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+# -*- coding: utf-8 -*-
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+"""
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+Created on Sat Sep 25 23:26:27 2021
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+
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+@author: admin
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+"""
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+import pandas as pd
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+import numpy as np
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+from LIB.BACKEND.DataPreProcess import DataPreProcess
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+import os
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+import math
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+import datetime
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+import time
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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_in,column_name):
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+ df=df_in.copy()
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+ df.index=range(len(df))
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+ time=df[column_name]
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+ timeInSeries=[]
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+ time2=datetime.datetime.strptime(str(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(str(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[s]']=pd.DataFrame(timeInSeries,columns=['相对时间[s]'])
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+ return df
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+
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+#计算累积能量
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+def cal_accumKwh(df_in):
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+ df_in1=df_in.copy()
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+ df1=df_in1[['总电流[A]','总电压[V]','相对时间[s]']]
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+ df1=df1.dropna(axis=0,how='any')
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+ I=df1['总电流[A]'].values
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+ V=df1['总电压[V]'].values
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+ t=df1['相对时间[s]'].values
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+ accumAh=[0.0]
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+ for k in range(1,len(I)):
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+ accumAh_temp=(t[k]-t[k-1])*((I[k]+I[k-1])/(2*3600))*(V[k]+V[k-1])/2/1000
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+ accumAh.append(accumAh[-1]+accumAh_temp)
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+ df1.loc[:,'累积能量[Kwh]']=accumAh
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+ df_out=pd.merge(df_in1,df1[['累积能量[Kwh]']],how='left',left_index=True, right_index=True)
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+ return(df_out)
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+
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+#将时间格式化为整数
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+def str_data_to_num(str_data):
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+ # 格式时间成毫秒
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+ strptime = time.strptime(str_data,"%Y-%m-%d %H:%M:%S")
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+ # print("strptime",strptime)
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+ mktime = int(time.mktime(strptime)*1000)
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+ # print("mktime",mktime)
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+ return mktime
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+
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+def df_date_To_int(df_in):
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+ df=df_in.copy()
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+ for k in range(len(df)):
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+ df.loc[k,'绝对时间[ms]']=str_data_to_num(df['时间戳'][k])
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+ return df
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+
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+#根据经纬度获取两点之间的距离
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+def cal_dis_meters(radius,latitude1, longitude1,latitude2, longitude2):
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+ radLat1 = (math.pi/180)*latitude1
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+ radLat2 = (math.pi/180)*latitude2
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+ radLng1 = (math.pi/180)*longitude1
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+ radLng2= (math.pi/180)*longitude2
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+ d=2*math.asin(math.sqrt(math.pow(math.sin((radLat1-radLat2)/2.0),2)+math.cos(radLat1)*math.cos(radLat2)*math.pow(math.sin((radLng1-radLng2)/2.0),2)))*radius
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+ return d
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+
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+def cal_gps_distance(df_in):
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+ #根据gps数据计算△距离1和△距离2
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+ #△距离1:直接根据两次的经度和纬度计算得到的马氏距离
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+ #△距离2：当两次上报经纬度的时间间隔＜60sec时，如果车辆为行驶状态则使用两次的经纬度求得的马氏距离，
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+ #如果车辆不为行驶状态，则为0.
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+ df_gpsOnly=df_in[df_in['纬度']>0]
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+ index_list=df_gpsOnly.index
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+ pos_list=df_gpsOnly[['纬度','经度']].values
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+ time_list=df_in['相对时间[s]'].values
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+ # Energy_list=df_in['累积能量[Kwh]'].values
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+ df_gpsOnly.loc[index_list[0],'△距离1']=0
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+ df_gpsOnly.loc[index_list[0],'△距离2']=0
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+ for k in range(1,len(pos_list)):
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+ latitude1=pos_list[k-1][0]
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+ longitude1=pos_list[k-1][1]
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+ latitude2=pos_list[k][0]
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+ longitude2=pos_list[k][1]
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+ dlt_odo=cal_dis_meters(6378.137,latitude1, longitude1,latitude2, longitude2)
