_lfp_ram,df_ram_res4],ignore_index=True)
+                    df_lfp_ram1=df_lfp_ram1.drop(df_lfp_ram1[df_lfp_ram1.sn==sn].index)
+                    df_lfp_ram1=pd.concat([df_lfp_ram1,df_ram_res5],ignore_index=True)
+
+
+            #电池热安全预警..............................................................................................................................................................
+            #读取内短路、析锂和一致性结果数据库数据
+            df_short = pd.read_sql("select time_sp,sn,short_current from intershort_result where sn = '{}' and time_sp between '{}' and '{}'".format(sn,start_time1,end_time), db_engine)
+            # df_liplated = pd.read_sql("select time,sn,liplated,liplated_amount from mechanism_liplated where sn = '{}' and time between '{}' and '{}'".format(sn,start_time2,end_time), db_qxcas_engine)
+            df_uniform = pd.read_sql("select time,sn,cellsoc_diff,cellvolt_diff,cellmin_num,cellmax_num,cellvolt_rank from uniform_result where sn = '{}' and time between '{}' and '{}'".format(sn,start_time2,end_time), db_engine)
+            df_voltsigma = pd.read_sql("select time,sn,VolOl_Uni,VolChng_Uni from outlier_voltchangeratio_result where sn = '{}' and time between '{}' and '{}'".format(sn,start_time3,end_time), db_engine)
+            df_uniform=df_uniform.dropna(axis=0,how='any')
+            #获取sn的故障RAM
+            df_fault_ram_sn=df_fault_ram[df_fault_ram['sn']==sn]
+
+            #热安全预警
+            if df_fault_ram_sn.empty:
+                BatWarning=CBMSSafetyWarning.SafetyWarning(sn,celltype,df_short,df_uniform,df_voltsigma,df_soh)
+                df_warning_res=BatWarning.diag()
+                #当前热失控故障写入数据库
+                if not df_warning_res.empty:
+                    df_warning_res['add_time'] = datetime.datetime.now()
+                    df_warning_res['factory'] = factory
+                    df_warning_res.to_sql("fault_result",con=db_engine, if_exists="append",index=False)
+                    logger.info(u"{} fault_result写入成功！！！\n".format(sn), exc_info=True)
+
+            else:
+                fault_time=df_fault_ram_sn.iloc[-1]['time_st']
+                if (now_time-fault_time).total_seconds()>7*24*3600:   #df_warning_end历史故障筛选并更改数据库故障结束时间
+                    df_fault_ram_sn['time_sp']=end_time
+                    try:
+                        cursor.execute('''
+                                update fault_result set update_time='{}',time_sp='{}' where sn='{}' and faultcode={} and time_sp='0000-00-00 00:00:00'
+                                '''.format(datetime.datetime.now(), end_time, sn, 110))
+                        conn.commit()
+                    except:
+                        logger.error(traceback.format_exc)
+                        logger.error(u"{} ：{},{} 任务运行错误\n".format(sn,start_time,end_time), exc_info=True)
+
+            logger.info("pid-{} FACTORY:{} - SN: {} DONE!".format(os.getpid(), factory, sn))
+        except:
+            logger.error(u"pid-{} FACTORY:{} - SN: {} ERROR!\n".format(os.getpid(), factory, sn), exc_info=True)
+            logger.error(traceback.format_exc)
+    db_engine.dispose()
+    logger.info("time range from {} to {} done!!!!!!!!! ".format(start_time, end_time))
+
+
+def heart_beat(host, port, db, user, password, log):
+    while True:
+        try:
+            db_engine = create_engine(
+                "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+                    user, parse.quote_plus(password), host, port, db
+                ))
+            while True:
+                now = datetime.datetime.now()
+                db_engine.execute("update status set algo_safetywarning='{}'".format(now))
+                log.info("safetywarning心跳信号" + now.strftime("%Y-%m-%d %H:%M:%S"))
+                time.sleep(5)
+        except:
+            log.error(u"safetywarning心跳错误", exc_info=True)
+            log.error(traceback.format_exc)
+            time.sleep(5)
+        finally:
+            db_engine.dispose()
+
+if __name__ == '__main__':
+    warnings.filterwarnings("ignore")
+    env_dist = os.environ
+    # 配置信息信息
+    host = env_dist.get("ALI_HOST", '120.25.223.1')
+    port = env_dist.get("ALI_PORT", '4901')
+    db = env_dist.get("ALI_DB", 'ali')
+    user = env_dist.get("ALI_ROOT", 'root')
+    password = env_dist.get("ALI_PASSWORD", '123456')
+    # host = env_dist.get("ALI_HOST", '192.168.31.141')
+    # port = env_dist.get("ALI_PORT", '3306')
+    # db = env_dist.get("ALI_DB", 'ali')
+    # user = env_dist.get("ALI_ROOT", 'root')
+    # password = env_dist.get("ALI_PASSWORD", 'Ali@123456')
+    runenv = env_dist.get("ALI_RUNENV", 'dev')
+    period_second = env_dist.get("ALI_PERIOD_SECOND", '5,5,5,5').split(',')
+    period_second = env_dist.get("ALI_PERIOD_SECOND", '10')
+
+
+
+    algo_name = 'safetywarning'
+    # 日志配置
+    log_path = './log' + '/' + algo_name
+    if not os.path.exists(log_path):
+        os.makedirs(log_path)
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/info.log'.format(log_path), maxBytes=1024 * 1024 * 1024, backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.suffix = "%Y-%m-%d_%H-%M.log"
+    fh.extMatch = re.compile(r"^\d{4}-\d{2}-\d{2}_\d{2}-\d{2}.log$")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.DEBUG)
+    logger.addHandler(fh)
+
+    fh = logging.handlers.RotatingFileHandler(filename='{}/error.log'.format(log_path), maxBytes=1024 * 1024 * 1024, backupCount=5, encoding="utf-8", mode="a")
+    formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
+    fh.setFormatter(formatter)
+    fh.setLevel(logging.ERROR)
+    logger.addHandler(fh)
+
+    #参数初始化
+    #............................模块运行前，先读取数据库中所有结束时间为0的数据，需要从数据库中读取...................................
+    df_warning_ram=pd.DataFrame(columns=['sn','time','deltsoc','cellsoc'])
+    df_warning_ram1=pd.DataFrame(columns=['sn','time1','deltsoc1'])
+    df_warning_ram2=pd.DataFrame(columns=['sn','time2','deltAs2'])
+    df_warning_ram3=pd.DataFrame(columns=['sn','time3','standingtime','standingtime1','standingtime2'])
+    df_lfp_ram=pd.DataFrame()
+    df_lfp_ram1=pd.DataFrame()
+
+    # 开启定时任务
+    logger.info("算法初始化完毕，开始周期运行！！！！！！！！！！！！！！！！！！！！！")
+    p = Process(target=heart_beat, args=(host, port, db, user, password, logger,))
+    p.start()
+    scheduler = BlockingScheduler()
+    # heart_beat(host, port, db, user, password, logger)
+    # scheduler.add_job(func=heart_beat, args=(host, port, db, user, password, logger), trigger='interval', seconds=5, max_instances=1, coalesce=True)
+    fun(host, port, db, user, password, runenv, logger, period_second)
+    scheduler.add_job(func=fun, args=(host, port, db, user, password, runenv, logger, period_second), trigger='interval', seconds=int(period_second),
+                      max_instances=1, coalesce=True)
+
+
+    try:
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        logger.error(str(e))

USER/SPF/alibaba/08BatCrtl/CBMSBatCtrl.py

@@ -0,0 +1,93 @@
+import pandas as pd
+import numpy as np
+import datetime
+import pika
+import json
+from USER.SPF.alibaba.Common import BatParam
+
+class BatCrtl():
+    def __init__(self,sn,celltype,host,port,df_statcrtl_ram,df_rlycrtl_ram,df_diag,df_bms1,df_bms2,df_bms3):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.host=host
+        self.port=port
+        self.param=BatParam.BatParam(celltype)
+        
+        df_bms1['time']= pd.to_datetime(df_bms1['time'], format='%Y-%m-%d %H:%M:%S')
+        df_bms2['time']= pd.to_datetime(df_bms2['time'], format='%Y-%m-%d %H:%M:%S')
+        df_bms3['time']= pd.to_datetime(df_bms2['time'], format='%Y-%m-%d %H:%M:%S')
+        self.df_bms1=df_bms1
+        self.df_bms2=df_bms2
+        self.df_bms3=df_bms3
+        self.df_statcrtl_ram=df_statcrtl_ram.copy()
+        self.df_rlycrtl_ram=df_rlycrtl_ram.copy()
+        self.df_diag=df_diag[(df_diag['fault_code']==110) | (df_diag['fault_code']==119)]
+        
+        # self.bmstime=df_bms['time']
+        # self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
+        # self.bms_soc=df_bms['PackSOC']
+        # self.bms_soh=df_bms['PackSOH']
+        # self.bmsstat=df_bms['BMSStat']
+        # self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        # self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+
+    #主调用函数........................................................................
+    def bat_crtl(self):
+        df_res1=self._bat_rlycrtl()
+        df_res2=self._bat_statecrtl()
+        return df_res1,df_res2
+    
+    #Ribbit队列发送数据..............................................................
+    def _rabbit_send(self, dict_send):
+        # 建立一个实例
+        credentials = pika.PlainCredentials('admin', 'admin')
+        connection = pika.BlockingConnection(pika.ConnectionParameters(self.host,self.port,credentials=credentials))
+        # 声明一个管道，在管道里发消息
+        channel = connection.channel()
+        # 在管道里声明queue
+        channel.queue_declare(queue='cmdQueue',durable=True)
+        channel.basic_publish(exchange='cmdExchange',routing_key='CmdDirectRouting',body=json.dumps(dict_send)) # 消息内容
+        connection.close()
+
+    #电池继电器下行控制.........................................................................................................................
+    def _bat_rlycrtl(self):
+        time_now=datetime.datetime.now()
+        for i in range(1,4):
+            sn='KPD00'+str(i)
+            if sn in list(self.df_diag['sn']):  #该sn发生4级或5级故障
+                if (sn in list(self.df_rlycrtl_ram['sn'])): #ram是否包含该sn
+                    if self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'batrlycrtl']==0:
+                        self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'time']=time_now
+                        self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'batrlycrtl']=1
+                        dict_send={"cmd_type":1,"sn":sn,"batrlycrtl":1}
+                        self._rabbit_send(dict_send)
+                        return self.df_rlycrtl_ram
+                    else:
+                        pass
+                else:
+                    self.df_rlycrtl_ram[len(self.df_rlycrtl_ram)]=[time_now,sn,1]
+                    dict_send={"cmd_type":1,"sn":sn,"batrlycrtl":1}
+                    self._rabbit_send(dict_send)
+                    return self.df_rlycrtl_ram
+            else:   #该sn没有4级和5级故障
+                if (sn in list(self.df_rlycrtl_ram['sn'])): #ram是否包含该sn
+                    if self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'batrlycrtl']==1:
+                        self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'time']=time_now
+                        self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'batrlycrtl']=0
+                        dict_send={"cmd_type":1,"sn":sn,"batrlycrtl":0}
+                        self._rabbit_send(dict_send)
+                        return self.df_rlycrtl_ram
+                    else:
+                        pass
+                else:
+                    self.df_rlycrtl_ram[len(self.df_rlycrtl_ram)]=[time_now,sn,0]
+                    dict_send={"cmd_type":1,"sn":sn,"batrlycrtl":0}
+                    self._rabbit_send(dict_send)
+                    return self.df_rlycrtl_ram
+        return self.df_rlycrtl_ram
+
+    #电池继电器下行控制.........................................................................................................................
+    def _bat_statecrtl(self):
+        time_now=datetime.datetime.now()
+

USER/SPF/alibaba/08BatCrtl/main.py

@@ -0,0 +1,64 @@
+import datetime
+import pandas as pd
+import datetime
+from USER.SPF.alibaba.Common import DBDownload
+from USER.SPF.alibaba.Common import log
+import CBMSBatSoh
+
+if __name__ == "__main__":
+    SNnums= ['LISHEN01']
+
+    end_time=datetime.datetime.now()-datetime.timedelta(seconds=10)
+    start_time=end_time-datetime.timedelta(seconds=12000)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    #log信息配置............................................................................................................................................................................
+    mylog=log.Mylog('log_soh.txt','error')
+    mylog.logcfg()
+
+    for sn in SNnums:
+        try:
+            # if 'PK500' in sn:
+            #     celltype=1 #6040三元电芯
+            # elif 'PK502' in sn:
+            #     celltype=2 #4840三元电芯
+            # elif 'PK504' in sn:
+            #     celltype=99    #60ah林磷酸铁锂电芯
+            # elif 'MGMLXN750' in sn:
+            #     celltype=3 #力信50ah三元电芯
+            # elif 'MGMCLN750' in sn: 
+            #     celltype=4 #CATL 50ah三元电芯
+            # else:
+            #     print('未找到对应电池编号！！！\n',sn)
+            #     # sys.exit()
+
+            celltype=99
+
+            #............................................................读取原始数据库数据...........................................................................
+            df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\力神40Ah-SOH原始数据.xlsx')
+
+            #............................................................读取结果数据库数据.........................................................................
+            host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+            port=3306
+            db='qx_cas'
+            user='qx_read'
+            password='Qx@123456'
+            mode=1
+            tablename='cellstateestimation_soh'
+            DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
+            with DBRead as DBRead:
+                df_soh=DBRead.getdata(param='time_st,time_sp,sn,method,soh,cellsoh,packsoh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+            df_soh=pd.DataFrame()
+            # print(df_soh)
+
+            #.................................................................计算soh...................................................................................
+            BatSoh=CBMSBatSoh.BatSoh(sn,celltype,df_bms,df_soh)
+            df_res=BatSoh.batsoh()
+            df_res.to_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\99Result\\'+'CBMS_SOH_'+sn+'.xlsx')
+            # print('done！！！')
+             
+        except Exception as e:
+            print(repr(e))
+            mylog.logopt(sn,e)
+            pass

USER/SPF/alibaba/alibaba/01BatSoh/01BatSoh/CBMSBatSoh.py

@@ -0,0 +1,823 @@
+import random
+import pandas as pd
+import numpy as np
+import datetime
+import matplotlib.pyplot as plt
+from USER.alibaba.Common import BatParam
+
+class BatSoh():
+    def __init__(self,sn,celltype,df_bms,df_soh):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_bms=df_bms
+        self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
+        self.bms_soc=df_bms['PackSOC']
+        self.bms_soh=df_bms['PackSOH']
+        self.bmsstat=df_bms['BMSStat']
+        self.bmstime= pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        self.df_soh=df_soh
+
+        self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+
+    def batsoh(self):
+        if self.celltype<50:
+            df_res=self._ncmsoh_twopoint()
+            return df_res
+            
+        else:
+            df_res=self._lfpsoh()
+            return df_res
+
+    #定义滑动滤波函数.........................................................................................................................
+    def _np_move_avg(self, a, n, mode):
+        return (np.convolve(a, np.ones((n,)) / n, mode=mode))
+
+    #筛选充电数据..............................................................................................................................
+    def _chrgdata(self):    
+        self.ChgStart=[]
+        self.ChgEnd=[]
+        if len(self.df_bms)>100:
+            for i in range(3, len(self.bmstime) - 3):
+                if i==3 and self.bmsstat[i]==2 and self.bmsstat[i+1]==2 and self.bmsstat[i+2]==2:
+                    self.ChgStart.append(i)
+                elif self.bmsstat[i-2]!=2 and self.bmsstat[i-1]!=2 and self.bmsstat[i]==2:
+                    self.ChgStart.append(i)
+                elif self.bmsstat[i-1]==2 and self.bmsstat[i]!=2 and self.bmsstat[i+1]!=2:
+                    self.ChgEnd.append(i-1)
+                elif i == (len(self.bmstime) - 4) and self.bmsstat[len(self.bmsstat)-1] == 2 and self.bmsstat[len(self.bmsstat)-2] == 2:
+                    self.ChgEnd.append(len(self.bmstime)-2)
+    
+    #寻找当前行数据的最小温度值.................................................................................................................
+    def _celltemp_weight(self,num):   
+        celltemp = list(self.df_bms.loc[num,self.celltemp_name])
+        celltemp.remove(min(celltemp))
+        self.celltemp=celltemp
+        if self.celltype>50:
+            if min(celltemp)>=25:
+                self.tempweight=1
+                self.StandardStandingTime=3500
+            elif min(celltemp)>=15:
+                self.tempweight=0.5
+                self.StandardStandingTime=7100
+            elif min(celltemp)>=5:
+                self.tempweight=0.2
+                self.StandardStandingTime=10700
+            else:
+                self.tempweight=0
+                self.StandardStandingTime=10800
+        else:
+            if min(celltemp)>=25:
+                self.tempweight=1
+                self.StandardStandingTime=1800
+            elif min(celltemp)>=15:
+                self.tempweight=0.8
+                self.StandardStandingTime=3600
+            elif min(celltemp)>=5:
+                self.tempweight=0.3
+                self.StandardStandingTime=7200
+            else:
+                self.tempweight=0.1
+                self.StandardStandingTime=7200
+
+    #获取SOC差对应的SOH权重值...................................................................................................................
+    def _deltsoc_weight(self,deltsoc):   
+        if deltsoc>60:   
+            deltsoc_weight=1
+        elif deltsoc>50:
+            deltsoc_weight=0.9
+        elif deltsoc>40:
+            deltsoc_weight=0.3
+        elif deltsoc>30:
+            deltsoc_weight=0
+        elif deltsoc>20:
+            deltsoc_weight=0
+        else:
+            deltsoc_weight=0
+        return deltsoc_weight
+
+    #获取当前行所有电压数据......................................................................................................................
+    def _cellvolt_get(self,num): 
+        cellvolt = np.array(self.df_bms.loc[num,self.cellvolt_name])
+        return cellvolt
+    
+    #获取单个电压值.................................................................................................
+    def _singlevolt_get(self,num,series,mode):  #mode==1取当前行单体电压值，mode==2取某个单体所有电压值
+        s=str(series)
+        if mode==1:
+            singlevolt=self.df_bms.loc[num,'CellVolt' + s]
+            return singlevolt
+        else:
+            singlevolt=self.df_bms['CellVolt' + s]
+            return singlevolt
+    
+    #dvdq方法计算soh...........................................................................................................................
+    def _dvdq_soh(self, chrg_st, chrg_end,cellvolt):    
+        Ah = 0  #参数赋初始值
+        Volt = cellvolt[chrg_st]
+        DV_Volt=[]
+        DQ_Ah = []
+        DVDQ = []
+        time2 = []
+        soc2 = []
+        Ah_tatal=[0]
+        xvolt=[]
+        #计算DV和DQ值
+        for j in range(chrg_st,chrg_end):
+            Step=(self.bmstime[j+1]-self.bmstime[j]).total_seconds()
+            Ah=Ah-self.packcrnt[j]*Step/3600
+            if (cellvolt[j]-Volt)>0.0015 and Ah>0:
+                Ah_tatal.append(Ah_tatal[-1]+Ah)
+                DQ_Ah.append(Ah)
+                DV_Volt.append(cellvolt[j]-Volt)
+                DVDQ.append((DV_Volt[-1])/DQ_Ah[-1])
+                xvolt.append(cellvolt[j])
+                Volt=cellvolt[j]
+                Ah = 0
+                time2.append(self.bmstime[j])
+                soc2.append(self.bms_soc[j])
+
+        #切片，去除前后10min的数据
+        df_Data1 = pd.DataFrame({'time': time2,
+                                'SOC': soc2,
+                                'DVDQ': DVDQ,
+                                'Ah_tatal': Ah_tatal[:-1],
+                                'DQ_Ah':DQ_Ah,
+                                'DV_Volt':DV_Volt,
+                                'XVOLT':xvolt})
+        start_time=df_Data1.loc[0,'time']
+        start_time=start_time+datetime.timedelta(seconds=900)
+        end_time=df_Data1.loc[len(time2)-1,'time']
+        end_time=end_time-datetime.timedelta(seconds=1200)
+        if soc2[0]<36:
+            df_Data1=df_Data1[(df_Data1['SOC']>40) & (df_Data1['SOC']<80)]
+        else:
+            df_Data1=df_Data1[(df_Data1['time']>start_time) & (df_Data1['SOC']<80)]
+        df_Data1=df_Data1[(df_Data1['XVOLT']>self.param.PeakVoltLowLmt) & (df_Data1['XVOLT']<self.param.PeakVoltUpLmt)]
+
+        # self._celltemp_weight(int((chrg_st+chrg_end)/2))
+        # print(self.packcrnt[int((chrg_st+chrg_end)/2)], min(self.celltemp))
+        # ax1 = plt.subplot(3, 1, 1)
+        # plt.plot(df_Data1['XVOLT'],df_Data1['DVDQ'],'r*-')
+        # plt.xlabel('Volt/V')
+        # plt.ylabel('DV/DQ')
+        # plt.legend()
+        # ax1 = plt.subplot(3, 1, 2)
+        # plt.plot(df_Data1['SOC'],df_Data1['XVOLT'],'y*-')
+        # plt.xlabel('SOC/%')
+        # plt.ylabel('Volt/V')
+        # plt.legend()
+        # ax1 = plt.subplot(3, 1, 3)
+        # plt.plot(df_Data1['SOC'], df_Data1['DVDQ'], 'r*-')
+        # plt.xlabel('SOC/%')
+        # plt.ylabel('DV/DQ')
+        # plt.legend()
+        # plt.show()
+
+        #寻找峰值并计算Soh
+        if len(df_Data1)>1:
+            PeakIndex=df_Data1['DVDQ'].idxmax()
+            #筛选峰值点附近±0.5%SOC内的数据
+            df_Data2=df_Data1[(df_Data1['SOC']>(df_Data1['SOC'][PeakIndex]-0.5)) & (df_Data1['SOC']<(df_Data1['SOC'][PeakIndex]+0.5))]
+            if len(df_Data2)>0 and df_Data1.loc[PeakIndex,'XVOLT']<self.param.PeakVoltUpLmt-0.015:
+                Ah_tatal1 = df_Data1['Ah_tatal']
+                DVDQ = df_Data1['DVDQ']
+                soc2 = df_Data1['SOC']
+                xvolt = df_Data1['XVOLT']
+                if soc2[PeakIndex]>40 and soc2[PeakIndex]<80:
+                    cellsoh_init=(Ah_tatal[-1]-Ah_tatal1[PeakIndex]) * 100 / ((self.param.FullChrgSoc - self.param.PeakSoc) * 0.01 * self.param.Capacity)
+                    if cellsoh_init<95:
+                        cellsoh_init=cellsoh_init*0.3926+58.14
+                        return cellsoh_init
+                    else:
+                        return cellsoh_init
+                else:
+                    return 0
+            else:
+                df_Data1=df_Data1.drop([PeakIndex])
+                PeakIndex = df_Data1['DVDQ'].idxmax()
+                df_Data2 = df_Data1[(df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
+                if len(df_Data2) > 0 and df_Data1.loc[PeakIndex,'XVOLT']<self.param.PeakVoltUpLmt-0.015:
+                    Ah_tatal1 = df_Data1['Ah_tatal']
+                    DVDQ = df_Data1['DVDQ']
+                    soc2 = df_Data1['SOC']
+                    xvolt = df_Data1['XVOLT']
+                    if soc2[PeakIndex]>40 and soc2[PeakIndex]<80:
+                        cellsoh_init=(Ah_tatal[-1]-Ah_tatal1[PeakIndex]) * 100 / ((self.param.FullChrgSoc - self.param.PeakSoc) * 0.01 * self.param.Capacity)
+                        if cellsoh_init<95:
+                            cellsoh_init=cellsoh_init*0.3926+58.14
+                            return cellsoh_init
+                        else:
+                            return cellsoh_init
+                    else:
+                        return 0
+                else:
+                    return 0
+        else:
+            return 0
+                
+    #NCM充电数据soh计算.........................................................................................................................
+    def _ncmsoh_chrg(self):      
+        self._chrgdata()
+        ChgStartValid=[]
+        ChgEndValid=[]
+        tempweightlist=[]
+        for i in range(min(len(self.ChgStart),len(self.ChgEnd))): 
+            self._celltemp_weight(self.ChgEnd[i])               #获取温度对应的静置时间及权重            
+            for k in range(self.ChgStart[i],self.ChgEnd[i]):   #去除电流0点
+                if self.packcrnt[k]<-0.5 and self.packcrnt[k+1]>-0.5 and self.packcrnt[k+2]>-0.5 and self.packcrnt[k+3]>-0.5:
+                    self.ChgEnd[i]=k
+   
+            #筛选满足2点法计算的数据
+            StandingTime=0
+            StandingTime1=0
+            if self.bms_soc[self.ChgEnd[i]]>70 and self.bms_soc[self.ChgStart[i]]<50:
+                for m in range(min(len(self.packcrnt)-self.ChgEnd[i]-2,self.ChgStart[i]-2)):
+                    if abs(self.packcrnt[self.ChgStart[i] - m - 1]) < 0.5:
+                        StandingTime = StandingTime + (self.bmstime[self.ChgStart[i] - m] - self.bmstime[self.ChgStart[i] - m - 1]).total_seconds()
+                    if abs(self.packcrnt[self.ChgEnd[i] + m + 1]) < 0.5:
+                        StandingTime1 = StandingTime1 + (self.bmstime[self.ChgEnd[i] + m + 1] - self.bmstime[self.ChgEnd[i] + m]).total_seconds()
+                    if StandingTime > self.StandardStandingTime and StandingTime1>self.StandardStandingTime and ((self.bmstime[self.ChgEnd[i]]-self.bmstime[self.ChgStart[i]]).total_seconds())/(self.ChgEnd[i]-self.ChgStart[i])<60:  #筛选静置时间>15min且慢充过程丢失数据少
+                        if abs(self.packcrnt[self.ChgEnd[i] + m + 2])>0.5 or m==len(self.packcrnt)-self.ChgEnd[i]-3: #如果电流<0.5,继续寻找充电后的静置电压，直到末尾
+                            ChgStartValid.append(self.ChgStart[i])
+                            ChgEndValid.append(self.ChgEnd[i]+m)
+                            tempweightlist.append(self.tempweight)
+                            break
+                    if abs(self.packcrnt[self.ChgStart[i] - m - 2])>0.5 and abs(self.packcrnt[self.ChgEnd[i] + m + 2])>0.5:
+                        break
+
+        if len(ChgStartValid)>0:   #两点法计算Soh
+            df_res=pd.DataFrame(columns=('time','sn','soh','soh1'))
+            soh2=[]
+            if not self.df_soh.empty:  #获取数据库中上次计算的Soh值
+                soh_init=list(self.df_soh['soh'])[-1]
+            else:
+                soh_init=list(self.bms_soh)[-1]
+
+            for i in range(len(ChgStartValid)):
+                Ah=0
+                for j in range(ChgStartValid[i],ChgEndValid[i]):  #计算Ah
+                    Step=(self.bmstime[j+1]-self.bmstime[j]).total_seconds()
+                    Ah=Ah-self.packcrnt[j+1]*Step/3600
+
+                for j in range(1, self.param.CellVoltNums+1):    #计算每个电芯的Soh
+                    s = str(j)
+                    OCVStart=self.df_bms.loc[ChgStartValid[i]-2,'单体电压' + s]/1000
+                    OCVEnd=self.df_bms.loc[ChgEndValid[i]-1,'单体电压' + s]/1000
+                    #soh
+                    ocv_Soc1=np.interp(OCVStart,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                    ocv_Soc2=np.interp(OCVEnd,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                
+                    soh2.append(Ah*100/((ocv_Soc2-ocv_Soc1)*0.01*self.param.Capacity))
+                soh1=np.mean(soh2)
+                delt_ocv_soc=ocv_Soc2-ocv_Soc1
+                self._deltsoc_weight(delt_ocv_soc)
+                soh_res=soh_init*(1-self.deltsoc_weight*tempweightlist[i])+soh1*self.deltsoc_weight*tempweightlist[i]
+                soh_init=soh_res
+                df_res.loc[i]=[self.bmstime[ChgStartValid[i]],self.sn,soh_res,soh1]
+            
+            return df_res
+        return pd.DataFrame()
+
+    #两点法计算三元SOH.........................................................................................................................
+    def _ncmsoh_twopoint(self):
+        standingpoint_st=[]
+        standingpoint_sp=[]
+        tempweightlist=[]
+        standingtime=0
+        as_accum=0
+        for i in range(3,len(self.df_bms)-3):
+            delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+            as_accum=self.packcrnt[i]*delttime
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     #电流为0
+                standingtime=standingtime+delttime
+                self._celltemp_weight(i)     #获取不同温度对应的静置时间
+
+                if standingtime>self.StandardStandingTime:      #静置时间满足要求
+                    cellvolt_now=self._cellvolt_get(i)
+                    cellvolt_min=min(cellvolt_now)
+                    cellvolt_max=max(cellvolt_now)
+                    cellvolt_last=self._cellvolt_get(i-1)
+                    deltvolt=max(abs(cellvolt_now-cellvolt_last))
+
+                    if 2<cellvolt_min<4.5 and 2<cellvolt_max<4.5 and deltvolt<0.003:    #前后两次电压波动<3mV
+                        if standingpoint_st:
+                            if len(standingpoint_st)>len(standingpoint_sp):     #开始时刻已获取，结束时刻未获取
+                                minocv_socnow=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                                cellvolt_st=self._cellvolt_get(standingpoint_st[-1])   #获取开始时刻静置后的电压数据
+                                minocv_socst=np.interp(min(cellvolt_st),self.param.LookTab_OCV,self.param.LookTab_SOC)
+
+                                if abs(minocv_socst-minocv_socnow)>=40:   #当前时刻SOC与开始时刻SOC差>=40
+                                    if abs(self.packcrnt[i+2])>=0.1:    #如果下一时刻电流>=0.5，则压入当前索引
+                                        standingpoint_sp.append(i)
+                                        standingpoint_st.append(i)
+                                        tempweightlist.append(self.tempweight)
+                                        standingtime=0
+                                        as_accum=0
+                                        continue
+                                    else:
+                                        if standingtime>3600 or i==len(self.df_bms)-2:   #仍处于静置，但静置时间>1h，则直接获取sp时刻，或者到了数据末尾
+                                            standingpoint_sp.append(i)
+                                            tempweightlist.append(self.tempweight)
+                                            continue
+                                else:
+                                    if abs(self.packcrnt[i+2])>=0.1:
+                                        standingtime=0
+                                        delt_soc=abs(minocv_socst-minocv_socnow)
+                                        delt_ahsoc=(as_accum*100)/(self.param.Capacity*3600)
+                                        if delt_ahsoc<0.01:
+                                            delt_ahsoc=0.01
+                                        if  minocv_socst<50 and minocv_socnow<minocv_socst:
+                                            standingpoint_st[-1]=i
+                                            as_accum=0
+                                            continue
+                                        elif minocv_socst>=50 and minocv_socnow>minocv_socst:
+                                            standingpoint_st[-1]=i
+                                            as_accum=0
+                                            continue
+                                        elif delt_soc/delt_ahsoc>5 or delt_soc/delt_ahsoc<0.2:
+                                            standingpoint_st[-1]=i
+                                            as_accum=0
+                                            continue
+                                        else:
+                                            continue
+                                    else:
+                                        continue
+                            else:
+                                if abs(self.packcrnt[i+2])>=0.1:
+                                    standingpoint_st.append(i)
+                                    standingtime=0
+                                    as_accum=0
+                                    continue
+                                else:
+                                    continue
+                        else:
+                            if abs(self.packcrnt[i+2])>0.1:
+                                standingpoint_st.append(i)
+                                standingtime=0
+                                as_accum=0
+                                continue
+                            else:
+                                continue
+                    else:
+                        continue
+                else:
+                    continue
+            else:
+                standingtime=0
+                continue
+
+        #计算SOH......................................................................................................................
+        if standingpoint_sp:
+            column_name=['time_st','time_sp','sn','method','bmssoh','packsoh','soh','cellsohmin','cellsohmax','sohmin_num','sohmax_num','cellsoh_diff','cellsoh']
+            df_res=pd.DataFrame(columns=column_name)
+
+            for i in range(len(standingpoint_sp)):
+                cellocv_st=self._cellvolt_get(standingpoint_st[i])    #获取静置点所有电芯的电压
+                cellocv_sp=self._cellvolt_get(standingpoint_sp[i])
+                # accumtime=self.accumtime.to_list()  #累计量的时间列表
+                timepoint_bms_st=self.bmstime[standingpoint_st[i]]   #获取静置点的时间
+                timepoint_bms_sp=self.bmstime[standingpoint_sp[i]]
+                # timepoint_accum_st=bisect.bisect(accumtime,timepoint_bms_st)   #获取最接近静置点时间的累计量时间点
+                # timepoint_accum_sp=bisect.bisect(accumtime,timepoint_bms_sp)
+                # if timepoint_accum_sp>=len(accumtime):  #防止指针超出数据范围
+                #     timepoint_accum_sp=len(accumtime)-1
+                
+                ah_packcrnt_dis=0
+                ah_packcrnt_chg=0
+                for j in range(standingpoint_st[i]+1,standingpoint_sp[i]): #计算累计Ah
+                    Step=(self.bmstime[j+1]-self.bmstime[j]).total_seconds()
+                    if Step<120:
+                        if self.packcrnt[j+1]>=0:
+                            ah_packcrnt_dis=ah_packcrnt_dis+self.packcrnt[j+1]*Step
+                        else:
+                            ah_packcrnt_chg=ah_packcrnt_chg-self.packcrnt[j+1]*Step  
+                ah_packcrnt_chg=ah_packcrnt_chg/3600
+                ah_packcrnt_dis=ah_packcrnt_dis/3600
+                ah_packcrnt=ah_packcrnt_chg-ah_packcrnt_dis     #两个静置点的总累计AH，负值代表放电，正值代表充电
+     
+                # ah_accum_dis=self.df_accum.loc[timepoint_accum_sp,'累计放电电量']-self.df_accum.loc[timepoint_accum_st,'累计放电电量']  #两个静置点之间的放电电量
+                # ah_accum_chg=self.df_accum.loc[timepoint_accum_sp,'累计充电电量']-self.df_accum.loc[timepoint_accum_st,'累计充电电量']  #两个静置点之间的充电电量
+                # ah_accum_tatol=ah_accum_chg-ah_accum_dis  #两个静置点的总累计AH，负值代表放电，正值代表充电
+                ah_accum=ah_packcrnt
+
+                delt_days=(self.bmstime[standingpoint_sp[i]]-self.bmstime[standingpoint_st[i]]).total_seconds()/(3600*24)
+                if delt_days<=1: #两次时间间隔对计算结果的影响
+                    soh_weight1=1
+                elif delt_days<=2:
+                    soh_weight1=0.7
+                elif delt_days<=3:
+                    soh_weight1=0.4
+                else:
+                    soh_weight1=0
+                
+                # if ah_packcrnt_dis<self.param.Capacity: #放电ah数对结果的影响
+                #     soh_weight1=(1-ah_packcrnt_dis/(self.param.Capacity*1.5))*soh_weight1
+                # else:
+                #     soh_weight1=0.1
+            
+                # if self.param.Capacity**0.7*0.4 < abs(ah_accum_tatol) < self.param.Capacity:    #累计量的权重
+                #     if abs(ah_accum_tatol-ah_packcrnt)<self.param.Capacity/20:
+                #         soh_weight1=soh_weight1*1
+                #     elif abs(ah_accum_tatol-ah_packcrnt) < self.param.Capacity/10:
+                #         soh_weight1=soh_weight1*0.8
+                #     else:
+                #         soh_weight1=soh_weight1*0.5
+                # else:
+                #     if self.param.Capacity*0.7*0.4< abs(ah_packcrnt) <self.param.Capacity:
+                #         soh_weight1=soh_weight1*0.3
+                #     else:
+                #         soh_weight1=0
+
+                #计算每个电芯的SOH值
+                cellsoh=[]
+                for j in range(self.param.CellVoltNums):    
+                    ocv_soc1=np.interp(cellocv_st[j],self.param.LookTab_OCV,self.param.LookTab_SOC)
+                    ocv_soc2=np.interp(cellocv_sp[j],self.param.LookTab_OCV,self.param.LookTab_SOC)
+                    delt_ocv_soc=ocv_soc2-ocv_soc1
+                    delt_ocv_soc_weight=self._deltsoc_weight(abs(delt_ocv_soc))
+                    soh_weight=soh_weight1*tempweightlist[i]*delt_ocv_soc_weight*0.5
+                    cellsoh_init=ah_accum*100/((ocv_soc2-ocv_soc1)*0.01*self.param.Capacity)
+
+                    if cellsoh_init>60 and cellsoh_init<120 and soh_weight>0.1:    #判断soh值的有效区间
+                        if len(df_res)<1:
+                            if not self.df_soh.empty:
+                                cellsoh_last=eval(self.df_soh.loc[len(self.df_soh)-1,'cellsoh'])
+                                # if soh_weight>1/abs(cellsoh_init-cellsoh_last[j]):
+                                #     soh_weight=1/abs(cellsoh_init-cellsoh_last[j])
+                                #     cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                                # else:
+                                cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                            else:
+                                cellsoh_cal=cellsoh_init
+                        else:
+                            cellsoh_last=eval(df_res.loc[len(df_res)-1,'cellsoh'])
+                            # if soh_weight>1/abs(cellsoh_init-cellsoh_last[j]):
+                            #     soh_weight=1/abs(cellsoh_init-cellsoh_last[j])
+                            #     cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                            # else:
+                            cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                        cellsoh_cal=eval(format(cellsoh_cal,'.1f'))
+                        cellsoh.append(cellsoh_cal)
+                    else:
+                        cellsoh=[]
+                        break
+                #计算电池包SOH
+                ocv_soc1=np.interp(min(cellocv_st),self.param.LookTab_OCV,self.param.LookTab_SOC)
+                ocv_soc2=np.interp(max(cellocv_sp),self.param.LookTab_OCV,self.param.LookTab_SOC)
+                delt_ocv_soc=ocv_soc2-ocv_soc1
+                delt_ocv_soc_weight=self._deltsoc_weight(abs(delt_ocv_soc))
+                soh_weight=soh_weight1*tempweightlist[i]*delt_ocv_soc_weight*0.5
+                packsoh_init=ah_accum*100/((ocv_soc2-ocv_soc1)*0.01*self.param.Capacity)
+
+                if packsoh_init>55 and packsoh_init<120 and soh_weight>0.1:    #判断soh值的有效区间
+                    if len(df_res)<1:
+                        if not self.df_soh.empty:
+                            packsoh_last=self.df_soh.loc[len(self.df_soh)-1,'packsoh']
+                            # if soh_weight>1/abs(packsoh_init-packsoh_last):
+                            #     soh_weight=1/abs(packsoh_init-packsoh_last)
+                            #     packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
+                            # else:
+                            packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
+                        else:
+                            packsoh=packsoh_init
+                    else:
+                        packsoh_last=df_res.loc[len(df_res)-1,'packsoh']
+                        # if soh_weight>1/abs(packsoh_init-packsoh_last):
+                        #         soh_weight=1/abs(packsoh_init-packsoh_last)
+                        #         packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
+                        # else:
+                        packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
+                    packsoh=eval(format(packsoh,'.1f'))
+                else:
+                    packsoh=0
+                    continue
+
+                if cellsoh and 55<min(cellsoh)<120 and 50<packsoh<120:
+                    soh=min(cellsoh)
+                    cellsohmin=soh
+                    cellsohmax=max(cellsoh)
+                    sohmin_num=cellsoh.index(cellsohmin)+1
+                    sohmax_num=cellsoh.index(cellsohmax)+1
+                    cellsoh_diff=cellsohmax-cellsohmin
+                    bmssoh=self.bms_soh[standingpoint_sp[i]]
+                    df_res.loc[len(df_res)]=[timepoint_bms_st, timepoint_bms_sp, self.sn, 1, bmssoh, packsoh, soh, cellsohmin, cellsohmax, sohmin_num, sohmax_num, cellsoh_diff, str(cellsoh)]
+                else:
+                    continue
+            if df_res.empty:
+                return pd.DataFrame()
+            else:
+                return df_res
+        return pd.DataFrame()
+
+    #两点法和DVDQ法计算磷酸铁锂电池SOH..................................................................................................................
+    def _lfpsoh(self):
+        standingpoint_st=[]
+        standingpoint_sp=[]
+        tempweightlist1=[]
+        cellmaxvolt_number1=[]
+        standingtime=0
+        chrg_start=[]
+        chrg_end=[]
+        tempweightlist2=[]
+        cellmaxvolt_number2=[]
+        charging=0
+
+        for i in range(3,len(self.df_bms)-3):
+
+            #获取两点法法所需数据-开始.................................................................................................................
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     #判断非平台区静置状态
+                delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                standingtime=standingtime+delttime
+                self._celltemp_weight(i)     #获取不同温度对应的静置时间
+
+                if standingtime>self.StandardStandingTime:  #静置时间满足要求
+                    if abs(self.packcrnt[i+2])>=0.1:  #下一时刻电流>0.1A
+                        standingtime=0
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))
+                        if 2<cellvolt_max<self.param.OcvInflexionBelow-0.002  and 2<cellvolt_min<4.5 and deltvolt<0.003:      #当前最大电芯电压<OCV下拐点
+                            if standingpoint_st:
+                                if len(standingpoint_st)>len(standingpoint_sp):
+                                    if self.packcrnt[standingpoint_st[-1]]<-1:     #判断上一次静置点的是否为满充
+                                        standingpoint_sp.append(i)
+                                        standingpoint_st.append(i)
+                                        tempweightlist1.append(self.tempweight)
+                                    else:
+                                        standingpoint_st[-1]=i
+                                        tempweightlist1[-1]=self.tempweight
+                                else:
+                                    standingpoint_st.append(i)
+                                    tempweightlist1.append(self.tempweight)
+                            else:
+                                standingpoint_st.append(i)
+                                tempweightlist1.append(self.tempweight)
+                        else:
+                            pass
+                    else:
+                        pass
+                else:
+                    pass
+            
+            elif self.packcrnt[i]<=-1 and self.packcrnt[i-1]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i+2]>-1:    #判读满充状态
+                standingtime=0
+                self._celltemp_weight(i)
+                cellvolt_now=self._cellvolt_get(i).tolist()
+                if max(cellvolt_now)>self.param.CellFullChrgVolt:
+                    if standingpoint_st:
+                        if len(standingpoint_st)>len(standingpoint_sp):
+                            if abs(self.packcrnt[standingpoint_st[-1]])<0.5:     #判断上一次静置点是否为下拐点
+                                standingpoint_sp.append(i)
+                                standingpoint_st.append(i)
+                                tempweightlist1.append(self.tempweight)
+                                cellmaxvolt_number1.append(cellvolt_now.index(max(cellvolt_now))) #获取最大电压索引
+                                cellmaxvolt_number1.append(cellvolt_now.index(max(cellvolt_now))) #获取最大电压索引
+                            else:
+                                standingpoint_st[-1]=i
+                                tempweightlist1[-1]=self.tempweight
+                                cellmaxvolt_number1[-1]=cellvolt_now.index(max(cellvolt_now))
+                    else:
+                        standingpoint_st.append(i)
+                        tempweightlist1.append(self.tempweight)
+                        cellmaxvolt_number1.append(cellvolt_now.index(max(cellvolt_now)))
+                else:
+                    pass
+            
+            else:
+                standingtime=0
+                pass
+
+            #获取DVDQ算法所需数据——开始.............................................................................................................
+            if charging==0:
+                if self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i+2]<=-1 and self.bms_soc[i]<40:     #充电开始
+                    self._celltemp_weight(i)
+                    charging=1
+                    if len(chrg_start)>len(chrg_end):
+                        chrg_start[-1]=i
+                        tempweightlist2[-1]=self.tempweight
+                    else:
+                        chrg_start.append(i)
+                        tempweightlist2.append(self.tempweight)
+                else:
+                    pass
+
+            else: #充电中
+                if (self.bmstime[i+1]-self.bmstime[i]).total_seconds()>180 or (self.packcrnt[i]<-self.param.Capacity-5 and self.packcrnt[i+1]<-self.param.Capacity-5):  #如果充电过程中时间间隔>180s，则舍弃该次充电
+                    chrg_start.remove(chrg_start[-1])
+                    tempweightlist2.remove(tempweightlist2[-1])
+                    charging=0
+                    continue
+                elif self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and  self.packcrnt[i+2]>-1:  #判断电流波动时刻
+                    cellvolt_now=self._cellvolt_get(i+1).tolist()
+                    if max(cellvolt_now)>self.param.CellFullChrgVolt:   #电压>满充电压
+                        chrg_end.append(i+1)
+                        cellmaxvolt_number2.append(cellvolt_now.index(max(cellvolt_now)))   #获取最大电压索引
+                        charging=0
+                        continue
+                    else:
+                        pass
+                elif self.packcrnt[i+1]>-0.1 and self.packcrnt[i+2]>-0.1:   #判断充电结束
+                    charging=0
+                    if len(chrg_start)>len(chrg_end):
+                        chrg_start.remove(chrg_start[-1])
+                        tempweightlist2.remove(tempweightlist2[-1])
+                        continue
+                    else:
+                        continue
+                elif i==len(self.packcrnt)-4 and self.packcrnt[i+1]<-1 and self.packcrnt[i+2]<-1:
+                    charging=0
+                    if len(chrg_start)>len(chrg_end):
+                        cellvolt_now=self._cellvolt_get(i).tolist()
+                        if max(cellvolt_now)>self.param.CellFullChrgVolt:   #电压>满充电压
+                            chrg_end.append(i)
+                            cellmaxvolt_number2.append(cellvolt_now.index(max(cellvolt_now)))   #获取最大电压索引
+                            continue
+                        else:
+                            chrg_start.remove(chrg_start[-1])
+                            tempweightlist2.remove(tempweightlist2[-1])
+                            continue
+                    else:
+                        continue  
+                else:
+                    continue 
+        
+        #开始计算SOH.............................................................................................................................................
+        if standingpoint_sp or chrg_end:       
+            df_res=pd.DataFrame(columns=['time_st','time_sp','sn','method','bmssoh','packsoh','soh','cellsohmin','cellsohmax','sohmin_num','sohmax_num','cellsoh_diff','cellsoh','soh_weight'])
+
+            #两点法计算SOH........................................................................................................................................
+            if standingpoint_sp:    
+                for i in range(len(standingpoint_sp)):  #判断为满充点或者下拐点
+                    if self.packcrnt[standingpoint_sp[i]]<=-1:  #计算单体电芯soh
+                        cellocv_st=self._cellvolt_get(standingpoint_st[i])    
+                        ocv_soc1=np.interp(cellocv_st[cellmaxvolt_number1[i]],self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        ocv_packsoc1=np.interp(min(cellocv_st),self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        ocv_soc2=self.param.FullChrgSoc
+                        ocv_packsoc2=ocv_soc2
+                    else:
+                        cellocv_sp=self._cellvolt_get(standingpoint_sp[i])
+                        ocv_soc1=self.param.FullChrgSoc
+                        ocv_packsoc1=ocv_soc1
+                        ocv_soc2=np.interp(cellocv_sp[cellmaxvolt_number1[i]],self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        ocv_packsoc2=np.interp(min(cellocv_sp),self.param.LookTab_OCV,self.param.LookTab_SOC)
+
+                    cellocv_sp=self._cellvolt_get(standingpoint_sp[i])
+                    # accumtime=self.accumtime.to_list()  #累计量的时间列表
+                    timepoint_bms_st=self.bmstime[standingpoint_st[i]]   #获取静置点的时间
+                    timepoint_bms_sp=self.bmstime[standingpoint_sp[i]]
+                    # timepoint_accum_st=bisect.bisect(accumtime,timepoint_bms_st)   #获取最接近静置点时间的累计量时间点
+                    # timepoint_accum_sp=bisect.bisect(accumtime,timepoint_bms_sp)
+                    # if timepoint_accum_sp>=len(accumtime):  #防止指针超出数据范围
+                    #     timepoint_accum_sp=len(accumtime)-1
+                    
+                    ah_packcrnt_dis=0
+                    ah_packcrnt_chg=0
+                    for j in range(standingpoint_st[i]+1,standingpoint_sp[i]+1): #计算累计Ah
+                        Step=(self.bmstime[j+1]-self.bmstime[j]).total_seconds()
+                        if Step<120:
+                            if self.packcrnt[j+1]>=0:
+                                ah_packcrnt_dis=ah_packcrnt_dis+self.packcrnt[j+1]*Step
+                            else:
+                                ah_packcrnt_chg=ah_packcrnt_chg-self.packcrnt[j+1]*Step
+                    ah_packcrnt_chg=ah_packcrnt_chg/3600
+                    ah_packcrnt_dis=ah_packcrnt_dis/3600          
+                    ah_packcrnt=ah_packcrnt_chg-ah_packcrnt_dis     #两个静置点的总累计AH，负值代表放电，正值代表充电
+        
+                    # ah_accum_dis=self.df_accum.loc[timepoint_accum_sp,'累计放电电量']-self.df_accum.loc[timepoint_accum_st,'累计放电电量']  #两个静置点之间的放电电量
+                    # ah_accum_chg=self.df_accum.loc[timepoint_accum_sp,'累计充电电量']-self.df_accum.loc[timepoint_accum_st,'累计充电电量']  #两个静置点之间的充电电量
+                    # ah_accum_tatol=ah_accum_chg-ah_accum_dis  #两个静置点的总累计AH，负值代表放电，正值代表充电
+                    ah_accum=ah_packcrnt
+
+                    delt_days=(self.bmstime[standingpoint_sp[i]]-self.bmstime[standingpoint_st[i]]).total_seconds()/(3600*24)
+                    if delt_days<=1: #两次时间间隔对计算结果的影响
+                        soh_weight=1
+                    elif delt_days<=2:
+                        soh_weight=0.7
+                    elif delt_days<=3:
+                        soh_weight=0.4
+                    else:
+                        soh_weight=0
+                    
+                    # if self.param.Capacity*0.65*0.7 < abs(ah_packcrnt) < self.param.Capacity:    #累计量的权重
+                    #     if abs(ah_accum_tatol-ah_packcrnt)<self.param.Capacity/20:
+                    #         soh_weight=soh_weight*1
+                    #     elif abs(ah_accum_tatol-ah_packcrnt)<self.param.Capacity/10:
+                    #         soh_weight=soh_weight*0.8
+                    #     else:
+                    #         soh_weight=soh_weight*0.5
+                    # else:
+                    #     if self.param.Capacity*0.65*0.7 < abs(ah_accum) < self.param.Capacity:
+                    #         soh_weight=soh_weight*0.3
+                    #     else:
+                    #         soh_weight=0
+
+                    delt_ocv_soc=ocv_soc2-ocv_soc1
+                    delt_ocv_packsoc=ocv_packsoc2-ocv_packsoc1
+                    delt_ocv_soc_weight=self._deltsoc_weight(abs(delt_ocv_soc))
+                    soh_weight=soh_weight*tempweightlist1[i]*delt_ocv_soc_weight*0.5
+                    cellsoh_init=ah_accum*100/(delt_ocv_soc*0.01*self.param.Capacity)
+                    packsoh_init=ah_accum*100/(delt_ocv_packsoc*0.01*self.param.Capacity)
+                    bmssoh=self.bms_soh[standingpoint_sp[i]]
+
+                    if 55<cellsoh_init<120 and 50<packsoh_init<120:    #判断soh值的有效区间
+                        df_res.loc[len(df_res)]=[timepoint_bms_st, timepoint_bms_sp, self.sn, 1, bmssoh, packsoh_init, cellsoh_init,0,0,0,0,0,0, soh_weight]
+                    else:
+                        pass
+            else:
+                pass
+
+            #DVDQ法计算SOH.......................................................................................................................................
+            if chrg_end:
+                for i in range(len(chrg_end)):
+                    cellsoh=[]
+                    for j in range(1, self.param.CellVoltNums + 1):
+                        cellvolt1 = self._singlevolt_get(i,j,2)  #取单体电压j的所有电压值
+                        cellvolt=self._np_move_avg(cellvolt1, 3, "same")     #对电压进行滑动平均滤
+                        cellsoh_init=self._dvdq_soh(chrg_start[i],chrg_end[i],cellvolt)     #dvdq计算soh
+                        cellsoh.append(cellsoh_init)
+                    
+                    soh_weight=tempweightlist2[i]*0.3
+                    cellsoh_init=cellsoh[cellmaxvolt_number2[i]+1]
+                    cellsoh_new=[k for k in cellsoh if 50<k<120]
+                    packsoh_init=min(cellsoh_new)
+                    bmssoh=self.bms_soh[chrg_end[i]]
+                    if 55<cellsoh_init<120:    #判断soh值的有效区间
+                        df_res.loc[len(df_res)]=[self.bmstime[chrg_start[i]], self.bmstime[chrg_end[i]], self.sn, 2, bmssoh, packsoh_init, cellsoh_init,0,0,0,0,0,0, soh_weight]
+                    else:
+                        pass
+        
+            #对SOH结果进行滤波处理................................................................................................................................
+            if df_res.empty:
+                return pd.DataFrame()
+            else:
+                df_res=df_res.sort_values(by='time_st',ascending=True)
+                df_res.index=range(len(df_res))
+                for i in range(len(df_res)):
+                    cellsoh_init=df_res.loc[i,'soh']
+                    packsoh_init=df_res.loc[i,'packsoh']
+                    soh_weight=df_res.loc[i,'soh_weight']
+                    if i<1:
+                        if not self.df_soh.empty and 60<self.df_soh.loc[len(self.df_soh)-1,'soh']<115:
+                            cellsoh_last=self.df_soh.loc[len(self.df_soh)-1,'soh']
+                            packsoh_last=self.df_soh.loc[len(self.df_soh)-1,'packsoh']
+                            if soh_weight>1/abs(cellsoh_init-cellsoh_last):
+                                soh_weight=1/abs(cellsoh_init-cellsoh_last)
+                                cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last*(1-soh_weight)
+                                packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
+                            else:
+                                cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last*(1-soh_weight)
+                                packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
+                        else:
+                            cellsoh_cal=cellsoh_init
+                            packsoh=packsoh_init
+                        cellsoh_cal=eval(format(cellsoh_cal,'.1f'))
+                        packsoh=eval(format(packsoh,'.1f'))
+                        if 55<cellsoh_cal<120:
+                            cellsoh=[cellsoh_cal+random.uniform(-1.5,1.5) for i in range(self.param.CellVoltNums)]
+                            cellsoh=list(map(lambda x:eval(format(x,'.1f')), cellsoh))
+                            df_res.loc[i,'soh']=min(cellsoh)
+                            df_res.loc[i,'packsoh']=packsoh
+                            df_res.loc[i,'cellsoh']=str(cellsoh)
+                            df_res.loc[i,'cellsohmin']=min(cellsoh)
+                            df_res.loc[i,'cellsohmax']=max(cellsoh)
+                            df_res.loc[i,'sohmin_num']=cellsoh.index(min(cellsoh))+1
+                            df_res.loc[i,'sohmax_num']=cellsoh.index(max(cellsoh))+1
+                            df_res.loc[i,'cellsoh_diff']=max(cellsoh)-min(cellsoh)
+                        else:
+                            df_res=df_res.drop(i,axis=0)
+                    else:
+                        cellsoh_last=df_res.loc[i-1,'soh']
+                        packsoh_last=df_res.loc[i-1,'packsoh']
+                        if soh_weight>1/abs(cellsoh_init-cellsoh_last):
+                            soh_weight=1/abs(cellsoh_init-cellsoh_last)
+                            cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last*(1-soh_weight)
+                            packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
+                        else:
+                            cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last*(1-soh_weight)
+                            packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
+                        cellsoh_cal=eval(format(cellsoh_cal,'.1f'))
+                        packsoh=eval(format(packsoh,'.1f'))
+                        if 55<cellsoh_cal<120:
+                            cellsoh=[cellsoh_cal+random.uniform(-1.5,1.5) for i in range(self.param.CellVoltNums)]
+                            cellsoh=list(map(lambda x:eval(format(x,'.1f')), cellsoh))
+                            df_res.loc[i,'soh']=min(cellsoh)
+                            df_res.loc[i,'packsoh']=packsoh
+                            df_res.loc[i,'cellsoh']=str(cellsoh)
+                            df_res.loc[i,'cellsohmin']=min(cellsoh)
+                            df_res.loc[i,'cellsohmax']=max(cellsoh)
+                            df_res.loc[i,'sohmin_num']=cellsoh.index(min(cellsoh))+1
+                            df_res.loc[i,'sohmax_num']=cellsoh.index(max(cellsoh))+1
+                            df_res.loc[i,'cellsoh_diff']=max(cellsoh)-min(cellsoh)
+                        else:
+                            df_res=df_res.drop(i,axis=0)
+                df_res=df_res.drop('soh_weight',axis=1)
+                return df_res
+
+        return pd.DataFrame()
+
+

