QX_BatteryParam.py更改项：

1.故障报警的温度GAP：由30修改为40；2.修改温度数量：由4修改为3；由于第四个温度异常；
main函数更改项：
1.将学琦和鹏飞的故障诊断合并；
2.通过飞书判断故障车辆是否已经返厂处理，已处理车辆的Batpos返回1，未处理车辆的Batpos返回0；
3.同一sn号的现有故障合并至相邻行，并通过start_time排序；

LIB/MIDDLE/CellStateEstimation/Common/V1_0_1/log.py

@@ -17,7 +17,7 @@ class Mylog:
                 Level=logging.WARNING
             else:
                 Level=logging.ERROR
-        logging.basicConfig(filename=r'D:\Platform\platform_python\data_analyze_platform\USER\spf\101log\\'+self.name, level=Level,format='%(asctime)s - %(levelname)s - %(message)s')
+        logging.basicConfig(filename=r'D:\Work\Code_write\data_analyze_platform\USER\lzx\SaftyCenter_CODE_V1_1_DIDI\\'+self.name, level=Level,format='%(asctime)s - %(levelname)s - %(message)s')
 
     def logopt(self,*info):
         logging.error(info)

LIB/MIDDLE/SaftyCenter/Common/QX_BatteryParam.py

@@ -5,7 +5,7 @@ class BatteryInfo():
         self.AllowDsChgTemp=-5
         self.AvgVolGap=1
         self.AvgCellTempGap=10
-        self.AvgOtherTempGap=30
+        self.AvgOtherTempGap=40
         self.PackOTlmt=65
         self.PackUTlmt=-20
         self.OtherOTlmt=91
@@ -19,7 +19,7 @@ class BatteryInfo():
             self.PackFullChrgVolt=69.99
             self.CellFullChrgVolt=4.2
             self.CellVoltNums=17
-            self.CellTempNums=4
+            self.CellTempNums=3
             self.OtherTempNums=5
             self.FullChrgSoc=98
             self.PeakSoc=57
@@ -37,7 +37,7 @@ class BatteryInfo():
             self.PackFullChrgVolt=69.99
             self.CellFullChrgVolt=4.2
             self.CellVoltNums=14
-            self.CellTempNums=4
+            self.CellTempNums=3
             self.OtherTempNums=5            
             self.FullChrgSoc=98
             self.PeakSoc=57
@@ -96,7 +96,7 @@ class BatteryInfo():
             self.OcvInflexion3=3.328
             self.OcvInflexionAbove=3.4
             self.CellVoltNums=20
-            self.CellTempNums=4
+            self.CellTempNums=3
             self.OtherTempNums=5 
             self.FullChrgSoc=98
             self.PeakSoc=59

LIB/MIDDLE/SaftyCenter/diagfault/CBMSBatDiag.py

@@ -0,0 +1,557 @@
+import pandas as pd
+import numpy as np
+import datetime
+from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import BatParam
+
+class BatDiag:
+    def __init__(self,sn,celltype,df_bms,df_soh,df_uniform,df_diag_Ram):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_bms=df_bms
+        self.df_soh=df_soh
+        self.df_uniform=df_uniform
+        self.df_diag_ram=df_diag_Ram
+        self.packcrnt=df_bms['总电流[A]']*self.param.PackCrntDec
+        self.packvolt=df_bms['总电压[V]']
+        self.bms_soc=df_bms['SOC[%]']
+        self.bmstime= pd.to_datetime(df_bms['时间戳'], format='%Y-%m-%d %H:%M:%S')
+        self.bmsfault1=self.df_bms['故障代码']
+        # self.bmsfault2=self.df_bms['alarm2'].tolist()
+        # self.bmsfault3=self.df_bms['alarm3'].tolist()
+        # self.bmsfault4=self.df_bms['fault'].tolist()
+
+        self.cellvolt_name=['单体电压'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        self.celltemp_name=['单体温度'+str(x) for x in range(1,self.param.CellTempNums+1)]
+    
+    def diag(self):
+        if self.celltype<=50:
+            df_res=self._ncm_diag()
+            return df_res    
+        else:
+            df_res=self._ncm_diag()
+            return df_res
+        
+
+    #定义滑动滤波函数.............................................................................................
