Merge branch 'dev' of http://git.fast-fun.cn:92/lmstack/data_analyze_platform into dev

LIB/MIDDLE/SaftyCenter/diagfault/SC_SamplingSafty.py

@@ -0,0 +1,84 @@
+import sys
+import numpy as np
+import pandas as pd
+import string
+import os
+from pandas import Series
+from matplotlib import pyplot as plt
+from pandas.core.frame import DataFrame
+from pandas.core.indexes.base import Index
+from pymysql import NULL
+from LIB.BACKEND import DBManager
+import datetime
+import time
+import string
+import re
+
+
+
+class SamplingSafty:
+    def __init__(self):
+        pass
+    def main(sn,param,bms_info,df_Diag_Ram_in):
+        df_Diag_Ram_Update_inside=DataFrame(columns=['start_time', 'end_time', 'product_id', 'code', 'level', 'info','advice'])
+        df_Diag_Ram_Update_inside=df_Diag_Ram_Update_inside.append(df_Diag_Ram_in)
+        global QuitErrCount 
+        VolStarkCount=[0 for i in range(param.CellVoltNums)]
+        VolCount=0
+        QuitErrCount=[0 for i in range(param.FaultCount)]
+        df_Diag_Ram=DataFrame(columns=['start_time', 'end_time', 'product_id', 'code', 'level', 'info','advice'])
+        st_tp=[]
+        #--------------该电池的所有当前故障-------------
+
+        VoltageNum=['单体电压'+str(i) for i in range(1,param.CellVoltNums+1)]#单体电压替换cellvolt_
+        CellVoltage=bms_info[VoltageNum]/1000
+        CellMaxVoltage=CellVoltage.max(axis=1)
+        CellMinVoltage=CellVoltage.min(axis=1)
+        InVMaxBatNo=CellVoltage[CellVoltage>=param.CellOVlmt].dropna(axis=0,how='all',inplace=False)
+        InVMinBatNo=CellVoltage[CellVoltage<=param.CellUVlmt].dropna(axis=0,how='all',inplace=False)
+        #date替换为'时间戳'
+        if len(InVMaxBatNo):
+            indexValue=InVMaxBatNo.index.values
+            df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[0],'时间戳'],'0000-00-00 00:00:00',sn,50,3,'单体电压大于{:.2f}V,采样无效'.format(param.CellOVlmt),'建议返厂维修']
+            for i in range(1,len(indexValue)):
+                if indexValue[i]- indexValue[i-1]>10:
+                    df_Diag_Ram.loc[len(df_Diag_Ram)-1,'end_time']=bms_info.loc[indexValue[i-1],'时间戳']
+                    df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[i],'时间戳'],'0000-00-00 00:00:00',sn,50,3,'单体电压大于{:.2f}V,采样无效'.format(param.CellOVlmt),'建议返厂维修']                         
+        if len(InVMinBatNo):
+            indexValue=InVMinBatNo.index.values
+            df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[0],'时间戳'],'0000-00-00 00:00:00',sn,50,3,'电压小于{:.2f}V,采样无效'.format(param.CellUVlmt),'建议返厂维修']
+            for i in range(1,len(indexValue)):
+                if indexValue[i]- indexValue[i-1]>10:
+                    df_Diag_Ram.loc[len(df_Diag_Ram)-1,'end_time']=bms_info.loc[indexValue[i-1],'时间戳']
+                    df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[i],'时间戳'],'0000-00-00 00:00:00',sn,50,3,'电压小于{:.2f}V,采样无效'.format(param.CellUVlmt),'建议返厂维修']           
+        MedianVol=CellVoltage.median(axis=1).tolist()
+        MediaVolGap=abs(CellVoltage.sub(MedianVol,axis=0))
+        OutlineVol=MediaVolGap[MediaVolGap>=param.AvgVolGap][CellMaxVoltage<param.CellOVlmt][CellMinVoltage>param.CellUVlmt][abs(bms_info['总电流[A]'])<1].dropna(axis=0,how='all',inplace=False)
+        #OutlineVol=OutlineVol[CellMaxVoltage<param.CellOVlmt][CellMinVoltage>param.CellUVlmt].dropna(axis=0,how='all',inplace=False)
+        
+        
+        if len(OutlineVol):
+            indexValue=OutlineVol.index.values
+            df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[0],'时间戳'],'0000-00-00 00:00:00',sn,50,3,sn,10,3,'电池电压发生偏移或发生断线','建议返厂维修']
+            for i in range(1,len(indexValue)):
+                if indexValue[i]- indexValue[i-1]>10:
+                    df_Diag_Ram.loc[len(df_Diag_Ram)-1,'end_time']=bms_info.loc[indexValue[i-1],'时间戳']
+                    df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[i],'时间戳'],'0000-00-00 00:00:00',sn,10,3,'电池电压发生偏移或发生断线','建议返厂维修'] 
+        CellTemp=bms_info['单体温度1']
+        InVMaxTempBatNo=CellTemp[CellTemp>=param.PackOTlmt].dropna(axis=0,how='all',inplace=False)
+        InVMinTempBatNo=CellTemp[CellTemp<=param.PackUTlmt].dropna(axis=0,how='all',inplace=False)
+        if len(InVMaxTempBatNo):