USER/SPF/alibaba/alibaba/01BatSoh/01BatSoh/main.py

@@ -0,0 +1,64 @@
+import datetime
+import pandas as pd
+import datetime
+from USER.SPF.alibaba.Common import DBDownload
+from USER.SPF.alibaba.Common import log
+import CBMSBatSoh
+
+if __name__ == "__main__":
+    SNnums= ['LISHEN01']
+
+    end_time=datetime.datetime.now()-datetime.timedelta(seconds=10)
+    start_time=end_time-datetime.timedelta(seconds=12000)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    #log信息配置............................................................................................................................................................................
+    mylog=log.Mylog('log_soh.txt','error')
+    mylog.logcfg()
+
+    for sn in SNnums:
+        try:
+            # if 'PK500' in sn:
+            #     celltype=1 #6040三元电芯
+            # elif 'PK502' in sn:
+            #     celltype=2 #4840三元电芯
+            # elif 'PK504' in sn:
+            #     celltype=99    #60ah林磷酸铁锂电芯
+            # elif 'MGMLXN750' in sn:
+            #     celltype=3 #力信50ah三元电芯
+            # elif 'MGMCLN750' in sn: 
+            #     celltype=4 #CATL 50ah三元电芯
+            # else:
+            #     print('未找到对应电池编号！！！\n',sn)
+            #     # sys.exit()
+
+            celltype=99
+
+            #............................................................读取原始数据库数据...........................................................................
+            df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\力神40Ah-SOH原始数据.xlsx')
+
+            #............................................................读取结果数据库数据.........................................................................
+            host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+            port=3306
+            db='qx_cas'
+            user='qx_read'
+            password='Qx@123456'
+            mode=1
+            tablename='cellstateestimation_soh'
+            DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
+            with DBRead as DBRead:
+                df_soh=DBRead.getdata(param='time_st,time_sp,sn,method,soh,cellsoh,packsoh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+            df_soh=pd.DataFrame()
+            # print(df_soh)
+
+            #.................................................................计算soh...................................................................................
+            BatSoh=CBMSBatSoh.BatSoh(sn,celltype,df_bms,df_soh)
+            df_res=BatSoh.batsoh()
+            df_res.to_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\99Result\\'+'CBMS_SOH_'+sn+'.xlsx')
+            # print('done！！！')
+             
+        except Exception as e:
+            print(repr(e))
+            mylog.logopt(sn,e)
+            pass

USER/SPF/alibaba/alibaba/02BatUniform/CBMSBatUniform.py

@@ -0,0 +1,390 @@
+import pandas as pd
+import numpy as np
+import datetime
+import bisect
+import matplotlib.pyplot as plt
+from USER.alibaba.Common import BatParam
+
+class BatUniform():
+    def __init__(self,sn,celltype,df_bms):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_bms=df_bms
+        self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
+        self.bms_soc=df_bms['PackSOC']
+        self.bms_soh=df_bms['PackSOH']
+        self.bmstime= pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+
+        self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+
+    def batuniform(self):
+        if self.celltype<50:
+            df_res=self._ncm_uniform()
+            return df_res
+                     
+        else:
+            df_res=self._lfp_uniform()
+            return df_res
+
+    #定义滑动滤波函数........................................................................................................................................
+    def _np_move_avg(self,a, n, mode="same"): 
+        return (np.convolve(a, np.ones((n,)) / n, mode=mode))
+    
+    #寻找当前行数据的最小温度值................................................................................................................................
+    def _celltemp_weight(self,num):   
+        celltemp = list(self.df_bms.loc[num,self.celltemp_name])
+        celltemp.remove(min(celltemp))
+        self.celltemp=celltemp
+        if self.celltype==99:
+            if min(celltemp)>=25:
+                self.tempweight=1
+                self.StandardStandingTime=2400
+            elif min(celltemp)>=15:
+                self.tempweight=0.6
+                self.StandardStandingTime=3600
+            elif min(celltemp)>=5:
+                self.tempweight=0.3
+                self.StandardStandingTime=7200
+            else:
+                self.tempweight=0.1
+                self.StandardStandingTime=7200
+        else:
+            if min(celltemp)>=25:
+                self.tempweight=1
+                self.StandardStandingTime=1800
+            elif min(celltemp)>=15:
+                self.tempweight=0.8
+                self.StandardStandingTime=2400
+            elif min(celltemp)>=5:
+                self.tempweight=0.6
+                self.StandardStandingTime=3600
+            else:
+                self.tempweight=0.2
+                self.StandardStandingTime=7200
+        
+    #获取当前行所有电压数据............................................................................................................................
+    def _cellvolt_get(self,num):
+        cellvolt = np.array(self.df_bms.loc[num,self.cellvolt_name])
+        return cellvolt
+    
+    #获取单个电压值.................................................................................................
+    def _singlevolt_get(self,num,series,mode):  #mode==1取当前行单体电压值，mode==2取某个单体所有电压值
+        s=str(series)
+        if mode==1:
+            singlevolt=self.df_bms.loc[num,'CellVolt_' + s]
+            return singlevolt
+        else:
+            singlevolt=self.df_bms['CellVolt' + s]
+            return singlevolt
+
+    #寻找DVDQ的峰值点，并返回..........................................................................................................................
+    def _dvdq_peak(self, time, soc, cellvolt, packcrnt):
+        cellvolt = self._np_move_avg(cellvolt, 3, mode="same")
+        Soc = 0
+        Ah = 0
+        Volt = cellvolt[0]
+        DV_Volt = []
+        DQ_Ah = []
+        DVDQ = []
+        time1 = []
+        soc1 = []
+        soc2 = []
+        xvolt=[]
+
+        for m in range(1, len(time)):
+            Step = (time[m] - time[m - 1]).total_seconds()
+            Soc = Soc - packcrnt[m] * Step * 100 / (3600 * self.param.Capacity)
+            Ah = Ah - packcrnt[m] * Step / 3600
+            if (cellvolt[m]-Volt)>0.0015 and Ah>0:
+                DQ_Ah.append(Ah)
+                DV_Volt.append(cellvolt[m]-Volt)
+                DVDQ.append((DV_Volt[-1])/DQ_Ah[-1])
+                xvolt.append(cellvolt[m])
+                Volt=cellvolt[m]
+                Ah = 0
+                soc1.append(Soc)
+                time1.append(time[m])
+                soc2.append(soc[m])
+
+        #切片，去除前后10min的数据
+        df_Data1 = pd.DataFrame({'time': time1,
+                                'SOC': soc2,
+                                'DVDQ': DVDQ,
+                                'AhSoc': soc1,
+                                'DQ_Ah':DQ_Ah,
+                                'DV_Volt':DV_Volt,
+                                'XVOLT':xvolt})
+        start_time=df_Data1.loc[0,'time']
+        start_time=start_time+datetime.timedelta(seconds=900)
+        end_time=df_Data1.loc[len(time1)-1,'time']
+        end_time=end_time-datetime.timedelta(seconds=1200)
+        if soc2[0]<36:
+            df_Data1=df_Data1[(df_Data1['SOC']>40) & (df_Data1['SOC']<80)]
+        else:
+            df_Data1=df_Data1[(df_Data1['time']>start_time) & (df_Data1['SOC']<80)]
+        df_Data1=df_Data1[(df_Data1['XVOLT']>self.param.PeakVoltLowLmt) & (df_Data1['XVOLT']<self.param.PeakVoltUpLmt)]
+
+        # print(packcrnt[int(len(time)/2)], min(self.celltemp))
+        # ax1 = plt.subplot(3, 1, 1)
+        # plt.plot(df_Data1['XVOLT'],df_Data1['DVDQ'],'g*-')
+        # plt.xlabel('Volt/V')
+        # plt.ylabel('DV/DQ')
+        # plt.legend()
+        # ax1 = plt.subplot(3, 1, 2)
+        # plt.plot(df_Data1['SOC'],df_Data1['XVOLT'],'y*-')
+        # plt.xlabel('SOC/%')
+        # plt.ylabel('Volt/V')
+        # plt.legend()
+        # ax1 = plt.subplot(3, 1, 3)
+        # plt.plot(df_Data1['SOC'], df_Data1['DVDQ'], 'r*-')
+        # plt.xlabel('SOC/%')
+        # plt.ylabel('DV/DQ')
+        # plt.legend()
+        # plt.show()
+
+        if len(df_Data1)>2:     #寻找峰值点，且峰值点个数>2
+            PeakIndex = df_Data1['DVDQ'].idxmax()
+            df_Data2 = df_Data1[(df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
+            if len(df_Data2) > 0 and df_Data1.loc[PeakIndex,'XVOLT']<self.param.PeakVoltUpLmt-0.019:
+                return df_Data1['AhSoc'][PeakIndex]
+            else:
+                df_Data1 = df_Data1.drop([PeakIndex])
+                PeakIndex = df_Data1['DVDQ'].idxmax()
+                df_Data2 = df_Data1[(df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
+                if len(df_Data2) > 0 and df_Data1.loc[PeakIndex,'XVOLT']<self.param.PeakVoltUpLmt-0.019:
+                    return df_Data1['AhSoc'][PeakIndex]
+                else:
+                    return 0
+        else:
+            return 0
+ 
+    #三元电池一致性计算.................................................................................................................................
+    def _ncm_uniform(self):
+        column_name=['time','sn','cellsoc_diff','cellvolt_diff','cellmin_num','cellmax_num']
+        df_res=pd.DataFrame(columns=column_name)
+        standingtime=0
+
+        for i in range(1,len(self.df_bms)-2):
+
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     #电流为0
+                delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                standingtime=standingtime+delttime
+                self._celltemp_weight(i)     #获取不同温度对应的静置时间
+
+                if standingtime>self.StandardStandingTime:      #静置时间满足要求
+                    if abs(self.packcrnt[i+2]) >= 0.1:
+                        standingtime=0  
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))                
+                        if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.004:
+                            
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num]
+                    elif standingtime>3600*12:
+                        standingtime=0
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))                  
+                        
+                        if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.004:
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num]
+                    elif i>=len(self.df_bms)-4:
+                        standingtime=0
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))     
+                        
+                        if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.004:
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num]
+                        break
+                    else:
+                        continue
+                else:
+                    continue
+            else:
+                standingtime=0
+                continue
+
+        if df_res.empty:    #返回计算结果
+            return pd.DataFrame()
+        else:
+            return df_res
+
+    #磷酸铁锂电池一致性计算.........................................................................................................................
+    def _lfp_uniform(self):
+        column_name=['time','sn','cellsoc_diff','cellvolt_diff','cellmin_num','cellmax_num']
+        df_res=pd.DataFrame(columns=column_name)
+        standingtime=0
+        chrg_start=[]
+        chrg_end=[]
+        charging=0
+
+        for i in range(3,len(self.df_bms)-3):
+
+            #静置电压法计算电芯一致性
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     #电流为0
+                delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                standingtime=standingtime+delttime
+                self._celltemp_weight(i)     #获取不同温度对应的静置时间
+
+                if standingtime>self.StandardStandingTime:      #静置时间满足要求
+                    if abs(self.packcrnt[i+2]) >= 0.1:     
+                        standingtime=0  
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))  
+        
+                        if 2 < cellvolt_max < self.param.OcvInflexionBelow-0.002 and 2<cellvolt_min<4.5 and deltvolt<0.003:                 
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num]
+                    elif i>=len(self.df_bms)-4:
+                        standingtime=0
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last)) 
+                        if 2 < cellvolt_max < self.param.OcvInflexionBelow-0.002 and 2<cellvolt_min<4.5 and deltvolt<0.003:
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num]
+                    else:
+                        pass
+                else:
+                    pass
+            else:
+                standingtime=0
+                pass   
+
+            #获取DVDQ算法所需数据——开始............................................................................................................
+            if charging==0: #判断充电开始
+                if self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i+2]<=-1 and self.bms_soc[i]<40:     #充电开始
+                    charging=1
+                    if len(chrg_start)>len(chrg_end):
+                        chrg_start[-1]=i
+                    else:
+                        chrg_start.append(i)
+                else:
+                    pass
+
+            else: #充电中
+                if (self.bmstime[i+1]-self.bmstime[i]).total_seconds()>180 or (self.packcrnt[i]<-self.param.Capacity-5 and self.packcrnt[i+1]<-self.param.Capacit-5):  #如果充电过程中时间间隔>180s，则舍弃该次充电
+                    chrg_start.remove(chrg_start[-1])
+                    charging=0
+                    continue
+                elif self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and  self.packcrnt[i+2]>-1:  #判断电流波动时刻
+                    cellvolt_now=self._cellvolt_get(i+1)
+                    if max(cellvolt_now)>self.param.CellFullChrgVolt:   #电压>满充电压
+                        chrg_end.append(i+1)
+                        charging=0
+                        continue
+                    else:
+                        pass
+                elif self.packcrnt[i+1]>-0.1 and self.packcrnt[i+2]>-0.1:   #判断充电结束
+                    charging=0
+                    if len(chrg_start)>len(chrg_end):
+                        if self.bms_soc[i]>90:
+                            chrg_end.append(i)
+                        else:
+                            chrg_start.remove(chrg_start[-1])
+                            continue
+                    else:
+                        continue
+                elif i==len(self.packcrnt)-4 and self.packcrnt[i+1]<-1 and self.packcrnt[i+2]<-1:
+                    charging=0
+                    if len(chrg_start)>len(chrg_end):
+                        if self.bms_soc[i]>90:   #soc>90
+                            chrg_end.append(i)
+                            continue
+                        else:
+                            chrg_start.remove(chrg_start[-1])
+                            continue
+                    else:
+                        continue
+                else:
+                    continue   
+
+        if chrg_end:    #DVDQ方法计算soc差
+            peaksoc_list=[]
+            for i in range(len(chrg_end)):
+                peaksoc_list = []
+                self._celltemp_weight(chrg_start[i])
+                if min(self.celltemp)>10:
+                    for j in range(1, self.param.CellVoltNums + 1):
+                        cellvolt = self._singlevolt_get(i,j,2)  #取单体电压j的所有电压值
+                        cellvolt = list(cellvolt[chrg_start[i]:chrg_end[i]])
+                        time = list(self.bmstime[chrg_start[i]:chrg_end[i]])
+                        packcrnt = list(self.packcrnt[chrg_start[i]:chrg_end[i]])
+                        soc = list(self.bms_soc[chrg_start[i]:chrg_end[i]])
+                        peaksoc = self._dvdq_peak(time, soc, cellvolt, packcrnt)
+                        if peaksoc>1:
+                            peaksoc_list.append(peaksoc)    #计算到达峰值点的累计Soc
+                        else:
+                            pass
+                    if len(peaksoc_list)>self.param.CellVoltNums/2:
+                        peaksoc_max=max(peaksoc_list)
+                        peaksoc_min=min(peaksoc_list)
+                        peaksoc_maxnum=peaksoc_list.index(peaksoc_min)+1
+                        peaksoc_minnum=peaksoc_list.index(peaksoc_max)+1
+                        cellsoc_diff=peaksoc_max-peaksoc_min
+                        cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                        df_res.loc[len(df_res)]=[self.bmstime[chrg_start[i]], self.sn, cellsoc_diff, 0, peaksoc_minnum, peaksoc_maxnum]
+                    else:
+                        pass
+                    plt.show()
+                else:
+                    pass
+
+        if df_res.empty:
+            return pd.DataFrame()
+        else:
+            df_res.sort_values(by='time', ascending=True, inplace=True)
+            return df_res

USER/SPF/alibaba/alibaba/02BatUniform/main.py

@@ -0,0 +1,52 @@
+import CBMSBatUniform
+import datetime
+import pandas as pd
+import datetime
+from USER.alibaba.Common import DBDownload
+from USER.alibaba.Common import log
+
+if __name__ == "__main__":
+
+    SNnums= ['HUATING01']
+
+    end_time=datetime.datetime.now()-datetime.timedelta(seconds=10)
+    start_time=end_time-datetime.timedelta(seconds=12000)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    #log信息配置............................................................................................................................................................................
+    mylog=log.Mylog('log_uniform.txt','error')
+    mylog.logcfg()
+
+    for sn in SNnums:
+        try:
+            # if 'PK500' in sn:
+            #     celltype=1 #6040三元电芯
+            # elif 'PK502' in sn:
+            #     celltype=2 #4840三元电芯
+            # elif 'PK504' in sn:
+            #     celltype=99    #60ah林磷酸铁锂电芯
+            # elif 'MGMLXN750' in sn:
+            #     celltype=3 #力信50ah三元电芯
+            # elif 'MGMCLN750' in sn: 
+            #     celltype=4 #CATL 50ah三元电芯
+            # else:
+            #     print('未找到对应电池编号！！！\n',sn)
+            #     # sys.exit()
+
+            celltype=2
+
+            #............................................................读取原始数据库数据...........................................................................
+            df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-SOH原始数据.xlsx')
+
+
+            #..............................................................计算一致性..............................................................................
+            BatUniform=CBMSBatUniform.BatUniform(sn,celltype,df_bms)
+            df_res=BatUniform.batuniform()
+            df_res.to_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\99Result\\'+'CBMS_Uniform'+sn+'_1.xlsx')
+            # print('done！！！')
+        
+        except IndexError as e:
+            print(repr(e))
+            mylog.logopt(sn,e)
+            pass

