zlwl-algos-new/USER/liuzhongxiao/lip_test_main.py

from datetime import datetime
from multiprocessing import Pool
import json
import os
import time
import traceback
import warnings
from li_plted.V1_0_0.corepro_V1 import *
from keras.models import load_model
import pickle
from sqlalchemy import text, delete, and_, or_, update
import pandas as pd

from ZlwlAlgosCommon.utils.ProUtils import *
from ZlwlAlgosCommon.service.iotp.IotpAlgoService import IotpAlgoService
from ZlwlAlgosCommon.service.iotp.Beans import DataField
from ZlwlAlgosCommon.orm.models import *

# from ALGOS.PERIODIC.task_day_1.group_1.socdiag.V_1_0_0.SOCBatDiag import SocDiag
# from ALGOS.PERIODIC.task_day_1.group_1.LowSocAlarm.V1_0_0.low_soc_alarm import Low_soc_alarm 
# from ALGOS.PERIODIC.task_day_1.group_1.SorCal.V_1_0_0.sorcal import sor_est  

from DataSplit.V_1_0_0  import data_status as ds  ##充电状态标准化程序
from DataSplit.V_1_0_0  import data_split  as dt ##分段函数程序
from DataSplit.V_1_0_0  import data_drive_stat as ddt ##行驶数据按行驶段汇总统计
from DataSplit.V_1_0_0  import data_charge_stat as dct ##充电数据按充电段汇总
from DataSplit.V_1_0_0  import data_stand_stat as dst ##静置数据按静置段汇总
from DataSplit.V_1_0_0  import data_drive_stat_period as ddtp ##行驶数据按充电周期汇总统计
from DataSplit.V_1_0_0  import trans_day  as trd  ##解决跨天的问题



def main(process_num): 
    # 程序不能停止
    while(True):
        try:
            warnings.filterwarnings("ignore")
            try:   
                # 调用算法前的准备工作
                kafka_topic_key = 'topic_test_lzx'
                kafka_groupid_key = 'group_id_test_lzx'
                algo_list = ['socdiag','low_soc_diag','Sor_Diag','Li_Plted','Data_Split'] # 本调度所包含的算法名列表。
                
                loggers = sysUtils.get_loggers(algo_list, log_base_path, process_num) # 为每个算法分配一个logger
                logger_main.info(f"process-{process_num}: 配置中间件")
                
                # mysql
                mysql_algo_params = sysUtils.get_cf_param('mysql-algo')
                mysqlUtils = MysqlUtils()
                mysql_algo_engine, mysql_algo_Session= mysqlUtils.get_mysql_engine(mysql_algo_params)
                mysql_algo_conn = mysql_algo_engine.connect()
                
                
                mysql_iotp_data = sysUtils.get_cf_param('mysql-iotp')
                mysqlUtils = MysqlUtils()
                mysql_iotp_engine, mysql_iopt_Session= mysqlUtils.get_mysql_engine(mysql_iotp_data)
                mysql_iotp_conn = mysql_iotp_engine.connect()            
                
                


                # kafka
                kafka_params = sysUtils.get_cf_param('kafka')
                kafkaUtils = KafkaUtils()
                kafka_consumer = kafkaUtils.get_kafka_consumer(kafka_params, kafka_topic_key, kafka_groupid_key, client_id=kafka_topic_key)

                #Hbase
                hbase_params = sysUtils.get_cf_param('hbase-datafactory')
                iotp_service = IotpAlgoService(hbase_params=hbase_params)

                #redis
                redis_params = sysUtils.get_cf_param('redis')
                reidsUtils = RedisUtils()
                rc = reidsUtils.get_redis_conncect(redis_params)

            except Exception as e:
                logger_main.error(f'process-{process_num}: {e}')
                logger_main.error(f'process-{process_num}: {traceback.format_exc()}')
            
