{ "cells": [ { "cell_type": "code", "execution_count": 2, "source": [ "# 获取数据\r\n", "import sys\r\n", "from LIB.BACKEND import DBManager\r\n", "\r\n", "sn = \"MGMCLN750N215I057\"\r\n", "st = '2021-05-23 00:00:00'\r\n", "et = '2021-08-12 20:00:00'\r\n", "\r\n", "dbManager = DBManager.DBManager()\r\n", "df_data = dbManager.get_data(sn=sn, start_time=st, end_time=et, data_groups=['bms', 'gps', 'accum', 'system'])\r\n", "# \r\n", "df_bms = df_data['bms']\r\n", "df_gps = df_data['gps']\r\n", "df_accum = df_data['accum']\r\n", "df_system = df_data['system']" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "### start to get data MGMCLN750N215I057 from 2021-05-23 00:00:00 to 2021-08-12 20:00:00\n", "# get data from 2021-08-12 00:00:00 to 2021-08-12 20:00:00......... \n", "all data-getting done, bms_count is 53957, gps_count is 53959, system_count is 0, accum_count is 53959 \n", "\n" ] } ], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "#coding=utf-8\r\n", "import os\r\n", "import datetime\r\n", "import pandas as pd\r\n", "from LIB.BACKEND import DBManager, Log\r\n", "from LIB.MIDDLE import SignalMonitor\r\n", "from sqlalchemy import create_engine\r\n", "from sqlalchemy.orm import sessionmaker\r\n", "import time, datetime\r\n", "import traceback\r\n", "import pymysql\r\n", "from urllib import parse\r\n", "import dateutil.relativedelta\r\n", "dbManager = DBManager.DBManager()\r\n", "if __name__ == \"__main__\":\r\n", " \r\n", " # # 算法运行\r\n", " count = 0\r\n", " signalMonitor = SignalMonitor.SignalMonitor()\r\n", " cal_period = 24 # 计算间隔，单位h\r\n", " sn = 'MGMCLN750N215I057'\r\n", " st = datetime.datetime.strptime('2021-05-23 00:00:00', \"%Y-%m-%d %H:%M:%S\")\r\n", " et = datetime.datetime.strptime('2021-08-12 00:00:00', \"%Y-%m-%d %H:%M:%S\")\r\n", " df_last_state = pd.DataFrame(\r\n", " columns=['sn', 'current', 'Timestamp', 'PackState', 'LineState']) # 每日最后BMS数据\r\n", " df_last_state_gps = pd.DataFrame(\r\n", " columns=['sn', 'Timestamp', 'PackState', 'LineState', 'latitude', 'longitude']) # 每日最后GPS数据\r\n", " df_res = pd.DataFrame(columns=[\r\n", " 'sn', 'PackState', 'LineState', 'StartTime', 'EndTime', 'OfflineTime']) # 初始化BMS信号统计数据\r\n", " df_res_gps = pd.DataFrame(columns=[\r\n", " 'sn', 'PackState', 'LineState', 'StartTime', 'EndTime', 'OfflineTime', 'latitude', 'longitude']) # 初\r\n", " logger.info(\"pid-{} {}/{}, {} {} START!\".format(os.getpid(), str(count), str(len(df_sn)), sn, str(st)))\r\n", " while st < et:\r\n", "\r\n", " df_res, df_state, df_last_state = signalMonitor.get_bms_offline_stat(\r\n", " sn, st, et, df_res, df_last_state, cal_period) # 计算每日BMS信号统计数据\r\n", " df_res_gps, df_last_state_gps = signalMonitor.get_gps_offline_stat(\r\n", " sn, st, et, df_state, df_res_gps, df_last_state_gps, cal_period) # 计算每日GPS信号统计数据\r\n", " st = st + datetime.timedelta(hours=cal_period)\r\n", "\r\n", "\r\n", "\r\n", "\r\n" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "# 下载数据 \r\n", "import sys\r\n", "from LIB.BACKEND import Tools\r\n", "\r\n", "tools = Tools.Tools()\r\n", "write_path = r''\r\n", "sn = \"PK50001A100000680\"\r\n", "\r\n", "st = '2021-07-06 00:00:00'\r\n", "et = '2021-07-07 20:00:00'\r\n", "tools.data_download(write_path=write_path, sn=sn, start_time=st, end_time=et, data_groups=['bms'])" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "# 数据预处理\r\n", "import sys\r\n", "from LIB.BACKEND import DataPreProcess\r\n", "\r\n", "dataPrePro = DataPreProcess.DataPreProcess()\r\n", "# 时间完全相同的数据仅保留一行\r\n", "df_bms_pro, df_gps_pro = dataPrePro.time_filter(df_bms, df_gps)\r\n", "\r\n", "# bms数据按照电流和状态分段, 然后在状态分段内部，根据时间跳变继续分段（解决段内数据丢失）\r\n", "df_bms_pro = dataPrePro.data_split_by_status(df_bms_pro)\r\n", "df_bms_pro = dataPrePro.data_split_by_time(df_bms_pro)\r\n", "\r\n", "# bms数据将两次充电间的状态合并\r\n", "df_bms_pro = dataPrePro.combine_drive_stand(df_bms_pro)\r\n", "# bms 数据计算行车和充电开始前后的静置时间\r\n", "df_bms_pro = dataPrePro.cal_stand_time(df_bms_pro)\r\n", "# gps 数据可靠性判断, 并增加里程和速度至gps数据(根据未合并的数据段判断)\r\n", "df_bms_pro, df_gps_pro, res_record= dataPrePro.gps_data_judge(df_bms_pro, df_gps_pro)\r\n", "# gps 数据可靠性判断, 并增加里程和速度至gps数据(根据已合并的数据段判断)\r\n", "df_bms_pro, df_gps_pro, res_record= dataPrePro.data_gps_judge_after_combine(df_bms_pro, df_gps_pro)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": null, "source": [ "# 单cycle指标统计\r\n", "import sys\r\n", "from LIB.BACKEND import IndexStaByOneCycle\r\n", "\r\n", "indexSta = IndexStaByOneCycle.IndexStaByOneCycle()\r\n", "\r\n", "data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status'])))\r\n", "for data_number in data_number_list[:]:\r\n", " df_sel_bms = df_bms[df_bms['data_split_by_status'] == data_number]\r\n", " df_sel_bms = df_sel_bms.reset_index(drop=True)\r\n", " df_sel_gps = df_gps[(df_gps['时间戳']>df_sel_bms.loc[0,'时间戳']) & (df_gps['时间戳']