+    def _np_move_avg(self,a, n, mode="same"): 
+        return (np.convolve(a, np.ones((n,)) / n, mode=mode))
+    
+    #寻找当前行数据的所有温度值...................................................................................
+    def _celltemp_get(self,num):   
+        celltemp = list(self.df_bms.loc[num,self.celltemp_name])
+        return celltemp
+
+    #获取当前行所有电压数据............................................................................................
+    def _cellvolt_get(self,num): 
+        cellvolt = np.array(self.df_bms.loc[num,self.cellvolt_name]/1000)
+        return cellvolt
+
+    #..........................................三元电池诊断功能..................................................................
+    def _ncm_diag(self):
+
+        bmssoc_st=float(self.bms_soc[0])    #SOC卡滞初始参数
+        ah_accum=0  #SOC卡滞初始参数
+        as_chg=0    #过流诊断初始参数
+        as_dis=0    #过流诊断初始参数
+        time1=self.bmstime[0]   #温升速率初始参数
+        temp1=np.array(self._celltemp_get(0))   #温升速率初始参数
+        temprate_cnt=0
+        
+        end_time='0000-00-00 00:00:00'
+            
+        for i in range(1,len(self.df_bms)):
+            
+            #温度诊断功能.............................................................................................................
+            celltemp0=self._celltemp_get(i-1)
+            celltemp1=self._celltemp_get(i)
+            celltempmin0=min(celltemp0)
+            celltempmin1=min(celltemp1)
+            celltempmax0=max(celltemp0)
+            celltempmax1=max(celltemp1)
+            #温度有效性判断..........................................................................
+            if celltempmax0>self.param.CellTempUpLmt or celltempmin0<self.param.CellTempLwLmt:
+                celltempvalid=0
+            else:  
+                celltempvalid=1
+           
+
+            if celltempvalid==1:
+                #过温判断.............................................................................................................
+                if not 4 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if celltempmax0>self.param.CellTempHighLv2 and celltempmax1>self.param.CellTempHighLv2:    #二级高温进入
+                        time=self.bmstime[i]
+                        code=4
+                        faultlv=3
+                        faultinfo='温度{}高温二级'.format(celltemp1.index(celltempmax1)+1)
+                        faultadvice='技术介入诊断'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:   #ram当前故障中有该故障，则判断是否退出该故障
+                    if celltempmax0<self.param.CellTempHighLv1-5 and celltempmax1<self.param.CellTempHighLv1-5:    #二级高温恢复
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==4].index, 'end_time'] = time
+                    else:
+                        pass
+            
+                #欠温判断.................................................................................................................
+                if not 6 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if celltempmin0<self.param.CellTempLowLv2 and celltempmin1<self.param.CellTempLowLv2:  #二级低温进入
+                        time=self.bmstime[i]
+                        code=6
+                        faultlv=3
+                        faultinfo='温度{}低温二级'.format(celltemp1.index(celltempmin1)+1)
+                        faultadvice='技术介入诊断'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:   #ram当前故障中有该故障，则判断是否退出该故障
+                    if celltempmax0>self.param.CellTempLowLv1+2 and celltempmax1>self.param.CellTempLowLv1+2:    #二级高温恢复
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==6].index, 'end_time'] = time
+                    else:
+                        pass
+              
+                #温差判断.............................................................................................................................
