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+from LIB.MIDDLE.FaultDetection.V1_0_2.aelstm import *
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+import pymysql
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+import datetime
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+import pandas as pd
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+from LIB.BACKEND import DBManager
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+dbManager = DBManager.DBManager()
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+from sqlalchemy import create_engine
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+from urllib import parse
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+import datetime, time
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+from apscheduler.schedulers.blocking import BlockingScheduler
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+import traceback
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+import pickle
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+from keras.models import load_model
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+import logging
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+import logging.handlers
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+import os
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+import re
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+
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+
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+#...................................故障检测函数......................................................................................................................
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+def diag_cal():
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+ global SNnums
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+ global scaler_dict, scaler2_dict, model_dict, model2_dict
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+
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+ start=time.time()
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+ now_time=datetime.datetime.now()
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+ start_time=now_time-datetime.timedelta(hours=3)
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+ start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
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+ end_time=now_time.strftime('%Y-%m-%d %H:%M:%S')
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+
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+ #数据库配置
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+ host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
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+ port=3306
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+ db='safety_platform'
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+ user='qx_read'
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+ password='Qx@123456'
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+
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+ #读取结果库数据......................................................
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+ param='product_id,start_time,end_time,diff_min,SOC,AnoScoreV_sum_max,AnoScoreV_max_max,AnoScoreT_sum_max,AnoScoreT_max_max'
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+ tablename='fault_detection'
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+ mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db)
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+ cursor = mysql.cursor()
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+ sql = "select {} from {} where end_time='0000-00-00 00:00:00'".format(param,tablename)
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+ cursor.execute(sql)
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+ res = cursor.fetchall()
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+ df_diag_ram= pd.DataFrame(res,columns=param.split(','))
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+
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+
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+ db_res_engine = create_engine(
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+ "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
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+ user, parse.quote_plus(password), host, port, db
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+ ))
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+
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+
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+ #调用主函数................................................................................................................................................................
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+ for sn in SNnums:
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+ try:
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+ group=sn[:5]
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+ df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms'])
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+ data_bms = df_data['bms']
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+ data_bms['sn']=sn
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+ if len(data_bms)>0:
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+ logger.info("SN: {} 数据开始预处理".format(sn))
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+ data_stand=data_groups(data_bms,sn,start_time,end_time)
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+ df_stand=split(data_stand)
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+ res=pd.DataFrame()
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+ if len(df_stand)>0:
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+ #读取训练产出的缩放指标：均值&方差
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+ logger.info("SN: {} 数据开始模型预测".format(sn))
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+ scaler = scaler_dict[group]
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+ scaler2 = scaler2_dict[group]
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+ #读取训练产出的模型状态空间：电压模型&温度模型
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+ model = model_dict[group]
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+ model2 = model2_dict[group]
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+ res=prediction(df_stand,scaler,scaler2,model,model2)
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+ if len(res)>0:
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+ df_res2,diff=threshold(res,group,end_time)
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+ df_diag_ram_sn=df_diag_ram[df_diag_ram['product_id']==sn]
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+ if not df_diag_ram_sn.empty: #该sn相关结果非空
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+ new_res,update_res=arrange(df_res2,df_diag_ram_sn,start_time,diff)
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+ if len(update_res)>0:
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+ cursor.execute("DELETE FROM fault_detection WHERE end_time = '0000-00-00 00:00:00' and product_id='{}'".format(sn))
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+ mysql.commit()
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+ update_res.to_sql("fault_detection",con=db_res_engine, if_exists="append",index=False)
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+ #新增结果存入结果库................................................................
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+ if len(new_res)>0:
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+ new_res.to_sql("fault_detection",con=db_res_engine, if_exists="append",index=False)
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+ else:
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+ df_res2.to_sql("fault_detection",con=db_res_engine, if_exists="append",index=False)
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+
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+ # end=time.time()
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+ # print(end-start)
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+
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+ except Exception as e:
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+ logger.error(str(e))
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+ logger.error(traceback.format_exc())
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+
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+ cursor.close()
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+ mysql.close()
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+
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+#...............................................主函数起定时作用.......................................................................................................................
