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+#历史剩余充电时间统计 2021-10
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+
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+from LIB.BACKEND.Tools import Tools
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+from LIB.BACKEND.DataPreProcess import DataPreProcess
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+import pandas as pd
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+import numpy as np
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+import os
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+import math
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+import datetime
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+from time import strftime,gmtime
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+from LIB.BACKEND import DBManager
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+dbManager = DBManager.DBManager()
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+
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+#将时间戳由 "%Y-%m-%d %H:%M:%S" 切换为 sec
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+def timeconvert(df):
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+ df.index=range(len(df))
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+ time=df.时间戳
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+ timeInSeries=[]
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+ time2=datetime.datetime.strptime(time[0],"%Y-%m-%d %H:%M:%S")
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+ for k in range(len(time)):
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+ time1=datetime.datetime.strptime(time[k],"%Y-%m-%d %H:%M:%S")
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+ t=(time1-time2)
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+ timeInSeries.append(t.days*86400+t.seconds)
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+ df.loc[:,'相对时间']=pd.DataFrame(timeInSeries,columns=['相对时间'])
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+ return df
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+
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+#构建新数据表统计剩余充电时间
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+def data_handle(df_in):
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+ #数据预处理
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+ df_in=timeconvert(df_in)
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+ dfOut_temp=DataPreProcess.data_split_by_status(DataPreProcess, df_in, drive_interval_threshold=120, charge_interval_threshold=300,drive_stand_threshold=120, charge_stand_threshold=300)
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+ dfOut=dfOut_temp.copy()
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+ df_charge=dfOut[dfOut['data_status']=='charge']
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+ unique_status_idx=np.unique(df_charge.data_split_by_status.values)
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+ #计算剩余充电时间
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+ df_statisticsInfo=pd.DataFrame()
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+ for n in unique_status_idx:
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+ df_charge_buff=df_charge[df_charge.data_split_by_status==n]
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+ if(np.max(df_charge_buff['SOC[%]'].values[-1])==100): #满充状态数据提取
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+ times=df_charge_buff['相对时间'].values
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+ times1=times[:-1]
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+ times2=times[1:]
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+ delta=times2-times1
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+ if(np.max(delta)<50):
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+ times_inv=[times[-1]-t for t in times ]
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+ df_charge_buff.loc[:,'剩余充电时间[sec]']=times_inv
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+ times_hms=[]
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+ for k in list(range(len(times_inv))):
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+ time_hms=strftime("%H:%M:%S", gmtime(df_charge_buff.loc[:,'剩余充电时间[sec]'].values[k]))
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+ times_hms.append(time_hms)
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+ df_charge_buff.loc[:,'剩余充电时间[时分秒]']=times_hms
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+ df_dataset_temp=df_charge_buff.drop(['GSM信号','故障等级','故障代码','外电压','绝缘电阻','总输出状态','上锁状态','充电状态','加热状态','单体压差','单体均衡状态','相对时间','data_split_by_crnt','data_split_by_status','data_status'],axis=1)
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+ df_statisticsInfo=df_statisticsInfo.append(df_dataset_temp)
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+ #简化特征
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+ list_col=list(df_statisticsInfo)
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+ list1=[s for s in list_col if '单体电压' in s]
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+ list2=[s for s in list_col if '单体温度' in s]
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+ list3=[s for s in list_col if '其他温度' in s]
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+ list_v=['单体电压'+str(i) for i in range(1,len(list1)+1)]
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+ list_T=['单体温度'+str(i) for i in range(1,len(list2)+1)]
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+ A_cellVolt=df_statisticsInfo[list_v].values
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+ celVolt_max=np.max(A_cellVolt,axis=1)
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+ celVolt_min=np.min(A_cellVolt,axis=1)
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+ A_cellTemp=df_statisticsInfo[list_T].values
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+ celTemp_max=np.max(A_cellTemp,axis=1)
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+ celTemp_min=np.min(A_cellTemp,axis=1)
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+ df_statisticsInfo.loc[:,'最高单体电压[V]']=celVolt_max
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+ df_statisticsInfo.loc[:,'最高单体温度[℃]']=celTemp_max
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+ df_statisticsInfo.loc[:,'最低单体温度[℃]']=celTemp_min
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+ df_statisticsInfo=df_statisticsInfo.drop(list_v,axis=1)
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+ df_statisticsInfo=df_statisticsInfo.drop(list_T,axis=1)
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+ df_statisticsInfo=df_statisticsInfo.drop(['其他温度'+str(i) for i in range(1,len(list3)+1)],axis=1)
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+ return(df_statisticsInfo)
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+
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+#Dataset
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+data_sn=pd.read_excel('sn-20210903.xlsx',sheet_name='sn-20210903')
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+fileNames=data_sn['sn']
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+df_dataset=pd.DataFrame()
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+for k in range(2):
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+ df_data = dbManager.get_data(sn=fileNames[k], start_time='2021-10-01 00:00:00', end_time='2021-11-01 00:00:00', data_groups=['bms'])
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+ if len(df_data)==0:
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+ continue
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+ dataIn = df_data['bms']
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+ dataIn=data_handle(dataIn)
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+ dataIn['sn']=fileNames[k]
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+ df_dataset=df_dataset.append(dataIn)
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+
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+df_dataset.to_csv('df_datatest.csv')
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+
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