USER/SPF/alibaba/alibaba/03BatInterShort/CBMSBatInterShort.py

@@ -0,0 +1,555 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from USER.alibaba.Common import BatParam
+
+class BatInterShort():
+    def __init__(self,sn,celltype,df_bms,df_soh):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_bms=df_bms
+        self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
+        self.bms_soc=df_bms['PackSOC']
+        self.bms_soh=df_bms['PackSOH']
+        self.bmstime= pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        self.df_soh=df_soh
+
+        self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+    
+    def intershort(self):
+        if self.celltype<50:
+            df_res=self._ncm_intershort()
+            return df_res
+            
+        elif self.celltype>50:
+            df_res=self._lfp_intershort()
+            return df_res
+        
+        else:
+            return pd.DataFrame()
+
+    #定义滑动滤波函数....................................................................................
+    def _np_move_avg(self,a, n, mode="same"): 
+        return (np.convolve(a, np.ones((n,)) / n, mode=mode))
+    
+    #寻找当前行数据的最小温度值.............................................................................
+    def _celltemp_weight(self,num):   
+        celltemp = list(self.df_bms.loc[num,self.celltemp_name])
+        celltemp=min(celltemp)
+        self.celltemp=celltemp
+        if self.celltype==99:
+            if celltemp>=20:
+                self.tempweight=1
+                self.StandardStandingTime=3600
+            elif celltemp>=10:
+                self.tempweight=0.6
+                self.StandardStandingTime=7200
+            elif celltemp>=5:
+                self.tempweight=0.
+                self.StandardStandingTime=7200
+            else:
+                self.tempweight=0.1
+                self.StandardStandingTime=10800
+        else:
+            if celltemp>=20:
+                self.tempweight=1
+                self.StandardStandingTime=3600
+            elif celltemp>=10:
+                self.tempweight=0.8
+                self.StandardStandingTime=3600
+            elif celltemp>=5:
+                self.tempweight=0.6
+                self.StandardStandingTime=7200
+            else:
+                self.tempweight=0.2
+                self.StandardStandingTime=10800
+
+    #获取当前行所有电压数据........................................................................................
+    def _cellvolt_get(self,num): 
+        cellvolt = np.array(self.df_bms.loc[num,self.cellvolt_name])
+        return cellvolt
+
+    #获取当前行所有soc差...........................................................................................
+    def _celldeltsoc_get(self,num,dict_baltime,capacity): 
+        cellsoc=[]
+        celldeltsoc=[]
+        for j in range(1, self.param.CellVoltNums+1):   #获取每个电芯电压对应的SOC值
+            cellvolt=self.df_bms.loc[num,'CellVolt' + str(j)]
+            ocv_soc=np.interp(cellvolt,self.param.LookTab_OCV,self.param.LookTab_SOC)
+            if j in dict_baltime.keys():
+                ocv_soc=ocv_soc+dict_baltime[j]*self.param.BalCurrent/(capacity*3600)   #补偿均衡电流
+            else:
+                pass
+            cellsoc.append(ocv_soc)
+        
+        cellsocmean=(sum(cellsoc)-max(cellsoc)-min(cellsoc))/(len(cellsoc)-2)
+        for j in range(len(cellsoc)):   #计算每个电芯的soc差
+            celldeltsoc.append(cellsoc[j]-cellsocmean)
+        return np.array(celldeltsoc)
+ 
+    #获取所有电芯的As差
+    def _cellDeltAs_get(self,chrg_st,chrg_end,dict_baltime):
+        cellAs=[]
+        celldeltAs=[]
+        for j in range(1, self.param.CellVoltNums+1):   #获取每个电芯电压>峰值电压的充入As数
+            if j in dict_baltime.keys():    #补偿均衡电流
+                As=-self.param.BalCurrent*dict_baltime[j]
+            else:    
+                As=0
+            As_tatol=0
+            symbol=0
+            for m in range(chrg_st+1,chrg_end):
+                As=As-self.packcrnt[m]*(self.bmstime[m]-self.bmstime[m-1]).total_seconds()
+                if symbol<5:
+                    if self.df_bms.loc[m,'CellVolt'+str(j)]>self.param.PeakCellVolt[symbol]:
+                        As_tatol=As_tatol+As
+                        symbol=symbol+1
+                    else:
+                        continue
+                else:
+                    cellAs.append(As_tatol/5)
+                    break
+   
+        cellAsmean=(sum(cellAs)-max(cellAs)-min(cellAs))/(len(cellAs)-2)
+        for j in range(len(cellAs)):   #计算每个电芯的soc差
+            celldeltAs.append(cellAs[j]-cellAsmean)
+        return np.array(celldeltAs)
+
+    #计算每个电芯的均衡时长..........................................................................................................................
+    def _bal_time(self,dict_bal):
+        dict_baltime={}
+        dict_baltime1={}
+        for key in dict_bal:
+            count=1
+            x=eval(key)
+            while x>0:
+                if x & 1==1:    #判断最后一位是否为1
+                    if count in dict_baltime.keys():
+                        dict_baltime[count] = dict_baltime[count] + dict_bal[key]
+                    else:
+                        dict_baltime[count] = dict_bal[key]
+                else:
+                    pass
+                count += 1
+                x >>= 1    #右移一位
+        
+        dict_baltime=dict(sorted(dict_baltime.items(),key=lambda dict_baltime:dict_baltime[0]))
+        for key in dict_baltime:    #解析均衡的电芯编号
+            if self.celltype==1:    #科易6040
+                if key<14:
+                    dict_baltime1[key]=dict_baltime[key]
+                elif key<18:
+                    dict_baltime1[key-1]=dict_baltime[key]
+                else:
+                    dict_baltime1[key-3]=dict_baltime[key]
+            elif self.celltype==1:    #科易4840
+                if key<4:
+                    dict_baltime1[key-1]=dict_baltime[key]
+                elif key<8:
+                    dict_baltime1[key-1]=dict_baltime[key]
+                elif key<14:
+                    dict_baltime1[key-3]=dict_baltime[key]
+                elif key<18:
+                    dict_baltime1[key-4]=dict_baltime[key]
+                else:
+                    dict_baltime1[key-6]=dict_baltime[key]
+            else:
+                dict_baltime1=dict_baltime
+        return dict_baltime1
+
+    #三元电池的内短路电流计算...........................................................................................................................................................
+    def _ncm_intershort(self):
+        column_name=['time_st', 'time_sp', 'sn', 'method','short_current','baltime']
+        df_res=pd.DataFrame(columns=column_name)
+        
+        if not self.df_bms.empty:
+            if self.df_soh.empty:
+                batsoh=self.df_bms.loc[0,'PackSOH']
+                capacity=self.param.Capacity*batsoh/100
+            else:
+                batsoh=self.df_soh.loc[len(self.df_soh)-1,'soh']
+                capacity=self.param.Capacity*batsoh/100
+            standingtime=0
+            standingtime1=0
+            firsttime=1
+            firsttime1=1
+            dict_bal={}
+            dict_bal1={}
+
+            for i in range(2,len(self.df_bms)-2):
+
+                if firsttime1==0:   #满电静置算法--计算均衡状态对应的均衡时间
+                    try:
+                        balstat=int(self.df_bms.loc[i,'单体均衡状态'])
+                        if balstat>0.5:
+                            bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                            bal_step=int(bal_step)
+                            if str(balstat) in dict_bal1.keys():
+                                dict_bal1[str(balstat)]=dict_bal1[str(balstat)]+bal_step
+                            else:
+                                dict_bal1[str(balstat)]=bal_step
+                        else:
+                            pass
+                    except:
+                        dict_bal1={}
+                else:
+                    pass
+
+                if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     
+                    delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                    standingtime=standingtime+delttime
+                    standingtime1=standingtime1+delttime
+                    self._celltemp_weight(i)
+
+                    #静置法计算内短路-开始.....................................................................................................................................
+                    if firsttime==1:    
+                        if standingtime>self.StandardStandingTime*2:      #静置时间满足要求
+                            standingtime=0
+                            cellvolt_now=self._cellvolt_get(i)
+                            cellvolt_min=min(cellvolt_now)
+                            cellvolt_max=max(cellvolt_now)
+                            cellvolt_last=self._cellvolt_get(i-1)
+                            deltvolt=max(abs(cellvolt_now-cellvolt_last))
+
+                            if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.003: 
+                                dict_baltime=self._bal_time(dict_bal)   #获取每个电芯的均衡时间
+                                deltsoc_last=self._celldeltsoc_get(i,dict_baltime,capacity)
+                                time_last=self.bmstime[i]
+                                firsttime=0
+                        else:
+                            pass                
+                    elif standingtime>3600*10:
+                        standingtime=0
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))
+                        
+                        if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.003:
+                            dict_baltime=self._bal_time(dict_bal)   #获取每个电芯的均衡时间
+                            deltsoc_now=self._celldeltsoc_get(i,dict_baltime,capacity)
+                            time_now=self.bmstime[i]
+                            
+                            list_sub=deltsoc_now-deltsoc_last
+                            list_pud=[0.01*capacity*3600*1000/(time_now-time_last).total_seconds()]*self.param.CellVoltNums
+                            leak_current=list_sub*list_pud
+                            # leak_current=np.array(leak_current)
+                            leak_current=np.round(leak_current,3)
+                            leak_current=list(leak_current)
+                            
+                            df_res.loc[len(df_res)]=[time_last,time_now,self.sn,1,str(leak_current),str(dict_baltime)]  #计算结果存入Dataframe
+                            time_last=time_now  #更新时间
+                            deltsoc_last=deltsoc_now    #更新soc差
+                            dict_bal={}
+                    else: 
+                        try:  
+                            balstat=int(self.df_bms.loc[i,'单体均衡状态'])
+                            if balstat>0.5:
+                                bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                                bal_step=int(bal_step)
+                                if str(balstat) in dict_bal.keys():
+                                    dict_bal[str(balstat)]=dict_bal[str(balstat)]+bal_step
+                                else:
+                                    dict_bal[str(balstat)]=bal_step
+                            else:
+                                pass
+                        except:
+                            dict_bal={}
+
+                    #满电静置法计算内短路-开始.....................................................................................................................................................
+                    if self.StandardStandingTime<standingtime1<self.StandardStandingTime*2:  
+                        standingtime1=0
+                        cellvolt_now1=self._cellvolt_get(i)
+                        cellvolt_max1=max(cellvolt_now1)
+                        cellvolt_min1=min(cellvolt_now1)
+                        cellvolt_last1=self._cellvolt_get(i-1)
+                        deltvolt1=max(abs(cellvolt_now1-cellvolt_last1))
+                        cellsoc_now1=np.interp(cellvolt_max1,self.param.LookTab_OCV,self.param.LookTab_SOC)
+
+                        if cellsoc_now1>=self.param.FullChrgSoc-8 and 3<cellvolt_min1<4.5 and 3<cellvolt_max1<4.5 and deltvolt1<0.003:
+                            if firsttime1==1:
+                                dict_baltime1=self._bal_time(dict_bal1)   #获取每个电芯的均衡时间
+                                deltsoc_last1=self._celldeltsoc_get(i,dict_baltime1,capacity)
+                                time_last1=self.bmstime[i]
+                                firsttime1=0
+                            else:
+                                dict_baltime1=self._bal_time(dict_bal1)   #获取每个电芯的均衡时间
+                                time_now1=self.bmstime[i]
+                                if (time_now1-time_last1).total_seconds()>3600*24:
+                                    deltsoc_now1=self._celldeltsoc_get(i,dict_baltime1,capacity)
+
+                                    list_sub1=deltsoc_now1-deltsoc_last1
+                                    list_pud1=[0.01*capacity*3600*1000/(time_now1-time_last1).total_seconds()]*self.param.CellVoltNums
+                                    leak_current1=list_sub1*list_pud1
+                                    # leak_current1=np.array(leak_current1)
+                                    leak_current1=np.round(leak_current1,3)
+                                    leak_current1=list(leak_current1)
+                                    
+                                    df_res.loc[len(df_res)]=[time_last1,time_now1,self.sn,2,str(leak_current1),str(dict_baltime1)]  #计算结果存入Dataframe
+                                    time_last1=time_now1  #更新时间
+                                    deltsoc_last1=deltsoc_now1    #更新soc差
+                                    dict_bal1={}
+                                else:
+                                    pass
+                        else:
+                            pass
+                    else:   
+                        pass
+
+                else:
+                    dict_bal={} 
+                    firsttime=1
+                    standingtime=0
+                    standingtime1=0
+                    pass
+
+        if df_res.empty:    #返回计算结果
+            return pd.DataFrame()
+        else:
+            return df_res
+
+    #磷酸铁锂电池内短路计算程序.............................................................................................................................
+    def _lfp_intershort(self):
+        column_name=['time_st', 'time_sp', 'sn', 'method','short_current','baltime']
+        df_res=pd.DataFrame(columns=column_name)
+        if not self.df_bms.empty:
+            if self.df_soh.empty:
+                batsoh=self.df_bms.loc[0,'PackSOH']
+                capacity=self.param.Capacity*batsoh/100
+            else:
+                batsoh=self.df_soh.loc[len(self.df_soh)-1,'soh']
+                capacity=self.param.Capacity*batsoh/100
+            standingtime=0
+            standingtime1=0
+            firsttime=1
+            firsttime1=1
+            dict_bal={}
+            dict_bal1={}
+
+            chrg_start=[]
+            chrg_end=[]
+            dict_bal_list=[]
+            charging=0
+            dict_bal3={}
+
+            for i in range(3,len(self.df_bms)-3):
+
+                #静置法计算内短路..........................................................................................................................
+                if firsttime1==0:   #满电静置算法--计算均衡状态对应的均衡时间
+                    try:
+                        balstat=int(self.df_bms.loc[i,'单体均衡状态'])
+                        if balstat>0.5:
+                            bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                            bal_step=int(bal_step)
+                            if str(balstat) in dict_bal1.keys():
+                                dict_bal1[str(balstat)]=dict_bal1[str(balstat)]+bal_step
+                            else:
+                                dict_bal1[str(balstat)]=bal_step
+                        else:
+                            pass
+                    except:
+                        dict_bal1={}
+                else:
+                    pass
+
+                if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     
+                    delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                    standingtime=standingtime+delttime
+                    standingtime1=standingtime1+delttime
+                    self._celltemp_weight(i)
+
+                    #静置法计算内短路-开始.....................................................................................................................................
+                    if firsttime==1:    
+                        if standingtime>self.StandardStandingTime:      #静置时间满足要求
+                            standingtime=0
+                            cellvolt_now=self._cellvolt_get(i)
+                            cellvolt_min=min(cellvolt_now)
+                            cellvolt_max=max(cellvolt_now)
+                            cellvolt_last=self._cellvolt_get(i-1)
+                            deltvolt=max(abs(cellvolt_now-cellvolt_last))
+
+                            if 2<cellvolt_max<self.param.OcvInflexionBelow-0.002 and 2<cellvolt_min<4.5 and abs(deltvolt)<0.003:
+                                dict_baltime=self._bal_time(dict_bal)   #获取每个电芯的均衡时间
+                                deltsoc_last=self._celldeltsoc_get(i,dict_baltime,capacity)
+                                time_last=self.bmstime[i]
+                                firsttime=0
+                            else:
+                                pass
+                        else:
+                            pass                
+                    elif standingtime>3600*12:
+                        standingtime=0
+                        cellvolt_now=np.array(self._cellvolt_get(i))
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=np.array(self._cellvolt_get(i-1))
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))
+
+                        if 2<cellvolt_max<self.param.OcvInflexionBelow-0.002 and 2<cellvolt_min<4.5  and abs(deltvolt)<0.003:
+                            dict_baltime=self._bal_time(dict_bal)   #获取每个电芯的均衡时间
+                            deltsoc_now=self._celldeltsoc_get(i, dict_baltime,capacity)    #获取每个电芯的SOC差
+                            time_now=self.bmstime[i]
+
+                            list_sub=deltsoc_now-deltsoc_last
+                            list_pud=[0.01*capacity*3600*1000/(time_now-time_last).total_seconds()]*self.param.CellVoltNums
+                            leak_current=list_sub*list_pud
+                            # leak_current=np.array(leak_current)
+                            leak_current=np.round(leak_current,3)
+                            leak_current=list(leak_current)
+                            
+                            df_res.loc[len(df_res)]=[time_last,time_now,self.sn,1,str(leak_current),str(dict_baltime)]  #计算结果存入Dataframe
+                            time_last=time_now  #更新时间
+                            deltsoc_last=deltsoc_now    #更新soc差
+                            dict_bal={}
+                        else:
+                            pass
+                    else: 
+                        try:  
+                            balstat=int(self.df_bms.loc[i,'单体均衡状态'])
+                            if balstat>0.5:
+                                bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                                bal_step=int(bal_step)
+                                if str(balstat) in dict_bal.keys():
+                                    dict_bal[str(balstat)]=dict_bal[str(balstat)]+bal_step
+                                else:
+                                    dict_bal[str(balstat)]=bal_step
+                            else:
+                                pass
+                        except:
+                            dict_bal={}
+
+                    #非平台区间静置法计算内短路-开始.....................................................................................................................................................
+                    if standingtime1>self.StandardStandingTime: 
+                        standingtime1=0
+                        cellvolt_now1=self._cellvolt_get(i)
+                        cellvolt_max1=max(cellvolt_now1)
+                        cellvolt_min1=min(cellvolt_now1)
+                        cellvolt_last1=self._cellvolt_get(i-1)
+                        deltvolt1=max(abs(cellvolt_now1-cellvolt_last1))
+                    
+                        if 2<cellvolt_max1<self.param.OcvInflexionBelow-0.002 and 2<cellvolt_min1<4.5 and deltvolt1<0.003:
+                            if firsttime1==1:
+                                dict_baltime1=self._bal_time(dict_bal1)   #获取每个电芯的均衡时间
+                                deltsoc_last1=self._celldeltsoc_get(i,dict_baltime1,capacity)
+                                time_last1=self.bmstime[i]
+                                firsttime1=0
+                            else:
+                                dict_baltime1=self._bal_time(dict_bal1)   #获取每个电芯的均衡时间
+                                deltsoc_now1=self._celldeltsoc_get(i,dict_baltime1,capacity)
+                                time_now1=self.bmstime[i]
+
+                                time_now1=self.bmstime[i]
+                                if abs(max(deltsoc_now1)-max(deltsoc_last1))<10 and (time_now1-time_last1).total_seconds()>3600*24:
+                                    list_sub1=deltsoc_now1-deltsoc_last1
+                                    list_pud1=[0.01*capacity*3600*1000/(time_now1-time_last1).total_seconds()]*self.param.CellVoltNums
+                                    leak_current1=list_sub1*list_pud1
+                                    # leak_current1=np.array(leak_current1)
+                                    leak_current1=np.round(leak_current1,3)
+                                    leak_current1=list(leak_current1)
+                                    
+                                    df_res.loc[len(df_res)]=[time_last1,time_now1,self.sn,2,str(leak_current1),str(dict_baltime1)]  #计算结果存入Dataframe
+                                    time_last1=time_now1  #更新时间
+                                    deltsoc_last1=deltsoc_now1    #更新soc差
+                                    dict_bal1={}
+                                else:
+                                    pass
+                                
+                        else:
+                            pass
+                    else:   
+                        pass
+
+                else:
+                    dict_bal={} 
+                    firsttime=1
+                    standingtime=0
+                    standingtime1=0
+                    pass
+
+                #获取充电数据——开始..............................................................................................................
+                try:
+                    balstat=int(self.df_bms.loc[i,'单体均衡状态'])  #统计均衡状态
+                    if balstat>0.5:
+                        bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                        bal_step=int(bal_step)
+                        if str(balstat) in dict_bal3.keys():
+                            dict_bal3[str(balstat)]=dict_bal3[str(balstat)]+bal_step
+                        else:
+                            dict_bal3[str(balstat)]=bal_step
+                    else:
+                        pass
+                except:
+                    dict_bal3={}
+
+                if charging==0:
+                    
+                    if self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i+2]<=-1 and self.bms_soc[i]<40:     #判断充电开始
+                        cellvolt_now=self._cellvolt_get(i)
+                        if min(cellvolt_now)<self.param.CellFullChrgVolt-0.15:
+                            charging=1
+                            if len(chrg_start)>len(chrg_end):
+                                chrg_start[-1]=i
+                            else:
+                                chrg_start.append(i)
+                        else:
+                            pass
+                    else:
+                        pass
+
+                else: #充电中
+                    if (self.bmstime[i+1]-self.bmstime[i]).total_seconds()>180 or (self.packcrnt[i]>self.param.Capacity/3 and self.packcrnt[i+1]>self.param.Capacity/3):  #如果充电过程中时间间隔>180s，则舍弃该次充电
+                        chrg_start.remove(chrg_start[-1])
+                        charging=0
+                        continue
+                    elif self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and  self.packcrnt[i+2]<-1:  
+                        cellvolt_now=self._cellvolt_get(i)
+                        if min(cellvolt_now)>self.param.CellFullChrgVolt-0.11:   #电压>满充电压-0.13V，即3.37V
+                            self._celltemp_weight(i)
+                            if i-chrg_start[-1]>10 and self.celltemp>10:
+                                chrg_end.append(i+1)
+                                dict_bal_list.append(dict_bal3)
+                                dict_bal3={}
+                                charging=0                       
+                                continue
+                            else:
+                                chrg_start.remove(chrg_start[-1])
+                                charging=0
+                                continue
+                        else:
+                            pass
+                    else:
+                        pass   
+            
+            #基于充电数据计算单体电芯的漏电流..........................................................................................................
+            if len(chrg_end)>1:    
+                for i in range(len(chrg_end)):
+                    if i<1:
+                        dict_baltime={}
+                        deltAs_last=self._cellDeltAs_get(chrg_start[i],chrg_end[i],dict_baltime)
+                        time_last=self.bmstime[chrg_end[i]]
+                    else:
+                        dict_baltime=self._bal_time(dict_bal_list[i])   #获取每个电芯的均衡时间
+                        deltAs_now=self._cellDeltAs_get(chrg_start[i],chrg_end[i],dict_baltime)  #获取每个电芯的As差
+                        time_now=self.bmstime[chrg_end[i]]
+
+                        list_sub=deltAs_now-deltAs_last
+                        list_pud=[-1000/(time_now-time_last).total_seconds()]*self.param.CellVoltNums
+                        leak_current=list_sub*list_pud
+                        # leak_current=np.array(leak_current)
+                        leak_current=np.round(leak_current,3)
+                        leak_current=list(leak_current)
+
+                        df_res.loc[len(df_res)]=[time_last,time_now,self.sn,3,str(leak_current),str(dict_baltime)]  #计算结果存入Dataframe
+                        deltAs_last=deltAs_now
+                        time_last=time_now
+            else:
+                pass
+
+        if df_res.empty:
+            return pd.DataFrame()
+        else:
+            return df_res

USER/SPF/alibaba/alibaba/03BatInterShort/main.py

@@ -0,0 +1,65 @@
+import CBMSBatInterShort
+import datetime
+import pandas as pd
+import datetime
+from USER.alibaba.Common import DBDownload
+from USER.alibaba.Common import log
+
+if __name__ == "__main__":
+
+    SNnums= ['HUATING01']
+
+    end_time=datetime.datetime.now()-datetime.timedelta(seconds=10)
+    start_time=end_time-datetime.timedelta(seconds=12000)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    #log信息配置............................................................................................................................................................................
+    mylog=log.Mylog('log_short.txt','error')
+    mylog.logcfg()
+
+    for sn in SNnums:
+        try:
+            # if 'PK500' in sn:
+            #     celltype=1 #6040三元电芯
+            # elif 'PK502' in sn:
+            #     celltype=2 #4840三元电芯
+            # elif 'PK504' in sn:
+            #     celltype=99    #60ah林磷酸铁锂电芯
+            # elif 'MGMLXN750' in sn:
+            #     celltype=3 #力信50ah三元电芯
+            # elif 'MGMCLN750' in sn: 
+            #     celltype=4 #CATL 50ah三元电芯
+            # else:
+            #     print('未找到对应电池编号！！！\n',sn)
+            #     # sys.exit()
+
+            celltype=2
+
+            #............................................................读取原始数据库数据...........................................................................
+            df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-内短路原始数据.xlsx')
+
+            #............................................................读取结果数据库数据.........................................................................
+            # host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+            # port=3306
+            # db='qx_cas'
+            # user='qx_read'
+            # password='Qx@123456'
+            # mode=1
+            # tablename='cellstateestimation_soh'
+            # DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
+            # with DBRead as DBRead:
+            #     df_soh=DBRead.getdata(param='time_st,time_sp,sn,method,soh,cellsoh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+            df_soh=pd.DataFrame()
+            # print(df_soh)
+            
+            #...........................................................................计算漏电流............................................................................
+            BatInterShort=CBMSBatInterShort.BatInterShort(sn,celltype,df_bms,df_soh)
+            df_res=BatInterShort.intershort()
+            df_res.to_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\99Result\\'+'CBMS_short_'+sn+'_2.xlsx')
+            # print('done！！！')
+        
+        except Exception as e:
+            print(repr(e))
+            mylog.logopt(sn,e)
+            pass

USER/SPF/alibaba/alibaba/04BatSoc/04BatSoc/CBMSBatSoc.py

@@ -0,0 +1,557 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from pymysql import paramstyle
+from scipy.interpolate import interp2d
+from USER.alibaba.Common import BatParam
+import datetime
+
+class BatSoc():
+    def __init__(self,sn,celltype,df_bms,df_soh,df_ram_sn,df_socdiff):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_soh=df_soh
+        self.df_socdiff=df_socdiff
+        self.df_ram_sn=df_ram_sn
+        df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        self.df_bms=df_bms
+
+        if not self.df_ram_sn.empty:
+            self.df_bms=self.df_bms[self.df_bms['time'] > self.df_ram_sn.iloc[-1]['time']]    #滤除原始数据中的重复数据
+            self.df_bms.reset_index(inplace=True,drop=True)     #重置索引
+        self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
+        self.bms_soc=df_bms['PackSOC']
+        self.bms_soh=df_bms['PackSOH']
+        self.bmstime= pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+
+        self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+
+    def batsoc(self):
+        if self.celltype<50:
+            if not self.df_bms.empty:
+                df_res, df_ram=self._ncm_soc()
+                return df_res, df_ram
+            else:
+                return pd.DataFrame(), pd.DataFrame()
+            
+        elif self.celltype>50:
+            if not self.df_bms.empty:
+                df_res, df_ram=self._lfp_soc()
+                return df_res, df_ram
+            else:
+                return pd.DataFrame(), pd.DataFrame()
+        
+        else:
+            return pd.DataFrame()
+
+    #........................................................寻找当前行数据的最小温度值...........................................................................................
+    def _celltemp_weight(self,num):   
+        celltemp = list(self.df_bms.loc[num,self.celltemp_name])
+        celltemp=np.mean(celltemp)
+        self.celltemp=celltemp
+        if self.celltype==99:
+            if celltemp>=25:
+                self.tempweight=1
+                self.StandardStandingTime=3600
+            elif celltemp>=15:
+                self.tempweight=0.6
+                self.StandardStandingTime=7200
+            elif celltemp>=5:
+                self.tempweight=0.
+                self.StandardStandingTime=10800
+            else:
+                self.tempweight=0.1
+                self.StandardStandingTime=18000
+        else:
+            if celltemp>=20:
+                self.tempweight=1
+                self.StandardStandingTime=1800
+            elif celltemp>=10:
+                self.tempweight=0.8
+                self.StandardStandingTime=3600
+            elif celltemp>=5:
+                self.tempweight=0.6
+                self.StandardStandingTime=7200
+            else:
+                self.tempweight=0.2
+                self.StandardStandingTime=10800
+
+    #.......................................................获取当前行所有电压数据...............................................................................................
+    def _cellvolt_get(self,num): 
+        cellvolt = np.array(self.df_bms.loc[num,self.cellvolt_name])
+        return cellvolt
+    
+    #......................................................判断单体电压离散度,并返回OCV值....................................................................................
+    def _ocv_dispersion(self):
+
+        cellvolt=self.df_bms[self.cellvolt_name]
+        cellocv=cellvolt.mean(axis=0)
+        cellocv_std=cellvolt.std(axis=0)
+        cellocv_std=cellocv_std.fillna(0)
+        if max(cellocv_std)<0.002:
+            return np.array(cellocv)
+        else:
+            return [0]
+
+    #...........................................................三元电池的soc计算...............................................................................................
+    def _ncm_soc(self):
+        column_name=['time', 'sn', 'bms_soc', 'packsoc', 'socdsp', 'cellsocmin', 'cellsocmax','socmin_num','socmax_num','cellsoc_diff','cellsoc','ocvweight','socstep']
+        df_res=pd.DataFrame(columns=column_name)
+        df_bms_len=len(self.df_bms)
+        rampackcrnt=self.packcrnt[df_bms_len-1]
+        ocvweight=0
+        
+        #获取电池包SOH................................................................................................
+        if self.df_soh.empty: 
+            batsoh=self.bms_soh[0]
+            if batsoh>30:
+                capacity=self.param.Capacity*batsoh/100
+            else:
+                capacity=self.param.Capacity
+        else:
+            batsoh=eval(self.df_soh.loc[0,'cellsoh'])
+            batsoh=np.array(batsoh)
+            capacity=self.param.Capacity*batsoh/100
+
+        #计算静置时间和累计As量..................................................................................................
+        if not self.df_ram_sn.empty:
+            standingtime=self.df_ram_sn.iloc[-1]['standingtime']
+            as_accum=0
+            for i in range(df_bms_len):
+                if i==0:
+                    step=(self.bmstime[i]-self.df_ram_sn.iloc[-1]['time']).total_seconds()
+                    if abs(self.packcrnt[i])<0.1 and abs(self.df_ram_sn.iloc[-1]['rampackcrnt'])<0.1:
+                        standingtime=standingtime+step
+                    else:
+                        standingtime=0
+                        if step<120:
+                            as_accum=as_accum-self.packcrnt[i]*step
+                        else:
+                            as_accum=as_accum+((self.bms_soc[i]-self.df_ram_sn.iloc[-1]['bms_soc'])*capacity*3600/100)
+                else:
+                    step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                    if abs(self.packcrnt[i])<0.1 and abs(self.packcrnt[i-1])<0.1:
+                        standingtime=standingtime+step
+                        as_accum=as_accum-self.packcrnt[i]*step
+                    else:
+                        standingtime=0
+                        as_accum=as_accum-self.packcrnt[i]*step
+        else:
+            standingtime=0
+            as_accum=0
+            for i in range(df_bms_len):
+                if i==0:
+                    pass
+                else:
+                    step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                    if abs(self.packcrnt[i])<0.1 and abs(self.packcrnt[i-1])<0.1:
+                        standingtime=standingtime+step
+                        as_accum=as_accum-self.packcrnt[i]*step
+                    else:
+                        standingtime=0
+                        as_accum=as_accum-self.packcrnt[i]*step
+        
+        if standingtime>3600*24*30:
+            standingtime=3600*24*30
+        else:
+            pass
+        
+        #计算单体SOC.............................................................................................................................
+        if not self.df_ram_sn.empty:
+            self._celltemp_weight(0)
+            ramcellvolt=self.df_ram_sn.iloc[-1]['ramcellvolt']
+            ramcellsoc=self.df_ram_sn.iloc[-1]['cellsoc']
+            ocvweight_2dlook=interp2d(self.param.OcvWeight_StandingTime, self.param.OcvWeight_Temp, self.param.OcvWeight, kind = 'linear')
+            ocvweight=ocvweight_2dlook(standingtime, self.celltemp)
+            ocvweight=ocvweight[0]
+
+            if self.bmstime[0]>datetime.datetime.strptime('2021-10-22 19:49:20','%Y-%m-%d %H:%M:%S'):
+                pass
+
+
+            if ocvweight>0.01:
+                cellocv=self._ocv_dispersion()
+
+                if df_bms_len>1:      #数据字段长度≥2
+                    if 2<max(cellocv)<4.5 and 2<min(cellocv)<4.5:
+                        ocvsoc=np.array(list(map(lambda x:np.interp(x,self.param.LookTab_OCV,self.param.LookTab_SOC),cellocv)))
+                        cellsoc=ocvsoc*ocvweight+ramcellsoc*(1-ocvweight)
+                        socstep=0
+                    else:
+                        cellsoc=ramcellsoc
+                        socstep=1
+                else:       #数据字段长度==1
+                    if 2<max(cellocv)<4.5 and 2<min(cellocv)<4.5 and max(abs(ramcellvolt-np.array(cellocv)))<0.004:
+                        ocvsoc=np.array(list(map(lambda x:np.interp(x,self.param.LookTab_OCV,self.param.LookTab_SOC),cellocv)))
+                        cellsoc=ocvsoc*ocvweight+ramcellsoc*(1-ocvweight)
+                        socstep=2
+                    else:
+                        cellsoc=ramcellsoc
+                        socstep=3
+
+            else:
+                cellsoc=ramcellsoc+(as_accum*100)/(3600*capacity)
+                socstep=4
+
+
+        else:
+            cellsoc= np.array([self.bms_soc[df_bms_len-1]])+np.random.rand(self.param.CellVoltNums)
+            socstep=5
+
+        #更新ram电芯电压
+        ramcellvolt=self._cellvolt_get(df_bms_len-1)
+        
+        #单体Soc满充修正........................................................................................................................................................................
+        cellvoltall=self.df_bms[self.cellvolt_name] #筛选所有单体电压
+        cellvoltall_min=cellvoltall.min(axis=0) #每一个单体电芯的最小值
+        if self.param.CellFullChrgVolt<max(cellvoltall_min)<5 and self.param.CellFullChrgVolt<max(cellvoltall_min)<5 and min(self.packcrnt)>self.param.CellFullChrgCrnt and max(self.packcrnt)<-0.5:
+            deltsoc=self.param.FullChrgSoc-max(cellsoc)
+            cellsoc=cellsoc+deltsoc
+        else:
+            pass
+
+        #计算电池包packsoc和最大最小SOC..................................................................................................................................................................................
+        cellsoc[cellsoc>100]=100    #限值最大值100
+        cellsoc[cellsoc<0]=0    #限值最小值0
+        cellsoc1=list(map(lambda x:float(format(x,'.1f')), cellsoc))
+        
+        cellsocmin=min(cellsoc1)
+        cellsocmax=max(cellsoc1)
+        cellsoc_diff=cellsocmax-cellsocmin
+        socmin_num=cellsoc1.index(cellsocmin)+1
+        socmax_num=cellsoc1.index(cellsocmax)+1
+        alpha=(cellsocmin+cellsocmax)*0.02-2    #blending系数计算
+        if cellsocmax>90 or alpha>1:
+            alpha=1
+        elif cellsocmin<10 or alpha<-1:
+            alpha=-1
+        else:
+            pass
+        packsoc=(cellsocmin+cellsocmax)*0.5+(cellsocmax-cellsocmin)*0.5*alpha
+        packsoc=eval(format(packsoc,'.1f'))
+        socdsp=packsoc
+
+        #输出ram结果....................................................................................................................................................................
+        list_ram=[self.bmstime[df_bms_len-1], self.sn, self.bms_soc[df_bms_len-1], packsoc, cellsoc, standingtime, rampackcrnt, ramcellvolt,0,0,ocvweight,as_accum,socstep]
+        df_ram=pd.DataFrame(columns=self.df_ram_sn.columns)
+        df_ram.loc[0]=list_ram
+
+        #输出计算结果........................................................................................................................................................................
+        df_res.loc[0]=[self.bmstime[df_bms_len-1], self.sn, self.bms_soc[df_bms_len-1], packsoc, socdsp, cellsocmin, cellsocmax,socmin_num,socmax_num,cellsoc_diff,str(cellsoc1),ocvweight,socstep]
+        return df_res, df_ram
+
+    #...........................................................磷酸铁锂电池的soc计算...............................................................................................
+    def _lfp_soc(self):
+        column_name=['time', 'sn', 'bms_soc', 'packsoc', 'socdsp', 'cellsocmin', 'cellsocmax','socmin_num','socmax_num','cellsoc_diff','cellsoc','ocvweight','socstep']
+        df_res=pd.DataFrame(columns=column_name)
+        df_bms_len=len(self.df_bms)
+        rampackcrnt=self.packcrnt[df_bms_len-1]
+        if not self.df_socdiff.empty:
+            cellsoc_diff=self.df_socdiff.iloc[-1]['cellsoc_diff']
+        else:
+            cellsoc_diff=2
+        
+        #获取电池包SOH................................................................................................
+        if self.df_soh.empty:
+            batsoh=self.bms_soh[0]
+            capacity=self.param.Capacity*batsoh/100
+        else:
+            batsoh=self.df_soh.loc[0,'soh']
+            capacity=self.param.Capacity*batsoh/100
+
+        #计算静置时间和累计As量以及OCV权重值..................................................................................................
+        if not self.df_ram_sn.empty:
+            standingtime=self.df_ram_sn.iloc[-1]['standingtime']
+            ocvweight=self.df_ram_sn.iloc[-1]['ocvweight']
+            as_accum=0
+            for i in range(df_bms_len):
+                if i==0:
+                    step=(self.bmstime[i]-self.df_ram_sn.iloc[-1]['time']).total_seconds()
+                    if abs(self.packcrnt[i])<0.1 and abs(self.df_ram_sn.iloc[-1]['rampackcrnt'])<0.1:
+                        standingtime=standingtime+step
+                    else:
+                        standingtime=0
+                        if step<120:
+                            as_accum=as_accum-self.packcrnt[i]*step
+                        else:
+                            as_accum=as_accum+((self.bms_soc[i]-self.df_ram_sn.iloc[-1]['bms_soc'])*capacity*3600/100)
+                else:
+                    step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                    if abs(self.packcrnt[i])<0.1 and abs(self.packcrnt[i-1])<0.1:
+                        standingtime=standingtime+step
+                        as_accum=as_accum-self.packcrnt[i]*step
+                    else:
+                        standingtime=0
+                        as_accum=as_accum-self.packcrnt[i]*step
+        
+        else:
+            standingtime=0
+            ocvweight=0.5
+            as_accum=0
+            for i in range(df_bms_len):
+                if i==0:
+                    pass
+                else:
+                    step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                    if abs(self.packcrnt[i])<0.1 and abs(self.packcrnt[i-1])<0.1:
+                        standingtime=standingtime+step
+                        as_accum=as_accum-self.packcrnt[i]*step
+                    else:
+                        standingtime=0
+                        as_accum=as_accum-self.packcrnt[i]*step
+
+        #静置时间限值..............................................
+        if standingtime>3600*24*30:     
+            standingtime=3600*24*30
+        else:
+            pass
+            
+        #权重值计算.................................................
+        ocvweight=ocvweight-as_accum*10/(capacity*3600)
+        if ocvweight>1:
+            ocvweight=1
+        elif ocvweight<0:
+            ocvweight=0
+        else:
+            pass
+        ocvweight=eval(format(ocvweight,'.2f'))
+        
+        #计算单体最大最小SOC.............................................................................................................................
+        cellocv=self._ocv_dispersion()  #获取每一列电压的平均值
+        if not self.df_ram_sn.empty:
+            self._celltemp_weight(0)
+            kocellvoltmin=self.df_ram_sn.iloc[-1]['kocellvoltmin']
+            kocellvoltmax=self.df_ram_sn.iloc[-1]['kocellvoltmax']
+            ramcellsoc=self.df_ram_sn.iloc[-1]['cellsoc']
+            #满足静置时间
+            if standingtime>self.StandardStandingTime: 
+                #数据字段长度≥2............................................................................................................... 
+                if df_bms_len>1:      
+                    ocvsocmin=np.interp(min(cellocv),self.param.LookTab_OCV,self.param.LookTab_SOC)
+                    ocvsocmax=np.interp(max(cellocv),self.param.LookTab_OCV,self.param.LookTab_SOC)
+                    if 2<min(cellocv)<self.param.OcvInflexionBelow and 2<max(cellocv)<self.param.OcvInflexionBelow and ocvsocmax<self.param.SocInflexion1:
+                        cellsocmin=ocvsocmin
+                        cellsocmax=ocvsocmax
+                        socstep=0
+                    elif 2<min(cellocv)<self.param.OcvInflexionBelow and max(cellocv)>=self.param.OcvInflexionBelow and ocvsocmin<self.param.SocInflexion1:
+                        cellsocmin=ocvsocmin
+                        cellsocmax=ocvsocmin+cellsoc_diff
+                        socstep=1
+                    else:
+                        #第一平台向第二平台修正
+                        if ocvsocmax>self.param.SocInflexion3+5 and max(ramcellsoc)<self.param.SocInflexion2-5:
+                            if  ocvsocmin>self.param.SocInflexion3+5 and min(ramcellsoc)<self.param.SocInflexion2-5:
+                                cellsocmin=self.param.SocInflexion3
+                                cellsocmax=cellsocmin+cellsoc_diff
+                                socstep=2
+                            else:
+                                cellsocmax=self.param.SocInflexion3
+                                cellsocmin=cellsocmax-cellsoc_diff
+                                socstep=3
+                        #第一非平台区向第一平台区修正
+                        elif ocvsocmax>self.param.SocInflexion1+5 and max(ramcellsoc)<self.param.SocInflexion1-2:
+                            if ocvsocmin>self.param.SocInflexion1+5 and min(ramcellsoc)<self.param.SocInflexion1-2:
+                                cellsocmin=self.param.SocInflexion1
+                                cellsocmax=cellsocmin+cellsoc_diff
+                                socstep=4
+                            else:
+                                cellsocmax=self.param.SocInflexion1
+                                cellsocmin=cellsocmax-cellsoc_diff
+                                socstep=5
+                        #第二平台区向第一平台区修正
+                        elif ocvsocmin<self.param.SocInflexion2-5 and min(ramcellsoc)>self.param.SocInflexion3+5:
+                            if ocvsocmax<self.param.SocInflexion2-5 and max(ramcellsoc)>self.param.SocInflexion3+5:
+                                cellsocmax=self.param.SocInflexion2
+                                cellsocmin=cellsocmax-cellsoc_diff
+                                socstep=6
+                            else:
+                                cellsocmin=self.param.SocInflexion2
+                                cellsocmax=cellsocmin+cellsoc_diff
+                                socstep=7
+                        else:
+                            cellsoc=ramcellsoc
+                            socstep=8
+                #数据字段长度==1..............................................................................................
+                else:       
+                    cellvolt0=self._cellvolt_get(0)
+                    ramcellvolt=self.df_ram_sn.iloc[-1]['ramcellvolt']
+                    if max(abs(cellvolt0-ramcellvolt))<0.004:
+                        ocvmax=max(cellvolt0)
+                        ocvmin=min(cellvolt0)
+                        ocvsocmin=np.interp(ocvmin,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        ocvsocmax=np.interp(ocvmax,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        if 2<ocvmin<self.param.OcvInflexionBelow and 2<ocvmax<self.param.OcvInflexionBelow and ocvsocmax<self.param.SocInflexion1:
+                            cellsocmin=ocvsocmin
+                            cellsocmax=ocvsocmax
+                            socstep=9
+                        elif 2<ocvmin<self.param.OcvInflexionBelow and ocvmax>=self.param.OcvInflexionBelow and ocvsocmin<self.param.SocInflexion1:
+                            cellsocmin=ocvsocmin
+                            cellsocmax=ocvsocmin+cellsoc_diff
+                            socstep=10
+                        else:
+                            #第一平台向第二平台修正
+                            if ocvsocmax>self.param.SocInflexion3+5 and max(ramcellsoc)<self.param.SocInflexion2-5:
+                                if  ocvsocmin>self.param.SocInflexion3+5 and min(ramcellsoc)<self.param.SocInflexion2-5:
+                                    cellsocmin=self.param.SocInflexion3
+                                    cellsocmax=cellsocmin+cellsoc_diff
+                                    socstep=11
+                                else:
+                                    cellsocmax=self.param.SocInflexion3
+                                    cellsocmin=cellsocmax-cellsoc_diff
+                                    socstep=12
+                            #第一非平台区向第一平台区修正
+                            elif ocvsocmax>self.param.SocInflexion1+5 and max(ramcellsoc)<self.param.SocInflexion1-2:
+                                if ocvsocmin>self.param.SocInflexion1+5 and min(ramcellsoc)<self.param.SocInflexion1-2:
+                                    cellsocmin=self.param.SocInflexion1
+                                    cellsocmax=cellsocmin+cellsoc_diff
+                                    socstep=13
+                                else:
+                                    cellsocmax=self.param.SocInflexion1
+                                    cellsocmin=cellsocmax-cellsoc_diff
+                                    socstep=14
+                            #第二平台区向第一平台区修正
+                            elif ocvsocmin<self.param.SocInflexion2-5 and min(ramcellsoc)>self.param.SocInflexion3+5:
+                                if ocvsocmax<self.param.SocInflexion2-5 and max(ramcellsoc)>self.param.SocInflexion3+5:
+                                    cellsocmax=self.param.SocInflexion2
+                                    cellsocmin=cellsocmax-cellsoc_diff
+                                    socstep=15
+                                else:
+                                    cellsocmin=self.param.SocInflexion2
+                                    cellsocmax=cellsocmin+cellsoc_diff
+                                    socstep=16
+                            else:
+                                cellsoc=ramcellsoc
+                                socstep=17
+                    else:
+                        cellsoc=ramcellsoc
+                        socstep=17
+            #不满足静置时间，判断电压回弹方向
+            elif standingtime>130 and 2<kocellvoltmax<4.5 and 2<kocellvoltmin<4.5:  
+                cellvoltmin=min(cellocv)
+                cellvoltmax=max(cellocv)
+                ocvsocmin1=np.interp(cellvoltmin,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                ocvsocmax1=np.interp(cellvoltmax,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                #最大最小电压向上回弹
+                if 2<cellvoltmin<4.5 and cellvoltmin>kocellvoltmin+0.005 and ocvsocmin1>min(ramcellsoc):
+                    if 2<cellvoltmax<4.5 and cellvoltmax>kocellvoltmax+0.005 and ocvsocmax1>max(ramcellsoc):
+                        #最大最小电压均在非平台区
+                        if ocvsocmax1<self.param.SocInflexion1:
+                            cellsocmin=ocvsocmin1
+                            cellsocmax=ocvsocmax1
+                            socstep=18
+                        #最小电压均在非平台区，最大电压在平台区
+                        elif ocvsocmin1<self.param.SocInflexion1:
+                            cellsocmin=ocvsocmin1
+                            cellsocmax=cellsocmin+cellsoc_diff
+                            socstep=19
+                        #最大最小电压均在平台区，ramsoc在非平台区
+                        elif ocvsocmin1>self.param.SocInflexion1+5 and max(ramcellsoc)<self.param.SocInflexion1 and min(ramcellsoc)<self.param.SocInflexion1:
+                            cellsocmin=self.param.SocInflexion1
+                            cellsocmax=cellsocmin+cellsoc_diff
+                            socstep=20
+                        else:
+                            cellsoc=ramcellsoc
+                            socstep=21
+                    else:
+                        cellsoc=ramcellsoc
+                        socstep=22
+                #最大最小电压向下回弹
+                elif 2<cellvoltmin<4.5 and cellvoltmin<kocellvoltmin-0.005 and ocvsocmin1<min(ramcellsoc):
+                    if 2<cellvoltmax<4.5 and cellvoltmax<kocellvoltmax-0.005 and ocvsocmax1>max(ramcellsoc):
+                        #最大最小电压均在非平台区
+                        if ocvsocmax1<self.param.SocInflexion1:
+                            cellsocmin=ocvsocmin1
+                            cellsocmax=ocvsocmax1
+                            socstep=23
+                        #最小电压在非平台区，最大电压在平台区
+                        elif ocvsocmin1<self.param.SocInflexion1:
+                            cellsocmin=ocvsocmin1
+                            cellsocmax=cellsocmin+cellsoc_diff
+                            socstep=24
+                        else:
+                            cellsoc=ramcellsoc
+                            socstep=25
+                    else:
+                        cellsoc=ramcellsoc
+                        socstep=26
+                #电压回弹方向一致
+                else:
+                    cellsoc=ramcellsoc
+                    socstep=27
+
+            #更新ko电压
+            elif 70<standingtime<=130:   
+                cellsoc=ramcellsoc
+                socstep=28
+                cellvolt=self._cellvolt_get(df_bms_len-1)
+                kocellvoltmin=min(cellvolt)
+                kocellvoltmax=max(cellvolt)
+                
+            else:
+                cellsoc=ramcellsoc+(as_accum*100)/(3600*capacity)
+                socstep=29
+                #更新ko电芯电压
+                kocellvoltmin=0
+                kocellvoltmax=0       
+
+        else:
+            cellsocmin= self.bms_soc[df_bms_len-1]-cellsoc_diff/2
+            cellsocmax= self.bms_soc[df_bms_len-1]+cellsoc_diff/2
+            socstep=30
+            #更新ko电芯电压
+            kocellvoltmin=0
+            kocellvoltmax=0
+        
+        #更新Ram电压.................
+        ramcellvolt=self._cellvolt_get(df_bms_len-1)
+        
+        #单体Soc满充修正........................................................................................................................................................................
+        cellvoltall=self.df_bms[self.cellvolt_name] #筛选所有单体电压
+        cellvoltall_min=cellvoltall.min(axis=0) #每一个单体电芯的最小值
+        if self.param.CellFullChrgVolt<max(cellvoltall_min)<5 and self.param.CellFullChrgVolt<max(cellvoltall_min)<5 and min(self.packcrnt)>self.param.CellFullChrgCrnt and max(self.packcrnt)<-0.5:
+            deltsoc=self.param.FullChrgSoc-max(cellsoc)
+            cellsoc=cellsoc+deltsoc
+        else:
+            pass
+
+        #计算所有电芯SOC...........................................................................................
+        if not socstep in [8,17,21,22,25,26,27,28,29]:
+            looktab_ocv=[min(cellocv),max(cellocv)]
+            looktab_soc=[cellsocmin,cellsocmax]
+            cellsoc=np.array(list(map(lambda x:np.interp(x,looktab_ocv,looktab_soc),cellocv)))
+        else:
+            pass
+
+        #计算电池包packsoc和最大最小SOC..................................................................................................................................................................................
+        cellsoc[cellsoc>100]=100    #限值最大值100
+        cellsoc[cellsoc<0]=0    #限值最小值0
+        cellsoc1=list(map(lambda x:eval(format(x,'.1f')), cellsoc))
+        
+        cellsocmin=min(cellsoc1)
+        cellsocmax=max(cellsoc1)
+        cellsoc_diff=cellsocmax-cellsocmin
+        socmin_num=cellsoc1.index(cellsocmin)+1
+        socmax_num=cellsoc1.index(cellsocmax)+1
+ 
+        alpha=(cellsocmin+cellsocmax)*0.02-2    #blending系数计算
+        if cellsocmax>90 or alpha>1:
+            alpha=1
+        elif cellsocmin<10 or alpha<-1:
+            alpha=-1
+        else:
+            pass
+        packsoc=(cellsocmin+cellsocmax)*0.5+(cellsocmax-cellsocmin)*0.5*alpha
+        packsoc=eval(format(packsoc,'.1f'))
+        socdsp=packsoc
+
+        #输出ram结果....................................................................................................................................................................
+        list_ram=[self.bmstime[df_bms_len-1], self.sn, self.bms_soc[df_bms_len-1], packsoc, cellsoc, standingtime, rampackcrnt, ramcellvolt,kocellvoltmin,kocellvoltmax,ocvweight,as_accum,socstep]
+        df_ram=pd.DataFrame(columns=self.df_ram_sn.columns)
+        df_ram.loc[0]=list_ram
+
+        #输出计算结果........................................................................................................................................................................
+        df_res.loc[0]=[self.bmstime[df_bms_len-1], self.sn, self.bms_soc[df_bms_len-1], packsoc, socdsp, cellsocmin, cellsocmax,socmin_num,socmax_num,cellsoc_diff,str(cellsoc1),ocvweight,socstep]
+        return df_res, df_ram

