{
"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 = \"PK10001A326000123\"\r\n",
"st = '2021-07-06 00:00:00'\r\n",
"et = '2021-07-07 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 PK10001A326000123 from 2021-07-06 00:00:00 to 2021-07-07 20:00:00\n",
"# get data from 2021-07-06 00:00:00 to 2021-07-07 00:00:00......... \n",
"Server Error, retry 1...... \n",
"# get data from 2021-07-07 00:00:00 to 2021-07-07 20:00:00......... \n",
"all data-getting done, bms_count is 0, gps_count is 0, system_count is 0, accum_count is 0 \n",
"\n"
]
}
],
"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']<df_sel_bms.loc[len(df_sel_bms)-1,'时间戳'])]\r\n",
" df_sel_gps = df_sel_gps.reset_index(drop=True)\r\n",
"\r\n",
" print(indexSta.odo_sta(np.array(df_sel_gps['odo'])))\r\n",
" print(indexSta.capacity_sta(40, np.array(df_sel_bms['SOC[%]']), np.array(df_sel_bms['SOH[%]'])))\r\n",
" print(indexSta.energy_sta(40, np.array(df_sel_bms['SOC[%]']), np.array(df_sel_bms['SOH[%]']),np.array(df_sel_bms['总电压[V]'])))\r\n",
" print(indexSta.acc_time_sta(np.array(df_sel_bms['时间戳'])))\r\n",
" print(indexSta.mean_temp_sta(np.array(df_sel_bms['单体温度1'])))\r\n",
" print(indexSta.temp_change_rate_sta(np.array(df_sel_bms['时间戳']), np.array(df_sel_bms['单体温度1'])))\r\n",
" print(indexSta.dischrg_max_pwr_sta(np.array(df_sel_bms['总电压[V]']), np.array(df_sel_bms['总电流[A]'])))\r\n",
" print(indexSta.chrg_max_pwr_sta(np.array(df_sel_bms['总电压[V]']), np.array(df_sel_bms['总电流[A]'])))\r\n",
" print(indexSta.speed_sta(indexSta.odo_sta(np.array(df_sel_gps['odo'])), indexSta.acc_time_sta(np.array(df_sel_gps['时间戳'])), np.array(df_sel_gps['speed'])))\r\n",
" break"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"source": [
"# 生成pydoc 说明文档\r\n",
"!python -m pydoc -w LIB\\BACKEND\\DataPreProcess.py"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"problem in LIB\\BACKEND\\DataPreProcess.py - ModuleNotFoundError: No module named 'DBManager'\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 1,
"source": [
"from LIB.BACKEND import DBManager, Log\r\n",
"log = Log.Mylog(log_name='signal_monitor', log_level = 'info')\r\n",
"log.set_file_hl(file_name='info.log', log_level='info')\r\n",
"log.set_file_hl(file_name='error.log', log_level='error')\r\n",
"logger = log.get_logger()\r\n"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"source": [
"logger.error(\"ttt5\")"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [],
"outputs": [],
"metadata": {}
}
],
"metadata": {
"interpreter": {
"hash": "b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.8.8 64-bit ('base': conda)"
},
"language_info": {
"name": "python",
"version": "3.8.8",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
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"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
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"nbformat": 4,
"nbformat_minor": 2
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