from LIB.MIDDLE.FaultDetection.V1_0_2.aelstm import * import pymysql import datetime import pandas as pd from LIB.BACKEND import DBManager dbManager = DBManager.DBManager() from sqlalchemy import create_engine from urllib import parse import datetime, time from apscheduler.schedulers.blocking import BlockingScheduler import traceback import pickle from keras.models import load_model import logging import logging.handlers import os import re #...................................故障检测函数...................................................................................................................... def diag_cal(): global SNnums global scaler_dict, scaler2_dict, model_dict, model2_dict start=time.time() now_time=datetime.datetime.now() start_time=now_time-datetime.timedelta(hours=3) 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='safety_platform' user='qx_read' password='Qx@123456' #读取结果库数据...................................................... param='product_id,start_time,end_time,diff_min,SOC,AnoScoreV_sum_max,AnoScoreV_max_max,AnoScoreT_sum_max,AnoScoreT_max_max' tablename='fault_detection' mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db) cursor = mysql.cursor() sql = "select {} from {} where end_time='0000-00-00 00:00:00'".format(param,tablename) cursor.execute(sql) res = cursor.fetchall() df_diag_ram= pd.DataFrame(res,columns=param.split(',')) db_res_engine = create_engine( "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format( user, parse.quote_plus(password), host, port, db )) #调用主函数................................................................................................................................................................ for sn in SNnums: try: group=sn[:5] if group=='UD020': group='MGMCL' df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms']) data_bms = df_data['bms'] data_bms['sn']=sn if len(data_bms)>0: logger.info("SN: {} 数据开始预处理".format(sn)) data_stand=data_groups(data_bms,sn,start_time,end_time) df_stand=split(data_stand) res=pd.DataFrame() if len(df_stand)>0: #读取训练产出的缩放指标：均值&方差 logger.info("SN: {} 数据开始模型预测".format(sn)) scaler = scaler_dict[group] scaler2 = scaler2_dict[group] #读取训练产出的模型状态空间：电压模型&温度模型 model = model_dict[group] model2 = model2_dict[group] res=prediction(df_stand,scaler,scaler2,model,model2) if len(res)>0: df_res2,diff=threshold(res,group,end_time) df_diag_ram_sn=df_diag_ram[df_diag_ram['product_id']==sn] if not df_diag_ram_sn.empty: #该sn相关结果非空 new_res,update_res=arrange(df_res2,df_diag_ram_sn,start_time,diff) if len(update_res)>0: cursor.execute("DELETE FROM fault_detection WHERE end_time = '0000-00-00 00:00:00' and product_id='{}'".format(sn)) mysql.commit() update_res.to_sql("fault_detection",con=db_res_engine, if_exists="append",index=False) #新增结果存入结果库................................................................ if len(new_res)>0: new_res.to_sql("fault_detection",con=db_res_engine, if_exists="append",index=False) else: df_res2.to_sql("fault_detection",con=db_res_engine, if_exists="append",index=False) # end=time.time() # print(end-start) except Exception as e: logger.error(str(e)) logger.error(traceback.format_exc()) cursor.close() mysql.close() #...............................................主函数起定时作用....................................................................................................................... if __name__ == "__main__": # 日志 log_path = 'log/' if not os.path.exists(log_path): os.makedirs(log_path) logger = logging.getLogger("main") logger.setLevel(logging.DEBUG) # 根据日期滚动(每天产生1个文件) fh = logging.handlers.TimedRotatingFileHandler(filename='{}/main_info.log'.format(log_path), when="D", interval=1, backupCount=30, encoding="utf-8") formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s") fh.suffix = "%Y-%m-%d_%H-%M-%S" fh.extMatch = re.compile(r"^\d{4}-\d{2}-\d{2}_\d{2}-\d{2}-\d{2}") fh.setFormatter(formatter) fh.setLevel(logging.INFO) logger.addHandler(fh) fh = logging.handlers.TimedRotatingFileHandler(filename='{}/main_error.log'.format(log_path), when="D", interval=1, backupCount=30, encoding="utf-8") formatter = logging.Formatter("%(asctime)s - %(name)s-%(levelname)s %(message)s") fh.suffix = "%Y-%m-%d_%H-%M-%S" fh.extMatch = re.compile(r"^\d{4}-\d{2}-\d{2}_\d{2}-\d{2}-\d{2}") fh.setFormatter(formatter) fh.setLevel(logging.ERROR) logger.addHandler(fh) logger.info("pid is {}".format(os.getpid())) # # 更新sn列表 host='rm-bp10j10qy42bzy0q7.mysql.rds.aliyuncs.com' port=3306 db='qixiang_oss' user='qixiang_oss' password='Qixiang2021' conn = pymysql.connect(host=host, port=port, user=user, password=password, database=db) cursor = conn.cursor() cursor.execute("select sn, imei, add_time from app_device where status in (1,2,3)") res = cursor.fetchall() df_sn = pd.DataFrame(res, columns=['sn', 'imei', 'add_time']) df_sn = df_sn.reset_index(drop=True) conn.close(); SNnums = list(df_sn['sn']) scaler_list=[] scaler2_list=[] model_list=[] model2_list=[] for group in ['MGMLX','PK504','PK502','PK500','MGMCL']: scaler = pickle.load(open('D:/deploy/python_platform/data_analyze_platform/LIB/MIDDLE/FaultDetection/V1_0_2/train_out/scalerV_'+group+'_10.pkl', 'rb')) scaler2 = pickle.load(open('D:/deploy/python_platform/data_analyze_platform/LIB/MIDDLE/FaultDetection/V1_0_2/train_out/scalerT_'+group+'_10.pkl', 'rb')) model = load_model('D:/deploy/python_platform/data_analyze_platform/LIB/MIDDLE/FaultDetection/V1_0_2/train_out/modelV_'+group+'_10.h5') model2 = load_model('D:/deploy/python_platform/data_analyze_platform/LIB/MIDDLE/FaultDetection/V1_0_2/train_out/modelT_'+group+'_10.h5') scaler_list.append(scaler) scaler2_list.append(scaler2) model_list.append(model) model2_list.append(model2) scaler_dict={'MGMLX':scaler_list[0],'PK504':scaler_list[1],'PK502':scaler_list[2],'PK500':scaler_list[3],'MGMCL':scaler_list[4]} scaler2_dict={'MGMLX':scaler2_list[0],'PK504':scaler2_list[1],'PK502':scaler2_list[2],'PK500':scaler2_list[3],'MGMCL':scaler2_list[4]} model_dict={'MGMLX':model_list[0],'PK504':model_list[1],'PK502':model_list[2],'PK500':model_list[3],'MGMCL':model_list[4]} model2_dict={'MGMLX':model2_list[0],'PK504':model2_list[1],'PK502':model2_list[2],'PK500':model2_list[3],'MGMCL':model2_list[4]} logger.info("模型加载完成") diag_cal() #定时任务....................................................................................................................................................................... scheduler = BlockingScheduler() scheduler.add_job(diag_cal, 'interval', hours=3) try: scheduler.start() except Exception as e: scheduler.shutdown() logger.error(str(e)) logger.error(traceback.format_exc())