USER/SPF/alibaba/alibaba/04BatSoc/04BatSoc/main.py

@@ -0,0 +1,100 @@
+import datetime
+import pandas as pd
+import time
+from apscheduler.schedulers.blocking import BlockingScheduler
+from USER.alibaba.Common import DBDownload
+from USER.alibaba.Common import log
+import CBMSBatSoc
+
+#...................................SOC计算函数......................................................................................................................
+def soc_cal():
+    global df_ram
+    global SNnums
+
+    end_time=datetime.datetime.now()
+    start_time=end_time-datetime.timedelta(seconds=11)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    for sn in SNnums:
+        if 'PK500' in sn:
+            celltype=1 #6040三元电芯
+        elif 'PK502' in sn:
+            celltype=2 #4840三元电芯
+        elif 'K504B' in sn:
+            celltype=99    #60ah林磷酸铁锂电芯
+        elif 'MGMLXN750' in sn:
+            celltype=3 #力信50ah三元电芯
+        elif 'MGMCLN750' or 'UD' in sn: 
+            celltype=4 #CATL 50ah三元电芯
+        else:
+            print('SN：{}，未找到对应电池类型！！！'.format(sn))
+            continue
+            sys.exit()
+
+        #读取原始数据库数据........................................................................................................................................................
+        df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\力神40Ah-SOC原始数据.xlsx')
+
+
+        #读取结果数据库数据........................................................................................................................................................
+        host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+        port=3306
+        db='qx_cas'
+        user='qx_read'
+        password='Qx@123456'
+        mode=1
+        tablename='cellstateestimation_soh'
+        DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
+        with DBRead as DBRead:
+            df_soh=DBRead.getdata(param='time_st,sn,soh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+
+        if celltype>50:
+            tablename='cellstateestimation_uniform_socvoltdiff'
+            with DBRead as DBRead:
+                df_socdiff=DBRead.getdata(param='time,sn,cellsoc_diff', tablename=tablename, sn=sn, timename='time', st=start_time, sp=end_time)
+        else:
+            df_socdiff=pd.DataFrame()
+
+        # print(df_bms)
+        # print(df_soh)
+        
+        #socram...............................................................................................................................................................
+        if not df_bms.empty:
+            df_ram_sn=df_ram[df_ram['sn']==sn]
+            
+            BatSoc=CBMSBatSoc.BatSoc(sn,celltype,df_bms,df_soh,df_ram_sn,df_socdiff)
+            df_res, df_ram_sn=BatSoc.batsoc()
+            if not df_ram_sn.empty:
+                sn_index=df_ram.loc[df_ram['sn']==sn].index
+                df_ram=df_ram.drop(index=sn_index)
+                df_ram=df_ram.append(df_ram_sn)
+                df_ram.reset_index(inplace=True,drop=True)     #重置索引
+            
+            #结果存入数据库...........................................................
+            if not df_res.empty:
+                pass
+
+#...............................................主函数.......................................................................................................................
+if __name__ == "__main__":
+
+    #所有SN号
+    SNnums= ['LISHEN01']
+
+    #log信息配置
+    mylog=log.Mylog('log_soc.txt','error')
+    mylog.logcfg()
+
+    #参数初始化.........................................................................................................................................................................
+    column_name=['time', 'sn', 'bms_soc', 'soc','cellsoc','standingtime','rampackcrnt','ramcellvolt','kocellvoltmin','kocellvoltmax','ocvweight','as_accum','socstep']
+    df_ram=pd.DataFrame(columns=column_name)  
+
+    scheduler = BlockingScheduler()
+    scheduler.add_job(soc_cal, 'interval', seconds=0.05, id='soc_job')
+    
+    try:
+        scheduler.start()
+
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/alibaba/04BatSoc/04BatSoc/main_test.py

@@ -0,0 +1,128 @@
+import datetime
+import pandas as pd
+import time
+from apscheduler.schedulers.blocking import BlockingScheduler
+from USER.alibaba.Common import DBDownload
+from USER.alibaba.Common import log
+import CBMSBatSoc
+
+#...................................SOC计算函数......................................................................................................................
+def soc_cal():
+    global start_time
+    global df_ram
+    global df_bms
+    SNnums= ['HUATING01']
+
+    start=time.time()
+    # end_time=datetime.datetime.now()-datetime.timedelta(seconds=10)
+    # start_time=end_time-datetime.timedelta(seconds=11)
+    
+    start_time=start_time+datetime.timedelta(seconds=10)
+    end_time=start_time+datetime.timedelta(seconds=11)
+    # start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    # end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    for sn in SNnums:
+        # if 'PK500' in sn:
+        #     celltype=1 #6040三元电芯
+        # elif 'PK502' in sn:
+        #     celltype=2 #4840三元电芯
+        # elif 'K504B' in sn:
+        #     celltype=99    #60ah林磷酸铁锂电芯
+        # elif 'MGMLXN750' in sn:
+        #     celltype=3 #力信50ah三元电芯
+        # elif 'MGMCLN750' or 'UD' in sn: 
+        #     celltype=4 #CATL 50ah三元电芯
+        # else:
+        #     print('SN：{}，未找到对应电池类型！！！'.format(sn))
+        #     continue
+            # sys.exit()
+
+        celltype=2
+        # start_time='2021-05-02 09:12:26'
+        # end_time='2021-06-03 19:12:26'
+        # # df_bms= pd.read_csv(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\98Download\\'+'BMS_'+sn+'.csv',encoding='GB18030')
+
+        #读取原始数据库数据........................................................................................................................................................
+        # df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-SOH原始数据.xlsx')
+        # df_bms.to_csv(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\99Result\\''BMS_'+sn+'.csv',encoding='GB18030')
+        # df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        df_bms1=df_bms[(df_bms['time'] > start_time) & (df_bms['time'] < end_time)]
+        df_bms1.reset_index(inplace=True,drop=True)
+        # print(df_bms1)
+
+        #读取结果数据库数据........................................................................................................................................................
+        # host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+        # port=3306
+        # db='qx_cas'
+        # user='qx_read'
+        # password='Qx@123456'
+        # mode=1
+        # tablename='cellstateestimation_soh'
+        # DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
+        # with DBRead as DBRead:
+        #     df_soh=DBRead.getdata(param='time_st,sn,soh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+        df_soh=pd.DataFrame({'time_st':[1],'sn':[sn],'soh':[100]})
+
+        if celltype>50:
+            # tablename='cellstateestimation_uniform_socvoltdiff'
+            # with DBRead as DBRead:
+            #     df_socdiff=DBRead.getdata(param='time,sn,cellsoc_diff', tablename=tablename, sn=sn, timename='time', st=start_time, sp=end_time)
+            df_socdiff=pd.DataFrame({'time':[1],'sn':[sn],'cellsoc_diff':[1.2]})
+        else:
+            df_socdiff=pd.DataFrame()
+
+        # print(df_bms)
+        # print(df_soh)
+        
+        #socram存储...............................................................................................................................................................
+        if not df_bms1.empty:
+            df_ram_sn=df_ram[df_ram['sn']==sn]
+
+            if df_ram_sn.empty:
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\04BatSoc\\'+'soc_'+sn+'.txt','a') as file:
+                    file.write(str(tuple(df_ram_sn.columns))+'\n')
+            
+            BatSoc=CBMSBatSoc.BatSoc(sn,celltype,df_bms1,df_soh,df_ram_sn,df_socdiff)
+            df_res, df_ram_sn=BatSoc.batsoc()
+            if not df_ram_sn.empty:
+                sn_index=df_ram.loc[df_ram['sn']==sn].index
+                df_ram=df_ram.drop(index=sn_index)
+                df_ram=df_ram.append(df_ram_sn)
+                df_ram.reset_index(inplace=True,drop=True)     #重置索引
+            
+            # print(df_res)
+            # print(df_ram)
+
+            if not df_ram_sn.empty:
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\04BatSoc\\'+'soc_'+sn+'.txt','a') as file:
+                    file.write(str(tuple(df_ram_sn.iloc[-1]))+'\n')
+                    
+        
+    end=time.time()
+    print(end-start)
+        
+#...............................................主函数.......................................................................................................................
+if __name__ == "__main__":
+
+    #log信息配置
+    mylog=log.Mylog('log_soc.txt','error')
+    mylog.logcfg()
+
+    #参数初始化.........................................................................................................................................................................
+    column_name=['time', 'sn', 'bms_soc', 'soc','cellsoc','standingtime','rampackcrnt','ramcellvolt','kocellvoltmin','kocellvoltmax','ocvweight','as_accum','socstep']
+    df_ram=pd.DataFrame(columns=column_name)
+    start_time=datetime.datetime.strptime('2021-10-22 14:59:00','%Y-%m-%d %H:%M:%S')
+    df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-SOC原始数据.xlsx')
+    df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+
+    scheduler = BlockingScheduler()
+    scheduler.add_job(soc_cal, 'interval', seconds=0.05, id='soc_job')
+    
+    try:
+        scheduler.start()
+
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/alibaba/04BatSoc/04BatSoc/soc_HUATING01.txt

@@ -0,0 +1,5034 @@
+('time', 'sn', 'bms_soc', 'soc', 'cellsoc', 'standingtime', 'rampackcrnt', 'ramcellvolt', 'kocellvoltmin', 'kocellvoltmax', 'ocvweight', 'as_accum', 'socstep')
+(Timestamp('2021-10-22 15:00:10'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 0, 0.0, array([4.1282, 4.1241, 4.1282, 4.1241], dtype=object), 0, 0, 0, 0, 5)
+(Timestamp('2021-10-22 15:00:20.045000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 10.044999999999998, 0.0, array([4.1282, 4.1241, 4.1282, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:00:29.090000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 19.08999999999999, 0.0, array([4.1282, 4.1241, 4.1282, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:00:40.145000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 30.14499999999998, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:00:49.190000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 39.19, 0.0, array([4.1282, 4.1241, 4.1282, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:01:00.245000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 50.245000000000026, 0.0, array([4.1282, 4.1241, 4.1282, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:01:09.290000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 59.29000000000005, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:01:20.345000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 70.34500000000003, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:01:29.390000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 79.38999999999999, 0.0, array([4.1282, 4.1241, 4.1282, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:01:40.445000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 90.44499999999994, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:01:49.490000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 99.4899999999999, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:02:00.545000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 110.54499999999985, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:02:09.590000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 119.5899999999998, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:02:20.645000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 130.64499999999975, 0.0, array([4.1282, 4.1241, 4.1282, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:02:29.690000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 139.6899999999997, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:02:40.745000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 150.74499999999966, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:02:49.790000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 159.78999999999962, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:03:00.845000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 170.84499999999957, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:03:09.890000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 179.88999999999953, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:03:20.945000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 190.94499999999948, 0.0, array([4.1282, 4.1241, 4.1282, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:03:29.990000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 199.98999999999944, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:03:40.040000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 210.0399999999994, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:03:49.085000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 219.08499999999935, 0.0, array([4.1279, 4.1238, 4.1279, 4.1238], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:04:00.140000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 230.1399999999993, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:04:09.185000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 239.18499999999926, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:04:20.240000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 250.2399999999992, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:04:29.285000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 259.2849999999992, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:04:40.340000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 270.3399999999991, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:04:49.385000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 279.3849999999991, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:05:00.440000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 290.43999999999903, 0.0, array([4.1279, 4.1238, 4.1279, 4.1238], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:05:09.485000'), 'HUATING01', 97.0, 97.8, array([97.45866753, 97.6066418 , 97.83881243, 97.77218001]), 299.484999999999, 0.0, array([4.1279, 4.1241, 4.1279, 4.1241], dtype=object), 0, 0, 0.0, 0.0, 4)
+(Timestamp('2021-10-22 15:05:20.540000'), 'HUATING01', 96.74615431693358, 97.6, array([97.20736029, 97.35533457, 97.5875052 , 97.52087277]), 0, 2.00080004882813, array([4.0798, 4.077, 4.0798, 4.077], dtype=object), 0, 0, 0.0, -18.09412084350587, 4)
+(Timestamp('2021-10-22 15:05:29.585000'), 'HUATING01', 96.49221415142709, 97.3, array([96.95595954, 97.10393381, 97.33610445, 97.26947202]), 0, 2.00140002441406, array([4.0699, 4.0674, 4.0699, 4.0674], dtype=object), 0, 0, 0.0, -18.100854294433592, 4)
+(Timestamp('2021-10-22 15:05:40.640000'), 'HUATING01', 96.18187104730923, 97.0, array([96.64871986, 96.79669414, 97.02886477, 96.96223234]), 0, 2.00140002441406, array([4.0631, 4.0609, 4.0631, 4.0609], dtype=object), 0, 0, 0.0, -22.121256441650413, 4)
+(Timestamp('2021-10-22 15:05:49.685000'), 'HUATING01', 95.92795627720999, 96.8, array([96.39734423, 96.54531851, 96.77748914, 96.71085671]), 0, 2.00140002441406, array([4.0578, 4.0559, 4.0578, 4.0559], dtype=object), 0, 0, 0.0, -18.099045368042013, 4)
+(Timestamp('2021-10-22 15:06:00.740000'), 'HUATING01', 95.6176047138471, 96.5, array([96.09009619, 96.23807046, 96.4702411 , 96.40360867]), 0, 2.00080004882813, array([4.0541, 4.0522, 4.0541, 4.0522], dtype=object), 0, 0, 0.0, -22.121859417114273, 4)
+(Timestamp('2021-10-22 15:06:09.785000'), 'HUATING01', 95.36368146684651, 96.2, array([95.83871218, 95.98668646, 96.21885709, 96.15222466]), 0, 2.00080004882813, array([4.0507, 4.0491, 4.0507, 4.0491], dtype=object), 0, 0, 0.0, -18.099648343505873, 4)
+(Timestamp('2021-10-22 15:06:20.840000'), 'HUATING01', 95.05332144422803, 95.9, array([95.53145576, 95.67943004, 95.91160067, 95.84496824]), 0, 2.00080004882813, array([4.0479, 4.0463, 4.0479, 4.0463], dtype=object), 0, 0, 0.0, -22.122462392578132, 4)
+(Timestamp('2021-10-22 15:06:29.885000'), 'HUATING01', 94.79938975563346, 95.7, array([95.28006338, 95.42803766, 95.66020829, 95.59357586]), 0, 2.00140002441406, array([4.0454, 4.0442, 4.0454, 4.0442], dtype=object), 0, 0, 0.0, -18.100251318969732, 4)
+(Timestamp('2021-10-22 15:06:40.940000'), 'HUATING01', 94.4890551107659, 95.4, array([94.97283208, 95.12080636, 95.35297699, 95.28634456]), 0, 2.00080004882813, array([4.0429, 4.0417, 4.0429, 4.0417], dtype=object), 0, 0, 0.0, -22.120653466186553, 4)
+(Timestamp('2021-10-22 15:06:49.985000'), 'HUATING01', 94.23514032301034, 95.1, array([94.72145645, 94.86943073, 95.10160136, 95.03496893]), 0, 2.00140002441406, array([4.0411, 4.0398, 4.0411, 4.0398], dtype=object), 0, 0, 0.0, -18.099045368042013, 4)
+(Timestamp('2021-10-22 15:07:00.035000'), 'HUATING01', 93.9530156243551, 94.8, array([94.44215299, 94.59012726, 94.8222979 , 94.75566547]), 0, 2.00080004882813, array([4.0395, 4.0383, 4.0395, 4.0383], dtype=object), 0, 0, 0.0, -20.109849417114283, 4)
+(Timestamp('2021-10-22 15:07:09.080000'), 'HUATING01', 93.69909237736509, 94.6, array([94.19076898, 94.33874326, 94.57091389, 94.50428146]), 0, 2.00080004882813, array([4.038, 4.037, 4.038, 4.037], dtype=object), 0, 0, 0.0, -18.099648343505873, 4)
+(Timestamp('2021-10-22 15:07:20.135000'), 'HUATING01', 93.38876619175313, 94.3, array([93.88354606, 94.03152034, 94.26369097, 94.19705854]), 0, 2.00080004882813, array([4.0361, 4.0355, 4.0361, 4.0355], dtype=object), 0, 0, 0.0, -22.120050490722694, 4)
+(Timestamp('2021-10-22 15:07:29.180000'), 'HUATING01', 93.13485988090949, 94.0, array([93.6321788 , 93.78015308, 94.01232371, 93.94569128]), 0, 2.00080004882813, array([4.0349, 4.0339, 4.0349, 4.0339], dtype=object), 0, 0, 0.0, -18.098442392578153, 4)
+(Timestamp('2021-10-22 15:07:40.235000'), 'HUATING01', 92.82453369529225, 93.7, array([93.32495588, 93.47293016, 93.70510079, 93.63846836]), 0, 2.00080004882813, array([4.0336, 4.0327, 4.0336, 4.0327], dtype=object), 0, 0, 0.0, -22.120050490722694, 4)
+(Timestamp('2021-10-22 15:07:49.280000'), 'HUATING01', 92.57061890754726, 93.5, array([93.07358025, 93.22155453, 93.45372516, 93.38709273]), 0, 2.00080004882813, array([4.0324, 4.0314, 4.0324, 4.0314], dtype=object), 0, 0, 0.0, -18.099045368042013, 4)
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USER/SPF/alibaba/alibaba/04BatSoc/04BatSoc/soc_LISHEN01.txt

@@ -0,0 +1,1623 @@
+('time', 'sn', 'bms_soc', 'soc', 'cellsoc', 'standingtime', 'rampackcrnt', 'ramcellvolt', 'kocellvoltmin', 'kocellvoltmax', 'ocvweight', 'as_accum', 'socstep')
+('time', 'sn', 'bms_soc', 'soc', 'cellsoc', 'standingtime', 'rampackcrnt', 'ramcellvolt', 'kocellvoltmin', 'kocellvoltmax', 'ocvweight', 'as_accum', 'socstep')
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USER/SPF/alibaba/alibaba/05BatDiag/05BatDiag/CBMSBatDiag.py

@@ -0,0 +1,692 @@
+import pandas as pd
+import numpy as np
+import datetime
+from USER.alibaba.Common import BatParam
+
+class BatDiag:
+    def __init__(self,sn,celltype,df_bms,df_soh,df_soc,df_uniform,df_diag_ram):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_bms=df_bms
+        self.df_soh=df_soh
+        self.df_soc=df_soc
+        self.df_uniform=df_uniform
+        self.df_diag_ram=df_diag_ram.copy()
+        self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
+        self.packvolt=df_bms['PackVolt']
+        self.bms_soc=df_bms['PackSOC']
+        self.bmstime= pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+
+        self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+    
+    def diag(self):
+        if len(self.df_bms)>1:
+            if self.celltype<=50:
+                df_res=self._ncm_diag()
+                return df_res    
+            else:
+                df_res=self._ncm_diag()
+                return df_res
+        else:
+            return pd.DataFrame()
+        
+    #定义滑动滤波函数.............................................................................................
+    def _np_move_avg(self,a, n, mode="same"): 
+        return (np.convolve(a, np.ones((n,)) / n, mode=mode))
+    
+    #寻找当前行数据的所有温度值...................................................................................
+    def _celltemp_get(self,num):   
+        celltemp = list(self.df_bms.loc[num,self.celltemp_name])
+        return celltemp
+
+    #获取当前行所有电压数据............................................................................................
+    def _cellvolt_get(self,num): 
+        cellvolt = list(self.df_bms.loc[num,self.cellvolt_name])
+        return cellvolt
+
+
+    #..........................................三元电池诊断功能..................................................................
+    def _ncm_diag(self):
+
+        #参数初始化
+        bmssoc_st=float(self.bms_soc[0])    #SOC卡滞初始参数
+        ah_accum=0  #SOC卡滞初始参数
+        as_chg=0    #过流诊断初始参数
+        as_dis=0    #过流诊断初始参数
+        time1=self.bmstime[0]   #温升速率初始参数
+        temp1=np.array(self._celltemp_get(0))   #温升速率初始参数
+        temprate_cnt=0
+        ot_time=0
+        ut_time=0
+        dt_time=0
+        cov_time=0
+        cuv_time=0
+        cdv_time=0
+        pov_time=0
+        puv_time=0
+        vv_time=0
+        tv_time=0
+        
+        time_sp='0000-00-00 00:00:00'
+            
+        for i in range(1,len(self.df_bms)):
+            time=self.bmstime[i]
+            
+            #温度诊断功能.............................................................................................................
+            celltemp0=self._celltemp_get(i-1)
+            celltemp1=self._celltemp_get(i)
+            celltempmin0=min(celltemp0)
+            celltempmin1=min(celltemp1)
+            celltempmax0=max(celltemp0)
+            celltempmax1=max(celltemp1)
+            #温度有效性判断............................................................................................
+            if not 2 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                if celltempmax0>self.param.CellTempUpLmt or celltempmin0<self.param.CellTempLwLmt:
+                    tv_time=tv_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                    if tv_time>55:
+                        celltempvalid=0
+                        faultcode=2
+                        faultlv=2
+                        cellnum1=np.where(np.array(celltemp1)<self.param.CellTempLwLmt)[0]+1
+                        cellnum2=np.where(np.array(celltemp1)>self.param.CellTempUpLmt)[0]+1
+                        cellnum=list(cellnum1)+list(cellnum2)
+                        faultinfo='电芯温度{}无效'.format(cellnum)
+                        faultadvice='丢失温度信息，无法全面监控电池安全；请停止充放电，并检修电池采样系统及数据传输系统'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:   #不作处理
+                    celltempvalid=1
+                    tv_time=0
+            else:   #ram当前故障中有该故障，则判断是否退出该故障
+                if celltempmax0<self.param.CellTempUpLmt-10 and celltempmin0>self.param.CellTempLwLmt+10:
+                    celltempvalid=1
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==2].index[0], 'time_sp'] = time
+                else:
+                    celltempvalid=0
+
+            if celltempvalid==1:
+                #过温判断.............................................................................................................
+                if not 4 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if celltempmax0>self.param.CellTempHighLv2 and celltempmax1>self.param.CellTempHighLv2:    #二级高温进入
+                        ot_time=ot_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                        if ot_time>self.param.temp_time:
+                            faultcode=4
+                            faultlv=3
+                            cellnum=np.where(np.array(celltemp1)>self.param.CellTempHighLv2)[0]+1
+                            faultinfo='电芯温度{}过温'.format(list(cellnum))
+                            faultadvice='高温下充放电，会导致容量衰减过快，并存在热失控风险；请停止充放电，开启电池冷却功能，并通知电池技术人员介入分析'
+                            self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                        else:
+                            pass
+                    else:
+                        ot_time=0
+                else:   #ram当前故障中有该故障，则判断是否退出该故障
+                    if celltempmax0<self.param.CellTempHighLv1-5 and celltempmax1<self.param.CellTempHighLv1-5:    #二级高温恢复
+                        self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==4].index[0], 'time_sp'] = time
+                    else:
+                        pass
+            
+                #欠温判断.................................................................................................................
+                if not 6 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if celltempmin0<self.param.CellTempLowLv2 and celltempmin1<self.param.CellTempLowLv2:  #二级低温进入
+                        ut_time=ut_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                        if ut_time>self.param.temp_time:
+                            faultcode=6
+                            faultlv=3
+                            cellnum=np.where(np.array(celltemp1)<self.param.CellTempLowLv2)[0]+1
+                            faultinfo='电芯温度{}过低'.format(list(cellnum))
+                            faultadvice='低温下充电，会导致析锂，存在电池安全与寿命衰减过快风险；请停止充放电，并开启电池加热功能'
+                            self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                        else:
+                            pass
+                    else:
+                        ut_time=0
+                else:   #ram当前故障中有该故障，则判断是否退出该故障
+                    if celltempmax0>self.param.CellTempLowLv1+2 and celltempmax1>self.param.CellTempLowLv1+2:    #二级高温恢复
+                        self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==6].index[0], 'time_sp'] = time
+                    else:
+                        pass
+                
+                #温差判断.............................................................................................................................
+                if not 8 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if (celltempmax0-celltempmin0)>self.param.CellTempDiffLv2 and (celltempmax1-celltempmax0)>self.param.CellTempDiffLv2:  #二级温差进入
+                        dt_time=dt_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                        if dt_time>self.param.temp_time:
+                            faultcode=8
+                            faultlv=3
+                            faultinfo='电芯温度{}和{}温差大'.format(celltemp1.index(celltempmax1)+1,celltemp1.index(celltempmin1)+1)
+                            faultadvice='存在电芯的老化速率不一致的风险；请检查电池温度采样系统'
+                            self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                        else:
+                            pass
+                    else:
+                        dt_time=0
+                else:   #ram当前故障中有该故障，则判断是否退出该故障
+                    if (celltempmax0-celltempmin0)<self.param.CellTempDiffLv1-2 and (celltempmax1-celltempmax0)>self.param.CellTempDiffLv1-2:  #二级温差恢复
+                        self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==8].index[0], 'time_sp'] = time
+                    else:
+                        pass
+
+                #温升判断
+                time2=self.bmstime[i]
+                delttime=(time2-time1).total_seconds()
+                if delttime>20:
+                    temp2=np.array(self._celltemp_get(i))
+                    celltemp_rate=(max(temp2-temp1)*60)/delttime    #计算最大温升速率
+                    temp1=temp2 #更新初始温度
+                    time1=time2 #更新初始时间
+                    if not 9 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                        if celltemp_rate>self.param.CellTempRate:
+                            temprate_cnt=temprate_cnt+1
+                            if temprate_cnt>2:  #温升故障进入
+                                faultcode=9
+                                faultlv=3
+                                faultinfo='电芯温升速率过快：{}℃/min'.format(celltemp_rate)
+                                faultadvice='温升速率过快，存在热失控风险；禁止充放电，并通知电池技术人员介入分析'
+                                self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                            else:
+                                pass
+                        else:   #ram当前故障中有该故障，则判断是否退出该故障
+                            pass
+                    else:
+                        if celltemp_rate<self.param.CellTempRate-1: #温升故障恢复
+                            self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==9].index[0], 'time_sp'] = time
+                else:
+                    pass
+            
+            else:
+                ot_time=0
+                ut_time=0
+                dt_time=0
+            
+            #电压诊断功能.................................................................................................
+            cellvolt0=self._cellvolt_get(i-1)
+            cellvolt1=self._cellvolt_get(i)
+            cellvoltmin0=min(cellvolt0)
+            cellvoltmax0=max(cellvolt0)
+            cellvoltmin1=min(cellvolt1)
+            cellvoltmax1=max(cellvolt1)
+            cellvoltmin_index0=cellvolt0.index(cellvoltmin0)
+            cellvoltmax_index0=cellvolt0.index(cellvoltmax0)
+            cellvoltmin_index1=cellvolt1.index(cellvoltmin1)
+            cellvoltmax_index1=cellvolt1.index(cellvoltmax1)
+            #电压断线诊断...................................................................................................
+            if not 10 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                if (cellvoltmin0<2 and cellvoltmax0>4.5 and abs(cellvoltmax_index0-cellvoltmin_index0)==1) and (cellvoltmin1<2 and cellvoltmax1>4.5 and abs(cellvoltmax_index1-cellvoltmin_index1)==1):   #电压断线故障进入
+                    vv_time=vv_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                    if vv_time>55:
+                        cellvoltvalid=0
+                        faultcode=10
+                        faultlv=2
+                        faultinfo='{}号电芯和{}号电芯电压采样断线'.format(cellvolt0.index(cellvoltmax0)+1,cellvolt0.index(cellvoltmin0)+1)
+                        faultadvice='丢失部分电压信息，无法全面监控电池安全；禁止充放电，请检查电池电压采样系统'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:
+                    cellvoltvalid=1
+                    vv_time=0
+            else:   #ram当前故障中有该故障，则判断是否退出该故障
+                if cellvoltmin0>2 and cellvoltmax0<4.5: #电压断线故障恢复
+                    cellvoltvalid=1
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==10].index[0], 'time_sp'] = time
+                else:
+                    cellvoltvalid=0
+            
+            #电压无效诊断.......................................................................................................
+            if not 11 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                if (cellvoltmin0<0.5 and cellvoltmin1<0.5) or (cellvoltmax0>4.5 and cellvoltmax1>4.5):
+                    cellvoltvalid=0
+                    faultcode=11
+                    faultlv=2
+                    cellnum1=np.where(np.array(cellvolt1)<0.5)[0]+1
+                    cellnum2=np.where(np.array(cellvolt1)>4.5)[0]+1
+                    cellnum=list(cellnum1)+list(cellnum2)
+                    faultinfo='电芯电压{}采样无效'.format(cellnum)
+                    faultadvice='丢失电压信息，无法全面监控电池安全；禁止充放电，请检查电池电压采样系统'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                else:
+                    cellvoltvalid=1
+            else:
+                if cellvoltmin0>2 and cellvoltmax0<4.5 and cellvoltmin1>2 and cellvoltmax1<4.5: #电压断线故障恢复
+                    cellvoltvalid=1
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==11].index[0], 'time_sp'] = time
+                else:
+                    cellvoltvalid=0
+            
+            if cellvoltvalid==1:
+                #过压诊断.............................................................................................................
+                if not 12 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if cellvoltmax0>self.param.CellOvLv2 and cellvoltmax1>self.param.CellOvLv2:  #二级过压进入
+                        cov_time=cov_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                        if cov_time>self.param.volt_time:
+                            faultcode=12
+                            faultlv=3
+                            cellnum=np.where(np.array(cellvolt1)>self.param.CellOvLv2)[0]+1
+                            faultinfo='{}号电芯过压'.format(list(cellnum))
+                            faultadvice='过压可能导致电池析锂，存在电池安全与寿命衰减过快风险；禁止充电，若持续>10min，请通知电池技术人员介入分析'
+                            self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                        else:
+                            pass
+                    else:
+                        cov_time=0
+                else:   #ram当前故障中有该故障，则判断是否退出该故障
+                    if cellvoltmax0<self.param.CellOvLv1-0.05 and cellvoltmax1<self.param.CellOvLv1-0.05:   #二级过压故障恢复
+                        self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==12].index[0], 'time_sp'] = time
+                    else:
+                        pass
+
+                #欠压诊断.................................................................................................................
+                if not 14 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if cellvoltmin0<self.param.CellUvLv2 and cellvoltmin1<self.param.CellUvLv2:  #二级欠压
+                        cuv_time=cuv_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                        if cuv_time>self.param.volt_time:
+                            faultcode=14
+                            faultlv=3
+                            cellnum=np.where(np.array(cellvolt1)<self.param.CellUvLv2)[0]+1
+                            faultinfo='{}号电芯欠压'.format(list(cellnum))
+                            faultadvice='欠压可能导致电池过放；禁止放电，请充电，若欠压持续>10min，通知电池技术人员介入分析'
+                            self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                        else:
+                            pass
+                    else:
+                        cuv_time=0
+                else:
+                    if cellvoltmin0>self.param.CellUvLv1+0.1 and cellvoltmin1>self.param.CellUvLv1+0.1:
+                        self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==14].index[0], 'time_sp'] = time
+                    else:
+                        pass
+                
+                #电芯压差大.....................................................................................................................................................
+                if not 16 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if (cellvoltmax0-cellvoltmin0)>self.param.CellVoltDiffLv2 and (cellvoltmax1-cellvoltmin1)>self.param.CellVoltDiffLv2:  #二级电芯压差
+                        cdv_time=cdv_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                        if cdv_time>self.param.volt_time:
+                            faultcode=16
+                            faultlv=3
+                            faultinfo='{}号电芯和{}号电芯压差大'.format(cellvolt1.index(cellvoltmax1)+1,cellvolt1.index(cellvoltmin1)+1)
+                            faultadvice='容量/内阻不一致；请均衡电池，若均衡无法解决问题，请通知电池技术人员介入分析'
+                            self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                        else:
+                            pass
+                    else:
+                        cdv_time=0
+                else:
+                    if (cellvoltmax0-cellvoltmin0)<self.param.CellVoltDiffLv1-0.05 and (cellvoltmax1-cellvoltmin1)<self.param.CellVoltDiffLv1-0.05: #二级欠压恢复
+                        self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==16].index[0], 'time_sp'] = time
+                    else:
+                        pass
+            else:
+                cov_time=0
+                cuv_time=0
+                cdv_time=0
+                
+            #电池包诊断.....................................................................................................................................
+            if self.packvolt[i-1]>2*self.param.CellVoltNums and self.packvolt[i]<4.5*self.param.CellVoltNums:   #电池包电压有效性
+                packvoltvalid=1
+            else:
+                packvoltvalid=0
+            
+            if packvoltvalid==1:
+                if not 18 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if self.packvolt[i-1]>self.param.PackVoltOvLv2 and self.packvolt[i]>self.param.PackVoltOvLv2:   #电池包过压二级进入
+                        pov_time=pov_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                        if pov_time>self.param.volt_time:
+                            faultcode=18
+                            faultlv=3
+                            faultinfo='电池包过压'
+                            faultadvice='过压会导致电池析锂，存在电池安全与寿命衰减过快风险；请停止充电，若过压持续>10min，通知电池技术人员介入分析'
+                            self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                        else:
+                            pass
+                    else:
+                        pov_time=0
+                else:
+                    if self.packvolt[i-1]<self.param.PackVoltOvLv1 and self.packvolt[i]<self.param.PackVoltOvLv1: #电池包过压二级恢复
+                        self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==18].index[0], 'time_sp'] = time
+                    else:
+                        pass
+                
+                if not 20 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if self.packvolt[i-1]<self.param.PackVoltUvLv2 and self.packvolt[i]<self.param.PackVoltUvLv2:   #电池包二级欠压进入
+                        puv_time=puv_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                        if puv_time>self.param.volt_time:
+                            faultcode=20
+                            faultlv=3
+                            faultinfo='电池包欠压'
+                            faultadvice='欠压可能导致电池过放；请停止放电，并充电，若欠压持续>10min，请通知电池技术人员介入分析'
+                            self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                        else:
+                            pass
+                    else:
+                        puv_time=0
+                else:
+                    if self.packvolt[i-1]>self.param.PackVoltUvLv1 and self.packvolt[i]>self.param.PackVoltUvLv1:   #电池包二级欠压恢复
+                        self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==20].index[0], 'time_sp'] = time
+                    else:
+                        pass
+            else:
+                pov_time=0
+                puv_time=0
+            
+            #电流过流诊断.......................................................................................................................
+            step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+            if step<120 and self.packcrnt[i]>self.param.PackDisOc and self.packcrnt[i-1]>self.param.PackDisOc:
+                as_dis=as_dis+(self.packcrnt[i]-self.param.self.PackDisOc)*step    #ah累计
+            elif step<120 and self.packcrnt[i]<self.param.PackChgOc and self.packcrnt[i-1]<self.param.PackChgOc:
+                as_chg=as_chg+(self.param.PackDisOc-self.packcrnt[i])*step    #ah累计
+            else:
+                as_dis=0
+                as_chg=0
+            
+            if not 22 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                if as_dis>100:
+                    faultcode=22
+                    faultlv=3
+                    faultinfo='电池包放电过流'
+                    faultadvice='长时间过流会导致电池欠压及温升过快；请停止放电，若持续>5min，断开继电器，并通知电池技术人员介入分析'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if self.packcrnt[i-1]<self.param.PackDisOc-10 and self.packcrnt[i]<self.param.PackDisOc-10:
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==22].index[0], 'time_sp'] = time
+                else:
+                    pass
+            
+            if not 21 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                if as_chg>100:
+                    faultcode=21
+                    faultlv=3
+                    faultinfo='电池包充电过流'
+                    faultadvice='过流会导致电池析锂，存在电池安全与寿命衰减过快风险；请停止充电，若持续>5min，断开继电器，并通知电池技术人员介入分析'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if self.packcrnt[i-1]>self.param.PackChgOc+10 and self.packcrnt[i]>self.param.PackChgOc+10:
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==21].index[0], 'time_sp'] = time
+                else:
+                    pass
+
+            #SOC卡滞、跳变诊断................................................................................................
+            step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+            if step<120:
+                ah_accum=ah_accum-self.packcrnt[i]*step/3600    #ah累计
+            else:
+                pass
+            #SOC卡滞............................................................................................................
+            if abs(ah_accum)>self.param.Capacity*0.1:   
+                bmssoc_now=float(self.bms_soc[i])
+                if not 27 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if abs(bmssoc_now-bmssoc_st)<self.param.SocClamp:   #SOC卡滞故障进入
+                        faultcode=27
+                        faultlv=1
+                        faultinfo='电池SOC卡滞'
+                        faultadvice='请通知电池供应商检查电池BMS软件是否正常'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:
+                    if abs(bmssoc_now-bmssoc_st)>self.param.SocClamp:   #SOC卡滞故障退出
+                        self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==27].index[0], 'time_sp'] = time
+                    else:
+                        pass
+                bmssoc_st=bmssoc_now
+                ah_accum=0
+            else:
+                pass
+
+            #SOC跳变....................................................................................................................
+            bmssoc_last=float(self.bms_soc[i-1])
+            bmssoc_now=float(self.bms_soc[i])
+            if not 28 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                if step<30 and abs(bmssoc_now-bmssoc_last)>self.param.SocJump and 1<bmssoc_now<=100 and 1<bmssoc_last<=100:  #SOC跳变进入
+                    faultcode=28
+                    faultlv=1
+                    faultinfo='电池SOC跳变'
+                    faultadvice='请通知电池供应商检查电池BMS软件是否正常'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if abs(bmssoc_now-bmssoc_st)<self.param.SocJump:    #SOC跳变故障退出
+                    time=self.bmstime[i]
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==28].index[0], 'time_sp'] = time
+                else:
+                    pass
+
+            # #BMS故障报警........................................................................................................
+            # if not 1 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+            #     if self.bmsfault1[i-1] is None or self.bmsfault1[i] is None:
+            #         self.bmsfault1[i-1]=0
+            #         self.bmsfault1[i]=0
+            #     if self.bmsfault1[i-1]>0 or self.bmsfault1[i]>0:   #BMS故障进入
+            #         time=self.bmstime[0]
+            #         faultcode=1
+            #         faultlv=2
+            #         faultinfo='BMS故障报警：{}'.format(self.bmsfault1[i-1])
+            #         faultadvice='检修电池'
+                    # self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+            #     else:
+            #         pass
+            # else:
+            #     if self.bmsfault1[i-1]==0 and self.bmsfault1[i]==0:   #BMS故恢复
+            #         time=self.bmstime[i]
+            #         self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['faultcode']==1].index[0], 'time_sp'] = time
+        
+        #SOC过低故障报警............................................................................................................
+        if not self.df_soc.empty:
+            if not 26 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                if self.df_soc.loc[0, 'packsoc']<self.param.SocLow:   #SOC过低故障进入
+                    time=self.df_soc.loc[0, 'time']
+                    faultcode=26
+                    faultlv=1
+                    faultinfo='电池包电量过低'
+                    faultadvice='电量过低会引起电池过放；请停止放电，并充电'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:   
+                if self.df_soc.loc[0, 'packsoc']>self.param.SocLow:   #SOC过低故障退出
+                    time=self.df_soc.loc[0, 'time']
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==26].index[0], 'time_sp'] = time
+                else:
+                    pass
+        else:
+            pass
+        
+        #SOC一致性故障报警..........................................................................................................
+        if not self.df_uniform.empty:
+            cellsoc_diff=self.df_uniform.loc[0,'cellsoc_diff']
+            if not 25 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                if cellsoc_diff>self.param.SocDiff: #SOC一致性差故障进入
+                    time=self.bmstime[0]
+                    faultcode=25
+                    faultlv=1
+                    faultinfo='电芯{}和{}SOC差过大:{}'.format(self.df_uniform.loc[0,'cellmin_num'],self.df_uniform.loc[0,'cellmax_num'],cellsoc_diff)
+                    faultadvice='请均衡电池，若无法解决，请通知电池技术人员介入分析'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if cellsoc_diff<self.param.SocDiff: #SOC一致性差故障恢复
+                    time=self.bmstime[0]
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==25].index[0], 'time_sp'] = time
+        else:
+            cellsoc_diff=3
+
+        #容量过低和一致性故障报警................................................................................................
+        if not self.df_soh.empty:
+            soh=self.df_soh.loc[0,'soh']
+            cellsoh=eval(self.df_soh.loc[0,'cellsoh'])
+            cellsoh=np.array(cellsoh)
+            cellsoh_lowindex=np.argwhere(cellsoh<self.param.SohLow)
+            cellsoh_lowindex=cellsoh_lowindex+1
+            cellsoh_diff=max(cellsoh)-min(cellsoh)
+            if not 23 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                if soh<self.param.SohLow:   #soh过低故障进入
+                    time=self.bmstime[0]
+                    faultcode=23
+                    faultlv=1
+                    faultinfo='电池包容量过低:{}号电芯'.format(cellsoh_lowindex)
+                    faultadvice='电池容量过低，会导致电池放电电量不足；建议更换容量过低的电芯或模组'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if soh>self.param.SohLow+2:   #soh过低故障恢复
+                    time=self.bmstime[0]
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==23].index[0], 'time_sp'] = time
+                else:
+                    pass
+            
+            if not 24 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
+                cellsohmin=min(cellsoh)
+                cellsohmax=max(cellsoh)
+                if cellsoh_diff>self.param.SohDiff:
+                    time=self.bmstime[0]
+                    faultcode=24
+                    faultlv=1
+                    faultinfo='电池包容量一致性差：{}号和{}号电芯'.format(list(cellsoh).index(cellsohmin)+1,list(cellsoh).index(cellsohmax)+1)
+                    faultadvice='电池容量不一致性差，会导致电池放电电量不足；建议更换容量过低的电芯或模组'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if cellsoh_diff<self.param.SohDiff-2:
+                    time=self.bmstime[0]
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==24].index[0], 'time_sp'] = time
+                else:
+                    pass
+            #soh长时间未标定
+            if not 100 in list(self.df_diag_ram['faultcode']):
+                time=self.bmstime[0]
+                if (time-self.df_soh.loc[0,'time_st']).total_seconds()>30*24*3600:
+                    faultcode=100
+                    faultlv=1
+                    faultinfo='电池包状态长时间未标定'
+                    faultadvice='电池长时间未标定，存在电池状态估算不准确风险，请对电池进行:放电至SOC<10%-静置>1h-充满，操作'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                time=self.bmstime[0]
+                if (time-self.df_soh.loc[0,'time_st']).total_seconds()<29*24*3600:
+                    self.df_diag_ram.loc[self.df_diag_ram[self.df_diag_ram['faultcode']==100].index[0], 'time_sp'] = time
+                else:
+                    pass
+
+                
+        else:
+            #soh长时间未标定
+            if not 100 in list(self.df_diag_ram['faultcode']):
+                time=self.bmstime[0]
+                faultcode=100
+                faultlv=1
+                faultinfo='电池包状态长时间未标定'
+                faultadvice='电池长时间未标定，存在电池状态估算不准确风险，请对电池进行:放电至SOC<10%-静置>1h-充满，操作'
+                self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+            else:
+                pass
+
+        #电池健康度评分.....................................................................................................
+        column_name=['time', 'sn', 'healthstate', 'healthadvice']
+        df_health=pd.DataFrame(columns=column_name)
+
+        healthstate=100
+        healthadvice=''
+
+        df_fault_now=self.df_diag_ram[self.df_diag_ram['time_sp']=='0000-00-00 00:00:00']
+        df_fault_now.reset_index(inplace=True,drop=True)
+        fault_num=len(df_fault_now)
+        fltlv=df_fault_now['faultlv']
+        fltlv1=np.sum(fltlv<1.5)
+        fltlv2=np.sum(fltlv<2.5)-fltlv1
+        fltlv3=np.sum(fltlv<3.5)-fltlv1-fltlv2
+        fltlv4=np.sum(fltlv<4.5)-fltlv1-fltlv2-fltlv3
+        fltlv5=np.sum(fltlv<5.5)-fltlv1-fltlv2-fltlv3-fltlv4
+        if fltlv5>0:
+            healthstate=healthstate-fltlv1*10-fltlv2*20-fltlv3*30-fltlv4*50-fltlv5*100
+            healthadvice=healthadvice+'，电池发生{}个故障'.format(fault_num)
+        elif fltlv4>0:
+            healthstate=healthstate-50
+            healthadvice=healthadvice+'，电池发生{}个故障'.format(fault_num)
+        elif fltlv3>0:
+            healthstate=healthstate-30
+            healthadvice=healthadvice+'，电池发生{}个故障'.format(fault_num)
+        elif fltlv2>0:
+            healthstate=healthstate-20
+            healthadvice=healthadvice+'，电池发生{}个故障'.format(fault_num)
+        elif fltlv1>0:
+            healthstate=healthstate-10
+            healthadvice=healthadvice+'，电池发生{}个故障'.format(fault_num)
+
+        #电池寿命
+        if not self.df_soh.empty:
+            if soh>85:
+                pass
+            elif soh>=80:
+                healthstate=healthstate-5
+                healthadvice=healthadvice+'，电池SOH较低'
+            elif soh>=70:
+                healthstate=healthstate-15
+                healthadvice=healthadvice+'，电池SOH过低'
+            else:
+                healthstate=healthstate-30
+                healthadvice=healthadvice+'，电池寿命用尽'
+            #电池寿命一致性 
+            if cellsoh_diff>15:
+                healthstate=healthstate-10
+                healthadvice=healthadvice+'，电池SOH一致性很差'
+            elif cellsoh_diff>10:
+                healthstate=healthstate-5
+                healthadvice=healthadvice+'，电池SOH一致性较差'
+            elif cellsoh_diff>5:
+                healthstate=healthstate-1
+            else:
+                pass
+        #SOC一致性
+        if not self.df_uniform.empty:
+            if cellsoc_diff>20:
+                healthstate=healthstate-10
+                healthadvice=healthadvice+'，电池SOC一致性很差'
+            elif cellsoc_diff>10:
+                healthstate=healthstate-5
+                healthadvice=healthadvice+'，电池SOC一致性较差'
+            elif cellsoc_diff>5:
+                healthstate=healthstate-1
+            else:
+                pass
+        if healthstate<0:
+            healthstate=0
+        elif healthstate>100:
+            healthstate=100
+        else:
+            pass
+       
+        healthstate=int(healthstate)
+        if len(healthadvice)>0.5:
+            healthadvice=healthadvice[1:]
+        else:
+            healthadvice='电池运行状态良好'
+        df_health.loc[0]=[self.bmstime[len(self.bmstime)-1], self.sn, healthstate, healthadvice]
+
+        #返回诊断结果...........................................................................................................
+        df_res=self.df_diag_ram
+        if not df_res.empty:
+            pass
+        else:
+            df_res=pd.DataFrame()
+        
+        if not df_health.empty:
+            pass
+        else:
+            df_health=pd.DataFrame()
+        
+        return df_res, df_health
+