+                if not 8 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if (celltempmax0-celltempmin0)>self.param.CellTempDiffLv2 and (celltempmax1-celltempmin1)>self.param.CellTempDiffLv2:  #二级温差进入
+                        time=self.bmstime[i]
+                        code=8
+                        faultlv=3
+                        faultinfo='温度{}和{}温差大二级'.format(celltemp1.index(celltempmax1)+1,celltemp1.index(celltempmin1)+1)
+                        faultadvice='技术介入诊断'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:   #ram当前故障中有该故障，则判断是否退出该故障
+                    if (celltempmax0-celltempmin0)<self.param.CellTempDiffLv1-2 and (celltempmax1-celltempmax0)>self.param.CellTempDiffLv1-2:  #二级温差恢复
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==8].index, 'end_time'] = time
+                    else:
+                        pass
+
+                #温升判断
+                time2=self.bmstime[i]
+                delttime=(time2-time1).total_seconds()
+                if delttime>20:
+                    temp2=np.array(self._celltemp_get(i))
+                    celltemp_rate=(max(temp2-temp1)*60)/delttime    #计算最大温升速率
+                    temp1=temp2 #更新初始温度
+                    time1=time2 #更新初始时间
+                    if celltemp_rate>self.param.CellTempRate:
+                        temprate_cnt=temprate_cnt+1
+                        if not 9 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                            if temprate_cnt>2:  #温升故障进入
+                                time=self.bmstime[i]
+                                code=9
+                                faultlv=3
+                                faultinfo='温升速率过快：{}℃/min'.format(celltemp_rate)
+                                faultadvice='技术介入诊断'
+                                self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                            else:
+                                pass
+                        else:   #ram当前故障中有该故障，则判断是否退出该故障
+                            if celltemp_rate<self.param.CellTempRate-1: #温升故障恢复
+                                time=self.bmstime[i]
+                                self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==9].index, 'end_time'] = time
+                    else:
+                        pass
+                else:
+                    pass
+            
+            else:
+                pass
+            
+            #电压诊断功能.................................................................................................
+            cellvolt0=self._cellvolt_get(i-1)
+            cellvolt1=self._cellvolt_get(i)
+            cellvoltmin0=min(cellvolt0)
+            cellvoltmax0=max(cellvolt0)
+            cellvoltmin1=min(cellvolt1)
+            cellvoltmax1=max(cellvolt1)
+            #电压断线诊断...................................................................................................
+            if (cellvoltmin0<2 and cellvoltmax0>4.5) or cellvoltmin0<0.1 or cellvoltmax0>5:
+                cellvoltvalid=0
+            else:
+                cellvoltvalid=1
+            
+            if cellvoltvalid==1:
+                #过压诊断.............................................................................................................
+                if not 12 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if cellvoltmax0>self.param.CellOvLv2 and cellvoltmax1>self.param.CellOvLv2:  #二级过压进入
+                        time=self.bmstime[i]
+                        code=12
+                        faultlv=3
+                        faultinfo='电芯{}过压二级'.format(cellvolt1.index(cellvoltmax1)+1)
+                        faultadvice='联系用户询问用车场景，技术介入诊断'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:   #ram当前故障中有该故障，则判断是否退出该故障
+                    if cellvoltmax0<self.param.CellOvLv1-0.05 and cellvoltmax1<self.param.CellOvLv1-0.05:   #二级过压故障恢复
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==12].index, 'end_time'] = time
+                    else:
+                        pass
+              
+
+                #欠压诊断.................................................................................................................
+                if not 14 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if cellvoltmin0<self.param.CellUvLv2 and cellvoltmin1<self.param.CellUvLv2:  #二级欠压
+                        time=self.bmstime[i]
+                        code=14
+                        faultlv=3
+                        faultinfo='电芯{}欠压二级'.format(cellvolt1.index(cellvoltmin1)+1)
+                        faultadvice='联系用户询问用车场景，技术介入诊断'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:
+                    if cellvoltmin0>self.param.CellUvLv1+0.1 and cellvoltmin1>self.param.CellUvLv1+0.1:
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==14].index, 'end_time'] = time
+                    else:
+                        pass
+             
+                #电芯压差大.....................................................................................................................................................
+                if not 16 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if (cellvoltmax0-cellvoltmin0)>self.param.CellVoltDiffLv2 and (cellvoltmax1-cellvoltmin1)>self.param.CellVoltDiffLv2:  #二级电芯压差
+                        time=self.bmstime[i]
+                        code=16
+                        faultlv=3
+                        faultinfo='电芯{}和{}压差大二级'.format(cellvolt1.index(cellvoltmax1)+1,cellvolt1.index(cellvoltmin1)+1)
+                        faultadvice='技术介入诊断'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:
+                    if (cellvoltmax0-cellvoltmin0)<self.param.CellVoltDiffLv1-0.05 and (cellvoltmax1-cellvoltmin1)>self.param.CellVoltDiffLv1-0.05: #二级欠压恢复
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==16].index, 'end_time'] = time
+                    else:
+                        pass
+            else:
+                pass
+                
+            #电池包诊断.....................................................................................................................................