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+ df_gpsOnly.loc[index_list[k],'△距离1']=dlt_odo
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+ if time_list[index_list[k]]-time_list[index_list[k-1]]<60:#两次上传GPS数据时间间隔小于60sec
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+ if df_gpsOnly.loc[index_list[k],'data_status']=='drive' :
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+ df_gpsOnly.loc[index_list[k],'△距离2']=dlt_odo
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+ elif df_gpsOnly.loc[index_list[k],'data_status']=='none' :
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+ df_gpsOnly.loc[index_list[k],'△距离2']=dlt_odo
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+ else:
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+ df_gpsOnly.loc[index_list[k],'△距离2']=0
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+ return(df_gpsOnly)
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+
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+
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+def real_odo(df_in,avg_cost):
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+ # df_handle=df_in.copy()
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+ df=timeconvert(df_in,"时间戳")#计算相对时间
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+ df=cal_accumKwh(df)#计算累积能量
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+
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+ positive_lat_index=df[df['纬度']>0].index
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+ if len(positive_lat_index)>2:
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+ first_index=positive_lat_index[0]
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+ end_index=positive_lat_index[-1]
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+ #将数据分割为有第一个GPS数据之前（part1）、
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+ #有最后一个GPS数据之后(part3)以及
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+ #有第一个GPS数据和最后一个GPS数据中间的数据(part3)
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+ part1=df[0:first_index+1]
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+ part2=df[first_index:end_index+1]
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+ part3=df[end_index:-1]
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+
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+ #第一部分距离计算
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+ part1.index=[i for i in range(len(part1))]
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+ part1.loc[0,'△距离']=0
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+ AccumEnergy1=part1['累积能量[Kwh]'].values
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+ for k1 in range(1,len(part1)):
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+ if (AccumEnergy1[k1]-AccumEnergy1[k1-1])>0:
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+ part1.loc[k1,'△距离']=(AccumEnergy1[k1]-AccumEnergy1[k1-1])*avg_cost
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+ part1.loc[k1,'方法']=4
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+ else:
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+ part1.loc[k1,'△距离']=0
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+ part1.loc[k1,'方法']=5
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+
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+ #第二部分计算
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+ part2_gps=part2[part2['纬度']>0]
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+ part2_gps.index=[i for i in range(len(part2_gps))]
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+ times_list=part2_gps['相对时间[s]'].values
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+ lat=part2_gps['纬度'].values
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+ lng=part2_gps['经度'].values
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+ AccumEnergy2=part2_gps['累积能量[Kwh]'].values
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+ part2_gps.loc[0,'△距离']=0
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+ for k2 in range(1,len(part2_gps)):
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+ delta_energy=AccumEnergy2[k2]-AccumEnergy2[k2-1]
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+ delta_span=cal_dis_meters(6378.137,lat[k2], lng[k2],lat[k2-1], lng[k2-1])
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+ v_spd=3600*delta_span/(times_list[k2]-times_list[k2-1])
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+ if times_list[k2]-times_list[k2-1]<60 and not delta_energy<=0:
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+ if v_spd>50 :
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+ part2_gps.loc[k2,'△距离']=delta_energy*avg_cost
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+ part2_gps.loc[k2,'方法']=1_1
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+ else:
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+ part2_gps.loc[k2,'△距离']=delta_span
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+ part2_gps.loc[k2,'方法']=1_2
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+ else:
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+ if delta_energy<=0:
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+ part2_gps.loc[k2,'△距离']=0
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+ part2_gps.loc[k2,'方法']=2
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+ else:
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+ if v_spd>50:
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+ part2_gps.loc[k2,'△距离']=delta_energy*avg_cost
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+ part2_gps.loc[k2,'方法']=3_1
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+ else:
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+ part2_gps.loc[k2,'△距离']=max(delta_span,delta_energy*avg_cost)
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+ part2_gps.loc[k2,'方法']=3_2
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+
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+
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+ #第三部分距离计算
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+ part3.index=[i for i in range(len(part3))]
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+ part3.loc[0,'△距离']=0
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+ AccumEnergy3=part3['累积能量[Kwh]'].values
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+ for k3 in range(1,len(part3)):
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+ if (AccumEnergy3[k3]-AccumEnergy3[k3-1])>0:
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+ part3.loc[k3,'△距离']=(AccumEnergy3[k3]-AccumEnergy3[k3-1])*avg_cost
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+ part3.loc[k3,'方法']=4
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+ else:
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+ part3.loc[k3,'△距离']=0
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+ part3.loc[k3,'方法']=5
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+ df_out=pd.concat([part1,part2_gps[1:],part3[1:]])
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+ else:
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+ part1=df
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+ part2=df[0:0]
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+ part3=df[0:0]
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+
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+ #第一部分距离计算
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+ part1.index=[i for i in range(len(part1))]
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+ part1.loc[0,'△距离']=0
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+ AccumEnergy1=part1['累积能量[Kwh]'].values
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+ for k1 in range(1,len(part1)):
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+ if (AccumEnergy1[k1]-AccumEnergy1[k1-1])>0:
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+ part1.loc[k1,'△距离']=(AccumEnergy1[k1]-AccumEnergy1[k1-1])*avg_cost
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+ part1.loc[k1,'方法']=4
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+ else:
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+ part1.loc[k1,'△距离']=0
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+ part1.loc[k1,'方法']=5
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+ df_out=part1.copy()
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+
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+ #计算累积里程
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+ df_out.loc[0,'累积里程[km]']=0
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+ df_out.index=[i for i in range(len(df_out))]
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+
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+ for k in range(1,len(df_out)):
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+ df_out.loc[k,'累积里程[km]']=df_out.loc[k-1,'累积里程[km]']+df_out.loc[k,'△距离']
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+ return(df_out)
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+
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+def calcul_mileage(sn,data_bms,data_gps):
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+
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+ #合并两张表格
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+ df_bms=data_bms.copy()
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+ df_gps=data_gps.copy()
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+ #删除纬度小于10的点
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+ for k in range(1,len(df_gps)):
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+ if df_gps.loc[k,'纬度']<10:
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+ df_gps=df_gps.drop(k)
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+ df_bms.set_index(["时间戳"], inplace=True)
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+ df_gps.set_index(["时间戳"], inplace=True)
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+ merge_df1=pd.merge(df_bms, df_gps,how='outer', left_index=True, right_index=True)
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+ merge_df1.loc[:,'时间戳']=merge_df1.index
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+ merge_df1.index=[i for i in range(len(merge_df1))]
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+
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+ #参数定义
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+ cost_min=34 #最小能耗km/kwh
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+ cost_max=45 #最高能耗km/kwh
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+
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+ df_input=merge_df1[['时间戳','总电流[A]', '总电压[V]','SOC[%]','充电状态','纬度', '经度']]
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+ #电流电压数据修复
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+ df_input=timeconvert(df_input,"时间戳")#计算相对时间
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+ related_times=df_input['相对时间[s]'].values
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+ lat_list=df_input['纬度'].values
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+ lng_list=df_input['经度'].values
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+ Vlt_list=df_input['总电压[V]'].values
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+ for k in range(1,len(df_input)):
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+ if math.isnan(df_input.loc[k,'总电流[A]']):
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+ if related_times[k]-related_times[k-1]<5:
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+ df_input.loc[k,'总电流[A]']=df_input.loc[k-1,'总电流[A]']
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+ df_input.loc[k,'总电压[V]']=df_input.loc[k-1,'总电压[V]']
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+ df_input.loc[k,'修复']=1
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+ elif lat_list[k-1]>0:
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+ delta_odo=cal_dis_meters(6378.137,lat_list[k], lng_list[k],lat_list[k-1], lng_list[k-1])
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+ df_input.loc[k,'总电流[A]']=1000*3600*(delta_odo/cost_min)/70/(related_times[k]-related_times[k-1])
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+ df_input.loc[k,'总电压[V]']=70
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+ df_input.loc[k,'修复']=2
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+ elif related_times[k]-related_times[k-1]<30:
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+ df_input.loc[k,'总电流[A]']=df_input.loc[k-1,'总电流[A]']
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+ df_input.loc[k,'总电压[V]']=df_input.loc[k-1,'总电压[V]']
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+ df_input.loc[k,'修复']=3
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+ else:
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+ df_input=df_input.drop(k)
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+
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+ df_input.index=[i for i in range(len(df_input))]
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+ df_input['日期']=[df_input.loc[i,'时间戳'][0:10] for i in range(len(df_input))]
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+ date_list=np.unique(df_input['日期'].values)
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+
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+ list_result=[]
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+ input_res=pd.DataFrame()
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+ res = pd.DataFrame()