USER/SPF/alibaba/alibaba/05BatDiag/05BatDiag/main.py

@@ -0,0 +1,139 @@
+import CBMSBatDiag
+import pymysql
+import datetime
+import pandas as pd
+from LIB.BACKEND import DBManager
+import datetime
+from apscheduler.schedulers.blocking import BlockingScheduler
+from USER.alibaba.Common import DBDownload
+from USER.alibaba.Common import log
+
+#...................................电池包电芯安全诊断函数......................................................................................................................
+def diag_cal():
+    global SNnums
+
+    now_time=datetime.datetime.now()
+    start_time=now_time-datetime.timedelta(seconds=65)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=now_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    #数据库配置
+    host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+    port=3306
+    db='qx_cas'
+    user='qx_read'
+    password='Qx@123456'
+
+    #读取故障结果库中当前故障......................................................
+    param='time_st,time_sp,sn,faultcode,faultlv,faultinfo,faultadvice'
+    tablename=''
+    mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db)
+    cursor = mysql.cursor()
+    sql =  "select %s from %s where time_sp='0000-00-00 00:00:00'" %(param,tablename)
+    cursor.execute(sql)
+    res = cursor.fetchall()
+    df_diag_ram= pd.DataFram(res,columns=param.split(','))
+    cursor.close()
+    mysql.close()
+
+    for sn in SNnums:
+        if 'PK500' in sn:
+            celltype=1 #6040三元电芯
+        elif 'PK502' in sn:
+            celltype=2 #4840三元电芯
+        elif 'K504B' in sn:
+            celltype=99    #60ah林磷酸铁锂电芯
+        elif 'MGMLXN750' in sn:
+            celltype=3 #力信50ah三元电芯
+        elif 'MGMCLN750' or 'UD' in sn: 
+            celltype=4 #CATL 50ah三元电芯
+        else:
+            print('SN：{}，未找到对应电池类型！！！'.format(sn))
+            continue
+            # sys.exit()
+
+        #读取原始数据库数据........................................................................................................................................................
+        dbManager = DBManager.DBManager()
+        df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms'])
+        df_bms = df_data['bms']
+
+        #读取结果数据库数据........................................................................................................................................................
+        mode=1
+        tablename1='cellstateestimation_soh'
+        tablename2='cellstateestimation_uniform_socvoltdiff'
+        tablename3='cellstateestimation_soc'
+        DBRead=DBDownload.DBDownload(host, port, db, user, password,mode)
+        with DBRead as DBRead:
+            df_soh=DBRead.getdata('time_st,sn,soh,cellsoh', tablename=tablename1, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+            df_uniform=DBRead.getdata('time,sn,cellsoc_diff,cellmin_num,cellmax_num', tablename=tablename2, sn=sn, timename='time', st=start_time, sp=end_time)
+            df_soc=DBRead.getdata('time','sn','packsoc', tablename=tablename3, sn=sn)
+
+        #电池诊断................................................................................................................................................................
+        if not df_bms.empty:
+            df_diag_ram_sn=df_diag_ram[df_diag_ram['sn']==sn]
+            df_diag_ram_sn.reset_index(inplace=True,drop=True)
+
+            batdiag=CBMSBatDiag.BatDiag(sn,celltype,df_bms,df_soh,df_soc,df_uniform,df_diag_ram_sn)
+            df_diag_res, df_health_res=batdiag.diag()   #获取电池故障结果和电池评分结果
+
+            #电池评分写入数据库
+            if not df_health_res.empty:   #变为历史故障更改数据库
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\05BatDiag\电池评分.txt','a') as file:
+                    file.write(str(tuple(df_health_res))+'\n') 
+
+            #历史故障筛选并更改数据库故障结束时间.........................................................
+            if not df_diag_res.empty:
+                df_diag_now=df_diag_res[df_diag_res['time_sp'] == '0000-00-00 00:00:00']   #去除历史故障
+                df_diag_new = pd.concat([df_diag_res,df_diag_ram_sn,df_diag_ram_sn]).drop_duplicates(subset=['time_st','faultcode'],keep=False)#此次判断中新增故障
+                df_diag_end=pd.concat([df_diag_res,df_diag_new,df_diag_new]).drop_duplicates(subset=['time_st','faultcode'],keep=False)#此次判断中新增故障
+                df_diag_end=df_diag_end[df_diag_end['time_sp'] != '0000-00-00 00:00:00']
+                df_diag_end.reset_index(inplace=True,drop=True)  #重置索引
+
+                if not df_diag_end.empty:   #变为历史故障更改数据库
+                    with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\05BatDiag\故障报警.txt','a') as file:
+                        file.write(str(tuple(df_diag_end))+'\n')          
+
+                #新增故障筛选并存入数据库.....................................................................
+                if not df_diag_new.empty:  #新增写入数据库
+                    with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\05BatDiag\故障报警.txt','a') as file:
+                        file.write(str(tuple(df_diag_new))+'\n')        
+
+                #更新diag的Ram数据
+                df_diag_ram=df_diag_ram.drop(df_diag_ram[df_diag_ram.sn==sn].index)
+                df_diag_ram=df_diag_ram.append(df_diag_now, ignore_index=True)
+                df_diag_ram.reset_index(inplace=True,drop=True)     #重置索引
+
+            #健康度评分存入数据库
+
+#...............................................主函数起定时作用.......................................................................................................................
+if __name__ == "__main__":
+    
+    excelpath=r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\sn-20210903.xlsx'
+    SNdata_6060 = pd.read_excel(excelpath, sheet_name='科易6060')
+    SNdata_6040 = pd.read_excel(excelpath, sheet_name='科易6040')
+    SNdata_4840 = pd.read_excel(excelpath, sheet_name='科易4840')
+    SNdata_L7255 = pd.read_excel(excelpath, sheet_name='格林美-力信7255')
+    SNdata_C7255 = pd.read_excel(excelpath, sheet_name='格林美-CATL7255')
+    SNdata_U7255 = pd.read_excel(excelpath, sheet_name='优旦7255')
+    SNnums_6060=SNdata_6060['SN号'].tolist()
+    SNnums_6040=SNdata_6040['SN号'].tolist()
+    SNnums_4840=SNdata_4840['SN号'].tolist()
+    SNnums_L7255=SNdata_L7255['SN号'].tolist()
+    SNnums_C7255=SNdata_C7255['SN号'].tolist()
+    SNnums_U7255=SNdata_U7255['SN号'].tolist()
+    SNnums=SNnums_L7255 + SNnums_C7255 + SNnums_U7255, SNnums_6040 + SNnums_4840 + SNnums_6060
+    SNnums=['HUATING']
+    
+    mylog=log.Mylog('log_diag.txt','error')
+    mylog.logcfg()
+
+    #定时任务.......................................................................................................................................................................
+    scheduler = BlockingScheduler()
+    scheduler.add_job(diag_cal, 'interval', seconds=60)
+
+    try:  
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/alibaba/05BatDiag/05BatDiag/main_test.py

@@ -0,0 +1,148 @@
+import CBMSBatDiag
+import pymysql
+import datetime
+import pandas as pd
+from LIB.BACKEND import DBManager
+import time, datetime
+from apscheduler.schedulers.blocking import BlockingScheduler
+from USER.alibaba.Common import DBDownload
+from USER.alibaba.Common import log
+
+#...................................电池包电芯安全诊断函数......................................................................................................................
+def diag_cal():
+    global SNnums
+    global start_time
+    global df_bms
+
+    start=time.time()
+    start_time=start_time+datetime.timedelta(seconds=55)
+    end_time=start_time+datetime.timedelta(seconds=60)
+
+    #数据库配置
+    host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+    port=3306
+    db='qx_cas'
+    user='qx_read'
+    password='Qx@123456'
+
+    #读取故障结果库中的当前故障...............................
+    # param='time_st,time_sp,sn,faultcode,faultlv,faultinfo,faultadvice'
+    # tablename=''
+    # mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db)
+    # cursor = mysql.cursor()
+    # sql =  "select %s from %s where time_sp='0000-00-00 00:00:00'" %(param,tablename)
+    # cursor.execute(sql)
+    # res = cursor.fetchall()
+    # df_diag_ram= pd.DataFram(res,columns=param.split(','))
+    # cursor.close()
+    # mysql.close()
+    df_diag_ram=pd.DataFrame(columns=['time_st','time_sp','sn','faultcode','faultlv','faultinfo','faultadvice'])
+
+    for sn in SNnums:
+        if 'PK500' in sn:
+            celltype=1 #6040三元电芯
+        elif 'PK502' in sn:
+            celltype=2 #4840三元电芯
+        elif 'K504B' in sn:
+            celltype=99    #60ah林磷酸铁锂电芯
+        elif 'MGMLXN750' in sn:
+            celltype=3 #力信50ah三元电芯
+        elif 'MGMCLN750' or 'UD' in sn: 
+            celltype=4 #CATL 50ah三元电芯
+        else:
+            print('SN：{}，未找到对应电池类型！！！'.format(sn))
+            continue
+            # sys.exit()
+        celltype=2
+
+        #读取原始数据库数据........................................................................................................................................................
+        # dbManager = DBManager.DBManager()
+        # df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms'])
+        # df_bms = df_data['bms']
+        df_bms1=df_bms[(df_bms['time'] > start_time) & (df_bms['time'] < end_time)]
+        df_bms1.reset_index(inplace=True,drop=True)
+        print(df_bms1)
+
+        #读取结果数据库数据........................................................................................................................................................
+        mode=1
+        tablename1='cellstateestimation_soh'
+        tablename2='cellstateestimation_uniform_socvoltdiff'
+        tablename3='cellstateestimation_soc'
+        DBRead=DBDownload.DBDownload(host, port, db, user, password,mode)
+        with DBRead as DBRead:
+            df_soh=DBRead.getdata('time_st,sn,soh,cellsoh', tablename=tablename1, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+            df_uniform=DBRead.getdata('time,sn,cellsoc_diff,cellmin_num,cellmax_num', tablename=tablename2, sn=sn, timename='time', st=start_time, sp=end_time)
+            # df_soc=DBRead.getdata('time','sn','packsoc', tablename=tablename3, sn=sn)
+            df_soc=pd.DataFrame()
+
+        #电池诊断................................................................................................................................................................
+        if not df_bms1.empty:
+            df_diag_ram_sn=df_diag_ram[df_diag_ram['sn']==sn]
+
+            batdiag=CBMSBatDiag.BatDiag(sn,celltype,df_bms1,df_soh,df_soc,df_uniform,df_diag_ram_sn)
+            df_diag_res, df_health_res=batdiag.diag()   #获取电池故障结果和电池评分结果
+
+            #电池评分写入数据库
+            if not df_health_res.empty:   #变为历史故障更改数据库
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\05BatDiag\电池评分.txt','a',encoding='utf8') as file:
+                    file.write(str(df_health_res)+'\n') 
+
+            #历史故障筛选并更改数据库故障结束时间.........................................................
+            df_diag_end=df_diag_res[df_diag_res['time_sp'] != '0000-00-00 00:00:00']
+            if not df_diag_end.empty:   #变为历史故障更改数据库
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\05BatDiag\故障报警.txt','a',encoding='utf8') as file:
+                    file.write(str(df_diag_end)+'\n')          
+
+            #新增故障筛选并存入数据库.....................................................................
+            df_diag_now=df_diag_res[df_diag_res['time_sp'] == '0000-00-00 00:00:00']   #去除历史故障
+            df_diag_all=df_diag_now.append(df_diag_ram_sn) 
+            df_diag_new=df_diag_all.drop_duplicates(subset=['faultcode'],keep=False)
+            if not df_diag_new.empty:  #新增写入数据库
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\05BatDiag\故障报警.txt','a',encoding='utf8') as file:
+                    file.write(str(df_diag_new)+'\n')        
+
+            #更新diag的Ram数据
+            df_diag_ram=df_diag_ram.drop(df_diag_ram[df_diag_ram.sn==sn].index)
+            df_diag_ram=df_diag_ram.append(df_diag_now, ignore_index=True)
+            df_diag_ram.reset_index(inplace=True,drop=True)     #重置索引
+
+            #健康度评分存入数据库
+    end=time.time()
+    print(end-start)
+
+#...............................................主函数起定时作用.......................................................................................................................
+if __name__ == "__main__":
+    
+    # excelpath=r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\sn-20210903.xlsx'
+    # SNdata_6060 = pd.read_excel(excelpath, sheet_name='科易6060')
+    # SNdata_6040 = pd.read_excel(excelpath, sheet_name='科易6040')
+    # SNdata_4840 = pd.read_excel(excelpath, sheet_name='科易4840')
+    # SNdata_L7255 = pd.read_excel(excelpath, sheet_name='格林美-力信7255')
+    # SNdata_C7255 = pd.read_excel(excelpath, sheet_name='格林美-CATL7255')
+    # SNdata_U7255 = pd.read_excel(excelpath, sheet_name='优旦7255')
+    # SNnums_6060=SNdata_6060['SN号'].tolist()
+    # SNnums_6040=SNdata_6040['SN号'].tolist()
+    # SNnums_4840=SNdata_4840['SN号'].tolist()
+    # SNnums_L7255=SNdata_L7255['SN号'].tolist()
+    # SNnums_C7255=SNdata_C7255['SN号'].tolist()
+    # SNnums_U7255=SNdata_U7255['SN号'].tolist()
+    # SNnums=SNnums_L7255 + SNnums_C7255 + SNnums_U7255, SNnums_6040 + SNnums_4840 + SNnums_6060
+    SNnums=['HUATING']
+    
+    mylog=log.Mylog('log_diag.txt','error')
+    mylog.logcfg()
+
+    start_time=datetime.datetime.strptime('2021-10-29 14:27:40','%Y-%m-%d %H:%M:%S')
+    df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-故障诊断原始数据.xlsx')
+    df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+
+    #定时任务.......................................................................................................................................................................
+    scheduler = BlockingScheduler()
+    scheduler.add_job(diag_cal, 'interval', seconds=1)
+
+    try:  
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/alibaba/06BatSafetyAlarm/06BatSafetyAlarm/CBMSSafetyAlarm.py

@@ -0,0 +1,271 @@
+import pandas as pd
+import numpy as np
+import datetime
+from USER.alibaba.Common import BatParam
+
+class SafetyAlarm:
+    def __init__(self,sn,celltype,df_bms,df_bms_ram_sn,df_alarm_ram_sn):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_bms=df_bms
+        self.df_ram_bms=df_bms_ram_sn.copy()
+        df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        self.df_ram_alarm=df_alarm_ram_sn.copy()
+
+        if (not df_bms_ram_sn.empty) and (not self.df_bms.empty):
+            self.df_bms=self.df_bms[self.df_bms['time'] > df_bms_ram_sn.iloc[-1]['time']]    #滤除原始数据中的重复数据
+            self.df_bms.reset_index(inplace=True,drop=True)     #重置索引
+
+        self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
+        self.packvolt=df_bms['PackCrnt']
+        self.bmstime= df_bms['time']
+
+        self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        # othertemp=['其他温度'+str(x) for x in range(1,self.param.OtherTempNums+1)]
+        self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+        # self.celltemp_name=celltemp+othertemp
+
+    
+    def safety_alarm_diag(self):
+        if self.celltype<=50:
+            df_res=self._alarm_diag()
+            return df_res    
+        else:
+            df_res=self._alarm_diag()
+            return df_res
+        
+
+    #定义滑动滤波函数.............................................................................................
+    def _np_move_avg(self,a, n, mode="same"): 
+        return (np.convolve(a, np.ones((n,)) / n, mode=mode))
+    
+    #寻找当前行数据的所有温度值...................................................................................
+    def _celltemp_get(self,num):   
+        celltemp = np.array(self.df_bms.loc[num,self.celltemp_name])
+        return celltemp
+
+    #获取当前行所有电压数据............................................................................................
+    def _cellvolt_get(self,num): 
+        cellvolt = np.array(self.df_bms.loc[num,self.cellvolt_name])
+        return cellvolt
+
+    #..........................................三元电池诊断功能..................................................................
+    def _alarm_diag(self):
+
+        column_name=['time_st','time_sp','sn','faultcode','faultlv','faultinfo','faultadvice']
+        df_res=pd.DataFrame(columns=column_name)
+
+        end_time='0000-00-00 00:00:00'
+        time_now=datetime.datetime.now()
+        celltemprise=0
+        celltemphigh=0
+        cellvoltfall=0
+        packvoltfall=0
+
+        if not self.df_ram_alarm.empty:
+            safetywarning1=self.df_ram_alarm.iloc[-1]['safetywarning1']
+            safetywarning2=self.df_ram_alarm.iloc[-1]['safetywarning2']
+        else:
+            safetywarning1=0
+            safetywarning2=0
+
+
+        if not self.df_bms.empty:
+            #温升参数初始化
+            if not self.df_ram_bms.empty:
+                time_temp_st=self.df_ram_bms.iloc[-1]['time']
+                temp_st=self.df_ram_bms.iloc[-1]['celltemp']
+            else:
+                time_temp_st=self.bmstime[0]
+                temp_st=self._celltemp_get(0)
+            
+            for i in range(len(self.df_bms)):
+                #温度诊断功能.............................................................................................................
+                if i<1:
+                    if not self.df_ram_bms.empty:
+                        temp1=self.df_ram_bms.iloc[-1]['celltemp']
+                        temp2=self._celltemp_get(i)
+                        time1=self.df_ram_bms.iloc[-1]['time']
+                        time2=self.bmstime[i]
+                    else:
+                        temp1=self._celltemp_get(i)
+                        temp2=self._celltemp_get(i)
+                        time1=self.bmstime[i]-datetime.timedelta(seconds=10)
+                        time2=self.bmstime[i]
+                else:
+                    temp1=self._celltemp_get(i-1)
+                    temp2=self._celltemp_get(i)
+                    time1=self.bmstime[i-1]
+                    time2=self.bmstime[i]
+                
+                temp1=temp1[np.where((temp1<self.param.CellTempUpLmt) & (temp1>self.param.CellTempLwLmt))]
+                temp2=temp2[np.where((temp2<self.param.CellTempUpLmt) & (temp2>self.param.CellTempLwLmt))]
+                
+                #温度有效性判断...........................................................................
+                if len(temp1)>0.5 and len(temp2)>0.5 and len(temp1)==len(temp2):
+                    celltempvalid=1
+                else:   #不作处理
+                    celltempvalid=0
+
+                if celltempvalid==1:
+                    celltempmax=max(temp2)
+                    #过温判断............................................................................................................................
+                    if celltempmax>self.param.TrwTempHigh:
+                        celltemphigh=1
+                    else:
+                        pass
+                    
+                    #温升判断.............................................................................................................................
+                    delttime=(time2-time_temp_st).total_seconds()
+                    if delttime>5:
+                        celltemp_rate=(max(temp2-temp_st))/delttime    #计算最大温升速率
+                        temp_st=temp2
+                        time_temp_st=time2
+                        if celltemp_rate>self.param.TrwTempRate and self.param.CellTempLwLmt<min(temp1) and max(temp1)<self.param.CellTempUpLmt:
+                            celltemprise=celltemprise+1
+                        else:
+                            pass
+                
+                else:
+                    pass
+                
+                #电压诊断功能.........................................................................................................................................
+                if i<1:
+                    cellvolt2=self._cellvolt_get(i)
+                    time2=self.bmstime[i]
+                    if not self.df_ram_bms.empty:
+                        cellvolt1=self.df_ram_bms.iloc[-1]['cellvolt']
+                        time1=self.df_ram_bms.iloc[-1]['time']
+                    else:
+                        cellvolt1=cellvolt2
+                        time1=time2
+                else:
+                    cellvolt2=self._cellvolt_get(i)
+                    cellvolt1=self._cellvolt_get(i-1)
+                    time2=self.bmstime[i]
+                    time1=self.bmstime[i-1]
+                    
+
+                #电压有效性..........................................................................................................................................
+                cellvoltmin2=min(cellvolt2)
+                cellvoltmax2=max(cellvolt2)
+                cellvoltmin_index2=list(cellvolt2).index(cellvoltmin2)
+                cellvoltmax_index2=list(cellvolt2).index(cellvoltmax2)
+                cellvoltmin1=min(cellvolt1)
+                cellvoltmax1=max(cellvolt1)
+                cellvoltmin_index1=list(cellvolt1).index(cellvoltmin1)
+                cellvoltmax_index1=list(cellvolt1).index(cellvoltmax1)
+                
+                if (cellvoltmin2<2 and cellvoltmax2>4.5 and abs(cellvoltmax_index2-cellvoltmin_index2)==1) or (cellvoltmin1<2 and cellvoltmax1>4.5 and abs(cellvoltmax_index1-cellvoltmin_index1)==1):   #电压断线故障进入
+                    cellvoltvalid=0
+                else:
+                    cellvolt1=cellvolt1[np.where((cellvolt1>0.01) & (cellvolt1<4.5))]
+                    cellvolt2=cellvolt2[np.where((cellvolt2>0.01) & (cellvolt2<4.5))]
+                    if len(cellvolt1)>1 and len(cellvolt2)>1 and len(cellvolt1)==len(cellvolt2):
+                        cellvoltvalid=1
+                    else:
+                        cellvoltvalid=0
+                
+                if cellvoltvalid==1:
+                    #单体电压跌落诊断...........................................................................................................................
+                    delttime=(time2-time1).total_seconds()
+                    if delttime<360:
+                        if self.packcrnt[i]<0.5 and max(cellvolt1-cellvolt2)>self.param.TrwCellVoltFall:
+                            cellvoltfall=cellvoltfall+1
+                        elif self.packcrnt[i]>0.5 and max(cellvolt1-cellvolt2)-self.packcrnt[i]*0.01>self.param.TrwCellVoltFall:
+                            cellvoltfall=cellvoltfall+1
+                        else:
+                            pass
+                    else:
+                        pass
+                else:
+                    pass
+                    
+                #电池包诊断.....................................................................................................................................
+                packvolt2=self.packvolt[i]
+                #电池包电压有效性............................................................................................................................
+                if self.param.CellVoltNums<packvolt2<self.param.CellVoltNums*4.5:
+                    packvoltvalid=1
+                else:
+                    packvoltvalid=0
+                
+                if packvoltvalid==1:
+                    if i<1:
+                        if not self.df_ram_bms.empty:
+                            time1=self.df_ram_bms.iloc[-1]['time']
+                            time2=self.bmstime[i]
+                            delttime=(time2-time1).total_seconds()
+                            packvolt1=self.df_ram_bms.iloc[-1]['packvolt']
+                        
+                            if delttime<360:
+                                if self.packcrnt[i]<5 and (packvolt1-packvolt2)>self.param.TrwPackVoltFall and self.param.CellVoltNums<packvolt1<self.param.CellVoltNums*4.5:
+                                    packvoltfall=packvoltfall+1
+                                else:
+                                    pass
+                            else:
+                                pass
+                        else:
+                            pass
+                    else:
+                        packvolt1=self.packvolt[i-1]
+                        if self.packcrnt[i]<5 and (packvolt1-packvolt2)>self.param.TrwPackVoltFall and self.param.CellVoltNums<packvolt1<self.param.CellVoltNums*4.5:
+                            packvoltfall=packvoltfall+1
+                        else:
+                            pass
+                else:
+                    pass
+            
+            #热失控故障判断........................................................................................................................
+            df_bms_ram=pd.DataFrame(columns=['time', 'sn', 'packvolt',  'cellvolt', 'celltemp'])
+            temp2=self._celltemp_get(len(self.bmstime)-1)
+            df_bms_ram.loc[0]=[self.bmstime[0], self.sn, packvolt2, cellvolt2, temp2]
+            df_ram_alarm=self.df_ram_alarm
+            trwtemp=max(celltemprise,celltemphigh)
+            trwcellvolt=cellvoltfall
+            trwpackvolt=packvoltfall
+            trw_array=np.array([trwtemp, trwcellvolt, trwpackvolt])
+
+            if np.sum(trw_array)>3.5 and np.sum(trw_array>0.5)>1.5:
+                fltcode=119
+                df_res.loc[0]=[self.bmstime[len(self.bmstime)-1], end_time, self.sn, fltcode, 5, '电池发生热失控', '断开继电器，远离电池，立刻消防处理，并通知技术人员介入']
+                df_ram_alarm=pd.DataFrame(columns=['sn','time','safetywarning1','safetywarning2'])
+            elif safetywarning1>2.5 and np.sum(trw_array)>1.5 and np.sum(trw_array>0.5)>1.5:
+                fltcode=119
+                df_res.loc[0]=[self.bmstime[len(self.bmstime)-1], end_time, self.sn, fltcode, 5, '电池发生热失控', '断开继电器，远离电池，立刻消防处理，并通知技术人员介入']
+                df_ram_alarm=pd.DataFrame(columns=['sn','time','safetywarning1','safetywarning2'])
+            else:
+                df_res=pd.DataFrame()
+                #更新df_ram_alarm信息
+                if np.sum(trw_array)>1.5 and np.sum(trw_array>0.5)>1.5:
+                    safetywarning1=3
+                    df_ram_alarm.loc[0]=[self.sn,time2,safetywarning1,safetywarning2]
+                else:
+                    safetywarning1=0
+                    df_ram_alarm=pd.DataFrame(columns=['sn','time','safetywarning1','safetywarning2'])
+                if max(cellvolt2)>5 or min(cellvolt2)<1:
+                    safetywarning2=3
+                    df_ram_alarm.loc[0]=[self.sn,time2,safetywarning1,safetywarning2]
+                elif max(cellvolt2)<4.5 and min(cellvolt2)>2.5:
+                    safetywarning2=0
+                    if safetywarning1==0:
+                        df_ram_alarm=pd.DataFrame(columns=['sn','time','safetywarning1','safetywarning2'])
+                    else:
+                        df_ram_alarm.loc[0]=[self.sn,time2,safetywarning1,safetywarning2]
+                else:
+                    df_ram_alarm.loc[0]=[self.sn,time2,safetywarning1,safetywarning2]
+            return df_res, df_bms_ram, df_ram_alarm
+            
+        else:
+            df_ram_alarm=self.df_ram_alarm
+            df_ram_bms=self.df_ram_bms
+            if (safetywarning1>2.5 or safetywarning2>2.5) and (time_now-df_ram_alarm.loc[0,'time']).total_seconds()>60:
+                fltcode=119
+                time_now=time_now.strftime('%Y-%m-%d %H:%M:%S')
+                time_now=datetime.datetime.strptime(time_now,'%Y-%m-%d %H:%M:%S')
+                df_res.loc[0]=[time_now, end_time, self.sn, fltcode, 5, '电池发生热失控', '断开继电器，远离电池，立刻消防处理，并通知技术人员介入']
+                df_ram_alarm=pd.DataFrame(columns=['sn','time','safetywarning1','safetywarning2'])
+            else:
+                df_res=pd.DataFrame()
+            return df_res, df_ram_bms, df_ram_alarm