+            if not 18 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                if self.packvolt[i-1]>self.param.PackVoltOvLv2 and self.packvolt[i]>self.param.PackVoltOvLv2:   #电池包过压二级进入
+                    time=self.bmstime[i]
+                    code=18
+                    faultlv=3
+                    faultinfo='电池包过压二级'
+                    faultadvice='联系用户询问用车场景，技术介入诊断'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if self.packvolt[i-1]<self.param.PackVoltOvLv1-0.05*self.param.CellVoltNums and self.packvolt[i]<self.param.PackVoltOvLv1-0.05*self.param.CellVoltNums: #电池包过压二级恢复
+                    time=self.bmstime[i]
+                    self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==18].index, 'end_time'] = time
+                else:
+                    pass
+          
+            #电池包诊断.....................................................................................................................................
+            if not 20 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                if self.packvolt[i-1]<self.param.PackVoltUvLv2 and self.packvolt[i]<self.param.PackVoltUvLv2:   #电池包二级欠压进入
+                    time=self.bmstime[i]
+                    code=20
+                    faultlv=3
+                    faultinfo='电池包欠压二级'
+                    faultadvice='联系用户询问用车场景，技术介入诊断'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if self.packvolt[i-1]>self.param.PackVoltUvLv1+0.1*self.param.CellVoltNums and self.packvolt[i]>self.param.PackVoltUvLv1+0.1*self.param.CellVoltNums:   #电池包二级欠压恢复
+                    time=self.bmstime[i]
+                    self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==20].index, 'end_time'] = time
+                else:
+                    pass
+            
+            #电流过流诊断.......................................................................................................................
+            step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+            if step<120 and self.packcrnt[i]>self.param.PackDisOc:
+                as_dis=as_dis+(self.packcrnt[i]-self.param.self.PackDisOc)*step    #ah累计
+            elif step<120 and self.packcrnt[i]<self.param.PackChgOc:
+                as_chg=as_chg+(self.param.PackDisOc-self.packcrnt[i])*step    #ah累计
+            else:
+                as_dis=0
+                as_chg=0
+            
+            if not 22 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                if as_dis>100:
+                    time=self.bmstime[i]
+                    code=22
+                    faultlv=3
+                    faultinfo='电池包放电过流'
+                    faultadvice='联系用户询问用车场景，技术介入诊断'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if self.packcrnt[i-1]<self.param.PackDisOc-10 and self.packcrnt[i]<self.param.PackDisOc-10:
+                    time=self.bmstime[i]
+                    self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==22].index, 'end_time'] = time
+                else:
+                    pass
+            
+            if not 21 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                if as_chg>100:
+                    time=self.bmstime[i]
+                    code=21
+                    faultlv=3
+                    faultinfo='电池包充电过流'
+                    faultadvice='联系用户询问用车场景，技术介入诊断'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if self.packcrnt[i-1]>self.param.PackChgOc+10 and self.packcrnt[i]>self.param.PackChgOc+10:
+                    time=self.bmstime[i]
+                    self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==22].index, 'end_time'] = time
+                else:
+                    pass
+
+            #SOC卡滞、跳变诊断................................................................................................
+            step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
+            if step<120:
+                ah_accum=ah_accum-self.packcrnt[i]*step/3600    #ah累计
+            else:
+                pass
+            #SOC卡滞............................................................................................................