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+ df_input_copy=df_input.copy()
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+ for date_str in date_list:
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+ df_input_copy=df_input[df_input['日期']==date_str]
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+ data_len=len(df_input_copy)
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+ time_list=df_input_copy['时间戳'].values
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+ #初始化参数
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+ start_time=datetime.datetime.strptime(time_list[0], "%Y-%m-%d %H:%M:%S")
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+ last_time=time_list[0]
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+ reset_time=start_time
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+ start_index=0
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+ end_index=1
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+ cal_count=0
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+ df_deltaData_last=df_input_copy[0:0]
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+ #初始化输出
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+ df_result=pd.DataFrame()
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+
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+ df_result.loc[0,'时间']=start_time
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+ df_result.loc[0,'时间']=str(df_result.loc[0,'时间'])
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+ df_result.loc[0,'时间'] =df_result.loc[0,'时间'].rpartition(':')[0]
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+
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+ df_result.loc[0,'累积里程[km]']=0
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+ df_result.loc[0,'能耗[km/kwh]']=cost_min
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+ process_store=[]
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+ avg_cost=34
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+ cross_odo=0
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+ for k in range(1,data_len):
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+
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+ target_Time=start_time+datetime.timedelta(minutes=5)
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+ timenow=datetime.datetime.strptime(time_list[k], "%Y-%m-%d %H:%M:%S")
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+ if target_Time>=timenow:
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+ delta_time=(target_Time-timenow).seconds
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+ cal_flag=0
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+ if delta_time<60:
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+ end_index=k
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+ cal_flag=1
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+ else:
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+ end_index=k
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+ cal_flag=1
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+
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+ if cal_flag==1:
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+ cal_count=cal_count+1
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+
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+ df_deltaData=df_input_copy[max(start_index-100,0):end_index+1]
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+ avg_cost=max(cost_min,df_result.loc[cal_count-1,'能耗[km/kwh]'])
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+ #avg_cost=cost_min
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+ df_span=real_odo(df_deltaData,avg_cost)
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+ st_str=df_span['时间戳'].values[0]
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+ st_time=datetime.datetime.strptime(st_str, "%Y-%m-%d %H:%M:%S")
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+ last_time_s=datetime.datetime.strptime(last_time, "%Y-%m-%d %H:%M:%S")
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+ if last_time_s>=st_time:
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+ last_time=last_time
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+ cross_odo=cross_odo
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+ else:
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+ last_time=start_time
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+ cross_odo=0
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+ delta_odo=df_span['累积里程[km]'].values[-1]-df_span[df_span['时间戳']==str(last_time)]['累积里程[km]'].values[0]
|
|
|
+ delta_energy=df_span['累积能量[Kwh]'].values[-1]-df_span[df_span['时间戳']==str(last_time)]['累积能量[Kwh]'].values[0]
|
|
|
+ df_result.loc[cal_count,'时间']=timenow
|
|
|
+ df_result.loc[cal_count,'时间']=str(df_result.loc[cal_count,'时间'])
|
|
|
+ df_result.loc[cal_count,'时间'] = df_result.loc[cal_count,'时间'].rpartition(':')[0]
|
|
|
+
|
|
|
+ df_result.loc[cal_count,'累积里程[km]']=df_result.loc[cal_count-1,'累积里程[km]']+delta_odo-cross_odo
|
|
|
+ if delta_energy>0:
|
|
|
+ df_result.loc[cal_count,'能耗[km/kwh]']=min(max(delta_odo/delta_energy,cost_min),cost_max)
|
|
|
+ else:
|
|
|
+ df_result.loc[cal_count,'能耗[km/kwh]']=cost_min
|
|
|
+
|
|
|
+ # df_span[df_span['纬度']>0][-1:]
|
|
|
+ if len(df_span[df_span['纬度']>0])>0:
|
|
|
+ last_gps=df_span[df_span['纬度']>0][-1:]
|
|
|
+ idx=last_gps.index.values[0]
|
|
|
+ last_time=df_span.loc[idx,'时间戳']
|
|
|
+ cross_odo=df_span['累积里程[km]'].values[-1]-df_span.loc[idx,'累积里程[km]']
|
|
|
+ else:
|
|
|
+ last_time=df_span['时间戳'].values[-1]
|
|
|
+ cross_odo=0
|
|
|
+
|
|
|
+ start_index=end_index
|
|
|
+ start_time=timenow
|
|
|
+ cal_flag=0
|
|
|
+
|
|
|
+ res=res.append(df_result)
|
|
|
+ input_res=input_res.append(df_input_copy)
|
|
|
+
|
|
|
+ l= len(input_res['时间戳'])
|
|
|
+ for k in range(l):
|
|
|
+ input_res.loc[k,'时间戳'] =input_res.loc[k,'时间戳'].rpartition(':')[0]
|
|
|
+
|
|
|
+ res.index=[i for i in range(len(res))]
|
|
|
+ res['日期']=[res.loc[i,'时间'][0:10] for i in range(len(res))]
|
|
|
+
|
|
|
+ merge_res=pd.DataFrame()
|
|
|
+ for date_str in date_list:
|
|
|
+ input_res_copy=input_res[input_res['日期']==date_str]
|
|
|
+ input_res_copy=input_res_copy.drop_duplicates('时间戳')
|
|
|
+ res_copy=res[res['日期']==date_str]
|
|
|
+ df_merge=pd.DataFrame()
|
|
|
+ df_merge=pd.merge(input_res_copy,res_copy,left_on='时间戳',right_on='时间',how='right')
|
|
|
+ merge_res=merge_res.append(df_merge)
|
|
|
+
|
|
|
+ merge_res['sn']=sn
|
|
|
+ merge_res=merge_res[['时间戳','sn','总电流[A]','总电压[V]','SOC[%]','充电状态','纬度','经度','能耗[km/kwh]','累积里程[km]']]
|
|
|
+ merge_res=merge_res.rename(columns={'累积里程[km]':'每日累积里程[km]'})
|
|
|
+
|
|
|
+ return merge_res
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+
|