USER/SPF/alibaba/alibaba/06BatSafetyAlarm/06BatSafetyAlarm/main test.py

@@ -0,0 +1,131 @@
+import CBMSSafetyAlarm
+import pymysql
+import datetime
+import pandas as pd
+from LIB.BACKEND import DBManager, Log
+import datetime, time
+from apscheduler.schedulers.blocking import BlockingScheduler
+from USER.alibaba.Common import log
+
+#...................................电池包电芯安全诊断函数......................................................................................................................
+def diag_cal():
+    global SNnums
+    global df_bms_ram
+    global start_time
+    global df_bms
+
+    start=time.time()
+    start_time=start_time+datetime.timedelta(seconds=60)
+    end_time=start_time+datetime.timedelta(seconds=60)
+    now_time=datetime.datetime.now()
+
+    #数据库配置
+    host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+    port=3306
+    user='qx_algo_readonly'
+    password = 'qx@123456'
+
+    #读取故障结果库中code==119且end_time='0000-00-00 00:00:00'...............................
+    db='safety_platform'
+    mysql = pymysql.connect (host=host, port=port, user=user, password=password, database=db)
+    cursor = mysql.cursor()
+    param='start_time,end_time,product_id,code,level,info,advice'
+    tablename='all_fault_info'
+    sql =  "select %s from %s where code=119 and end_time='0000-00-00 00:00:00'" %(param,tablename)
+    cursor.execute(sql)
+    res = cursor.fetchall()
+    df_diag_ram= pd.DataFrame(res,columns=param.split(','))
+    cursor.close()
+    mysql.close()
+
+    for sn in SNnums:
+        if 'PK500' in sn:
+            celltype=1 #6040三元电芯
+        elif 'PK502' in sn:
+            celltype=2 #4840三元电芯
+        elif 'K504B' in sn:
+            celltype=99    #60ah林磷酸铁锂电芯
+        elif 'MGMLXN750' in sn:
+            celltype=3 #力信50ah三元电芯
+        elif 'MGMCLN750' or 'UD' in sn: 
+            celltype=4 #CATL 50ah三元电芯
+        else:
+            print('SN：{}，未找到对应电池类型！！！'.format(sn))
+            continue
+            # sys.exit()
+        celltype=2
+
+        #读取原始数据库数据........................................................................................................................................................
+        # df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-故障诊断原始数据.xlsx')
+        df_bms1=df_bms[(df_bms['time'] > start_time) & (df_bms['time'] < end_time)]
+        df_bms1.reset_index(inplace=True,drop=True)
+        print(df_bms1)
+
+        #电池诊断................................................................................................................................................................
+        df_diag_ram_sn=df_diag_ram[df_diag_ram['product_id']==sn]
+        df_bms_ram_sn=df_bms_ram[df_bms_ram['sn']==sn]
+        if df_diag_ram_sn.empty:
+            if not df_bms1.empty:
+                SafetyAlarm=CBMSSafetyAlarm.SafetyAlarm(sn,celltype,df_bms1, df_bms_ram_sn)
+                df_diag_res, df_bms_res=SafetyAlarm.diag() 
+
+                #更新bms的ram数据
+                sn_index=df_bms_ram.loc[df_bms_ram['sn']==sn].index
+                df_bms_ram=df_bms_ram.drop(index=sn_index)
+                df_bms_ram=df_bms_ram.append(df_bms_res)
+
+                #当前热失控故障写入数据库
+                if not df_diag_res.empty:
+                    with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\06BatSafetyAlarm\热失控报警.txt','a') as file:
+                        file.write(str(tuple(df_diag_res.iloc[-1]))+'\n')
+                
+        #当前热失控已超过一天变为历史故障并更改数据库
+        else:
+            fault_time=datetime.datetime.strptime(df_diag_ram_sn.iloc[-1]['start_time'], '%Y-%m-%d %H:%M:%S')
+            if (now_time-fault_time).total_seconds()>24*3600:
+                df_diag_ram_sn['end_time']=end_time
+                df_diag_ram_sn['Batpos']=1
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\06BatSafetyAlarm\热失控报警.txt','a') as file:
+                    file.write(str(tuple(df_diag_ram_sn.iloc[-1]))+'\n')
+
+        end=time.time()
+        print(end-start)
+
+#...............................................主函数起定时作用.......................................................................................................................
+if __name__ == "__main__":
+    
+    # excelpath=r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\sn-20210903.xlsx'
+    # SNdata_6060 = pd.read_excel(excelpath, sheet_name='科易6060')
+    # SNdata_6040 = pd.read_excel(excelpath, sheet_name='科易6040')
+    # SNdata_4840 = pd.read_excel(excelpath, sheet_name='科易4840')
+    # SNdata_L7255 = pd.read_excel(excelpath, sheet_name='格林美-力信7255')
+    # SNdata_C7255 = pd.read_excel(excelpath, sheet_name='格林美-CATL7255')
+    # SNdata_U7255 = pd.read_excel(excelpath, sheet_name='优旦7255')
+    # SNnums_6060=SNdata_6060['SN号'].tolist()
+    # SNnums_6040=SNdata_6040['SN号'].tolist()
+    # SNnums_4840=SNdata_4840['SN号'].tolist()
+    # SNnums_L7255=SNdata_L7255['SN号'].tolist()
+    # SNnums_C7255=SNdata_C7255['SN号'].tolist()
+    # SNnums_U7255=SNdata_U7255['SN号'].tolist()
+    # SNnums=SNnums_L7255 + SNnums_C7255 + SNnums_U7255 + SNnums_6040 + SNnums_4840 + SNnums_6060
+    SNnums=['HUATING']
+    
+    mylog=log.Mylog('log_alarm.txt','error')
+    mylog.logcfg()
+    
+    #参数初始化
+    df_bms_ram=pd.DataFrame(columns=['time', 'sn', 'packvolt', 'cellvolt', 'celltemp'])
+    start_time=datetime.datetime.strptime('2021-10-29 18:10:24','%Y-%m-%d %H:%M:%S')
+    df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-故障诊断原始数据.xlsx')
+    df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+
+    #定时任务.......................................................................................................................................................................
+    scheduler = BlockingScheduler()
+    scheduler.add_job(diag_cal, 'interval', seconds=0.5, id='diag_job')
+
+    try:  
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/alibaba/06BatSafetyAlarm/06BatSafetyAlarm/main.py

@@ -0,0 +1,127 @@
+import CBMSSafetyAlarm
+import pymysql
+import datetime
+import pandas as pd
+import datetime
+from apscheduler.schedulers.blocking import BlockingScheduler
+from USER.alibaba.Common import log
+
+#...................................电池包电芯安全诊断函数......................................................................................................................
+def diag_cal():
+    global SNnums
+    global df_bms_ram, df_alarm_ram
+
+
+    # start=time.time()
+    now_time=datetime.datetime.now()
+    start_time=now_time-datetime.timedelta(seconds=60)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=now_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    #数据库配置
+    host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+    port=3306
+    user='qx_algo_readonly'
+    password = 'qx@123456'
+
+    #读取故障结果库中code==119且end_time='0000-00-00 00:00:00'...............................
+    db='safety_platform'
+    mysql = pymysql.connect (host=host, port=port, user=user, password=password, database=db)
+    cursor = mysql.cursor()
+    param='time_st,time_sp,sn,faultcode,faultlv,faultinfo,faultadvice'
+    tablename='all_fault_info'
+    sql =  "select %s from %s where faultcode=119 and time_sp='0000-00-00 00:00:00'" %(param,tablename)
+    cursor.execute(sql)
+    res = cursor.fetchall()
+    df_diag_ram= pd.DataFrame(res,columns=param.split(','))
+    cursor.close()
+    mysql.close()
+
+    for sn in SNnums:
+        if 'PK500' in sn:
+            celltype=1 #6040三元电芯
+        elif 'PK502' in sn:
+            celltype=2 #4840三元电芯
+        elif 'K504B' in sn:
+            celltype=99    #60ah林磷酸铁锂电芯
+        elif 'MGMLXN750' in sn:
+            celltype=3 #力信50ah三元电芯
+        elif 'MGMCLN750' or 'UD' in sn: 
+            celltype=4 #CATL 50ah三元电芯
+        else:
+            print('SN：{}，未找到对应电池类型！！！'.format(sn))
+            continue
+            # sys.exit()
+
+        #读取原始数据库数据........................................................................................................................................................
+        df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\力神40Ah-SOH原始数据.xlsx')
+        # print(df_bms)
+
+        #电池诊断................................................................................................................................................................
+        df_diag_ram_sn=df_diag_ram[df_diag_ram['sn']==sn]
+        df_bms_ram_sn=df_bms_ram[df_bms_ram['sn']==sn]
+        df_alarm_ram_sn=df_alarm_ram[df_alarm_ram['sn']==sn]
+        if df_diag_ram_sn.empty:   
+            SafetyAlarm=CBMSSafetyAlarm.SafetyAlarm(sn,celltype,df_bms, df_bms_ram_sn, df_alarm_ram_sn)
+            df_diag_res, df_bms_res, df_ram_res=SafetyAlarm.safety_alarm_diag() 
+
+            #更新bms和结果ram的ram数据
+            sn_index=df_bms_ram.loc[df_bms_ram['sn']==sn].index
+            df_bms_ram=df_bms_ram.drop(index=sn_index)
+            df_bms_ram=df_bms_ram.append(df_bms_res)
+
+            sn_index=df_alarm_ram.loc[df_alarm_ram['sn']==sn].index
+            df_alarm_ram=df_alarm_ram.drop(index=sn_index)
+            df_alarm_ram=df_alarm_ram.append(df_ram_res)
+
+            #当前热失控故障写入数据库
+            if not df_diag_res.empty:
+                with open(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\06BatSafetyAlarm\热失控报警.txt','a') as file:
+                    file.write(str(tuple(df_diag_res.iloc[-1]))+'\n')
+                
+        #当前热失控已超过一天变为历史故障并更改数据库
+        else:
+            fault_time=df_diag_ram_sn.iloc[-1]['time_st']
+            if (now_time-fault_time).total_seconds()>24*3600:
+                df_diag_ram_sn['time_sp']=end_time
+                with open(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\06BatSafetyAlarm\热失控报警.txt','a') as file:
+                    file.write(str(tuple(df_diag_ram_sn.iloc[-1]))+'\n')
+
+        # end=time.time()
+        # print(end-start)
+
+#...............................................主函数起定时作用.......................................................................................................................
+if __name__ == "__main__":
+    
+    excelpath=r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\sn-20210903.xlsx'
+    SNdata_6060 = pd.read_excel(excelpath, sheet_name='科易6060')
+    SNdata_6040 = pd.read_excel(excelpath, sheet_name='科易6040')
+    SNdata_4840 = pd.read_excel(excelpath, sheet_name='科易4840')
+    SNdata_L7255 = pd.read_excel(excelpath, sheet_name='格林美-力信7255')
+    SNdata_C7255 = pd.read_excel(excelpath, sheet_name='格林美-CATL7255')
+    SNdata_U7255 = pd.read_excel(excelpath, sheet_name='优旦7255')
+    SNnums_6060=SNdata_6060['SN号'].tolist()
+    SNnums_6040=SNdata_6040['SN号'].tolist()
+    SNnums_4840=SNdata_4840['SN号'].tolist()
+    SNnums_L7255=SNdata_L7255['SN号'].tolist()
+    SNnums_C7255=SNdata_C7255['SN号'].tolist()
+    SNnums_U7255=SNdata_U7255['SN号'].tolist()
+    SNnums=SNnums_L7255 + SNnums_C7255 + SNnums_U7255 + SNnums_6040 + SNnums_4840 + SNnums_6060
+    
+    mylog=log.Mylog('log_diag.txt','error')
+    mylog.logcfg()
+    
+    #参数初始化
+    df_bms_ram=pd.DataFrame(columns=['time', 'sn', 'packvolt', 'cellvolt', 'celltemp'])
+    df_alarm_ram=pd.DataFrame(columns=['sn','time','safetywarning1','safetywarning2'])
+
+    #定时任务.......................................................................................................................................................................
+    scheduler = BlockingScheduler()
+    scheduler.add_job(diag_cal, 'interval', seconds=60, id='diag_job')
+
+    try:  
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/alibaba/07BatSafetyWarning/07BatSafetyWarning/CBMSBatInterShort.py

@@ -0,0 +1,686 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import datetime
+from USER.alibaba.Common import BatParam
+
+class BatInterShort():
+    def __init__(self,sn,celltype,df_bms,df_soh,df_last,df_last1,df_last2,df_last3,df_lfp):  #参数初始化
+
+        if (not df_lfp.empty) and celltype>50:
+            df_lfp.drop(['sn'],axis=1)
+            df_bms=pd.concat([df_lfp, df_bms], ignore_index=True)
+        else:
+            pass
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
+        self.packvolt=df_bms['PackVolt']
+        self.bms_soc=df_bms['PackSOC']
+        df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        self.bmstime= df_bms['time']
+
+        self.df_bms=df_bms
+        self.df_soh=df_soh
+        self.df_last=df_last
+        self.df_last1=df_last1
+        self.df_last2=df_last2
+        self.df_last3=df_last3
+        self.df_lfp=df_lfp
+
+        self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+    
+    def intershort(self):
+        if self.celltype<=50:
+            df_res, df_ram_last, df_ram_last1, df_ram_last3=self._ncm_intershort()
+            return df_res, df_ram_last, df_ram_last1,self.df_last2, df_ram_last3,self.df_lfp
+            
+        else:
+            df_res, df_ram_last, df_ram_last1, df_ram_last2, df_ram_last3, df_ram_lfp=self._lfp_intershort()
+            return df_res, df_ram_last, df_ram_last1, df_ram_last2, df_ram_last3, df_ram_lfp
+
+
+    #定义滑动滤波函数....................................................................................
+    def _np_move_avg(self,a, n, mode="same"): 
+        return (np.convolve(a, np.ones((n,)) / n, mode=mode))
+    
+    #寻找当前行数据的最小温度值.............................................................................
+    def _celltemp_weight(self,num):   
+        celltemp = list(self.df_bms.loc[num,self.celltemp_name])
+        celltemp=min(celltemp)
+        self.celltemp=celltemp
+        if self.celltype>50:
+            if celltemp>=25:
+                self.tempweight=1
+                self.StandardStandingTime=4500
+            elif celltemp>=15:
+                self.tempweight=0.6
+                self.StandardStandingTime=7200
+            elif celltemp>=5:
+                self.tempweight=0.2
+                self.StandardStandingTime=10800
+            else:
+                self.tempweight=0.1
+                self.StandardStandingTime=10800
+        else:
+            if celltemp>=25:
+                self.tempweight=1
+                self.StandardStandingTime=3600
+            elif celltemp>=15:
+                self.tempweight=0.8
+                self.StandardStandingTime=5400
+            elif celltemp>=5:
+                self.tempweight=0.6
+                self.StandardStandingTime=7200
+            else:
+                self.tempweight=0.2
+                self.StandardStandingTime=10800
+
+    #获取前半个小时每个电压的平均值........................................................................................
+    def _avgvolt_get(self,num): 
+        time_now=self.df_bms.loc[num, 'time']
+        time_last=time_now-datetime.timedelta(seconds=1800)
+        df_volt=self.df_bms[(self.df_bms['time']>=time_last) & (self.df_bms['time']<=time_now)]
+        df_volt=df_volt[self.cellvolt_name]
+        cellvolt_std=df_volt.std(axis=0)
+        if len(df_volt)>2 and max(cellvolt_std)<1.5:
+            cellvolt_sum=df_volt.sum(0)-df_volt.max(0)-df_volt.min(0)
+            cellvolt_mean=cellvolt_sum/(len(df_volt)-2)
+            cellvolt=cellvolt_mean
+        elif len(df_volt)==2:
+            # df_volt=pd.DataFrame(df_volt,dtype=np.float)
+            if max(abs(df_volt.iloc[1]-df_volt.iloc[0]))<3:
+                cellvolt=df_volt.mean(0)
+            else:
+                cellvolt=pd.DataFrame()
+        else:
+            cellvolt=pd.DataFrame()
+        return cellvolt
+
+    #获取当前行所有电压数据........................................................................................
+    def _cellvolt_get(self,num):
+        cellvolt = np.array(self.df_bms.loc[num,self.cellvolt_name])
+        return cellvolt
+
+    #获取当前行所有soc差...........................................................................................
+    def _celldeltsoc_get(self,cellvolt_list,dict_baltime,capacity): 
+        cellsoc=[]
+        celldeltsoc=[]
+        for j in range(1, self.param.CellVoltNums+1):   #获取每个电芯电压对应的SOC值
+            cellvolt=cellvolt_list[j-1]
+            ocv_soc=np.interp(cellvolt,self.param.LookTab_OCV,self.param.LookTab_SOC)
+            if j in dict_baltime.keys():
+                ocv_soc=ocv_soc+dict_baltime[j]*self.param.BalCurrent/(capacity*3600)   #补偿均衡电流
+            else:
+                pass
+            cellsoc.append(ocv_soc)
+        
+        cellsocmean=(sum(cellsoc)-max(cellsoc)-min(cellsoc))/(len(cellsoc)-2)
+        for j in range(len(cellsoc)):   #计算每个电芯的soc差
+            celldeltsoc.append(cellsoc[j]-cellsocmean)
+        return np.array(celldeltsoc), np.array(cellsoc)
+
+    #获取所有电芯的As差
+    def _cellDeltAs_get(self,chrg_st,chrg_end,dict_baltime):
+        cellAs=[]
+        celldeltAs=[]
+        for j in range(1, self.param.CellVoltNums+1):   #获取每个电芯电压>峰值电压的充入As数
+            if j in dict_baltime.keys():    #补偿均衡电流
+                As=-self.param.BalCurrent*dict_baltime[j]
+            else:    
+                As=0
+            As_tatol=0
+            symbol=0
+            for m in range(chrg_st+1,chrg_end):
+                As=As-self.packcrnt[m]*(self.bmstime[m]-self.bmstime[m-1]).total_seconds()
+                if symbol<5:
+                    if self.df_bms.loc[m,'CellVolt'+str(j)]>self.param.PeakCellVolt[symbol]:
+                        As_tatol=As_tatol+As
+                        symbol=symbol+1
+                    else:
+                        continue
+                else:
+                    cellAs.append(As_tatol/5)
+                    break
+        cellAsmean=(sum(cellAs)-max(cellAs)-min(cellAs))/(len(cellAs)-2)
+        for j in range(len(cellAs)):   #计算每个电芯的soc差
+            celldeltAs.append(cellAs[j]-cellAsmean)
+        return np.array(celldeltAs)
+
+    #计算每个电芯的均衡时长..........................................................................................................................
+    def _bal_time(self,dict_bal):
+        dict_baltime={}
+        dict_baltime1={}
+        for key in dict_bal:
+            count=1
+            x=eval(key)
+            while x>0:
+                if x & 1==1:    #判断最后一位是否为1
+                    if count in dict_baltime.keys():
+                        dict_baltime[count] = dict_baltime[count] + dict_bal[key]
+                    else:
+                        dict_baltime[count] = dict_bal[key]
+                else:
+                    pass
+                count += 1
+                x >>= 1    #右移一位
+        
+        dict_baltime=dict(sorted(dict_baltime.items(),key=lambda dict_baltime:dict_baltime[0]))
+        for key in dict_baltime:    #解析均衡的电芯编号
+            if self.celltype==1:    #科易6040
+                if key<14:
+                    dict_baltime1[key]=dict_baltime[key]
+                elif key<18:
+                    dict_baltime1[key-1]=dict_baltime[key]
+                else:
+                    dict_baltime1[key-3]=dict_baltime[key]
+            elif self.celltype==1:    #科易4840
+                if key<4:
+                    dict_baltime1[key-1]=dict_baltime[key]
+                elif key<8:
+                    dict_baltime1[key-1]=dict_baltime[key]
+                elif key<14:
+                    dict_baltime1[key-3]=dict_baltime[key]
+                elif key<18:
+                    dict_baltime1[key-4]=dict_baltime[key]
+                else:
+                    dict_baltime1[key-6]=dict_baltime[key]
+            else:
+                dict_baltime1=dict_baltime
+        return dict_baltime1
+
+    #三元电池的内短路电流计算...........................................................................................................................................................
+    def _ncm_intershort(self):
+        df_res=pd.DataFrame(columns=['time_st', 'time_sp', 'sn', 'method','short_current','baltime'])
+        df_ram_last=self.df_last
+        df_ram_last1=self.df_last1
+        df_ram_last3=self.df_last3
+
+        #容量初始化
+        if self.df_soh.empty:
+            batsoh=self.df_bms.loc[0,'PackSOH']
+            capacity=self.param.Capacity*batsoh/100
+        else:
+            batsoh=self.df_soh.loc[len(self.df_soh)-1,'soh']
+            capacity=self.param.Capacity*batsoh/100
+
+        #参数初始化
+        if df_ram_last.empty:  
+            firsttime=1
+            dict_bal={}
+        else:
+            deltsoc_last=df_ram_last.loc[0,'deltsoc']
+            cellsoc_last=df_ram_last.loc[0,'cellsoc']
+            time_last=df_ram_last.loc[0,'time']
+            firsttime=0
+            dict_bal={}
+        if df_ram_last1.empty:
+            firsttime1=1
+            dict_bal1={}
+        else:
+            deltsoc_last1=df_ram_last1.loc[0,'deltsoc1']
+            time_last1=df_ram_last1.loc[0,'time1']
+            firsttime1=0
+            dict_bal1={}
+        if df_ram_last3.empty:
+            standingtime=0
+            standingtime1=0
+            standingtime2=0
+        else:
+            standingtime=df_ram_last3.loc[0,'standingtime']
+            standingtime1=df_ram_last3.loc[0,'standingtime1']
+            standingtime2=df_ram_last3.loc[0,'standingtime2']
+            dict_bal1={}
+            if abs(self.packcrnt[0])<0.01 and standingtime>1 and standingtime1>1:
+                standingtime=standingtime+(self.bmstime[0]-df_ram_last3.loc[0,'time3']).total_seconds()
+                standingtime1=standingtime1+(self.bmstime[0]-df_ram_last3.loc[0,'time3']).total_seconds()
+            else:
+                pass
+
+        for i in range(1,len(self.df_bms)-1):
+
+            if firsttime1==0:   #满电静置算法--计算均衡状态对应的均衡时间
+                try:
+                    balstat=int(self.df_bms.loc[i,'单体均衡状态'])
+                    if balstat>0.5:
+                        bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                        bal_step=int(bal_step)
+                        if str(balstat) in dict_bal1.keys():
+                            dict_bal1[str(balstat)]=dict_bal1[str(balstat)]+bal_step
+                        else:
+                            dict_bal1[str(balstat)]=bal_step
+                    else:
+                        pass
+                except:
+                    dict_bal1={}
+            else:
+                pass
+
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     
+                delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                standingtime=standingtime+delttime
+                standingtime1=standingtime1+delttime
+                self._celltemp_weight(i)
+
+                #长时间静置法计算内短路-开始.....................................................................................................................................
+                if firsttime==1:    
+                    if standingtime>self.StandardStandingTime*2:      #静置时间满足要求
+                        standingtime=0
+                        cellvolt_now=self._avgvolt_get(i)
+                        if not cellvolt_now.empty:
+                            cellvolt_min=min(cellvolt_now)
+                            cellvolt_max=max(cellvolt_now)
+                            # cellvolt_last=self._avgvolt_get(i-1)
+                            # deltvolt=max(abs(cellvolt_now-cellvolt_last))
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+
+                            if 2<cellvolt_min<4.5 and 2<cellvolt_max<4.5 and (45<cellsoc_max or cellsoc_max<30) and (45<cellsoc_min or cellsoc_min<30): 
+                                dict_baltime={}   #获取每个电芯的均衡时间
+                                deltsoc_last, cellsoc_last=self._celldeltsoc_get(cellvolt_now,dict_baltime,capacity)
+                                time_last=self.bmstime[i]
+                                firsttime=0
+                                df_ram_last.loc[0]=[self.sn,time_last,deltsoc_last,cellsoc_last]   #更新RAM信息
+                    else:
+                        pass                
+                elif standingtime>3600*12:
+                    standingtime=0
+                    cellvolt_now=self._avgvolt_get(i)
+                    if not cellvolt_now.empty:
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        # cellvolt_last=self._avgvolt_get(i-1)
+                        # deltvolt=max(abs(cellvolt_now-cellvolt_last))
+                        cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        
+                        if 2<cellvolt_min<4.5 and 2<cellvolt_max<4.5 and (45<cellsoc_max or cellsoc_max<30) and (45<cellsoc_min or cellsoc_min<30):
+                            dict_baltime=self._bal_time(dict_bal)   #获取每个电芯的均衡时间
+                            deltsoc_now, cellsoc_now=self._celldeltsoc_get(cellvolt_now,dict_baltime,capacity)
+                            time_now=self.bmstime[i]
+                            if -5<max(cellsoc_now-cellsoc_last)<5:
+                                df_ram_last.loc[0]=[self.sn,time_now,deltsoc_now,cellsoc_now] #更新RAM信息
+                                
+                                list_sub=deltsoc_now-deltsoc_last
+                                list_pud=(0.01*capacity*3600*1000)/(time_now-time_last).total_seconds()
+                                leak_current=list_sub*list_pud
+                                # leak_current=np.array(leak_current)
+                                leak_current=np.round(leak_current,3)
+                                leak_current=list(leak_current)
+                                
+                                df_res.loc[len(df_res)]=[time_last,time_now,self.sn,1,str(leak_current),str(dict_baltime)]  #计算结果存入Dataframe
+                                time_last=time_now  #更新时间
+                                deltsoc_last=deltsoc_now    #更新soc差
+                                dict_bal={}
+                            else:
+                                firsttime=1
+                else: 
+                    try:  
+                        balstat=int(self.df_bms.loc[i,'单体均衡状态'])
+                        if balstat>0.5:
+                            bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                            bal_step=int(bal_step)
+                            if str(balstat) in dict_bal.keys():
+                                dict_bal[str(balstat)]=dict_bal[str(balstat)]+bal_step
+                            else:
+                                dict_bal[str(balstat)]=bal_step
+                        else:
+                            pass
+                    except:
+                        dict_bal={}
+
+                #满电静置法计算内短路-开始.....................................................................................................................................................
+                if self.StandardStandingTime<standingtime1:  
+                    standingtime1=0
+                    cellvolt_now1=self._avgvolt_get(i)
+                    if not cellvolt_now1.empty:
+                        cellvolt_max1=max(cellvolt_now1)
+                        cellvolt_min1=min(cellvolt_now1)
+                        # cellvolt_last1=self._avgvolt_get(i-1)
+                        # deltvolt1=max(abs(cellvolt_now1-cellvolt_last1))
+                        cellsoc_now1=np.interp(cellvolt_max1,self.param.LookTab_OCV,self.param.LookTab_SOC)
+
+                        if cellsoc_now1>self.param.FullChrgSoc-10 and 2<cellvolt_min1<4.5 and 2<cellvolt_max1<4.5:
+                            if firsttime1==1:
+                                dict_baltime1={}   #获取每个电芯的均衡时间
+                                deltsoc_last1, cellsoc_last1=self._celldeltsoc_get(cellvolt_now1,dict_baltime1,capacity)
+                                time_last1=self.bmstime[i]
+                                firsttime1=0
+                                df_ram_last1.loc[0]=[self.sn,time_last1,deltsoc_last1]    #更新RAM信息
+                            else:
+                                dict_baltime1=self._bal_time(dict_bal1)   #获取每个电芯的均衡时间
+                                time_now1=self.bmstime[i]
+                                if (time_now1-time_last1).total_seconds()>3600*20:
+                                    deltsoc_now1, cellsoc_now1=self._celldeltsoc_get(cellvolt_now1,dict_baltime1,capacity)
+                                    df_ram_last1.loc[0]=[self.sn,time_now1,deltsoc_now1] #更新RAM信息
+
+                                    list_sub1=deltsoc_now1-deltsoc_last1
+                                    list_pud1=(0.01*capacity*3600*1000)/(time_now1-time_last1).total_seconds()
+                                    leak_current1=list_sub1*list_pud1
+                                    # leak_current1=np.array(leak_current1)
+                                    leak_current1=np.round(leak_current1,3)
+                                    leak_current1=list(leak_current1)
+                                    
+                                    df_res.loc[len(df_res)]=[time_last1,time_now1,self.sn,2,str(leak_current1),str(dict_baltime1)]  #计算结果存入Dataframe
+                                    time_last1=time_now1  #更新时间
+                                    deltsoc_last1=deltsoc_now1    #更新soc差
+                                    dict_bal1={}
+                                else:
+                                    pass
+                    else:
+                        pass
+                else:   
+                    pass
+
+            else:
+                df_ram_last=pd.DataFrame(columns=['sn','time','deltsoc','cellsoc'])   #电流>0,清空上次静置的SOC差
+                dict_bal={} 
+                firsttime=1
+                standingtime=0
+                standingtime1=0
+                pass
+        
+        #更新RAM的standingtime
+        df_ram_last3.loc[0]=[self.sn,self.bmstime[len(self.bmstime)-1],standingtime,standingtime1,standingtime2]
+
+        #返回计算结果
+        if df_res.empty:    
+            return pd.DataFrame(), df_ram_last, df_ram_last1, df_ram_last3
+        else:
+            return df_res, df_ram_last, df_ram_last1, df_ram_last3
+
+    #磷酸铁锂电池内短路计算程序.............................................................................................................................
+    def _lfp_intershort(self):
+        column_name=['time_st', 'time_sp', 'sn', 'method','short_current','baltime']
+        df_res=pd.DataFrame(columns=column_name)
+        df_ram_last=self.df_last
+        df_ram_last1=self.df_last1
+        df_ram_last2=self.df_last2
+        df_ram_last3=self.df_last3
+        df_ram_lfp=pd.DataFrame(columns=self.df_bms.columns.tolist())
+
+        #容量初始化
+        if self.df_soh.empty:
+            batsoh=self.df_bms.loc[0,'PackSOH']
+            capacity=self.param.Capacity*batsoh/100
+        else:
+            batsoh=self.df_soh.loc[len(self.df_soh)-1,'soh']
+            capacity=self.param.Capacity*batsoh/100
+        #参数初始化
+        if df_ram_last.empty:  
+            firsttime=1
+            dict_bal={}
+        else:
+            deltsoc_last=df_ram_last.loc[0,'deltsoc']
+            cellsoc_last=df_ram_last.loc[0,'cellsoc']
+            time_last=df_ram_last.loc[0,'time']
+            firsttime=0
+            dict_bal={}
+        if df_ram_last1.empty:
+            firsttime1=1
+            dict_bal1={}
+        else:
+            deltsoc_last1=df_ram_last1.loc[0,'deltsoc1']
+            time_last1=df_ram_last1.loc[0,'time1']
+            firsttime1=0
+            dict_bal1={}
+        if df_ram_last2.empty:
+            firsttime2=1
+            charging=0
+            dict_bal2={}
+        else:
+            deltAs_last2=df_ram_last2.loc[0,'deltAs2']
+            time_last2=df_ram_last2.loc[0,'time2']
+            firsttime2=0
+            charging=0
+            dict_bal2={}
+        if df_ram_last3.empty:
+            standingtime=0
+            standingtime1=0
+            standingtime2=0
+        else:
+            standingtime=df_ram_last3.loc[0,'standingtime']
+            standingtime1=df_ram_last3.loc[0,'standingtime1']
+            standingtime2=df_ram_last3.loc[0,'standingtime2']
+            dict_bal1={}
+            if abs(self.packcrnt[0])<0.01 and standingtime>1 and standingtime1>1:
+                standingtime=standingtime+(self.bmstime[0]-df_ram_last3.loc[0,'time3']).total_seconds()
+                standingtime1=standingtime1+(self.bmstime[0]-df_ram_last3.loc[0,'time3']).total_seconds()
+            else:
+                pass
+
+        for i in range(1,len(self.df_bms)-1):
+
+            #静置法计算内短路..........................................................................................................................
+            if firsttime1==0:   #满电静置算法--计算均衡状态对应的均衡时间
+                try:
+                    balstat=int(self.df_bms.loc[i,'单体均衡状态'])
+                    if balstat>0.5:
+                        bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                        bal_step=int(bal_step)
+                        if str(balstat) in dict_bal1.keys():
+                            dict_bal1[str(balstat)]=dict_bal1[str(balstat)]+bal_step
+                        else:
+                            dict_bal1[str(balstat)]=bal_step
+                    else:
+                        pass
+                except:
+                    dict_bal1={}
+            else:
+                pass
+
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     
+                delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                standingtime=standingtime+delttime
+                standingtime1=standingtime1+delttime
+                self._celltemp_weight(i)
+
+                #长时间静置法计算内短路-开始.....................................................................................................................................
+                if firsttime==1:    
+                    if standingtime>self.StandardStandingTime:      #静置时间满足要求
+                        standingtime=0
+                        cellvolt_now=self._avgvolt_get(i)
+                        if not cellvolt_now.empty:
+                            cellvolt_min=min(cellvolt_now)
+                            cellvolt_max=max(cellvolt_now)
+                            # cellvolt_last=self._avgvolt_get(i-1)
+                            # deltvolt=max(abs(cellvolt_now-cellvolt_last))
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+
+                            if cellsoc_max<self.param.SocInflexion1-2 and 12<cellsoc_min:
+                                dict_baltime={}  #获取每个电芯的均衡时间
+                                deltsoc_last, cellsoc_last=self._celldeltsoc_get(cellvolt_now,dict_baltime,capacity)
+                                time_last=self.bmstime[i]
+                                firsttime=0
+                                df_ram_last.loc[0]=[self.sn,time_last,deltsoc_last,cellsoc_last]   #更新RAM信息
+                            else:
+                                pass
+                    else:
+                        pass                
+                elif standingtime>3600*12:
+                    standingtime=0
+                    cellvolt_now=np.array(self._avgvolt_get(i))
+                    if not cellvolt_now.empty:
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        # cellvolt_last=np.array(self._avgvolt_get(i-1))
+                        # deltvolt=max(abs(cellvolt_now-cellvolt_last))
+                        cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+
+                        if cellsoc_max<self.param.SocInflexion1-2 and 12<cellsoc_min:
+                            dict_baltime=self._bal_time(dict_bal)   #获取每个电芯的均衡时间
+                            deltsoc_now, cellsoc_now=self._celldeltsoc_get(cellvolt_now, dict_baltime,capacity)    #获取每个电芯的SOC差
+                            time_now=self.bmstime[i]
+                            if -5<max(cellsoc_now-cellsoc_last)<5:
+                                df_ram_last.loc[0]=[self.sn,time_now,deltsoc_now,cellsoc_now]   #更新RAM信息
+
+                                list_sub=deltsoc_now-deltsoc_last
+                                list_pud=(0.01*capacity*3600*1000)/(time_now-time_last).total_seconds()
+                                leak_current=list_sub*list_pud
+                                # leak_current=np.array(leak_current)
+                                leak_current=np.round(leak_current,3)
+                                leak_current=list(leak_current)
+                                
+                                df_res.loc[len(df_res)]=[time_last,time_now,self.sn,1,str(leak_current),str(dict_baltime)]  #计算结果存入Dataframe
+                                time_last=time_now  #更新时间
+                                deltsoc_last=deltsoc_now    #更新soc差
+                                dict_bal={}
+                            else:
+                                firsttime=1
+                        else:
+                            pass
+                else: 
+                    try:  
+                        balstat=int(self.df_bms.loc[i,'单体均衡状态'])
+                        if balstat>0.5:
+                            bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                            bal_step=int(bal_step)
+                            if str(balstat) in dict_bal.keys():
+                                dict_bal[str(balstat)]=dict_bal[str(balstat)]+bal_step
+                            else:
+                                dict_bal[str(balstat)]=bal_step
+                        else:
+                            pass
+                    except:
+                        dict_bal={}
+
+                #非平台区间静置法计算内短路-开始.....................................................................................................................................................
+                if standingtime1>self.StandardStandingTime: 
+                    standingtime1=0
+                    cellvolt_now1=self._avgvolt_get(i)
+                    if not cellvolt_now1.empty:
+                        cellvolt_max1=max(cellvolt_now1)
+                        cellvolt_min1=min(cellvolt_now1)
+                        # cellvolt_last1=self._avgvolt_get(i-1)
+                        # deltvolt1=max(abs(cellvolt_now1-cellvolt_last1))
+                        cellsoc_max1=np.interp(cellvolt_max1,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        cellsoc_min1=np.interp(cellvolt_min1,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                    
+                        if cellsoc_max1<self.param.SocInflexion1-2 and 12<cellsoc_min1:
+                            if firsttime1==1:
+                                dict_baltime1=self._bal_time(dict_bal1)   #获取每个电芯的均衡时间
+                                deltsoc_last1, cellsoc_last1=self._celldeltsoc_get(cellvolt_now1,dict_baltime1,capacity)
+                                time_last1=self.bmstime[i]
+                                firsttime1=0
+                                df_ram_last1.loc[0]=[self.sn,time_last1,deltsoc_last1]    #更新RAM信息
+                            else:
+                                dict_baltime1=self._bal_time(dict_bal1)   #获取每个电芯的均衡时间
+                                deltsoc_now1, cellsoc_now1=self._celldeltsoc_get(cellvolt_now1,dict_baltime1,capacity)
+                                time_now1=self.bmstime[i]
+                                df_ram_last1.loc[0]=[self.sn,time_now1,deltsoc_now1]    #更新RAM信息
+
+                                if (time_now1-time_last1).total_seconds()>3600*24:
+                                    list_sub1=deltsoc_now1-deltsoc_last1
+                                    list_pud1=(0.01*capacity*3600*1000)/(time_now1-time_last1).total_seconds()
+                                    leak_current1=list_sub1*list_pud1
+                                    # leak_current1=np.array(leak_current1)
+                                    leak_current1=np.round(leak_current1,3)
+                                    leak_current1=list(leak_current1)
+                                    
+                                    df_res.loc[len(df_res)]=[time_last1,time_now1,self.sn,2,str(leak_current1),str(dict_baltime1)]  #计算结果存入Dataframe
+                                    time_last1=time_now1  #更新时间
+                                    deltsoc_last1=deltsoc_now1    #更新soc差
+                                    dict_bal1={}
+                                else:
+                                    pass
+                        else:
+                            pass
+                else:   
+                    pass
+
+            else:
+                df_ram_last=pd.DataFrame(columns=['sn','time','deltsoc','cellsoc'])   #电流>0,清空上次静置的SOC差
+                dict_bal={} 
+                firsttime=1
+                standingtime=0
+                standingtime1=0
+                pass
+
+            #获取充电数据——开始..............................................................................................................
+            try:
+                balstat=int(self.df_bms.loc[i,'单体均衡状态'])  #统计均衡状态
+                if balstat>0.5:
+                    bal_step=(self.bmstime[i+1]-self.bmstime[i]).total_seconds()  #均衡步长
+                    bal_step=int(bal_step)
+                    if str(balstat) in dict_bal2.keys():
+                        dict_bal2[str(balstat)]=dict_bal2[str(balstat)]+bal_step
+                    else:
+                        dict_bal2[str(balstat)]=bal_step
+                else:
+                    pass
+            except:
+                dict_bal2={}
+
+            #判断充电状态
+            if self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i-1]<=-1:
+                if charging==0:
+                    if self.bms_soc[i]<40:
+                        cellvolt_now=self._cellvolt_get(i)
+                        if min(cellvolt_now)<self.param.CellFullChrgVolt-0.15:
+                            charging=1
+                            chrg_start=i
+                        else:
+                            pass
+                    else:
+                        pass
+
+                else: #充电中
+                    cellvolt_now=self._cellvolt_get(i)
+                    if (self.bmstime[i+1]-self.bmstime[i]).total_seconds()>180 or (self.packcrnt[i]>self.param.Capacity/3 and self.packcrnt[i+1]>self.param.Capacity/3):  #如果充电过程中时间间隔>180s，则舍弃该次充电
+                        charging=0
+                        continue
+                    elif min(cellvolt_now)>self.param.CellFullChrgVolt-0.13:   #电压>满充电压-0.13V，即3.37V
+                        self._celltemp_weight(i)
+                        if i-chrg_start>10 and self.celltemp>20:
+                            chrg_end=i+1
+                            charging=0
+
+                            #计算漏电流值...................................................................
+                            if firsttime2==1:
+                                dict_baltime={}
+                                deltAs_last2=self._cellDeltAs_get(chrg_start,chrg_end,dict_baltime)
+                                time_last2=self.bmstime[chrg_end]
+                                df_ram_last2.loc[0]=[self.sn,time_last2,deltAs_last2]    #更新RAM信息
+                            else:
+                                dict_baltime=self._bal_time(dict_bal2)   #获取每个电芯的均衡时间
+                                deltAs_now2=self._cellDeltAs_get(chrg_start,chrg_end,dict_baltime)  #获取每个电芯的As差
+                                time_now2=self.bmstime[chrg_end]
+                                df_ram_last2.loc[0]=[self.sn,time_now2,deltAs_now2]    #更新RAM信息
+
+                                list_sub2=deltAs_now2-deltAs_last2
+                                list_pud2=-1000/(time_now2-time_last2).total_seconds()
+                                leak_current2=list_sub2*list_pud2
+                                # leak_current=np.array(leak_current)
+                                leak_current2=np.round(leak_current2,3)
+                                leak_current2=list(leak_current2)
+
+                                df_res.loc[len(df_res)]=[time_last2,time_now2,self.sn,3,str(leak_current2),str(dict_baltime)]  #计算结果存入Dataframe
+                                deltAs_last2=deltAs_now2
+                                time_last2=time_now2
+                                dict_bal2={}
+
+                        else:
+                            charging=0
+                            continue
+                    elif i==len(self.df_bms)-2:  #数据中断后仍在充电，将前段充电数据写入RAM
+                        df_ram_lfp=self.df_bms.iloc[chrg_start:]
+                        df_ram_lfp['sn']=self.sn
+                    else:
+                        pass
+            else:
+                pass
+
+    
+        #更新RAM
+        df_ram_last3.loc[0]=[self.sn,self.bmstime[len(self.bmstime)-1],standingtime,standingtime1,standingtime2]
+
+        #返回结果
+        if df_res.empty:    
+            return pd.DataFrame(), df_ram_last, df_ram_last1, df_ram_last2, df_ram_last3,df_ram_lfp
+        else:
+            return df_res, df_ram_last, df_ram_last1, df_ram_last2, df_ram_last3, df_ram_lfp