+            indexValue=InVMaxTempBatNo.index.values
+            df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[0],'时间戳'],'0000-00-00 00:00:00',sn,50,3,'电池温度大于{:.0f}摄氏度,采样无效'.format(param.PackOTlmt),'请立即确认状态']
+            for i in range(1,len(indexValue)):
+                if indexValue[i]- indexValue[i-1]>10:
+                    df_Diag_Ram.loc[len(df_Diag_Ram)-1,'end_time']=bms_info.loc[indexValue[i-1],'时间戳']
+                    df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[i],'时间戳'],'0000-00-00 00:00:00',sn,50,3,'电池温度大于{:.0f}摄氏度,采样无效'.format(param.PackOTlmt),'请立即确认状态'] 
+        if len(InVMinTempBatNo):
+            indexValue=InVMinTempBatNo.index.values
+            df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[0],'时间戳'],'0000-00-00 00:00:00',sn,50,3,'电池温度小于{:.0f}摄氏度,采样无效'.format(param.PackUTlmt),'建议返厂维修']
+            for i in range(1,len(indexValue)):
+                if indexValue[i]- indexValue[i-1]>10:
+                    df_Diag_Ram.loc[len(df_Diag_Ram)-1,'end_time']=bms_info.loc[indexValue[i-1],'时间戳']
+                    df_Diag_Ram.loc[len(df_Diag_Ram)]=[bms_info.loc[indexValue[i],'时间戳'],'0000-00-00 00:00:00',sn,50,3,'电池温度小于{:.0f}摄氏度,采样无效'.format(param.PackUTlmt),'建议返厂维修'] 
+        return df_Diag_Ram

LIB/MIDDLE/SaftyCenter/diagfault/main.py

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+import CBMSBatDiag
+from SC_SamplingSafty import SamplingSafty
+import datetime
+import pandas as pd
+from LIB.BACKEND import DBManager, Log
+from sqlalchemy import create_engine
+import time, datetime
+from apscheduler.schedulers.blocking import BlockingScheduler
+from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import DBDownload as DBDownload
+from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import log
+from pandas.core.frame import DataFrame
+import datacompy
+from LIB.MIDDLE.SaftyCenter.Common import FeiShuData
+from LIB.MIDDLE.SaftyCenter.Common import QX_BatteryParam
+from LIB.MIDDLE.SaftyCenter.Common import DBDownload as DBDw
+
+#...................................电池包电芯安全诊断函数......................................................................................................................
+def diag_cal():
+    global SNnums
+    global df_Diag_Ram
+  
+    start=time.time()
+    end_time=datetime.datetime.now()
+    start_time=end_time-datetime.timedelta(seconds=300)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+    df_read_Yunw = FeiShuData.getFeiShuDATA()#运维表格数据
+    df_read_Yunw.rename(columns={'电池编码':'product_id'},inplace=True)
+    df_read_Yunw.rename(columns={'内容描述':'info'},inplace=True)
+    df_read_Yunw.rename(columns={'发生时间':'start_time'},inplace=True)
+    df_read_Yunw.rename(columns={'维修信息':'advice'},inplace=True)
+    for sn in SNnums:
+        print(sn)
+        if 'PK500' in sn:
+            celltype=1 #6040三元电芯
+        elif 'PK502' in sn:
+            celltype=2 #4840三元电芯
+        elif 'K504B' in sn:
+            celltype=99    #60ah林磷酸铁锂电芯
+        elif 'MGMLXN750' in sn:
+            celltype=3 #力信50ah三元电芯
+        elif 'MGMCLN750' or 'UD' in sn: 
+            celltype=4 #CATL 50ah三元电芯
+        else:
+            print('SN：{}，未找到对应电池类型！！！'.format(sn))
+            continue
+            # sys.exit()
+        param=QX_BatteryParam.BatteryInfo(celltype)   
+        #读取原始数据库数据........................................................................................................................................................
+        dbManager = DBManager.DBManager()
+        df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms'])
+        df_bms = df_data['bms']
+
+        #读取结果数据库数据........................................................................................................................................................
+        host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+        port=3306
+        db='qx_cas'
+        user='qx_read'
+        password='Qx@123456'
+        mode=1
+        tablename1='cellstateestimation_soh'
+        tablename2='cellstateestimation_uniform_socvoltdiff'       
+        #电池诊断................................................................................................................................................................