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+if __name__ == "__main__":
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+
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+ # 日志
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+ log_path = 'log/'
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+ if not os.path.exists(log_path):
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+ os.makedirs(log_path)
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+ logger = logging.getLogger("main")
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+ logger.setLevel(logging.DEBUG)
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+
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+ # 根据日期滚动(每天产生1个文件)
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+ fh = logging.handlers.TimedRotatingFileHandler(filename='{}/main_info.log'.format(log_path), when="D", interval=1, backupCount=30,
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+ encoding="utf-8")
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+ formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
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+ fh.suffix = "%Y-%m-%d_%H-%M-%S"
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+ fh.extMatch = re.compile(r"^\d{4}-\d{2}-\d{2}_\d{2}-\d{2}-\d{2}")
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+ fh.setFormatter(formatter)
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+ fh.setLevel(logging.INFO)
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+ logger.addHandler(fh)
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+
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+ fh = logging.handlers.TimedRotatingFileHandler(filename='{}/main_error.log'.format(log_path), when="D", interval=1, backupCount=30,
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+ encoding="utf-8")
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+ formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s")
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+ fh.suffix = "%Y-%m-%d_%H-%M-%S"
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+ fh.extMatch = re.compile(r"^\d{4}-\d{2}-\d{2}_\d{2}-\d{2}-\d{2}")
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+ fh.setFormatter(formatter)
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+ fh.setLevel(logging.ERROR)
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+ logger.addHandler(fh)
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+
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+ logger.info("pid is {}".format(os.getpid()))
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+
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+ # # 更新sn列表
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+ host='rm-bp10j10qy42bzy0q7.mysql.rds.aliyuncs.com'
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+ port=3306
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+ db='qixiang_oss'
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+ user='qixiang_oss'
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+ password='Qixiang2021'
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+ conn = pymysql.connect(host=host, port=port, user=user, password=password, database=db)
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+ cursor = conn.cursor()
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+ cursor.execute("select sn, imei, add_time from app_device where status in (1,2,3)")
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+ res = cursor.fetchall()
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+ df_sn = pd.DataFrame(res, columns=['sn', 'imei', 'add_time'])
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+ df_sn = df_sn.reset_index(drop=True)
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+ conn.close();
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+
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+ SNnums = list(df_sn['sn'])
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+
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+ scaler_list=[]
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+ scaler2_list=[]
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+ model_list=[]
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+ model2_list=[]
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+ for group in ['MGMLX','PK504','PK502','PK500','MGMCL']:
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+ scaler = pickle.load(open('D:/deploy/python_platform/data_analyze_platform/LIB/MIDDLE/FaultDetection/V1_0_2/train_out/scalerV_'+group+'_10.pkl', 'rb'))
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+ scaler2 = pickle.load(open('D:/deploy/python_platform/data_analyze_platform/LIB/MIDDLE/FaultDetection/V1_0_2/train_out/scalerT_'+group+'_10.pkl', 'rb'))
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+ model = load_model('D:/deploy/python_platform/data_analyze_platform/LIB/MIDDLE/FaultDetection/V1_0_2/train_out/modelV_'+group+'_10.h5')
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+ model2 = load_model('D:/deploy/python_platform/data_analyze_platform/LIB/MIDDLE/FaultDetection/V1_0_2/train_out/modelT_'+group+'_10.h5')
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+ scaler_list.append(scaler)
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+ scaler2_list.append(scaler2)
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+ model_list.append(model)
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+ model2_list.append(model2)
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+ scaler_dict={'MGMLX':scaler_list[0],'PK504':scaler_list[1],'PK502':scaler_list[2],'PK500':scaler_list[3],'MGMCL':scaler_list[4]}
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+ scaler2_dict={'MGMLX':scaler2_list[0],'PK504':scaler2_list[1],'PK502':scaler2_list[2],'PK500':scaler2_list[3],'MGMCL':scaler2_list[4]}
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+ model_dict={'MGMLX':model_list[0],'PK504':model_list[1],'PK502':model_list[2],'PK500':model_list[3],'MGMCL':model_list[4]}
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+ model2_dict={'MGMLX':model2_list[0],'PK504':model2_list[1],'PK502':model2_list[2],'PK500':model2_list[3],'MGMCL':model2_list[4]}
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+ logger.info("模型加载完成")
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+
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+ diag_cal()
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+ #定时任务.......................................................................................................................................................................
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+ scheduler = BlockingScheduler()
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+ scheduler.add_job(diag_cal, 'interval', hours=3)
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+
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+ try:
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+ scheduler.start()
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+ except Exception as e:
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+ scheduler.shutdown()
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+ logger.error(str(e))
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+ logger.error(traceback.format_exc())
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