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- from math import radians, cos, sin, asin, sqrt
- import pandas as pd
- import numpy as np
- from datetime import datetime
- from datetime import timedelta
- from GpsRank import *
- from ProcessDfBms import *
- from ProcessDfGps import *
- from LIB.BACKEND import DBManager
- import DBManager
- #####################################配置环境分割线#################################################
- def GetDistInfo(input_sn,input_starttime,input_endtime):
- #####################################配置参数分割线#################################################
- dbManager = DBManager.DBManager()
- data_raw = dbManager.get_data(sn=input_sn, start_time=input_starttime,
- end_time=input_endtime)
- #拆包预处理
- df_bms_raw=data_raw['bms']
- df_gps_raw=data_raw['gps']
- df_bms=preprocess_Df_Bms(df_bms_raw)
- df_gps=preprocess_Df_Gps(df_gps_raw)
-
- #####################################数据预处理分割线#################################################
- # mode: 0：正常取数； 1：7255 取数
- if input_sn[0:2] == 'UD' or input_sn[0:2] == 'MG':
- mode = 1
- else:
- mode = 0
- #获取状态表,mode默认为0,mode=1放电时电流为负，mode=0充电时电流为正
- df_bms_drive_timetable=get_bms_drive_timetable(df_bms,mode)
- df_gps_drive_cycle_accum=pd.DataFrame()
- if len(df_bms_drive_timetable)>0:
- for index in range(len(df_bms_drive_timetable)):
- #筛选drivecycle数据
- drive_start_time=df_bms_drive_timetable.loc[index,'drive_start_time']#开始时间
- drive_end_time=df_bms_drive_timetable.loc[index,'drive_end_time']#结束时间
- time_condition=(df_gps['time']>drive_start_time)&(df_gps['time']<drive_end_time)#时间判断条件
- df_gps_drive_cycle=df_gps.loc[time_condition,:].copy()
- df_gps_drive_cycle=df_gps_drive_cycle.reset_index(drop=True)#重置index
- #计算drivecycle GPS累计里程,存入表格
- condition_a=df_gps_drive_cycle['deltatime']>60*3
- condition_b=(df_gps_drive_cycle['deltatime']>90*1)&(df_gps_drive_cycle['distance']>1000)
- drive_cycle_dist_array=df_gps_drive_cycle.loc[~(condition_a|condition_b),'distance'].values
- drive_cycle_dist_array=drive_cycle_dist_array[np.where((drive_cycle_dist_array>=1)&(drive_cycle_dist_array<1000))[0]]
- gps_dist=drive_cycle_dist_array.sum()
- df_bms_drive_timetable.loc[index,'gps_dist']=gps_dist#得到GPS路径
- #计算头-尾的空缺时间段对应的预估SOC
- if len(df_gps_drive_cycle)>2:
- gps_starttime=df_gps_drive_cycle.loc[1,'time']#gps开始时间
- gps_endtime=df_gps_drive_cycle.loc[df_gps_drive_cycle.index[-1],'time']#gps结束时间
- #从drive_start_time到gps开始时间，使用SOC计算的里程
- #gps结束时间到drive_end_time，使用SOC计算的里程
- unrecorded_odo_head=cal_deltasoc(df_bms,drive_start_time,gps_starttime)
- unrecorded_odo_tail=cal_deltasoc(df_bms,gps_endtime,drive_end_time)
- else:
- #计算数据丢失行unrecordeodo
- unrecorded_odo_head=cal_deltasoc(df_bms,drive_start_time,drive_end_time)
- unrecorded_odo_tail=0
- #计算中间的预估SOC
- predict_dist=cal_unrecorded_gps(df_gps_drive_cycle,df_bms)
- #计算总的预估SOC
- totaldist=predict_dist+unrecorded_odo_head+ unrecorded_odo_tail#得到GPS路径
- df_bms_drive_timetable.loc[index,'predict_dist']=totaldist
- else :
- pass
- #####################################统计行驶里程End#################################################
- #打印输出结果#
- index_list=list(range(len(df_bms_drive_timetable)))
- dist_gps=0
- dist_predict=0
- day_start_time=''#当日开始时间
- day_end_time=''#当日结束时间
- day_start_soc=0#当日开始soc
- day_end_soc=0#当日结束soc
- day_min_soc=101#当日最低soc
- drive_accum_soc=0#累计使用SOC
- if len(df_bms_drive_timetable)>0:
- #开始行
- day_start_soc=df_bms_drive_timetable.loc[1,'drive_start_soc']#开始soc
- day_start_time=df_bms_drive_timetable.loc[1,'drive_start_time']#开始时间
- #结束行
- day_end_time=df_bms_drive_timetable.loc[len(df_bms_drive_timetable)-1,'drive_end_time']#结束时间
- day_end_soc=df_bms_drive_timetable.loc[len(df_bms_drive_timetable)-1,'drive_end_soc']#结束soc
- for index in index_list:
- '''汇总里程'''
- dist_gps+=df_bms_drive_timetable.loc[index,'gps_dist']/1000#计算GPS里程
- dist_predict+=df_bms_drive_timetable.loc[index,'predict_dist']#计算预估里程
- drive_start_soc=df_bms_drive_timetable.loc[index,'drive_start_soc']#驾驶周期开始的soc
- drive_end_soc=df_bms_drive_timetable.loc[index,'drive_end_soc']#驾驶周期结束的soc
- day_min_soc=min(day_min_soc,drive_start_soc,drive_end_soc)
- delta_soc=drive_start_soc-drive_end_soc#驾驶周期SOC变化量
- drive_accum_soc+=abs(delta_soc)#所有drive cycle累计消耗的soc
- # gps_score=get_df_gps_score(input_starttime,input_endtime,df_gps)
- # gps_score=round(gps_score,1)
- #计算总里程
- dist_gps=round(dist_gps,3)
- dist_predict=round(dist_predict,3)
- dist_all=round(dist_gps+dist_predict,3)
- #输出统计结果
- # print ('为您查询到，从'+input_starttime+'到'+input_endtime+'时间段内：')
- # print('SOC变化量：'+str(df_bms['bmspacksoc'].max()-df_bms['bmspacksoc'].min())+' %')
- # print('行驶总里程:'+str(dist_all)+' km')
- return {'SN':input_sn,'range':dist_all,'accum_soc':drive_accum_soc,'day_start_soc':day_start_soc,
- 'day_end_soc':day_end_soc,'day_start_time':day_start_time,'day_end_time':day_end_time,
- 'day_min_soc':day_min_soc}
- # print('其中GPS信号在线时里程:'+str(dist_gps)+' km')
- # print('其中GPS信号掉线时预估里程:'+str(dist_predict)+' km')
- # print('GPS信号质量评分为：'+str(gps_score),'分\n')
- #####################################打印结果End#################################################
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