            # 开始准备调度
            logger_main.info(f"process-{process_num}: 监听topic {kafka_params[kafka_topic_key]}等待kafka 调度")
            timest = []
            timesp = []
            data_len = []
            snlst = []
            for message in kafka_consumer:
                try:
                    logger_main.info(f'收到调度')
                    if mysql_algo_conn.closed:
                        mysql_algo_conn = mysql_algo_engine.connect() # 从连接池中获取一个myslq连接
                        
                    schedule_params = json.loads(message.value)                    
                    if (schedule_params is None) or (schedule_params ==''):
                        logger_main.info('{} kafka数据异常，跳过本次运算'.format(str(message.value)))
                        continue
                    # kafka 调度参数解析
                    df_snlist = pd.DataFrame(schedule_params['snlist'])
                    df_algo_adjustable_param = pd.DataFrame([(d['algo_id'], d['param'],d['param_ai']) for d in schedule_params['adjustable_param']], columns=['algo_id', 'param','param_ai'])
                    df_algo_pack_param = json.loads(schedule_params['pack_param'][0]['param'])
                    df_algo_pack_param = {k: eval(v) if isinstance(v, str) else v for k, v in df_algo_pack_param.items()}
                    df_algo_param = pd.DataFrame(schedule_params['algo_list'])
                    start_time = schedule_params['start_time']
                    end_time = schedule_params['end_time']
                    pack_code = schedule_params['pack_code']
                    cell_type = schedule_params['cell_type']  
                    sn_list=df_snlist['sn'].tolist()
                    
                    # 取数
                    time_st = time.time()
                    logger_main.info(f"process-{process_num}: 开始取数{sn_list}")
                    
                    # new_end = datetime.datetime.strptime(end_time, '%Y-%m-%d %H:%M:%S')
                    # new_end_time = new_end - datetime.timedelta(days=18)
                    # new_end_timetd = new_end_time.strftime('%Y-%m-%d %H:%M:%S')
                    
                    columns = [ DataField.time, DataField.sn, DataField.pack_crnt, DataField.pack_volt, DataField.pack_soc,  
                                DataField.cell_voltage_count, DataField.cell_temp_count, DataField.cell_voltage, DataField.cell_temp, 
                                DataField.other_temp_value, DataField.bms_sta, DataField.charge_sta,DataField.latitude,DataField.longitude]
                    df_data = iotp_service.get_data(sn_list=sn_list, columns=columns, start_time=start_time, end_time=end_time)#结束时间修改
                    logger_main.info(f'process-{process_num},获取到{len(df_data)}条数据,取数耗时:{time.time()-time_st}')
                    print('获取的数据长度为：' + str(len(df_data)))
                    # 将字符串转换成datetime对象
                    str_date = start_time
                    date_time = datetime.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')
                    # 将datetime对象减去6小时
                    new_date_time = date_time - datetime.timedelta(hours=8)
                    
                    # 将datetime对象转换成字符串
                    start_time_8h = new_date_time.strftime('%Y-%m-%d %H:%M:%S')
                    df_data_8h = iotp_service.get_data(sn_list=sn_list, columns=columns, start_time=start_time_8h, end_time=start_time)
                    logger_main.info(f'process-{process_num},获取到{len(df_data_8h)}条数据,取数耗时:{time.time()-time_st}') 

                except Exception as e:
                    logger_main.error(f"process-{process_num}:获取原始数据出错")
                    logger_main.error(f"process-{process_num}:{e}")
                    logger_main.error(f"process-{process_num}:{traceback.format_exc()}")
                    continue
                
