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@@ -6,12 +6,13 @@ from LIB.BACKEND import DBManager, Log
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from sqlalchemy import create_engine
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import time, datetime
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from apscheduler.schedulers.blocking import BlockingScheduler
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-from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import DBDownload
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+from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import DBDownload as DBDownload
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from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import log
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from pandas.core.frame import DataFrame
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import datacompy
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from LIB.MIDDLE.SaftyCenter.Common import FeiShuData
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-from LIB.MIDDLE.SaftyCenter.SaftyCenter.Common import QX_BatteryParam
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+from LIB.MIDDLE.SaftyCenter.Common import QX_BatteryParam
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+from LIB.MIDDLE.SaftyCenter.Common import DBDownload as DB
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#...................................电池包电芯安全诊断函数......................................................................................................................
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def diag_cal():
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@@ -44,8 +45,7 @@ def diag_cal():
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print('SN:{},未找到对应电池类型!!!'.format(sn))
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continue
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# sys.exit()
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- param=QX_BatteryParam.BatteryInfo(celltype)
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- print(sn)
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+ param=QX_BatteryParam.BatteryInfo(celltype)
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#读取原始数据库数据........................................................................................................................................................
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dbManager = DBManager.DBManager()
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df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms'])
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@@ -71,8 +71,8 @@ def diag_cal():
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df_Diag_Ram_sn = df_Diag_Ram.loc[df_Diag_Ram['product_id']==sn]#历史故障
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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
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CellFltInfo = df_Diag_Ram_sn.drop('Batpos',axis=1)
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- df_Diag_Ram_fix = df_Diag_Ram.loc[df_Diag_Ram['Batpos'] == 1]
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- df_Diag_Ram_unfix = df_Diag_Ram.loc[df_Diag_Ram['Batpos'] == 0]
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+ df_Diag_Ram_add = pd.DataFrame()
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+ df_Diag_Ram_Update_change = pd.DataFrame()
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if not df_bms.empty:
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df_Diag_Batdiag_update_xq=SamplingSafty.main(sn,param,df_bms,CellFltInfo)#学琦计算故障
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BatDiag=CBMSBatDiag.BatDiag(sn,celltype,df_bms, df_soh, df_uniform, CellFltInfo)#鹏飞计算
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@@ -80,7 +80,6 @@ def diag_cal():
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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
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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
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df_Diag_cal_early_unfix = pd.DataFrame()
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- df_sn_car_fix = pd.DataFrame()
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df_Diag_Cal_finish = pd.DataFrame()
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df_Diag_cal_early_fix = pd.DataFrame()
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if not df_Diag_Cal_Update_temp.empty:
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@@ -91,14 +90,13 @@ def diag_cal():
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df_Diag_Cal_finish['Batpos'] = 1
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df_Diag_Cal_new['Batpos'] = 0
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df_feishu_sta = df_read_Yunw.loc[(df_read_Yunw['product_id'] == sn)]#飞书中该sn车辆状态
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- if df_feishu_sta.empty:
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+ if df_feishu_sta.empty:#飞书中没有该sn记录故障的新增
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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
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else:
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- 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
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+ 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的新增
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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
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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
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df_feishu_diag_unfix = (df_feishu_sta_latest['advice'] == '需正常返仓') | (df_feishu_sta_latest['advice'] == '需紧急返仓')
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- df_sn_car_unfix = pd.DataFrame()
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if any(df_feishu_diag_unfix):
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df_Diag_cal_early_unfix = df_Diag_Cal_new
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else:
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@@ -107,30 +105,18 @@ def diag_cal():
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if not df_Diag_cal_early_fix.empty:
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df_Diag_cal_early_fix['Batpos'] = 1
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df_Diag_Ram_Update = pd.concat([df_Diag_cal_early_unfix,df_Diag_cal_early_fix,df_Diag_Cal_finish])
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- 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
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- df_temp5 = pd.concat([df_Diag_Ram_Update,df_Diag_Ram_sn_else])
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- 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
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- df_tempnum = df_Diag_Ram_sum.groupby(['product_id']).size()#获取每个sn的故障总数
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- col1 = df_tempnum[df_tempnum>1].reset_index()[['product_id']]#多故障sn号
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- col2 = df_tempnum[df_tempnum==1].reset_index()[['product_id']]#单故障sn号
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- df_temp1 = pd.DataFrame()
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- if not col1.empty:
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- for item in col1['product_id']:
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- temp_data = df_Diag_Ram_sum.loc[df_Diag_Ram_sum['product_id'] == item]
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- 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
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- df_temp1 = df_temp1.append(temp_data)
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- df_temp2 = pd.merge(col2,df_Diag_Ram_sum,on=["product_id"])#单故障码数据筛选
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- df_temp3 = pd.concat([df_temp1,df_temp2])#多故障及单故障合并
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- df_temp4 = df_temp3.reset_index(drop=True)
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- df_Diag_Ram = df_temp4
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- df_Diag_Ram_fix = df_Diag_Ram.loc[df_Diag_Ram['Batpos'] == 1]
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- df_Diag_Ram_unfix = df_Diag_Ram.loc[df_Diag_Ram['Batpos'] == 0]
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- if len(df_Diag_Ram) > 0:#历史及现有故障
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+ 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
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+ 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
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+ 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
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+ 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
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+ df_Diag_Ram = pd.concat([df_Diag_Ram_sn_else,df_Diag_Cal_new])
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+
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+ if (len(df_Diag_Ram_add) > 0) | (len(df_Diag_Ram_Update_change) > 0):#历史及现有故障
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df_Diag_Ram.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problem\result.csv',index=False,encoding='GB18030')
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- if len(df_Diag_Ram_fix) > 0:#故障车辆已返仓
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- df_Diag_Ram_fix.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problem\result_fix.csv',index=False,encoding='GB18030')
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- if len(df_Diag_Ram_unfix) > 0:#故障车辆未返仓
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- df_Diag_Ram_unfix.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problemm\result_unfix.csv',index=False,encoding='GB18030')
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+ if len(df_Diag_Ram_add) > 0:#新增故障
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+ df_Diag_Ram_add.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problem\result_add.csv',index=False,encoding='GB18030')
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+ if len(df_Diag_Ram_Update_change) > 0:#更改故障
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+ df_Diag_Ram_Update_change.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problemm\result_change.csv',index=False,encoding='GB18030')
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end=time.time()
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print(end-start)
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@@ -158,13 +144,18 @@ if __name__ == "__main__":
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mylog=log.Mylog('log_diag.txt','error')
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mylog.logcfg()
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#............................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................
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- result=pd.read_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problem\result.csv',encoding='gbk')
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-
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- # df_Diag_Ram=result[result['end_time']=='0000-00-00 00:00:00']
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- df_Diag_Ram=result#[result['Batpos'] == 0]#将故障依然存在的赋值
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- print('----------------输入--------')
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- print(df_Diag_Ram)
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- print('-------计算中-----------')
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+ host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
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+ port=3306
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+ db='safety_platform'
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+ user='qx_read'
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+ password='Qx@123456'
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+ mode=2
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+ tablename2='all_fault_info'
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+ DBRead = DB.DBDownload(host, port, db, user, password,mode)
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+ with DBRead as DBRead:
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+ 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='')
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+ # result=pd.read_csv(r'D:\Work\Code_write\data_analyze_platform\USER\01Screen_Problem\result.csv',encoding='gbk')
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+
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#定时任务.......................................................................................................................................................................
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scheduler = BlockingScheduler()
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scheduler.add_job(diag_cal, 'interval', seconds=300, id='diag_job')
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