+            if abs(ah_accum)>self.param.Capacity*0.05:   
+                bmssoc_now=float(self.bms_soc[i])
+                if not 27 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if abs(bmssoc_now-bmssoc_st)<self.param.SocClamp:   #SOC卡滞故障进入
+                        time=self.bmstime[i]
+                        code=27
+                        faultlv=1
+                        faultinfo='电池SOC卡滞'
+                        faultadvice='技术介入诊断，检修电池BMS软件'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:
+                    if abs(bmssoc_now-bmssoc_st)>self.param.SocClamp:   #SOC卡滞故障退出
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==27].index, 'end_time'] = time
+                    else:
+                        pass
+                bmssoc_st=bmssoc_now
+                ah_accum=0
+            else:
+                pass
+
+            #SOC跳变....................................................................................................................
+            if step<30: 
+                bmssoc_last=float(self.bms_soc[i-1])
+                bmssoc_now=float(self.bms_soc[i])
+                if not 28 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if abs(bmssoc_now-bmssoc_last)>self.param.SocJump:  #SOC跳变进入
+                        time=self.bmstime[i]
+                        code=28
+                        faultlv=1
+                        faultinfo='电池SOC跳变'
+                        faultadvice='技术介入诊断，检修电池BMS软件'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:   
+                    if abs(bmssoc_now-bmssoc_st)<self.param.SocJump:    #SOC跳变故障退出
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==28].index, 'end_time'] = time
+                    else:
+                        pass
+            else:
+                pass
+
+            #SOC过低故障报警............................................................................................................
+            if not 26 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                if self.bms_soc[i-1]<self.param.SocLow and self.bms_soc[i]<self.param.SocLow:   #SOC过低故障进入
+                    time=self.bmstime[0]
+                    code=26
+                    faultlv=1
+                    faultinfo='电池包电量过低'
+                    faultadvice='联系用户，请立刻充电'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:   
+                if self.bms_soc[i-1]>self.param.SocLow and self.bms_soc[i]>self.param.SocLow:   #SOC过低故障退出
+                    time=self.bmstime[i]
+                    self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==26].index, 'end_time'] = time
+                else:
+                    pass
+
+            #BMS故障报警........................................................................................................
+            if not 1 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                if self.bmsfault1[i-1] is None or self.bmsfault1[i] is None:
+                    self.bmsfault1[i-1]=0
+                    self.bmsfault1[i]=0
+                if self.bmsfault1[i-1]>0 or self.bmsfault1[i]>0:   #BMS故障进入
+                    time=self.bmstime[0]
+                    code=1
+                    faultlv=2
+                    faultinfo='BMS故障报警：{}'.format(self.bmsfault1[i-1])
+                    faultadvice='技术介入诊断'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if self.bmsfault1[i-1]==0 and self.bmsfault1[i]==0:   #BMS故恢复
+                    time=self.bmstime[i]
+                    self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==1].index, 'end_time'] = time
+        
+        
+        #SOC一致性故障报警..........................................................................................................
+        if not self.df_uniform.empty:
+            cellsoc_diff=self.df_uniform.loc[0,'cellsoc_diff']
+            if not 25 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                if cellsoc_diff>self.param.SocDiff: #SOC一致性差故障进入
+                    time=self.bmstime[0]
+                    code=25
+                    faultlv=1
+                    faultinfo='电芯{}和{}SOC差过大:{}'.format(self.df_uniform.loc[0,'cellmin_num'],self.df_uniform.loc[0,'cellmax_num']),cellsoc_diff
+                    faultadvice='技术介入诊断'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if cellsoc_diff<self.param.SocDiff: #SOC一致性差故障恢复
+                    time=self.bmstime[i]
+                    self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==25].index, 'end_time'] = time
+        else:
+            cellsoc_diff=3
+
+        #容量过低和一致性故障报警................................................................................................