                # 数据清洗
                try:
                    time_st = time.time()
                    logger_main.info(f'process-{process_num}数据清洗')                        
                    df_data, df_table, cellvolt_name, celltemp_name = iotp_service.datacleaning(df_algo_pack_param,df_data)#进行数据清洗
                    df_data_8h, df_table_t, cellvolt_name_t, celltemp_name_t = iotp_service.datacleaning(df_algo_pack_param,df_data_8h)#进行数据清洗
                    try:
                        df_data_32h=pd.concat([df_data_8h,df_data])
                        df_data_32h=df_data_32h.reset_index(drop=True)
                    except Exception as e:
                        logger_main.error(f"process-{process_num}:32小时拼接出错")
                        logger_main.error(f"process-{process_num}:{e}")
                        logger_main.error(f"process-{process_num}:{traceback.format_exc()}")
                    if len(df_data) == 0:
                        logger_main.info(f"process-{process_num}: 数据清洗耗时{time.time()-time_st}, 无有效数据，跳过本次运算")      
                        continue
                    else:
                        logger_main.info(f"process-{process_num}: {pack_code}, time_type:{df_data.loc[0, 'time']} ~ {df_data.iloc[-1]['time']}, 数据清洗完成耗时{time.time()-time_st}")
                except Exception as e:
                    logger_main.error(f"process-{process_num}:数据清洗出错")
                    logger_main.error(f"process-{process_num}:{e}")
                    logger_main.error(f"process-{process_num}:{traceback.format_exc()}")
                    continue
                
                # mysql数据读取
                try:
                    time_st = time.time()
                    logger_main.info(f'process-{process_num}开始读取mysql故障数据')
                    if len(sn_list) == 1:
                        sn_tuple = f"('{sn_list[0]}')"
                    else:
                        sn_tuple = tuple(sn_list)
                    sql = "select * from algo_all_fault_info_ing where  sn in {}".format(sn_tuple) #fault_code='{}' or fault_code='{}') and 'C599','C590', 
                    df_diag_ram = pd.read_sql(sql, mysql_algo_conn)
                    sql = "select * from algo_ailipltd_result where  sn in {}".format(sn_tuple) #fault_code='{}' or fault_code='{}') and 'C599','C590', 
                    Li_pltd_his = pd.read_sql(sql, mysql_algo_conn)                
                    
                    logger_main.info(f'process-{process_num}读取mysql耗时{time.time()-time_st}')
                except Exception as e:
                    logger_main.error(f"process-{process_num}:读取redis出错")
                    logger_main.error(f"process-{process_num}:{e}")
                    logger_main.error(f"process-{process_num}:{traceback.format_exc()}")
                    continue

                #算法1_SOC诊断调用   
                # try:
                #     time_st = time.time()
                #     loggers['socdiag'].info(f'开始执行算法{pack_code}, time:{start_time}~{end_time},\n sn_list:{sn_list}')
                #     period = 24*60 #算法周期min
                #     soc_diag = SocDiag(cell_type, df_algo_pack_param, df_algo_adjustable_param, df_algo_param, end_time, period, pack_code, df_snlist, df_data)
                #     df_res_new_C109, df_res_end_C109= soc_diag.soc_block(df_diag_ram)
                #     df_res_end_C107 = soc_diag.soc_jump()

                #     df_res_new_soc = df_res_new_C109
                #     df_res_end_soc = pd.concat([df_res_end_C107, df_res_end_C109])

                #     loggers['socdiag'].info(f'算法运行完成{pack_code}，算法耗时{time.time()-time_st}')
                # except Exception as e:
                #     loggers['socdiag'].error('算法运行出错')
                #     loggers['socdiag'].error(str(e))
                #     loggers['socdiag'].error(traceback.format_exc())
                #     df_res_end_soc=pd.DataFrame()
                #     df_res_new_soc=pd.DataFrame()
                # 算法2_低电量调用   
                # try:
                #     time_st = time.time()
                #     loggers['low_soc_diag'].info(f'开始执行算法{pack_code}, time:{start_time}~{end_time},\n sn_list:{sn_list}')
                #     low_soc_warning = Low_soc_alarm(df_data,cellvolt_name)
                #     df_res_new_lw_soc, df_res_update_lw_soc,df_res_end_lw_soc= low_soc_warning.diag(df_algo_pack_param,df_algo_param,df_algo_adjustable_param,df_data,df_table,df_diag_ram,df_snlist)

