Anomaly Detection

LIB/MIDDLE/Anomaly_Detection/V1_0_0/main_detection.py

@@ -2,12 +2,16 @@
 #预测及异常预警
 
 from LIB.BACKEND import DBManager
+
 dbManager = DBManager.DBManager()
-from LIB.MIDDLE.CellStateEstimation.Common import log
+import datetime
+
+import joblib
 import pandas as pd
+import pymysql
+from LIB.MIDDLE.CellStateEstimation.Common import log
+
 from anomalyPCA import *
-import joblib
-import datetime
 
 dataSOH = pd.read_excel('sn-20210903.xlsx',sheet_name='sn-20210903')
 fileNames = dataSOH['sn']
@@ -23,13 +27,34 @@ start_time=str(start_time)
 mylog=log.Mylog('log.txt','error')
 mylog.logcfg()
 
+#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='C493' 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()
+
 anomalies=pd.DataFrame()
+df_res=pd.DataFrame(columns=['start_time','end_time','product_id','code','level','info','advice'])
 for k in range(l): 
     try: 
         sn = fileNames[k]
+        df_diag_ram_sn=df_diag_ram[df_diag_ram['product_id']==sn]
         df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms'])
         data_test = df_data['bms']
-
+        data_test=data_test[data_test['SOC[%]']>20]
         if len(data_test)>0:
             pca1 = joblib.load('pca1_'+sn+'.m')  
             pca2 = joblib.load('pca2_'+sn+'.m') 
@@ -45,8 +70,31 @@ for k in range(l):
                     outliers['sn']=sn
                     anomalies=anomalies.append(outliers)
                     anomalies.to_csv('anomalies.csv',encoding='gbk')
+                    if df_diag_ram_sn.empty: 
+                        product_id=sn
+                        start_time=outliers.loc[0,'时间']
+                        if outliers.loc[-1,'时间'] == pred1.loc[-1,'时间']:
+                            end_time='0000-00-00 00:00:00'
+                        else:
+                            end_time=outliers.loc[-1,'时间']
+                        code='C493'
+                        level=4
+                        info='热失控预警'
+                        advice='建议返厂维修'
+                        df_res=df_res.append([start_time, end_time,product_id, code, level, info,advice])    
+                        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_res.iloc[-1]))+'\n')    
+                    else:
+                        if outliers.loc[-1,'时间'] == pred1.loc[-1,'时间']:
+                            end_time='0000-00-00 00:00:00'
+                        else:
+                            end_time=outliers.loc[-1,'时间']
+                            df_diag_ram_sn['end_time']=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')
+                        
            
     except Exception as e:
         print(repr(e))
         mylog.logopt(sn,e)
-        pass 
+        pass