+        if not self.df_soh.empty:
+            soh=self.df_soh.loc[0,'soh']
+            cellsoh=eval(self.df_soh.loc[0,'cellsoh'])
+            cellsoh=np.array(cellsoh)
+            cellsoh_lowindex=np.argwhere(cellsoh<self.param.SohLow)
+            cellsoh_lowindex=cellsoh_lowindex+1
+            if self.celltype==1 or self.celltype==2 or self.celltype==3 or self.celltype==4: 
+                cellsoh_diff=max(cellsoh)-min(cellsoh)
+                if not 23 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if soh<self.param.SohLow:   #soh过低故障进入
+                        time=self.bmstime[0]
+                        code=23
+                        faultlv=1
+                        faultinfo='电池包容量过低：电芯{}'.format(cellsoh_lowindex)
+                        faultadvice='检修电池，更换容量过低的电芯或模组'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:
+                    if soh>self.param.SohLow+2:   #soh过低故障恢复
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==23].index, 'end_time'] = time
+                    else:
+                        pass
+
+                if not 24 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                    if cellsoh_diff>self.param.SohDiff:
+                        time=self.bmstime[0]
+                        code=24
+                        faultlv=1
+                        faultinfo='电池包容量一致性差：电芯{}'.format(cellsoh_lowindex)
+                        faultadvice='检修电池，更换容量过低的电芯或模组'
+                        self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                    else:
+                        pass
+                else:
+                    if cellsoh_diff<self.param.SohDiff-2:
+                        time=self.bmstime[i]
+                        self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==24].index, 'end_time'] = time
+                    else:
+                        pass
+            else:
+                pass
+        else:
+            cellsoh_diff=5
+        
+        # #电池健康度评分.....................................................................................................
+        # health_state=soh*0.6+(100-cellsoh_diff)*0.2+(100-cellsoc_diff)*0.2
+        # if health_state>100:
+        #     health_state=100
+        # elif health_state<0:
+        #     health_state=0
+        # else:
+        #     pass
+        # health_state=eval(format(health_state,'.1f'))
+
+        #返回诊断结果...........................................................................................................
+        df_res=self.df_diag_ram
+        if not df_res.empty:
+            return df_res
+        else:
+            return pd.DataFrame()
+
+#............................................................内短路故障诊断.............................................................................
+class ShortDiag():
+    def __init__(self,sn,celltype,df_short):  #参数初始化
+
+        self.sn=sn
+        self.celltype=celltype
+        self.param=BatParam.BatParam(celltype)
+        self.df_short=df_short
+
+    def shortdiag(self):
+        if len(self.df_short)>1:
+            df_res=self._short_diag()
+            return df_res
+        else:
+            return pd.DataFrame()
+
+    #内短路故障检测...................................................................................................................................
+    def _short_diag(self):
+        column_name=['start_time', 'end_time', 'product_id', 'code', 'level', 'info','advice']
+        df_res=pd.DataFrame(columns=column_name)
+        time=datetime.datetime.now()
+        end_time=datetime.datetime.strftime('0000-00-00 00:00:00')
+        end_time=datetime.datetime.strptime(end_time,'%Y-%m-%d %H:%M:%S')
+
+        # if self.df_diag.empty:
+        #     health_state=100
+        # else:
+        #     health_state=self.df_diag.loc[0,'health_state']
+
+        for i in range(self.param.CellVoltNums):
+            #将字符串分割为多列
+            short_current=self.df_short['short_current']
+            short_current=short_current.str.replace("[", '')
+            short_current=short_current.str.replace("]", '')
+            self.df_short['cellshort'+str(i+1)]=short_current.map(lambda x:x.split(',')[i])
+            self.df_short['cellshort'+str(i+1)]=self.df_short['cellshort'+str(i+1)].map(lambda x:eval(x))
+
+            #漏电流故障判断
+            cellshort=np.array(self.df_short['cellshort'+str(i+1)])
+            shortlv3=np.sum(cellshort>self.param.LeakCurrentLv3)
+            shortlv2=np.sum(cellshort>self.param.LeakCurrentLv2)-shortlv3
+            shortlv1=np.sum(cellshort>self.param.LeakCurrentLv1)-shortlv2-shortlv3
+            shortlv=shortlv3*3 + shortlv2*2 + shortlv1
+
+            if not 31 in list(self.df_diag_ram['code']):  #当前故障中没有该故障，则判断是否发生该故障
+                if shortlv>=5:
+                    time=self.bmstime[0]
+                    code=31
+                    faultlv=3
+                    faultinfo='电芯{}发生严重内短路'.format(i+1)
+                    faultadvice='禁止充放电，检修电池'
+                    self.df_diag_ram.loc[len(self.df_diag_ram)]=[time, end_time, self.sn, code, faultlv, faultinfo, faultadvice]
+                else:
+                    pass
+            else:
+                if shortlv<3:
+                    time=self.bmstime[i]
+                    self.df_diag_ram[self.df_diag_ram[self.df_diag_ram['code']==31].index, 'end_time'] = time
+        
+        if not df_res.empty:
+            return df_res
+        else:
+            return pd.DataFrame()
+
+

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]
+        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

@@ -0,0 +1,177 @@
+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
+from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import log
+from pandas.core.frame import DataFrame
+import datacompy
+from SaftyCenter.Common import FeiShuData
+from SaftyCenter.Common import QX_BatteryParam
+
+#...................................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=130)
+    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) 
+        print(sn)    
+        #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_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_fix = df_Diag_Ram.loc[df_Diag_Ram['Batpos'] == 1]
+        df_Diag_Ram_unfix = df_Diag_Ram.loc[df_Diag_Ram['Batpos'] == 0]
+        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([CellFltInfo,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_sn_car_fix = 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:
+                    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[df_Diag_Cal_new['start_time'] > max(df_feishu_sta['start_time'])]#故障表中故障时间晚于飞书记录时间
+                    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[df_feishu_sta['start_time'] == max(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')
+                    df_sn_car_unfix = pd.DataFrame()
+                    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.sort_values(by = ['start_time'], axis = 0, ascending=True,inplace=True)#对故障信息按照时间进行排序
+            df_temp5 = pd.concat([df_Diag_Ram_Update,df_Diag_Ram_sn_else])
+            df_Diag_Ram_sum = df_temp5.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_tempnum = df_Diag_Ram_sum.groupby(['product_id']).size()#获取每个sn的故障总数
+            col1 = df_tempnum[df_tempnum>1].reset_index()[['product_id']]#多故障sn号
+            col2 = df_tempnum[df_tempnum==1].reset_index()[['product_id']]#单故障sn号
+            df_temp1 = pd.DataFrame()
+            if not col1.empty:
+                for item in col1['product_id']:
+                    temp_data = df_Diag_Ram_sum.loc[df_Diag_Ram_sum['product_id'] == item]
+                    temp_data.sort_values(by = ['start_time'], axis = 0, ascending=True,inplace=True)#对故障信息按照时间进行排序
+                    df_temp1 = df_temp1.append(temp_data)
+            df_temp2 = pd.merge(col2,df_Diag_Ram_sum,on=["product_id"])#单故障码数据筛选
+            df_temp3 = pd.concat([df_temp1,df_temp2])#多故障及单故障合并
+            df_temp4 = df_temp3.reset_index(drop=True)
+            df_Diag_Ram = df_temp4
+            df_Diag_Ram_fix = df_Diag_Ram.loc[df_Diag_Ram['Batpos'] == 1]
+            df_Diag_Ram_unfix = df_Diag_Ram.loc[df_Diag_Ram['Batpos'] == 0]
+        if len(df_Diag_Ram) > 0:
+            df_Diag_Ram.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problem\result.csv',index=False,encoding='GB18030')
+        if len(df_Diag_Ram_fix) > 0:
+            df_Diag_Ram_fix.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problem\result_fix.csv',index=False,encoding='GB18030')
+        if len(df_Diag_Ram_unfix) > 0:
+            df_Diag_Ram_unfix.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problemm\result_unfix.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\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................
+    result=pd.read_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problem\result.csv',encoding='gbk')
+    
+    # df_Diag_Ram=result[result['end_time']=='0000-00-00 00:00:00']
+    df_Diag_Ram=result#[result['Batpos'] == 0]#将故障依然存在的赋值
+    print('----------------输入--------')
+    print(df_Diag_Ram)
+    print('-------计算中-----------')
+    #定时任务.......................................................................................................................................................................
+    scheduler = BlockingScheduler()
+    scheduler.add_job(diag_cal, 'interval', seconds=120, id='diag_job')
+
+    try:  
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)