                #     loggers['low_soc_diag'].info(f'算法运行完成{pack_code}，算法耗时{time.time()-time_st}')
                # except Exception as e:
                #     loggers['low_soc_diag'].error('算法运行出错')
                #     loggers['low_soc_diag'].error(str(e))
                #     loggers['low_soc_diag'].error(traceback.format_exc())
                #     df_res_new_lw_soc=pd.DataFrame()
                #     df_res_update_lw_soc=pd.DataFrame()
                #     df_res_end_lw_soc=pd.DataFrame()
                # 算法3_SOR计算调用  
                # try:
                #     time_st = time.time()
                #     loggers['Sor_Diag'].info(f'开始执行算法{pack_code}, time:{start_time}~{end_time},\n sn_list:{sn_list}')
                #     Diagsor_temp = sor_est(df_data, df_algo_pack_param)#计算内阻
                #     df_sor_add = Diagsor_temp.sor_cal()
                    
                #     loggers['Sor_Diag'].info(f'算法运行完成{pack_code}，算法耗时{time.time()-time_st}')
                # except Exception as e:
                #     loggers['Sor_Diag'].error('算法运行出错')
                #     loggers['Sor_Diag'].error(str(e))
                #     loggers['Sor_Diag'].error(traceback.format_exc()) 
                #     df_sor_add=pd.DataFrame()
                
                #算法5_日数据分段
                try:
                    loggers['Data_Split'].info(f'开始执行算法{pack_code}, time:{start_time}~{end_time},\n sn_list:{sn_list}')
                    df_merge=ds.data_status(df_data_32h,c_soc_dif_p=0.05,s_soc_dif_p=0,c_order_delta=1200,s_order_delta=300)
                   ##基于各个状态码，进行分段，分段函数
                    df_drive,df_charge,df_stand,df_data_split_rlt=dt.split(df_merge,celltemp_name,drive_interval_time_min=1200,charge_interval_time_min=1200,stand_interval_time_min=1200,single_num_min=3,drive_sts=3,charge_sts=[21,22],stand_sts=0)                    
                    loggers['Data_Split'].info(f'算法运行完成{pack_code}，算法耗时{time.time()-time_st}')
                except Exception as e:
                    loggers['Data_Split'].error('算法运行出错')
                    loggers['Data_Split'].error(str(e))
                    loggers['Data_Split'].error(traceback.format_exc()) 
                #结果写入mysql
                # try:
                #     df_res_new = pd.concat([df_res_new_soc, df_res_new_lw_soc]) #, res1
                #     df_res_update=df_res_update_lw_soc#pd.concat([df_res_update_lw_soc,df_res_update_crnt, df_res_update_temp]) #, res1
                #     df_res_end = pd.concat([df_res_end_soc,df_res_end_lw_soc]) #, res2
                #     df_res_new.reset_index(drop=True, inplace=True)
                #     df_res_update.reset_index(drop=True, inplace=True)
                #     df_res_end.reset_index(drop=True, inplace=True) 
                #     time_st = time.time()
                #     session = mysql_algo_Session()
                #     if not df_res_new.empty:
                #         df_res_new['date_info'] = df_res_new['start_time']
                #         df_res_new['create_time'] = datetime.now()
                #         df_res_new['create_by'] = 'algo'
                #         df_res_new['is_delete'] = 0
                #         df_res_new.to_sql("algo_all_fault_info_ing", con=mysql_algo_conn, if_exists="append", index=False)
                #         logger_main.info(f'process-{process_num}新增未结束故障入库{pack_code}完成')
                    
                #     if not df_res_end.empty:
                #         df_res_end=df_res_end.where(pd.notnull(df_res_end),None)
                #         df_res_end=df_res_end.fillna(0)
                #         for index in df_res_end.index:
                #             df_t = df_res_end.loc[index:index]
                #             sql = 'delete from algo_all_fault_info_ing where start_time=:start_time and fault_code=:fault_code and sn=:sn'
                #             params = {'start_time': df_t['start_time'].values[0],
                #                         'fault_code': df_t['fault_code'].values[0], 'sn': df_t['sn'].values[0]}
                #             session.execute(sql, params=params)