USER/SPF/alibaba/alibaba/07BatSafetyWarning/07BatSafetyWarning/CBMSBatUniform.py

@@ -0,0 +1,506 @@
+import pandas as pd
+import numpy as np
+import datetime
+from USER.alibaba.Common import BatParam
+
+class BatUniform():
+    def __init__(self,sn,celltype,df_bms,df_uniform,df_last3,df_lfp1):  #参数初始化
+
+        if (not df_lfp1.empty) and celltype>50:
+            df_lfp1.drop(['sn'],axis=1)
+            df_bms=pd.concat([df_lfp1, df_bms], ignore_index=True)
+        else:
+            pass
+        
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_bms=df_bms
+        self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
+        self.packvolt=df_bms['PackVolt']
+        self.bms_soc=df_bms['PackSOC']
+        self.bmstime= pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        # df_uniform['time']=pd.to_datetime(df_uniform['time'], format='%Y-%m-%d %H:%M:%S')
+
+        self.df_uniform=df_uniform
+        self.df_last3=df_last3
+        self.df_lfp1=df_lfp1
+
+        self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+
+    def batuniform(self):
+        if self.celltype<50:
+            df_res, df_ram_last3=self._ncm_uniform()
+            return df_res, df_ram_last3, self.df_lfp1
+        else:
+            df_res, df_ram_last3, df_ram_lfp1=self._lfp_uniform()
+            return df_res, df_ram_last3, df_ram_lfp1
+    
+    #定义滑动滤波函数........................................................................................................................................
+    def _np_move_avg(self,a, n, mode="same"): 
+        return (np.convolve(a, np.ones((n,)) / n, mode=mode))
+    
+    #寻找当前行数据的最小温度值................................................................................................................................
+    def _celltemp_weight(self,num):   
+        celltemp = list(self.df_bms.loc[num,self.celltemp_name])
+        celltemp.remove(min(celltemp))
+        self.celltemp=celltemp
+        if self.celltype>50:
+            if min(celltemp)>=25:
+                self.tempweight=1
+                self.StandardStandingTime=2400
+            elif min(celltemp)>=15:
+                self.tempweight=0.6
+                self.StandardStandingTime=3600
+            elif min(celltemp)>=5:
+                self.tempweight=0.2
+                self.StandardStandingTime=4800
+            else:
+                self.tempweight=0.1
+                self.StandardStandingTime=7200
+        else:
+            if min(celltemp)>=25:
+                self.tempweight=1
+                self.StandardStandingTime=1800
+            elif min(celltemp)>=15:
+                self.tempweight=0.8
+                self.StandardStandingTime=2400
+            elif min(celltemp)>=5:
+                self.tempweight=0.6
+                self.StandardStandingTime=3600
+            else:
+                self.tempweight=0.2
+                self.StandardStandingTime=7200
+        
+    #获取当前行所有电压数据............................................................................................................................
+    def _cellvolt_get(self,num):
+        cellvolt = np.array(self.df_bms.loc[num,self.cellvolt_name])
+        return cellvolt
+    
+    #获取单个电压值.................................................................................................
+    def _singlevolt_get(self,num,series,mode):  #mode==1取当前行单体电压值，mode==2取某个单体所有电压值
+        s=str(series)
+        if mode==1:
+            singlevolt=self.df_bms.loc[num,'CellVolt' + s]
+            return singlevolt
+        else:
+            singlevolt=self.df_bms['CellVolt' + s]
+            return singlevolt
+
+    #寻找DVDQ的峰值点，并返回..........................................................................................................................
+    def _dvdq_peak(self, time, soc, cellvolt, packcrnt):
+        cellvolt = self._np_move_avg(cellvolt, 3, mode="same")
+        Soc = 0
+        Ah = 0
+        Volt = cellvolt[0]
+        DV_Volt = []
+        DQ_Ah = []
+        DVDQ = []
+        time1 = []
+        soc1 = []
+        soc2 = []
+        xvolt=[]
+
+        for m in range(1, len(time)):
+            Step = (time[m] - time[m - 1]).total_seconds()
+            Soc = Soc - packcrnt[m] * Step * 100 / (3600 * self.param.Capacity)
+            Ah = Ah - packcrnt[m] * Step / 3600
+            if (cellvolt[m]-Volt)>0.0019 and Ah>0:
+                DQ_Ah.append(Ah)
+                DV_Volt.append(cellvolt[m]-Volt)
+                DVDQ.append((DV_Volt[-1])/Ah)
+                xvolt.append(cellvolt[m])
+                Volt=cellvolt[m]
+                Ah = 0
+                soc1.append(Soc)
+                time1.append(time[m])
+                soc2.append(soc[m])
+
+        #切片，去除前后10min的数据
+        df_Data1 = pd.DataFrame({'time': time1,
+                                'SOC': soc2,
+                                'DVDQ': DVDQ,
+                                'AhSoc': soc1,
+                                'DQ_Ah':DQ_Ah,
+                                'DV_Volt':DV_Volt,
+                                'XVOLT':xvolt})
+        start_time=df_Data1.loc[0,'time']
+        start_time=start_time+datetime.timedelta(seconds=900)
+        end_time=df_Data1.loc[len(time1)-1,'time']
+        end_time=end_time-datetime.timedelta(seconds=1200)
+        if soc2[0]<36:
+            df_Data1=df_Data1[(df_Data1['SOC']>40) & (df_Data1['SOC']<80)]
+        else:
+            df_Data1=df_Data1[(df_Data1['time']>start_time) & (df_Data1['SOC']<80)]
+        df_Data1=df_Data1[(df_Data1['XVOLT']>self.param.PeakVoltLowLmt) & (df_Data1['XVOLT']<self.param.PeakVoltUpLmt)]
+
+        # print(packcrnt[int(len(time)/2)], min(self.celltemp))
+        # ax1 = plt.subplot(3, 1, 1)
+        # plt.plot(df_Data1['SOC'],df_Data1['DQ_Ah'],'g*-')
+        # plt.xlabel('SOC/%')
+        # plt.ylabel('DQ_Ah')
+        # plt.legend()
+        # ax1 = plt.subplot(3, 1, 2)
+        # plt.plot(df_Data1['SOC'],df_Data1['XVOLT'],'y*-')
+        # plt.xlabel('SOC/%')
+        # plt.ylabel('Volt/V')
+        # plt.legend()
+        # ax1 = plt.subplot(3, 1, 3)
+        # plt.plot(df_Data1['SOC'], df_Data1['DVDQ'], 'r*-')
+        # plt.xlabel('SOC/%')
+        # plt.ylabel('DV/DQ')
+        # plt.legend()
+        # # plt.show()
+
+        if len(df_Data1)>2:     #寻找峰值点，且峰值点个数>2
+            PeakIndex = df_Data1['DVDQ'].idxmax()
+            df_Data2 = df_Data1[(df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
+            if len(df_Data2) > 2 and df_Data1.loc[PeakIndex,'XVOLT']<self.param.PeakVoltUpLmt-0.019:
+                return df_Data1['AhSoc'][PeakIndex]
+            else:
+                df_Data1 = df_Data1.drop([PeakIndex])
+                PeakIndex = df_Data1['DVDQ'].idxmax()
+                df_Data2 = df_Data1[(df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
+                if len(df_Data2) > 2 and df_Data1.loc[PeakIndex,'XVOLT']<self.param.PeakVoltUpLmt-0.019:
+                    return df_Data1['AhSoc'][PeakIndex]
+                else:
+                    return 0
+        else:
+            return 0
+ 
+    #三元电池一致性计算.................................................................................................................................
+    def _ncm_uniform(self):
+        column_name=['time','sn','cellsoc_diff','cellvolt_diff','cellmin_num','cellmax_num','cellvolt_rank']
+        df_res=pd.DataFrame(columns=column_name)
+
+        df_ram_last3=self.df_last3
+        if df_ram_last3.empty:
+            standingtime=0
+            standingtime1=0
+            standingtime2=0
+        else:
+            standingtime=df_ram_last3.loc[0,'standingtime']
+            standingtime1=df_ram_last3.loc[0,'standingtime1']
+            standingtime2=df_ram_last3.loc[0,'standingtime2']
+            if abs(self.packcrnt[0])<0.01 and standingtime2>1:
+                standingtime2=standingtime2+(self.bmstime[0]-df_ram_last3.loc[0,'time3']).total_seconds()
+            else:
+                pass
+        
+
+        for i in range(1,len(self.df_bms)-2):
+
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     #电流为0
+                delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                standingtime2=standingtime2+delttime
+                self._celltemp_weight(i)     #获取不同温度对应的静置时间
+
+                if standingtime2>self.StandardStandingTime:      #静置时间满足要求
+                    if abs(self.packcrnt[i+2]) >= 0.1:
+                        standingtime2=0  
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))                
+                        if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.005:
+                            cellvolt_sort=np.argsort(cellvolt_now)
+                            cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num, str(cellvolt_rank)]
+                    elif standingtime2>3600*6:
+                        standingtime2=0
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))                  
+                        
+                        if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.005:
+                            cellvolt_sort=np.argsort(cellvolt_now)
+                            cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num, str(cellvolt_rank)]
+                    elif i>=len(self.df_bms)-3:
+                        standingtime2=0
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))     
+                        
+                        if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.005:
+                            cellvolt_sort=np.argsort(cellvolt_now)
+                            cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num, str(cellvolt_rank)]
+                        break
+                    else:
+                        continue
+                else:
+                    continue
+            else:
+                standingtime2=0
+                continue
+        
+        #更新RAM的standingtime
+        df_ram_last3.loc[0]=[self.sn,self.bmstime[len(self.bmstime)-1],standingtime,standingtime1,standingtime2]
+
+        if df_res.empty:    #返回计算结果
+            return pd.DataFrame(), df_ram_last3
+        else:
+            return df_res, df_ram_last3
+
+    #磷酸铁锂电池一致性计算.........................................................................................................................
+    def _lfp_uniform(self):
+        column_name=['time','sn','cellsoc_diff','cellvolt_diff','cellmin_num','cellmax_num','cellvolt_rank']
+        df_res=pd.DataFrame(columns=column_name)
+        df_ram_lfp1=pd.DataFrame(columns=self.df_bms.columns.tolist())
+        chrg_start=[]
+        chrg_end=[]
+        charging=0
+
+        df_ram_last3=self.df_last3
+        if df_ram_last3.empty:
+            standingtime=0
+            standingtime1=0
+            standingtime2=0
+        else:
+            standingtime=df_ram_last3.loc[0,'standingtime']
+            standingtime1=df_ram_last3.loc[0,'standingtime1']
+            standingtime2=df_ram_last3.loc[0,'standingtime2']
+            if abs(self.packcrnt[0])<0.01 and standingtime2>1:
+                standingtime2=standingtime2+(self.bmstime[0]-df_ram_last3.loc[0,'time3']).total_seconds()
+            else:
+                pass
+
+        for i in range(2,len(self.df_bms)-2):
+
+            #静置电压法计算电芯一致性
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     #电流为0
+                delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+                standingtime2=standingtime2+delttime
+                self._celltemp_weight(i)     #获取不同温度对应的静置时间
+
+                if standingtime2>self.StandardStandingTime:      #静置时间满足要求
+                    if abs(self.packcrnt[i+2]) >= 0.1:     
+                        standingtime2=0  
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))  
+        
+                        if 2 < cellvolt_max < self.param.OcvInflexionBelow-0.002 and 2<cellvolt_min<4.5 and deltvolt<0.005: 
+                            cellvolt_sort=np.argsort(cellvolt_now)
+                            cellvolt_rank=list(np.argsort(cellvolt_sort)+1)               
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num, str(cellvolt_rank)]
+                        # elif 2<cellvolt_max<4.5 and 2<cellvolt_min<4.5 and deltvolt<0.005: 
+                        #     cellvolt_sort=np.argsort(cellvolt_now)
+                        #     cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
+                        #     if not df_res.empty:
+                        #         df_res.loc[len(df_res)]=df_res.loc[len(df_res)-1]
+                        #         df_res.loc[len(df_res)-1,'cellvolt_rank']=str(cellvolt_rank)
+                        #         df_res.loc[len(df_res)-1,'time']=self.bmstime[i]
+                        #     elif not self.df_uniform.empty:
+                        #         df_res.loc[len(df_res)]=self.df_uniform.iloc[-1]
+                        #         df_res.loc[len(df_res)-1,'cellvolt_rank']=str(cellvolt_rank)
+                        #         df_res.loc[len(df_res)-1,'time']=self.bmstime[i]
+                        #     else:
+                        #         pass
+                    elif standingtime2>3600*6:
+                        standingtime2=0
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last))                  
+                        
+                        if 2 < cellvolt_max < self.param.OcvInflexionBelow-0.002 and 2<cellvolt_min<4.5 and deltvolt<0.005: 
+                            cellvolt_sort=np.argsort(cellvolt_now)
+                            cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num, str(cellvolt_rank)]
+                        # elif 2<cellvolt_max<4.5 and 2<cellvolt_min<4.5 and deltvolt<0.005: 
+                        #     cellvolt_sort=np.argsort(cellvolt_now)
+                        #     cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
+                        #     if not df_res.empty:
+                        #         df_res.loc[len(df_res)]=df_res.loc[len(df_res)-1]
+                        #         df_res.loc[len(df_res)-1,'cellvolt_rank']=str(cellvolt_rank)
+                        #         df_res.loc[len(df_res)-1,'time']=self.bmstime[i]
+                        #     elif not self.df_uniform.empty:
+                        #         df_res.loc[len(df_res)]=self.df_uniform.iloc[-1]
+                        #         df_res.loc[len(df_res)-1,'cellvolt_rank']=str(cellvolt_rank)
+                        #         df_res.loc[len(df_res)-1,'time']=self.bmstime[i]
+                        #     else:
+                        #         pass
+                    
+                    elif i>=len(self.df_bms)-3:
+                        standingtime2=0
+                        cellvolt_now=self._cellvolt_get(i)
+                        cellvolt_min=min(cellvolt_now)
+                        cellvolt_max=max(cellvolt_now)
+                        cellvolt_last=self._cellvolt_get(i-1)
+                        deltvolt=max(abs(cellvolt_now-cellvolt_last)) 
+                        if 2 < cellvolt_max < self.param.OcvInflexionBelow-0.002 and 2<cellvolt_min<4.5 and deltvolt<0.003:
+                            cellvolt_sort=np.argsort(cellvolt_now)
+                            cellvolt_rank=list(np.argsort(cellvolt_sort)+1) 
+                            cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
+                            cellmax_num=list(cellvolt_now).index(cellvolt_max)+1
+                            cellsoc_min=np.interp(cellvolt_min,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellsoc_max=np.interp(cellvolt_max,self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            cellvolt_diff=(cellvolt_max-cellvolt_min)*1000
+                            cellsoc_diff=cellsoc_max-cellsoc_min
+                            cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                            cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
+                            df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num, str(cellvolt_rank)]
+                        # elif 2<cellvolt_max<4.5 and 2<cellvolt_min<4.5 and deltvolt<0.005: 
+                        #     cellvolt_sort=np.argsort(cellvolt_now)
+                        #     cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
+                        #     if not df_res.empty:
+                        #         df_res.loc[len(df_res)]=df_res.loc[len(df_res)-1]
+                        #         df_res.loc[len(df_res)-1,'cellvolt_rank']=str(cellvolt_rank)
+                        #         df_res.loc[len(df_res)-1,'time']=self.bmstime[i]
+                        #     elif not self.df_uniform.empty:
+                        #         df_res.loc[len(df_res)]=self.df_uniform.iloc[-1]
+                        #         df_res.loc[len(df_res)-1,'cellvolt_rank']=str(cellvolt_rank)
+                        #         df_res.loc[len(df_res)-1,'time']=self.bmstime[i]
+                        #     else:
+                        #         pass
+                    else:
+                        pass
+                else:
+                    pass
+            else:
+                standingtime2=0
+                pass   
+
+            #获取DVDQ算法所需数据——开始............................................................................................................
+            if charging==0: #判断充电开始
+                if self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i+2]<=-1 and self.bms_soc[i]<40:     #充电开始
+                    charging=1
+                    if len(chrg_start)>len(chrg_end):
+                        chrg_start[-1]=i
+                    else:
+                        chrg_start.append(i)
+                else:
+                    pass
+
+            else: #充电中
+                if (self.bmstime[i+1]-self.bmstime[i]).total_seconds()>180 or (self.packcrnt[i]<-self.param.Capacity/2 and self.packcrnt[i+1]<-self.param.Capacity/2):  #如果充电过程中时间间隔>180s，则舍弃该次充电
+                    chrg_start.remove(chrg_start[-1])
+                    charging=0
+                    continue
+                elif self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and  self.packcrnt[i+2]>-1:  #判断电流波动时刻
+                    cellvolt_now=self._cellvolt_get(i+1)
+                    if max(cellvolt_now)>self.param.CellFullChrgVolt:   #电压>满充电压
+                        chrg_end.append(i+1)
+                        charging=0
+                        continue
+                    else:
+                        pass
+                elif self.packcrnt[i+1]>-0.1 and self.packcrnt[i+2]>-0.1:   #判断充电结束
+                    charging=0
+                    if len(chrg_start)>len(chrg_end):
+                        if self.bms_soc[i]>90:
+                            chrg_end.append(i)
+                        else:
+                            chrg_start.remove(chrg_start[-1])
+                            continue
+                    else:
+                        continue
+                elif i==len(self.packcrnt)-3 and self.packcrnt[i+1]<-1 and self.packcrnt[i+2]<-1:
+                    charging=0
+                    if len(chrg_start)>len(chrg_end) and self.bms_soc[i]>90:   #soc>90
+                        chrg_end.append(i)
+                        continue
+                    else:
+                        df_ram_lfp1=self.df_bms.iloc[chrg_start[-1]:]
+                        df_ram_lfp1['sn']=self.sn
+                        chrg_start.remove(chrg_start[-1])
+                        continue
+                else:
+                    continue   
+
+        if chrg_end:    #DVDQ方法计算soc差
+            peaksoc_list=[]
+            for i in range(len(chrg_end)):
+                peaksoc_list = []
+                self._celltemp_weight(chrg_start[i])
+                if min(self.celltemp)>10:
+                    for j in range(1, self.param.CellVoltNums + 1):
+                        cellvolt = self._singlevolt_get(i,j,2)  #取单体电压j的所有电压值
+                        cellvolt = list(cellvolt[chrg_start[i]:chrg_end[i]])
+                        time = list(self.bmstime[chrg_start[i]:chrg_end[i]])
+                        packcrnt = list(self.packcrnt[chrg_start[i]:chrg_end[i]])
+                        soc = list(self.bms_soc[chrg_start[i]:chrg_end[i]])
+                        peaksoc = self._dvdq_peak(time, soc, cellvolt, packcrnt)
+                        if peaksoc>1:
+                            peaksoc_list.append(peaksoc)    #计算到达峰值点的累计Soc
+                        else:
+                            pass
+                    if len(peaksoc_list)>self.param.CellVoltNums/2:
+                        peaksoc_max=max(peaksoc_list)
+                        peaksoc_min=min(peaksoc_list)
+                        peaksoc_maxnum=peaksoc_list.index(peaksoc_min)+1
+                        peaksoc_minnum=peaksoc_list.index(peaksoc_max)+1
+                        cellsoc_diff=peaksoc_max-peaksoc_min
+                        cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
+                        if not df_res.empty:
+                            cellvolt_rank=df_res.iloc[-1]['cellvolt_rank']
+                            df_res.loc[len(df_res)]=[self.bmstime[chrg_start[i]], self.sn, cellsoc_diff, 0, peaksoc_minnum, peaksoc_maxnum, cellvolt_rank]
+                        elif not self.df_uniform.empty:
+                            cellvolt_rank=self.df_uniform.iloc[-1]['cellvolt_rank']
+                            df_res.loc[len(df_res)]=[self.bmstime[chrg_start[i]], self.sn, cellsoc_diff, 0, peaksoc_minnum, peaksoc_maxnum, cellvolt_rank]
+                        else:
+                            pass
+                    else:
+                        pass
+                else:
+                    pass
+
+        #更新RAM的standingtime
+        df_ram_last3.loc[0]=[self.sn,self.bmstime[len(self.bmstime)-1],standingtime,standingtime1,standingtime2]
+        
+        if df_res.empty:
+            return pd.DataFrame(), df_ram_last3, df_ram_lfp1
+        else:
+            df_res.sort_values(by='time', ascending=True, inplace=True)
+            return df_res, df_ram_last3, df_ram_lfp1

USER/SPF/alibaba/alibaba/07BatSafetyWarning/07BatSafetyWarning/CBMSSafetyWarning.py

@@ -0,0 +1,255 @@
+import pandas as pd
+import numpy as np
+import datetime
+import time
+from matplotlib import pyplot as plt
+import pymannkendall as mk
+from USER.alibaba.Common import BatParam
+
+class SafetyWarning:
+    def __init__(self,sn,celltype,df_short,df_uniform,OutLineVol_Rate,df_soh):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_short=df_short
+        self.df_uniform=df_uniform
+        self.OutLineVol_Rate=OutLineVol_Rate
+        self.df_soh=df_soh
+    
+    def diag(self):
+        if self.celltype<=50:
+            df_res=self._warning_diag()
+            return df_res    
+        else:
+            df_res=self._warning_diag()
+            return df_res
+        
+
+    #电池热安全预警诊断功能.................................................................................................
+    def _warning_diag(self):
+
+        df_res=pd.DataFrame(columns=['time_st','time_sp','sn','faultcode','faultlv','faultinfo','faultadvice'])
+
+        time_now=datetime.datetime.now()
+        time_now=time_now.strftime('%Y-%m-%d %H:%M:%S')
+        time_sp='0000-00-00 00:00:00'
+
+        #参数初始化.......................................
+        voltsigmafault=0
+        uniformfault=0
+        cellshortfault=0
+        volt_rate=[]
+        R2_list=[]
+        voltsigmafault_list=[]
+        uniformfault_list=[]
+        mk_trend_list=[]
+        mk_p_list=[]
+        mk_z_list=[]
+        mk_Tau_list=[]
+        mk_slope_list=[]
+        mk_s_list=[]
+        mk_svar_list=[]
+
+        if not self.df_short.empty:
+            short_current=self.df_short['short_current']
+            short_current=short_current.str.replace("[", '')
+            short_current=short_current.str.replace("]", '')
+        
+        if not self.OutLineVol_Rate.empty:
+            volt_column = ['CellVolt'+str(i) for i in range(1,self.param.CellVoltNums+1)]
+            self.OutLineVol_Rate['VolChng_Uni']=self.OutLineVol_Rate['VolChng_Uni'].str.replace("[","")
+            self.OutLineVol_Rate['VolChng_Uni']=self.OutLineVol_Rate['VolChng_Uni'].str.replace("]","")
+            self.OutLineVol_Rate['VolOl_Uni']=self.OutLineVol_Rate['VolOl_Uni'].str.replace("[","")
+            self.OutLineVol_Rate['VolOl_Uni']=self.OutLineVol_Rate['VolOl_Uni'].str.replace("]","")
+            Volt_3Sigma=self.OutLineVol_Rate['VolOl_Uni'].str.split(',',expand=True)
+            Volt_3Sigma.columns=volt_column
+
+            #电压变化率
+            VoltChange=self.OutLineVol_Rate['VolChng_Uni'].str.split(',',expand=True)
+            VoltChange.columns=volt_column
+            VoltChange['time']=self.OutLineVol_Rate['time']
+            VoltChange = VoltChange.reset_index(drop=True)
+            xtime1=VoltChange['time']
+            time0=time.mktime(VoltChange.loc[0,'time'].timetuple())
+            for i in range(0,len(VoltChange)):
+                VoltChange.loc[i,'time']=(time.mktime(VoltChange.loc[i,'time'].timetuple())-time0)/36000
+            
+        #计算漏电流离群度
+        if not self.df_short.empty:
+            self.df_short['cellshort_sigma']=0
+            for i in range(len(self.df_short)):
+                cellshort=eval(self.df_short.loc[i,'short_current'])
+                cellshort_std=np.std(cellshort)
+                cellshort_mean=np.mean(cellshort)
+                self.df_short.loc[i,'cellshort_sigma']=str(list((cellshort-cellshort_mean)/cellshort_std))
+            
+    
+        if not self.df_uniform.empty:
+            cellvolt_rank=self.df_uniform['cellvolt_rank']
+            cellvolt_rank=cellvolt_rank.str.replace("[", '')
+            cellvolt_rank=cellvolt_rank.str.replace("]", '')
+
+        # plt.figure()
+        for i in range(self.param.CellVoltNums):
+            #漏电流故障判断...........................................................................
+            if not self.df_short.empty:
+                self.df_short['cellshort'+str(i+1)]=short_current.map(lambda x:eval(x.split(',')[i]))
+                cellshort=self.df_short['cellshort'+str(i+1)]
+                index_list=cellshort[cellshort<self.param.LeakCurrentLv2].index
+                if len(index_list)>1:
+                    for j in range(1,len(index_list)):
+                        if index_list[j]-index_list[j-1]==1:
+                            cellshort_sigma1=eval(self.df_short.loc[index_list[j],'cellshort_sigma'])
+                            cellshort_sigma2=eval(self.df_short.loc[index_list[j-1],'cellshort_sigma'])
+                            if cellshort_sigma1[i]<-3 or cellshort_sigma2[i]<-3:
+                                cellshortfault=1                       
+                        else:
+                            pass
+                else:
+                    pass
+            
+            #电压变化率及电压离群度.................................................................................
+            if not self.OutLineVol_Rate.empty and VoltChange.iloc[-1]['time']*36000>18*3600 and len(VoltChange)>5:
+
+                volt3sigma=np.array(Volt_3Sigma[volt_column[i]].map(lambda x:eval(x)))
+                volt3sigma_sum=np.sum(volt3sigma<-3)
+                #电压变化率
+                VoltChange[volt_column[i]]=VoltChange[volt_column[i]].map(lambda x:eval(x))
+                y=VoltChange[volt_column[i]]
+                a1,b1=np.polyfit(VoltChange['time'].tolist(),y.tolist(),1)
+                y1=a1*VoltChange['time']+b1
+                y_mean=y.mean()
+                R2=1-(np.sum((y1-y)**2))/(np.sum((y-y_mean)**2))
+                R2_list.append(R2)
+                
+                volt_rate.append(a1)
+                # plt.plot(xtime1,y1,label=a1)
+                # plt.scatter( xtime1,VoltChange[volt_column[i]],marker='o')
+                # plt.legend(loc='best')
+                # plt.title('CellVolt'+str(i+1))
+                # plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
+                # plt.rcParams['axes.unicode_minus']=False #用来正常显示负号  
+                # plt.show()
+                
+                if volt3sigma_sum>len(volt3sigma)/2:
+                    voltsigmafault=1
+                else:
+                    voltsigmafault=0
+                voltsigmafault_list.append(voltsigmafault)
+                
+                #mana-kendell趋势检验
+                mk_res=mk.regional_test(np.array(y)) 
+                mk_trend_list.append(mk_res.trend)
+                mk_p_list.append(mk_res.p)
+                mk_z_list.append(mk_res.z)
+                mk_Tau_list.append(mk_res.Tau)
+                mk_slope_list.append(mk_res.slope)
+                mk_s_list.append(mk_res.s)
+                mk_svar_list.append(mk_res.var_s)
+                """
+                trend：趋势；
+                h：有无趋势；
+                p：趋势的显著水平，越小趋势越明显；
+                z：检验统计量，正代表随时间增大趋势，负代表随时间减小趋势；
+                Tau：反映两个序列的相关性，接近1的值表示强烈的正相关，接近-1的值表示强烈的负相关；
+                s：如果S是一个正数，那么后一部分的观测值相比之前的观测值会趋向于变大；如果S是一个负数，那么后一部分的观测值相比之前的观测值会趋向于变小
+                slope：趋势斜率
+                """
+                # print('单体电压{}：\n'.format(i+1), mk_res)
+            else:
+                volt_rate.append(0)
+                R2_list.append(0)
+                voltsigmafault_list.append(0)
+                mk_trend_list.append(0)
+                mk_p_list.append(0)
+                mk_z_list.append(0)
+                mk_Tau_list.append(0)
+                mk_slope_list.append(0)
+                mk_s_list.append(0)
+                mk_svar_list.append(0)
+            
+
+            #电芯SOC排名判断.............................................................................
+            if not self.df_uniform.empty:
+                self.df_uniform['cellvolt_rank'+str(i+1)]=cellvolt_rank.map(lambda x:eval(x.split(',')[i]))
+                
+                if max(self.df_uniform['cellvolt_rank'+str(i+1)])<5:
+                    uniformfault=1
+                else:
+                    uniformfault=0
+            else:
+                uniformfault=0
+            uniformfault_list.append(uniformfault)
+            
+            #漏电流热失控预警确认........................................................
+            if cellshortfault==1:
+                faultcode=110
+                faultlv=4
+                faultinfo='电芯{}发生热失控安全预警'.format(i+1)
+                faultadvice='断开继电器，远离电池，并通知电池技术人员介入分析'
+                df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+                break
+            else:
+                pass       
+        
+        #电池电压变化率离群度计算...............................................................................
+        volt_rate_std=np.std(volt_rate)
+        volt_rate_mean=np.mean(volt_rate)
+        volt_rate_3sigma=(np.array(volt_rate)-volt_rate_mean)/volt_rate_std
+        
+        #mk离群度计算
+        mk_slope_std=np.std(mk_slope_list)
+        mk_slope_mean=np.mean(mk_slope_list)
+        mk_slope_3sigma=(np.array(mk_slope_list)-mk_slope_mean)/mk_slope_std   
+
+        mk_z_std=np.std(mk_z_list)
+        mk_z_mean=np.mean(mk_z_list)
+        mk_z_3sigma=(np.array(mk_z_list)-mk_z_mean)/mk_z_std  
+
+        if not self.df_soh.empty and self.celltype<50:
+            cellsoh=eval(self.df_soh.loc[0,'cellsoh'])
+            cellsoh_std=np.std(cellsoh)
+            cellsoh_mean=np.mean(cellsoh)
+            cellsoh_3sigma=((np.array(cellsoh)-cellsoh_mean)/cellsoh_std)
+        else:
+            cellsoh_3sigma=[0]*self.param.CellVoltNums
+        
+        #电压/SOC变化率
+        # for i in range(len(volt_rate)):
+        #     if volt_rate[i]<self.param.TrwVoltRate and volt_rate_3sigma[i]<-3 and abs(cellsoh_3sigma[i])<2.5 and  voltsigmafault_list[i]==1:
+        #         faultcode=110
+        #         faultlv=4
+        #         faultinfo='电芯{}发生热失控安全预警'.format(i+1)
+        #         faultadvice='2联系用户远离电池，立刻召回电池'
+        #         df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+        #     else:
+        #         pass
+
+        #mana-kendall趋势检测
+        for i in range(len(mk_p_list)):
+            #适用动态工况判断
+            if mk_trend_list[i]=='decreasing' and mk_p_list[i]<self.param.mk_p and mk_z_list[i]<self.param.mk_z and mk_Tau_list[i]<self.param.mk_Tau and mk_s_list[i]<self.param.mk_s and mk_svar_list[i]>self.param.mk_svar and mk_slope_3sigma[i]<-3 and mk_slope_list[i]<self.param.mk_slope and abs(cellsoh_3sigma[i])<2.5 and volt_rate_3sigma[i]<-3:
+                faultcode=110
+                faultlv=4
+                faultinfo='电芯{}发生热失控安全预警'.format(i+1)
+                faultadvice='2联系用户远离电池，立刻召回电池'
+                df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+            #适用静态工况判断
+            elif self.celltype<=50 and mk_trend_list[i]=='decreasing' and mk_p_list[i]<self.param.mk_p and mk_z_list[i]<-6 and (mk_Tau_list[i]<-0.9 or mk_z_3sigma[i]<-3) and mk_s_list[i]<self.param.mk_s and mk_svar_list[i]>self.param.mk_svar and mk_slope_3sigma[i]<-3.5 and mk_slope_list[i]<-0.03 and volt_rate_3sigma[i]<-3:
+                faultcode=110
+                faultlv=4
+                faultinfo='电芯{}发生热失控安全预警'.format(i+1)
+                faultadvice='2联系用户远离电池，立刻召回电池'
+                df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+            elif self.celltype>50 and mk_trend_list[i]=='decreasing' and mk_p_list[i]<self.param.mk_p and mk_z_list[i]<-6 and (mk_Tau_list[i]<-0.95 or mk_z_3sigma[i]<-3) and mk_s_list[i]<self.param.mk_s and mk_svar_list[i]>self.param.mk_svar and mk_slope_3sigma[i]<-3.5 and mk_slope_list[i]<-0.4 and volt_rate_3sigma[i]<-3:
+                faultcode=110
+                faultlv=4
+                faultinfo='电芯{}发生热失控安全预警'.format(i+1)
+                faultadvice='2联系用户远离电池，立刻召回电池'
+                df_res.loc[0]=[time_now, time_sp, self.sn, faultcode, faultlv, faultinfo, faultadvice]
+            else:
+                pass
+
+        # plt.show()
+        return df_res