+        DBRead=DBDownload.DBDownload(host, port, db, user, password,mode)
+        with DBRead as DBRead:
+            df_soh=DBRead.getdata('time_st,sn,soh,cellsoh', tablename=tablename1, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+            df_uniform=DBRead.getdata('time,sn,cellsoc_diff,cellmin_num,cellmax_num', tablename=tablename2, sn=sn, timename='time', st=start_time, sp=end_time)
+
+        #电池诊断................................................................................................................................................................
+        CellFltInfo=DataFrame(columns=['start_time', 'end_time', 'product_id', 'code', 'level', 'info','advice'])
+        df_Diag_Ram = df_Diag_Ram.drop_duplicates(subset=['product_id','code','start_time','Batpos','info'],keep='first')#sn之外的故障
+        df_Diag_Ram_sn = df_Diag_Ram.loc[df_Diag_Ram['product_id']==sn]#历史故障
+        df_Diag_Ram_sn_else = pd.concat([df_Diag_Ram,df_Diag_Ram_sn,df_Diag_Ram_sn]).drop_duplicates(subset=['product_id','code','start_time','Batpos','info'],keep=False)#sn之外的故障
+        CellFltInfo = df_Diag_Ram_sn.drop('Batpos',axis=1)
+        df_Diag_Ram_add = pd.DataFrame()
+        df_Diag_Ram_Update_change = pd.DataFrame()
+        if not df_bms.empty:
+            df_Diag_Batdiag_update_xq=SamplingSafty.main(sn,param,df_bms,CellFltInfo)#学琦计算故障   
+            BatDiag=CBMSBatDiag.BatDiag(sn,celltype,df_bms, df_soh, df_uniform, CellFltInfo)#鹏飞计算
+            df_Diag_Batdiag_update=BatDiag.diag() 
+            df_Diag_Cal_Update_add = pd.concat([df_Diag_Batdiag_update_xq,df_Diag_Batdiag_update])#重新计算的该SN下的故障
+            df_Diag_Cal_Update_temp = df_Diag_Cal_Update_add.drop_duplicates(subset=['product_id','start_time','end_time','code','info'], keep='first', inplace=False, ignore_index=False)#去除相同故障
+            df_Diag_cal_early_unfix = pd.DataFrame()
+            df_Diag_Cal_finish = pd.DataFrame()
+            df_Diag_cal_early_fix = pd.DataFrame()
+            if not df_Diag_Cal_Update_temp.empty:
+                #------------------------------合并两者故障，并将同一sn号下的车辆故障放一起----------------------------------------------
+                df_Diag_Cal_Update = df_Diag_Cal_Update_temp#替换上一行
+                df_Diag_Cal_finish = df_Diag_Cal_Update.loc[df_Diag_Cal_Update['end_time'] != '0000-00-00 00:00:00']
+                df_Diag_Cal_new = df_Diag_Cal_Update.loc[df_Diag_Cal_Update['end_time'] == '0000-00-00 00:00:00']
+                df_Diag_Cal_finish['Batpos'] = 1
+                df_Diag_Cal_new['Batpos'] = 0
+                df_feishu_sta = df_read_Yunw.loc[(df_read_Yunw['product_id'] == sn)]#飞书中该sn车辆状态
+                if df_feishu_sta.empty:#飞书中没有该sn记录故障的新增
+                    df_Diag_cal_early_unfix = df_Diag_Cal_new#如果为新出故障，则直接记录在df_diag_frame中
+                else:
+                    df_Diag_cal_later = df_Diag_Cal_new.loc[pd.to_datetime(df_Diag_Cal_new['start_time']) > max(pd.to_datetime(df_feishu_sta['start_time']))]#故障表中故障时间晚于飞书记录时间的新增
+                    df_Diag_cal_early = pd.concat([df_Diag_Cal_new,df_Diag_cal_later,df_Diag_cal_later]).drop_duplicates(subset=['product_id','code','start_time'],keep=False)#故障表中故障时间早于飞书记录时间
+                    df_feishu_sta_latest = df_feishu_sta.loc[pd.to_datetime(df_feishu_sta['start_time']) == max(pd.to_datetime(df_feishu_sta['start_time']))]#飞书中该SN下的最新故障
+                    df_feishu_diag_unfix = (df_feishu_sta_latest['advice'] == '需正常返仓') | (df_feishu_sta_latest['advice'] == '需紧急返仓')
+                    if any(df_feishu_diag_unfix):
+                        df_Diag_cal_early_unfix = df_Diag_Cal_new
+                    else:
+                        df_Diag_cal_early_fix = df_Diag_cal_early
+                        df_Diag_cal_early_unfix = df_Diag_cal_later
+                if not df_Diag_cal_early_fix.empty:
+                    df_Diag_cal_early_fix['Batpos'] = 1
+            df_Diag_Ram_Update = pd.concat([df_Diag_cal_early_unfix,df_Diag_cal_early_fix,df_Diag_Cal_finish])