                #             sql = 'insert into algo_all_fault_info_done (date_info, start_time, end_time, sn, imei, model, fault_level, fault_code, fault_info,\
                #             fault_reason, fault_advice, fault_location, device_status,odo, create_time, create_by,update_time, update_by, is_delete,comment) values \
                #                 (:date_info, :start_time, :end_time, :sn, :imei, :model,:fault_level, :fault_code, :fault_info,\
                #             :fault_reason, :fault_advice, :fault_location, :device_status, :odo, :create_time, :create_by, :update_time,:update_by, :is_delete , :comment)'
                #             params = {'date_info': datetime.now(),
                #                         'start_time': df_t['start_time'].values[0],
                #                         'end_time': df_t['end_time'].values[0],
                #                         'sn': df_t['sn'].values[0],
                #                         'imei': df_t['imei'].values[0],
                #                         'model' :pack_code,
                #                         'fault_level': df_t['fault_level'].values[0],
                #                         'fault_code': df_t['fault_code'].values[0],
                #                         'fault_info': df_t['fault_info'].values[0],
                #                         'fault_reason': df_t['fault_reason'].values[0],
                #                         'fault_advice': df_t['fault_advice'].values[0],
                #                         'fault_location': df_t['fault_location'].values[0],
                #                         'device_status': df_t['device_status'].values[0],
                #                         'odo': df_t['odo'].values[0], 
                #                         'create_time': datetime.now(),
                #                         'create_by': 'algo',
                #                         'update_time': datetime.now(),
                #                         'update_by': None,
                #                         'is_delete': 0,
                #                         'comment': None}
                #             session.execute(sql, params=params)
                #             session.commit()
                #         logger_main.info(f'process-{process_num}结束故障入库{pack_code}完成')
                #     if not df_res_update.empty:
                #         df_res_update=df_res_update.where(pd.notnull(df_res_update),None)
                #         df_res_update=df_res_update.fillna(0)                    
                #         for index in df_res_update.index:
                #             df_t = df_res_update.loc[index:index]
                #             try:
                #                 # 更新数据
                #                 with mysql_algo_Session() as session:
                #                     session.execute(update(AlgoAllFaultInfoIng).where(
                #                         and_((AlgoAllFaultInfoIng.start_time == df_t['start_time'].values[0]), 
                #                                 (AlgoAllFaultInfoIng.fault_code == df_t['fault_code'].values[0]),
                #                                 (AlgoAllFaultInfoIng.sn == df_t['sn'].values[0]))).
                #                                     values(fault_level=df_t['fault_level'].values[0],
                #                                             comment=df_t['comment'].values[0]))
                #                     session.commit()
                #             except Exception as e:
                #                 logger_main.error(f"process-{process_num}:结果入库出错")
                #                 logger_main.error(f"process-{process_num}:{e}")
                #                 logger_main.error(f"process-{process_num}:{traceback.format_exc()}")
                #             finally:
                #                 session.close()
                #         logger_main.info(f"process-{process_num}: 更新入库完成")
                #     else:
                #         logger_main.info(f"process-{process_num}: 无更新故障")                                            
                #     # if not df_sor_add.empty:

                #     #     time_record = time.time()
                #     #     df_sor_rlt = df_sor_add#df_sor_rlt.append()
                #     #     df_sor_rlt.reset_index(drop = True, inplace = True)
                #     #     df_sor_rlt.to_sql("algo_mid_sorout",con=mysql_algo_conn, if_exists="append",index=False)
                #     #     write_mysql_time = write_mysql_time + time.time()-time_record
                #     #     logger_main.info(f'process-{process_num}新增未结束故障入库{pack_code}完成')
                #     # logger_main.info(f"process-{process_num}: 结果入库耗时：{time.time()-time_st}")

                # except Exception as e:
                #     logger_main.error(f"process-{process_num}:结果入库出错")
                #     logger_main.error(f"process-{process_num}:{e}")
                #     logger_main.error(f"process-{process_num}:{traceback.format_exc()}")