USER/SPF/alibaba/alibaba/07BatSafetyWarning/07BatSafetyWarning/VoltStray.py

@@ -0,0 +1,138 @@
+from re import X
+import pandas as pd
+import numpy as np
+from pandas.core.frame import DataFrame
+from USER.alibaba.Common import BatParam
+import pandas as pd
+# 计算充电过程
+def preprocess(df):
+    # 滤除前后电压存在一增一减的情况（采样异常）
+    pass
+
+# 计算SOC变化率  
+def cal_volt_change(dfin, volt_column):
+    
+    df = dfin.copy()
+    df_volt_rolling = df[volt_column] 
+
+    df_volt_rolling_sum=df_volt_rolling.sum(1)-df_volt_rolling.max(1)
+    df_volt_rolling_sum=df_volt_rolling_sum-df_volt_rolling.min(1)
+    mean1 = df_volt_rolling_sum/(len(volt_column)-2)
+    df_volt_rolling_norm = df_volt_rolling.sub(mean1, axis=0)#.div(std,axis=0)
+    df_volt_rolling_norm = df_volt_rolling_norm.reset_index(drop=True)#和均值的距离
+
+    return df_volt_rolling_norm
+
+# 计算电压离群  
+def cal_volt_sigma(dfin, volt_column):
+    
+    df = dfin.copy()
+    df_volt_rolling = df[volt_column] 
+    
+    mean1=df_volt_rolling.mean(axis=1)
+    std = df_volt_rolling.std(axis=1)
+    std = std.replace(0,0.000001)
+    df_volt_rolling = df_volt_rolling.sub(mean1, axis=0).div(std,axis=0)
+    df_volt_rolling = df_volt_rolling.reset_index(drop=True)#分布
+
+    return df_volt_rolling
+
+
+# # 计算电压变化量的偏离度    
+# def cal_voltdiff_uniform(dfin, volt_column, window=10, step=5, window2=10, step2=5,threshold=3):
+    
+#     df = dfin.copy()
+#     time_list = dfin['time'].tolist()
+
+#     # 电压滤波
+#     df_volt = df[volt_column] 
+#     df_volt_rolling = df_volt.rolling(window).mean()[window-1::step]  # 滑动平均值
+#     time_list = time_list[window-1::step] 
+
+#     # 计算电压变化量的绝对值（# 计算前后的差值的绝对值，  时间列-1）
+#     df_volt_diff = abs(df_volt_rolling.diff()[1:])
+#     df_volt_diff = df_volt_diff.reset_index(drop=True)
+#     time_list = time_list[1:]
+
+#     # 压差归一化（偏离度）
+#     # mean = df_volt_diff.mean(axis=1)
+#     # std = df_volt_diff.std(axis=1)
+#     # df_voltdiff_norm = df_volt_diff.sub(mean, axis=0).div(std,axis=0)
+#     df_voltdiff_norm = df_volt_diff.copy()
+
+#     # 压差偏离度滑动平均滤波
+#     df_voltdiff_rolling = df_voltdiff_norm.rolling(window2).mean()[window2-1::step2]  # 滑动平均值
+#     time_list = time_list[window2-1::step2]
+#     df_voltdiff_rolling_sum=df_voltdiff_rolling.sum(1)-df_voltdiff_rolling.max(1)
+#     df_voltdiff_rolling_sum=df_voltdiff_rolling_sum-df_voltdiff_rolling.min(1)
+#     mean = df_voltdiff_rolling_sum/(len(volt_column)-2)
+#     std = df_voltdiff_rolling.std(axis=1)
+#     # mean = [np.array(sorted(x)[1:-1]).mean() for x in df_voltdiff_rolling.values]
+#     # std = [np.array(sorted(x)[1:-1]).std() for x in df_voltdiff_rolling.values]
+#     df_voltdiff_rolling_norm = df_voltdiff_rolling.sub(mean, axis=0)#.div(std,axis=0)
+#     df_voltdiff_rolling_norm = df_voltdiff_rolling_norm.reset_index(drop=True)
+#     return df_voltdiff_rolling_norm, time_list
+def main(sn,df_bms,celltype):
+    param=BatParam.BatParam(celltype)
+    df_bms['PackCrnt']=df_bms['PackCrnt']*param.PackCrntDec
+    df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+    volt_column = ['CellVolt'+str(i) for i in range(1,param.CellVoltNums+1)]
+    columns=['time']+volt_column
+    df_bms=df_bms[(df_bms['PackSOC']>10)]
+    # df_bms=df_bms[(df_bms['PackCrnt']<1)]
+    # df_chrg=df_bms[(df_bms['PackCrnt']<-1)]
+
+    #电压/SOC变化率计算
+    if celltype<50:
+        df_ori = df_bms[columns]
+        df = df_ori.drop_duplicates(subset=['time']) # 删除时间相同的数据
+        df= df.set_index('time')
+        df=df[(df[volt_column]>2) & (df[volt_column]<4.5)]
+        df[volt_column]=pd.DataFrame(df[volt_column],dtype=np.float)
+        df=df.resample('H').mean()  #取一个小时的平均值
+        df=df.dropna(how='any')
+        time_list1=df.index.tolist()
+    
+        fun=lambda x: np.interp(x, param.LookTab_OCV, param.LookTab_SOC)
+        df_soc=df.applymap(fun)
+
+        VolChng = cal_volt_change(df_soc,volt_column)
+    else:
+        # df_bms=df_bms[(df_bms['PackCrnt']>-0.1) & (df_bms['PackCrnt']<0.1)]
+        df_ori = df_bms[columns]
+        df = df_ori.drop_duplicates(subset=['time']) # 删除时间相同的数据
+        df= df.set_index('time')
+        df=df[(df[volt_column]>3.2) & (df[volt_column]<3.4)]
+        df[volt_column]=pd.DataFrame(df[volt_column],dtype=np.float)
+        df=df.resample('H').mean()  #取一个小时的平均值
+        df=df.dropna(how='any')
+        time_list1=df.index.tolist()
+
+        VolChng = cal_volt_change(df,volt_column)
+    
+
+    VolSigma = cal_volt_sigma(df,volt_column)
+
+    OutLineVol=DataFrame(columns=['time','sn','VolOl_Uni','VolChng_Uni'])
+
+    #静置电压变化率和离群度计算
+    if len(VolChng)>5 and len(VolSigma)>5:
+        VolChng['time'] = time_list1
+        VolChng= VolChng.set_index('time')
+        VolChng_Uni_result=VolChng.values.tolist()#改
+        
+        VolSigma['time'] = time_list1
+        VolSigma= VolSigma.set_index('time')
+        VolOl_Uni_result=VolSigma.values.tolist()#改
+       
+        for i in range(0,len(VolChng)):
+            OutLineVol.loc[i,'VolOl_Uni']=str(list(np.around(VolOl_Uni_result[i],decimals=2)))
+            OutLineVol.loc[i,'VolChng_Uni']=str(list(np.around(VolChng_Uni_result[i],decimals=2)))
+        OutLineVol=OutLineVol[~OutLineVol['VolOl_Uni'].str.contains('nan')]
+        OutLineVol=OutLineVol[~OutLineVol['VolChng_Uni'].str.contains('nan')]
+        OutLineVol=OutLineVol.applymap((lambda x:''.join(x.split()) if type(x) is str else x)) 
+        OutLineVol=OutLineVol.reset_index(drop=True) 
+        OutLineVol['time']= VolSigma.index
+        OutLineVol['sn']=sn
+
+    return(OutLineVol)

USER/SPF/alibaba/alibaba/07BatSafetyWarning/07BatSafetyWarning/main.py

@@ -0,0 +1,222 @@
+import pandas as pd
+import pymysql
+from LIB.BACKEND import DBManager, Log
+from apscheduler.schedulers.blocking import BlockingScheduler
+import datetime
+from USER.alibaba.Common import DBDownload
+from USER.alibaba.Common import log
+import CBMSBatInterShort
+import CBMSBatUniform
+import VoltStray
+import CBMSSafetyWarning
+
+#电池热安全预警核心算法函数......................................................................................................................
+def saftywarning_cal():
+    global SNnums
+    global df_warning_ram
+    global df_warning_ram1
+    global df_warning_ram2
+    global df_warning_ram3
+    global df_lfp_ram
+    global df_lfp_ram1
+    
+    now_time=datetime.datetime.now()
+    start_time=now_time-datetime.timedelta(hours=6)
+    start_time1=now_time-datetime.timedelta(days=7)
+    start_time2=now_time-datetime.timedelta(days=3)
+    start_time3=now_time-datetime.timedelta(days=1)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    start_time1=start_time1.strftime('%Y-%m-%d %H:%M:%S')
+    start_time2=start_time2.strftime('%Y-%m-%d %H:%M:%S')
+    start_time3=start_time3.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=now_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    #数据库配置
+    host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+    port=3306
+    user='qx_read'
+    password='Qx@123456'
+
+    #读取故障结果库中faultcode==110且time_sp='0000-00-00 00:00:00'...............................
+    db='safety_platform'
+    mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db)
+    cursor = mysql.cursor()
+    param='time_st,time_sp,sn,faultcode,faultlv,faultinfo,faultadvice'
+    tablename='all_fault_info'
+    sql =  "select %s from %s where faultcode=110 and time_sp='0000-00-00 00:00:00'" %(param,tablename)
+    cursor.execute(sql)
+    res = cursor.fetchall()
+    df_fault_ram= pd.DataFrame(res,columns=param.split(','))
+    cursor.close()
+    mysql.close()
+
+    for sn in SNnums:
+        if 'PK500' in sn:
+            celltype=1 #6040三元电芯
+        elif 'PK502' in sn:
+            celltype=2 #4840三元电芯
+        elif 'K504B' in sn:
+            celltype=99    #60ah林磷酸铁锂电芯
+        elif 'MGMLXN750' in sn:
+            celltype=3 #力信50ah三元电芯
+        elif 'MGMCLN750' or 'UD' in sn: 
+            celltype=4 #CATL 50ah三元电芯
+        else:
+            print('SN：{}，未找到对应电池类型！！！'.format(sn))
+            continue
+            # sys.exit()
+
+        #读取原始数据库数据........................................................................................................................................................
+        dbManager = DBManager.DBManager()
+        df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms'])
+        df_bms = df_data['bms']
+        # print(df_bms)
+        # df_bms.to_csv(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\98Download\\'+sn+'_BMS_'+tim+'.csv',encoding='GB18030')
+
+        #读取结果数据库数据........................................................................................................................................................
+        db='qx_cas'
+        mode=1
+        DBRead=DBDownload.DBDownload(host, port, db, user, password,mode)
+        with DBRead as DBRead:
+            df_soh=DBRead.getdata('time_st,sn,soh,cellsoh', tablename='cellstateestimation_soh', sn=sn, timename='time_sp', st=start_time, sp=end_time)
+            df_uniform=DBRead.getdata('time,sn,cellsoc_diff,cellvolt_diff,cellmin_num,cellmax_num,cellvolt_rank', tablename='cellstateestimation_uniform_socvoltdiff', sn=sn, timename='time', st=start_time1, sp=end_time)
+
+        if not df_bms.empty:
+            #ram处理...............................................................................................................
+            df_warning_ram_sn=df_warning_ram[df_warning_ram['sn']==sn]
+            df_warning_ram_sn1=df_warning_ram1[df_warning_ram1['sn']==sn]
+            df_warning_ram_sn2=df_warning_ram2[df_warning_ram2['sn']==sn]
+            df_warning_ram_sn3=df_warning_ram3[df_warning_ram3['sn']==sn]
+            df_warning_ram_sn.reset_index(inplace=True,drop=True)     #重置索引
+            df_warning_ram_sn1.reset_index(inplace=True,drop=True)     #重置索引
+            df_warning_ram_sn2.reset_index(inplace=True,drop=True)     #重置索引
+            df_warning_ram_sn3.reset_index(inplace=True,drop=True)     #重置索引
+            if celltype>50 and (not df_lfp_ram.empty):
+                df_lfp_ram_sn=df_lfp_ram[df_lfp_ram['sn']==sn]
+                df_lfp_ram_sn.reset_index(inplace=True,drop=True)     #重置索引
+            else:
+                df_lfp_ram_sn=pd.DataFrame()
+                df_lfp_ram=pd.DataFrame(columns=df_bms.columns.tolist()+['sn'])
+            if celltype>50 and (not df_lfp_ram1.empty):
+                df_lfp_ram_sn1=df_lfp_ram1[df_lfp_ram1['sn']==sn]
+                df_lfp_ram_sn1.reset_index(inplace=True,drop=True)     #重置索引
+            else:
+                df_lfp_ram_sn1=pd.DataFrame()
+                df_lfp_ram1=pd.DataFrame(columns=df_bms.columns.tolist()+['sn'])
+
+            #内短路计算..................................................................................................................................................
+            BatShort=CBMSBatInterShort.BatInterShort(sn,celltype,df_bms,df_soh,df_warning_ram_sn,df_warning_ram_sn1,df_warning_ram_sn2,df_warning_ram_sn3,df_lfp_ram_sn)
+            df_short_res, df_ram_res, df_ram_res1, df_ram_res2, df_ram_res3, df_ram_res4=BatShort.intershort() 
+            if not df_short_res.empty:
+                with open(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\07BatSafetyWarning\内短路.txt','a') as file:
+                    file.write(str(df_short_res)+'\n')
+            
+            #静置电压排名..................................................................................................................................................
+            BatUniform=CBMSBatUniform.BatUniform(sn,celltype,df_bms,df_uniform,df_ram_res3,df_lfp_ram_sn1)
+            df_rank_res, df_ram_res3, df_ram_res5=BatUniform.batuniform()
+            if not df_rank_res.empty:
+                df_uniform=df_rank_res
+                with open(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\07BatSafetyWarning\电压排名.txt','a') as file:
+                    file.write(str(df_rank_res)+'\n')
+            
+            #电压离群.....................................................................................................................................................
+            df_voltsigma=VoltStray.main(sn,df_bms,celltype)
+            if not df_voltsigma.empty:
+                with open(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\07BatSafetyWarning\电压离群.txt','a') as file:
+                    file.write(str(df_voltsigma)+'\n')
+
+            #ram处理................................................................................................................
+            df_warning_ram=df_warning_ram.drop(df_warning_ram[df_warning_ram.sn==sn].index)
+            df_warning_ram1=df_warning_ram1.drop(df_warning_ram1[df_warning_ram1.sn==sn].index)
+            df_warning_ram2=df_warning_ram2.drop(df_warning_ram2[df_warning_ram2.sn==sn].index)
+            df_warning_ram3=df_warning_ram3.drop(df_warning_ram3[df_warning_ram3.sn==sn].index)
+
+            df_warning_ram=pd.concat([df_warning_ram,df_ram_res],ignore_index=True)
+            df_warning_ram1=pd.concat([df_warning_ram1,df_ram_res1],ignore_index=True)
+            df_warning_ram2=pd.concat([df_warning_ram2,df_ram_res2],ignore_index=True)
+            df_warning_ram3=pd.concat([df_warning_ram3,df_ram_res3],ignore_index=True)
+            
+            if celltype>50:
+                df_lfp_ram=df_lfp_ram.drop(df_lfp_ram[df_lfp_ram.sn==sn].index)
+                df_lfp_ram=pd.concat([df_lfp_ram,df_ram_res4],ignore_index=True)
+                df_lfp_ram1=df_lfp_ram1.drop(df_lfp_ram1[df_lfp_ram1.sn==sn].index)
+                df_lfp_ram1=pd.concat([df_lfp_ram1,df_ram_res5],ignore_index=True)
+            
+
+        #电池热安全预警..............................................................................................................................................................
+        #读取内短路、析锂和一致性结果数据库数据
+        db='qx_cas'
+        mode=2
+        tablename1='cellstateestimation_intershort'
+        tablename3='cellstateestimation_uniform_socvoltdiff'
+        tablename4='outlier_voltchangeratio'   #电压离群表单
+        DBRead=DBDownload.DBDownload(host, port, db, user, password,mode)
+        with DBRead as DBRead:
+            df_short=DBRead.getdata('time_sp,sn,short_current', tablename=tablename1, sn=sn, timename='time_sp', st=start_time1, sp=end_time)
+            # df_liplated=DBRead.getdata('time,sn,liplated,liplated_amount', tablename=tablename2, sn=sn, timename='time', st=start_time2, sp=end_time)
+            df_uniform=DBRead.getdata('time,sn,cellsoc_diff,cellvolt_diff,cellmin_num,cellmax_num,cellvolt_rank', tablename=tablename3, sn=sn, timename='time', st=start_time2, sp=end_time)
+            df_voltsigma=DBRead.getdata('time,sn,VolOl_Uni,VolChng_Uni', tablename=tablename4, sn=sn, timename='time', st=start_time3, sp=end_time)
+            df_uniform=df_uniform.dropna(axis=0,how='any')
+        
+        #获取sn的故障RAM
+        df_fault_ram_sn=df_fault_ram[df_fault_ram['sn']==sn]
+        
+        #热安全预警
+        if df_fault_ram_sn.empty:
+            BatWarning=CBMSSafetyWarning.SafetyWarning(sn,celltype,df_short,df_uniform,df_voltsigma,df_soh)
+            df_warning_res=BatWarning.diag()
+            #当前热失控故障写入数据库
+            if not df_warning_res.empty:
+                with open(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\07BatSafetyWarning\热失控预警.txt','a',encoding="utf-8") as file:
+                    file.write(str(tuple(df_warning_res.iloc[-1]))+'\n')
+        
+        else:
+            fault_time=df_fault_ram_sn.iloc[-1]['time_st']
+            if (now_time-fault_time).total_seconds()>7*24*3600:   #df_warning_end历史故障筛选并更改数据库故障结束时间
+                df_fault_ram_sn['time_st']=end_time
+                with open(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\07BatSafetyWarning\热失控预警.txt','a',encoding="utf-8") as file:
+                    file.write(str(tuple(df_warning_res.iloc[-1]))+'\n')
+
+#...............................................主函数起定时作用.......................................................................................................................
+if __name__ == "__main__":
+    
+    # excelpath=r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\sn-20210903.xlsx'
+    # SNdata_6060 = pd.read_excel(excelpath, sheet_name='科易6060')
+    # SNdata_6040 = pd.read_excel(excelpath, sheet_name='科易6040')
+    # SNdata_4840 = pd.read_excel(excelpath, sheet_name='科易4840')
+    # SNdata_L7255 = pd.read_excel(excelpath, sheet_name='格林美-力信7255')
+    # SNdata_C7255 = pd.read_excel(excelpath, sheet_name='格林美-CATL7255')
+    # SNdata_U7255 = pd.read_excel(excelpath, sheet_name='优旦7255')
+    # SNnums_6060=SNdata_6060['SN号'].tolist()
+    # SNnums_6040=SNdata_6040['SN号'].tolist()
+    # SNnums_4840=SNdata_4840['SN号'].tolist()
+    # SNnums_L7255=SNdata_L7255['SN号'].tolist()
+    # SNnums_C7255=SNdata_C7255['SN号'].tolist()
+    # SNnums_U7255=SNdata_U7255['SN号'].tolist()
+    # SNnums=SNnums_L7255 + SNnums_C7255 + SNnums_U7255 + SNnums_6040 + SNnums_4840 + SNnums_6060
+    SNnums=['HUATING']
+    
+    mylog=log.Mylog('log_warning.txt','error')
+    mylog.logcfg()
+
+    #............................模块运行前，先读取数据库中所有结束时间为0的数据，需要从数据库中读取...................................
+    df_warning_ram=pd.DataFrame(columns=['sn','time','deltsoc','cellsoc'])
+    df_warning_ram1=pd.DataFrame(columns=['sn','time1','deltsoc1'])
+    df_warning_ram2=pd.DataFrame(columns=['sn','time2','deltAs2'])
+    df_warning_ram3=pd.DataFrame(columns=['sn','time3','standingtime','standingtime1','standingtime2'])
+    df_lfp_ram=pd.DataFrame()
+    df_lfp_ram1=pd.DataFrame()
+
+    now_time='2021-12-08 15:00:00'
+    now_time=datetime.datetime.strptime(now_time,'%Y-%m-%d %H:%M:%S')
+
+    #定时任务.......................................................................................................................................................................
+    scheduler = BlockingScheduler()
+    scheduler.add_job(saftywarning_cal, 'interval', seconds=5)
+
+    try:  
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/alibaba/07BatSafetyWarning/07BatSafetyWarning/main_test.py

@@ -0,0 +1,250 @@
+import pandas as pd
+import pymysql
+from LIB.BACKEND import DBManager, Log
+from apscheduler.schedulers.blocking import BlockingScheduler
+import time, datetime
+from USER.alibaba.Common import DBDownload
+from USER.alibaba.Common import log
+import CBMSBatInterShort
+import CBMSBatUniform
+import VoltStray
+import CBMSSafetyWarning
+
+#电池热安全预警核心算法函数......................................................................................................................
+def saftywarning_cal():
+    global SNnums
+    global df_warning_ram
+    global df_warning_ram1
+    global df_warning_ram2
+    global df_warning_ram3
+    global df_lfp_ram
+    global df_lfp_ram1
+    global now_time
+    global df_bms
+    
+    start=time.time()
+    # now_time=datetime.datetime.now()
+    # start_time=now_time-datetime.timedelta(hours=12)
+    # start_time1=now_time-datetime.timedelta(days=7)
+    # start_time2=now_time-datetime.timedelta(days=90)
+    # start_time3=now_time-datetime.timedelta(days=3)
+    # start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    # start_time1=start_time1.strftime('%Y-%m-%d %H:%M:%S')
+    # start_time2=start_time2.strftime('%Y-%m-%d %H:%M:%S')
+    # start_time3=start_time3.strftime('%Y-%m-%d %H:%M:%S')
+    # end_time=now_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    now_time=now_time+datetime.timedelta(hours=6)
+    start_time=now_time-datetime.timedelta(hours=24)
+    start_time1=now_time-datetime.timedelta(days=7)
+    start_time2=now_time-datetime.timedelta(days=3)
+    start_time3=now_time-datetime.timedelta(days=1)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    start_time1=start_time1.strftime('%Y-%m-%d %H:%M:%S')
+    start_time2=start_time2.strftime('%Y-%m-%d %H:%M:%S')
+    start_time3=start_time3.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=now_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    #数据库配置
+    host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+    port=3306
+    user='qx_read'
+    password='Qx@123456'
+
+    #读取故障结果库中code==110且end_time='0000-00-00 00:00:00'...............................
+    # db='safety_platform'
+    # mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db)
+    # cursor = mysql.cursor()
+    # param='start_time,end_time,product_id,code,level,info,advice'
+    # tablename='all_fault_info'
+    # sql =  "select %s from %s where code=110 and end_time='0000-00-00 00:00:00'" %(param,tablename)
+    # cursor.execute(sql)
+    # res = cursor.fetchall()
+    # df_fault_ram= pd.DataFrame(res,columns=param.split(','))
+    # cursor.close()
+    # mysql.close()
+    df_fault_ram=pd.DataFrame(columns=['time_st','time_sp','sn','faultcode','faultlv','faultinfo','faultadvice'])
+
+    for sn in SNnums:
+        if 'PK500' in sn:
+            celltype=1 #6040三元电芯
+        elif 'PK502' in sn:
+            celltype=2 #4840三元电芯
+        elif 'K504B' in sn:
+            celltype=99    #60ah林磷酸铁锂电芯
+        elif 'MGMLXN750' in sn:
+            celltype=3 #力信50ah三元电芯
+        elif 'MGMCLN750' or 'UD' in sn: 
+            celltype=4 #CATL 50ah三元电芯
+        else:
+            print('SN：{}，未找到对应电池类型！！！'.format(sn))
+            continue
+            # sys.exit()
+        celltype=2
+
+        #读取原始数据库数据........................................................................................................................................................
+        df_bms1=df_bms[(df_bms['time'] > start_time) & (df_bms['time'] < end_time)]
+        df_bms1.reset_index(inplace=True,drop=True)
+        print(df_bms1)
+       
+        #读取结果数据库数据........................................................................................................................................................
+        db='qx_cas'
+        mode=1
+        DBRead=DBDownload.DBDownload(host, port, db, user, password,mode)
+        with DBRead as DBRead:
+            df_soh=DBRead.getdata('time_st,sn,soh,cellsoh', tablename='cellstateestimation_soh', sn=sn, timename='time_sp', st=start_time, sp=end_time)
+            df_uniform=DBRead.getdata('time,sn,cellsoc_diff,cellvolt_diff,cellmin_num,cellmax_num', tablename='cellstateestimation_uniform_socvoltdiff', sn=sn, timename='time', st=start_time1, sp=end_time)
+
+        if not df_bms1.empty:
+            #ram处理...............................................................................................................
+            df_warning_ram_sn=df_warning_ram[df_warning_ram['sn']==sn]
+            df_warning_ram_sn1=df_warning_ram1[df_warning_ram1['sn']==sn]
+            df_warning_ram_sn2=df_warning_ram2[df_warning_ram2['sn']==sn]
+            df_warning_ram_sn3=df_warning_ram3[df_warning_ram3['sn']==sn]
+            df_warning_ram_sn.reset_index(inplace=True,drop=True)     #重置索引
+            df_warning_ram_sn1.reset_index(inplace=True,drop=True)     #重置索引
+            df_warning_ram_sn2.reset_index(inplace=True,drop=True)     #重置索引
+            df_warning_ram_sn3.reset_index(inplace=True,drop=True)     #重置索引
+            if celltype>50 and (not df_lfp_ram.empty):
+                df_lfp_ram_sn=df_lfp_ram[df_lfp_ram['sn']==sn]
+                df_lfp_ram_sn.reset_index(inplace=True,drop=True)     #重置索引
+            else:
+                df_lfp_ram_sn=pd.DataFrame()
+                df_lfp_ram=pd.DataFrame(columns=df_bms.columns.tolist()+['sn'])
+            if celltype>50 and (not df_lfp_ram1.empty):
+                df_lfp_ram_sn1=df_lfp_ram1[df_lfp_ram1['sn']==sn]
+                df_lfp_ram_sn1.reset_index(inplace=True,drop=True)     #重置索引
+            else:
+                df_lfp_ram_sn1=pd.DataFrame()
+                df_lfp_ram1=pd.DataFrame(columns=df_bms.columns.tolist()+['sn'])
+
+            #内短路计算..................................................................................................................................................
+            BatShort=CBMSBatInterShort.BatInterShort(sn,celltype,df_bms,df_soh,df_warning_ram_sn,df_warning_ram_sn1,df_warning_ram_sn2,df_warning_ram_sn3,df_lfp_ram_sn)
+            df_short_res, df_ram_res, df_ram_res1, df_ram_res2, df_ram_res3, df_ram_res4=BatShort.intershort() 
+            if not df_short_res.empty:
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\07BatSafetyWarning\内短路.txt','a') as file:
+                    file.write(str(df_short_res)+'\n')
+            
+            #静置电压排名..................................................................................................................................................
+            BatUniform=CBMSBatUniform.BatUniform(sn,celltype,df_bms1,df_uniform,df_ram_res3,df_lfp_ram_sn1)
+            df_rank_res, df_ram_res3, df_ram_res5=BatUniform.batuniform()
+            if not df_rank_res.empty:
+                df_uniform=df_rank_res
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\07BatSafetyWarning\电压排名.txt','a') as file:
+                    file.write(str(df_rank_res)+'\n')
+            
+            #电压离群.....................................................................................................................................................
+            df_voltsigma=VoltStray.main(sn,df_bms1,celltype)
+            if not df_voltsigma.empty:
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\07BatSafetyWarning\电压离群.txt','a') as file:
+                    file.write(str(df_voltsigma)+'\n')
+
+            #ram处理................................................................................................................
+            df_warning_ram=df_warning_ram.drop(df_warning_ram[df_warning_ram.sn==sn].index)
+            df_warning_ram1=df_warning_ram1.drop(df_warning_ram1[df_warning_ram1.sn==sn].index)
+            df_warning_ram2=df_warning_ram2.drop(df_warning_ram2[df_warning_ram2.sn==sn].index)
+            df_warning_ram3=df_warning_ram3.drop(df_warning_ram3[df_warning_ram3.sn==sn].index)
+
+            df_warning_ram=pd.concat([df_warning_ram,df_ram_res],ignore_index=True)
+            df_warning_ram1=pd.concat([df_warning_ram1,df_ram_res1],ignore_index=True)
+            df_warning_ram2=pd.concat([df_warning_ram2,df_ram_res2],ignore_index=True)
+            df_warning_ram3=pd.concat([df_warning_ram3,df_ram_res3],ignore_index=True)
+            
+            if celltype>50:
+                df_lfp_ram=df_lfp_ram.drop(df_lfp_ram[df_lfp_ram.sn==sn].index)
+                df_lfp_ram=pd.concat([df_lfp_ram,df_ram_res4],ignore_index=True)
+                df_lfp_ram1=df_lfp_ram1.drop(df_lfp_ram1[df_lfp_ram1.sn==sn].index)
+                df_lfp_ram1=pd.concat([df_lfp_ram1,df_ram_res5],ignore_index=True)
+            
+
+        #电池热安全预警..............................................................................................................................................................
+        #读取内短路、析锂和一致性结果数据库数据
+        db='qx_cas'
+        mode=2
+        tablename1='cellstateestimation_intershort'
+        tablename3='cellstateestimation_uniform_socvoltdiff'
+        tablename4='outlier_voltchangeratio'   #电压离群表单
+        DBRead=DBDownload.DBDownload(host, port, db, user, password,mode)
+        with DBRead as DBRead:
+            df_short=DBRead.getdata('time_sp,sn,short_current', tablename=tablename1, sn=sn, timename='time_sp', st=start_time1, sp=end_time)
+            # df_liplated=DBRead.getdata('time,sn,liplated,liplated_amount', tablename=tablename2, sn=sn, timename='time', st=start_time2, sp=end_time)
+            df_uniform=DBRead.getdata('time,sn,cellsoc_diff,cellvolt_diff,cellmin_num,cellmax_num,cellvolt_rank', tablename=tablename3, sn=sn, timename='time', st=start_time2, sp=end_time)
+            # df_voltsigma=DBRead.getdata('time,sn,VolOl_Uni,VolChng_Uni', tablename=tablename4, sn=sn, timename='time', st=start_time3, sp=end_time)
+            df_uniform=df_uniform.dropna(axis=0,how='any')
+        
+        #获取sn的故障RAM
+        df_fault_ram_sn=df_fault_ram[df_fault_ram['sn']==sn]
+        
+        #热安全预警
+        if df_fault_ram_sn.empty:
+            BatWarning=CBMSSafetyWarning.SafetyWarning(sn,celltype,df_short,df_uniform,df_voltsigma,df_soh)
+            df_warning_res=BatWarning.diag()
+            #当前热失控故障写入数据库
+            if not df_warning_res.empty:
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\07BatSafetyWarning\热失控预警.txt','a',encoding="utf-8") as file:
+                    file.write(str(tuple(df_warning_res.iloc[-1]))+'\n')
+        
+        else:
+            fault_time=datetime.datetime.strptime(df_fault_ram_sn.iloc[-1]['start_time'], '%Y-%m-%d %H:%M:%S')
+            if (now_time-fault_time).total_seconds()>3*24*3600:   #df_warning_end历史故障筛选并更改数据库故障结束时间
+                df_fault_ram_sn['end_time']=end_time
+                df_fault_ram_sn['Batpos']=1
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\07BatSafetyWarning\热失控预警.txt','a',encoding="utf-8") as file:
+                    file.write(str(tuple(df_warning_res.iloc[-1]))+'\n')
+
+        
+        print(df_warning_ram)
+        print(df_warning_ram1)
+        print(df_warning_ram2)
+        print(df_warning_ram3)
+        print(df_lfp_ram)
+
+        end=time.time()
+        print(end-start)
+        # print(df_soh)
+
+#...............................................主函数起定时作用.......................................................................................................................
+if __name__ == "__main__":
+    
+    # excelpath=r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\sn-20210903.xlsx'
+    # SNdata_6060 = pd.read_excel(excelpath, sheet_name='科易6060')
+    # SNdata_6040 = pd.read_excel(excelpath, sheet_name='科易6040')
+    # SNdata_4840 = pd.read_excel(excelpath, sheet_name='科易4840')
+    # SNdata_L7255 = pd.read_excel(excelpath, sheet_name='格林美-力信7255')
+    # SNdata_C7255 = pd.read_excel(excelpath, sheet_name='格林美-CATL7255')
+    # SNdata_U7255 = pd.read_excel(excelpath, sheet_name='优旦7255')
+    # SNnums_6060=SNdata_6060['SN号'].tolist()
+    # SNnums_6040=SNdata_6040['SN号'].tolist()
+    # SNnums_4840=SNdata_4840['SN号'].tolist()
+    # SNnums_L7255=SNdata_L7255['SN号'].tolist()
+    # SNnums_C7255=SNdata_C7255['SN号'].tolist()
+    # SNnums_U7255=SNdata_U7255['SN号'].tolist()
+    # SNnums=SNnums_L7255 + SNnums_C7255 + SNnums_U7255 + SNnums_6040 + SNnums_4840 + SNnums_6060
+    SNnums=['HUATING']
+    
+    mylog=log.Mylog('log_warning.txt','error')
+    mylog.logcfg()
+
+    #............................模块运行前，先读取数据库中所有结束时间为0的数据，需要从数据库中读取...................................
+    df_warning_ram=pd.DataFrame(columns=['sn','time','deltsoc','cellsoc'])
+    df_warning_ram1=pd.DataFrame(columns=['sn','time1','deltsoc1'])
+    df_warning_ram2=pd.DataFrame(columns=['sn','time2','deltAs2'])
+    df_warning_ram3=pd.DataFrame(columns=['sn','time3','standingtime','standingtime1','standingtime2'])
+    df_lfp_ram=pd.DataFrame()
+    df_lfp_ram1=pd.DataFrame()
+
+    now_time='2021-10-29 14:28:40'
+    now_time=datetime.datetime.strptime(now_time,'%Y-%m-%d %H:%M:%S')
+    df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-故障诊断原始数据.xlsx')
+    df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+
+    #定时任务.......................................................................................................................................................................
+    scheduler = BlockingScheduler()
+    scheduler.add_job(saftywarning_cal, 'interval', seconds=5)
+
+    try:  
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/alibaba/BatParam.py

@@ -0,0 +1,196 @@
+
+#定义电池参数
+
+class BatParam:
+    def __init__(self,celltype):
+
+        #公用参数................................................................................................................................................
+        #热失控参数
+        self.TrwTempHigh=60
+        self.TrwTempRate=3
+        self.TrwTempDiff=15
+        self.TrwCellVoltDiff=2.5
+        self.TrwCellVoltFall=0.5
+        self.TrwCellVoltLow=1.5
+        self.TrwPackVoltFall=1.5
+        
+        #故障诊断参数
+        self.temp_time=10
+        self.volt_time=10
+
+        self.SocJump=10
+        self.SocClamp=0.1
+        self.SocLow=3
+        self.SocDiff=20
+
+        self.SohLow=65
+        self.SohDiff=15
+
+        #mana-kendall趋势检验参数
+        self.mk_p=0.001
+        self.mk_z=-5
+        self.mk_Tau=-0.7
+        self.mk_slope=-0.1
+        self.mk_s=-260
+        self.mk_svar=1600
+
+        self.OcvWeight_Temp=[-30,-20,-10,0,10,20,30,40,50]
+        self.OcvWeight_StandingTime=[0,500,600,1200,1800,3600,7200,10800]
+        self.OcvWeight            =[[0,0,  0,  0,    0,   0.1,0.3, 1],
+                                    [0,0,  0,  0,    0,   0.1,0.3, 1],
+                                    [0,0,  0,  0,    0,   0.2,0.5, 1],
+                                    [0,0,  0,  0,    0.2, 0.4,0.8, 1],
+                                    [0,0,  0,  0.1,  0.3, 0.6,1,   1],
+                                    [0,0,  0.1,0.2,  0.5, 0.8,1,   1],
+                                    [0,0,  0.1,0.3,  0.6, 1,  1,   1],
+                                    [0,0,  0.1,0.3,  0.7, 1,  1,   1],
+                                    [0,0,  0.2,0.3,  0.8, 1,  1,   1]]
+
+
+        if celltype==1: #永联+康普顿21700
+            self.Capacity = 4*2
+            self.PackFullChrgVolt=69.99
+            self.CellFullChrgVolt=4.2
+            self.CellFullChrgCrnt=-4
+            self.CellVoltNums=120
+            self.CellTempNums=40
+            self.FullChrgSoc=90
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+            self.LookTab_SOC = [0,	     5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100]
+            self.LookTab_OCV = [2.8497,	3.0924,	3.2868,	3.4117,	3.4461,	3.5032,	3.5602,	3.6049,	3.6368,	3.6709,	3.7121,	3.7593,	3.8237,	3.8755,	3.9093,	3.9518,	4.0119,	4.0615,	4.077,	4.0969,	4.1821]
+
+            self.CellOvLv1=4.2
+            self.CellOvLv2=4.25
+            self.CellUvLv1=2.8
+            self.CellUvLv2=2.5
+            self.CellVoltDiffLv1=0.3
+            self.CellVoltDiffLv2=0.5
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=10
+            self.CellTempDiffLv2=15
+            self.CellTempRate=5
+
+            self.PackChgOc=-40
+            self.PackDisOc=200
+
+            self.LeakCurrentLv1=-10
+            self.LeakCurrentLv2=-15
+            self.LeakCurrentLv3=-50
+
+            self.TrwVoltRate=-1
+
+        elif celltype==2: #中恒+华庭18650
+            self.Capacity = 2*9
+            self.CellVoltNums= 68
+            self.CellTempNums= 40
+            self.CellFullChrgVolt=4.2
+            self.CellFullChrgCrnt=-self.Capacity/2
+            self.FullChrgSoc=100
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+            self.LookTab_SOC = [0,	     5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100]
+            self.LookTab_OCV = [2.929,	3.2902,	3.3888,	3.425,	3.4846,	3.5382,	3.577,	3.6083,	3.638,	3.6786,	3.7127,	3.7596,	3.7853,	3.8371,	3.9081,	3.9127,	4.0097,	4.0314,	4.0463,	4.0969,	4.174]
+
+            self.CellOvLv1=4.04
+            self.CellOvLv2=4.05
+            self.CellUvLv1=2.99
+            self.CellUvLv2=2.950
+            self.CellVoltDiffLv1=0.35
+            self.CellVoltDiffLv2=0.4
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=10
+            self.CellTempDiffLv2=15
+            self.CellTempRate=5
+
+            self.PackChgOc=-45
+            self.PackDisOc=200
+
+            self.LeakCurrentLv1=-10
+            self.LeakCurrentLv2=-15
+            self.LeakCurrentLv3=-50
+
+            self.TrwVoltRate=-1
+
+        elif celltype==99:   #永联+力神
+            self.Capacity = 40*2
+            self.CellVoltNums=72
+            self.CellTempNums=32
+            self.CellFullChrgVolt=3.5
+            self.CellFullChrgCrnt=-self.Capacity/2
+
+            self.OcvInflexionBelow=3.285
+            self.OcvInflexion2=3.303
+            self.OcvInflexion3=3.343
+            self.OcvInflexionAbove=3.36
+            self.SocInflexion1=30
+            self.SocInflexion2=60
+            self.SocInflexion3=70
+
+            self.FullChrgSoc=97
+            self.PeakSoc=62.5
+            self.PeakVoltLowLmt=3.37
+            self.PeakVoltUpLmt=3.42
+            self.PeakCellVolt=[3.384,3.385,3.386,3.387,3.388]
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+
+            self.LookTab_SOC = [0,	5,	10,	15,	20,	25,	30,	35,	40,	45,	50,	55,	60,	61,	62,	63,	64,	65,	66,	67,	68,	69,	70,	75,	80,	85,	90,	95,	96,	97,	98,	99,	100]
+            self.LookTab_OCV = [2.6748,	3.2020,	3.2170,	3.2430,	3.2600,	3.2770,	3.2880,	3.2890,	3.2900,	3.2910,	3.2911,	3.2970,	3.3030,	3.3080,	3.3200,	3.3270,	3.3290,	3.3300,	3.3301,	3.3310,	3.3311,	3.3312,	3.3313,	3.3314,	3.3315,	3.3316,	3.3320,	3.3330,	3.3340,	3.3350,	3.3360,	3.3580,	3.3640]
+            self.LookTab_OCVChg=[2.6460, 3.1570, 3.2220, 3.2450,3.2760,	3.2970,	3.3060,	3.3070,	3.3080,	3.3090,	3.3110,	3.3140,	3.3220,	3.3270,	3.3330,	3.3380,	3.3420,	3.3421,	3.3422,	3.3423,	3.3424,	3.3431,	3.3432,	3.3433,	3.3434,	3.3441,	3.3442,	3.3443,	3.3444,	3.3445,	3.3446,	3.3447,	3.3640]
+
+            self.CellOvLv1=3.65
+            self.CellOvLv2=3.7
+            self.CellUvLv1=2.7
+            self.CellUvLv2=2.6
+            self.CellVoltDiffLv1=0.6
+            self.CellVoltDiffLv2=1.1
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=15
+            self.CellTempDiffLv2=20
+            self.CellTempRate=5
+
+            self.PackChgOc=-160
+            self.PackDisOc=180
+
+            self.LeakCurrentLv1=-20
+            self.LeakCurrentLv2=-30
+            self.LeakCurrentLv3=-100
+
+            self.TrwVoltRate=-8  
+            self.mk_slope=-0.8
+        
+
+        else:
+            print('未找到对应电池编号！！！')
+            # sys.exit()

USER/test.ipynb

@@ -177,27 +177,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "a    1\n",
-       "b    2\n",
-       "c    6\n",
-       "Name: 0, dtype: int64"
+       "[1, 2, 3]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a=df.loc[0]\n",
-    "\n",
-    "a"
+    "a=[1,2,3]\n",
+    "list(a)"
    ]
   },
   {
@@ -339,7 +335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.5"
   },
   "orig_nbformat": 4
  },