|
|
@@ -1,178 +0,0 @@
|
|
|
-
|
|
|
-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]
|
|
|
- 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('LIB/MIDDLE/FaultDetection/V1_0_2/train_out/scalerV_'+group+'_10.pkl', 'rb'))
|
|
|
- scaler2 = pickle.load(open('LIB/MIDDLE/FaultDetection/V1_0_2/train_out/scalerT_'+group+'_10.pkl', 'rb'))
|
|
|
- model = load_model('LIB/MIDDLE/FaultDetection/V1_0_2/train_out/modelV_'+group+'_10.h5')
|
|
|
- model2 = load_model('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())
|