                # try:
                #     if not df_data_split_rlt.empty:

                #         time_record = time.time()
                #         df_data_split_rlt.reset_index(drop = True, inplace = True)
                #         df_data_split_rlt.to_sql("algo_charge_info",con=mysql_algo_conn, if_exists="append",index=False)
                #         # write_mysql_time = write_mysql_time + time.time()-time_record
                #         logger_main.info(f'process-{process_num}新增未结束故障入库{pack_code}完成')
                #     logger_main.info(f"process-{process_num}: 结果入库耗时：{time.time()-time_st}")                    
                    
                # except Exception as e:
                #     logger_main.error(f"process-{process_num}:数据分段结果入库出错")
                #     logger_main.error(f"process-{process_num}:{e}")
                #     logger_main.error(f"process-{process_num}:{traceback.format_exc()}")   
                #算法4_析锂计算调用  
                try:
                    time_st = time.time()
                    loggers['Li_Plted'].info(f'开始执行算法{pack_code}, time:{start_time}~{end_time},\n sn_list:{sn_list}')
                    pkl_path='/home/liuzhongxiao/project/zlwl-algos/USER/liuzhongxiao/li_plted/V1_0_0/scaler.pkl'
                    md_path='/home/liuzhongxiao/project/zlwl-algos/USER/liuzhongxiao/li_plted/V1_0_0/model.h5'
                    scaler=pickle.load(open(pkl_path,'rb'))     #读取标准化参数
                    model=load_model(md_path)       #读取模型参数
                    data_set=df_data.groupby('sn').apply(prediction,scaler,model,cellvolt_name)
                    if not data_set.empty:
                        df_result=data_set.groupby('sn').apply(out_final,Li_pltd_his,df_algo_param,df_algo_pack_param)
                        # if not df_result.empty:
                        #     df_res_lipltdchange,df_res_lipltd_new = zip(*df_result.groupby('sn').apply(alarme_final,Li_pltd_his,df_algo_pack_param))
                    
                    
                    loggers['Li_Plted'].info(f'算法运行完成{pack_code}，算法耗时{time.time()-time_st}')
                except Exception as e:
                    loggers['Li_Plted'].error('算法运行出错')
                    loggers['Li_Plted'].error(str(e))
                    loggers['Li_Plted'].error(traceback.format_exc()) 
                    df_res_lipltdchange=pd.DataFrame()
                    df_res_lipltd_new=pd.DataFrame()    
        except Exception as e:
            logger_main.error(f'process-{process_num}: {e}')
            logger_main.error(f'process-{process_num}: {traceback.format_exc()}')

if __name__ == '__main__':
   while(True):
        try:
            # 配置量
            
            cur_env = 'dev' # 设置运行环境
            app_path = "/home/liuzhongxiao/project/zlwl-algos/" # 设置app绝对路径
            log_base_path = f"{os.path.dirname(os.path.abspath(__file__))}/log" # 设置日志路径

            app_name = "task_day_1" # 应用名
            
            sysUtils = SysUtils(cur_env, app_path)
            logger_main = sysUtils.get_logger(app_name, log_base_path)
            logger_main.info(f"本次主进程号: {os.getpid()}")
            # 读取配置文件 (该部分请不要修改)
            processes = int(sysUtils.env_params.get("PROCESS_NUM_PER_NODE", '1')) # 默认为1个进程
            pool = Pool(processes = int(processes))
            logger_main.info("开始分配子进程")
            for i in range(int(processes)):
                pool.apply_async(main, (i, ))
            pool.close()
            logger_main.info("进程分配结束，堵塞主进程")
            pool.join()
        except Exception as e:
            print(str(e))
            print(traceback.format_exc())
            logger_main.error(str(e))
            logger_main.error(traceback.format_exc())
        finally:
            handlers = logger_main.handlers.copy()
            for h in handlers:
                logger_main.removeHandler(h)
            pool.terminate()