+            df_Diag_Ram_Update['start_time'] = pd.to_dateime(df_Diag_Ram_Update['start_time'])
+            df_Diag_Ram_Update.sort_values(by = ['start_time'], axis = 0, ascending=True,inplace=True)#该sn下当次诊断的故障状态
+            df_Diag_Ram_add = pd.concat([df_Diag_Ram_Update,df_Diag_Ram_sn,df_Diag_Ram_sn]).drop_duplicates(subset=['start_time','code'],keep=False)#此次判断中新增故障
+            df_Diag_Ram_Update_old = pd.concat([df_Diag_Ram_Update,df_Diag_Ram_add,df_Diag_Ram_add]).drop_duplicates(subset=['start_time','code'],keep=False)#此次判断中新增故障
+            df_Diag_Ram_Update_change = pd.concat([df_Diag_Ram_Update_old,df_Diag_Ram_sn,df_Diag_Ram_sn]).drop_duplicates(subset=['start_time','code','Batpos'],keep=False)#此次判断中新增故障
+            df_Diag_Ram_sav = df_Diag_Ram_Update.loc[df_Diag_Ram_Update['Batpos'] == 0]
+            df_Diag_Ram = pd.concat([df_Diag_Ram_sn_else,df_Diag_Ram_sav])
+
+        if len(df_Diag_Ram_sav) > 0:#历史及现有故障
+            df_Diag_Ram.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\04故障诊断\01Screen_Problem\result.csv',index=False,encoding='GB18030')
+        if len(df_Diag_Ram_add) > 0:#新增故障
+            df_Diag_Ram_add.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\04故障诊断\01Screen_Problem\result_add.csv',index=False,encoding='GB18030')
+        if len(df_Diag_Ram_Update_change) > 0:#更改故障
+            df_Diag_Ram_Update_change.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\04故障诊断\01Screen_Problem\result_change.csv',index=False,encoding='GB18030')
+        end=time.time()
+        print(end-start)
+
+#...............................................主函数.......................................................................................................................
+if __name__ == "__main__":
+    global SNnums
+    
+    excelpath=r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\04故障诊断\01Screen_Problem\sn-20210903.xlsx'
+    SNdata_6060 = pd.read_excel(excelpath, sheet_name='科易6060')
+    SNdata_6040 = pd.read_excel(excelpath, sheet_name='科易6040')
+    SNdata_4840 = pd.read_excel(excelpath, sheet_name='科易4840')
+    SNdata_L7255 = pd.read_excel(excelpath, sheet_name='格林美-力信7255')
+    SNdata_C7255 = pd.read_excel(excelpath, sheet_name='格林美-CATL7255')
+    SNdata_U7255 = pd.read_excel(excelpath, sheet_name='优旦7255')
+    SNnums_6060=SNdata_6060['SN号'].tolist()
+    SNnums_6040=SNdata_6040['SN号'].tolist()
+    SNnums_4840=SNdata_4840['SN号'].tolist()
+    SNnums_L7255=SNdata_L7255['SN号'].tolist()
+    SNnums_C7255=SNdata_C7255['SN号'].tolist()
+    SNnums_U7255=SNdata_U7255['SN号'].tolist()
+    #SNnums=SNnums_L7255 + SNnums_C7255 + SNnums_6040 + SNnums_4840 + SNnums_U7255+ SNnums_6060
+    # SNnums=['MGMCLN750N215I005','PK504B10100004341','PK504B00100004172','MGMLXN750N2189014']
+    SNnums = ['MGMLXN750N21B5004'] #SNnums_6040
+    
+    mylog=log.Mylog('log_diag.txt','error')
+    mylog.logcfg()
+    #............................模块运行前，先读取数据库中所有结束时间为0的数据，需要从数据库中读取................
+    host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+    port=3306
+    db='safety_platform'
+    user='qx_read'
+    password='Qx@123456'
+    mode=2
+    tablename2='all_fault_info'
+    DBRead = DBDw.DBDownload(host, port, db, user, password,mode)
+    with DBRead as DBRead:
+        df_Diag_Ram = DBRead.getdata('start_time','end_time','product_id','code','level','info','advice','Batpos',tablename=tablename2,factory='骑享',sn='',timename='',st='',sp='')
+    # result=pd.read_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problem\result.csv',encoding='gbk')
+
+    #定时任务.......................................................................................................................................................................
+    scheduler = BlockingScheduler()
+    scheduler.add_job(diag_cal, 'interval', seconds=300, id='diag_job')
+
+    try:  
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)