data_analyze_platform/demo.py

# 获取数据
base_path = 'F:\work\QX\data_analyze_platform'
CONF_PATH = base_path + '\CONFIGURE\'
import sys
sys.path.append(CONF_PATH)
import PathSetting
sys.path.append(PathSetting.backend_path)
import DBManager

dbManager = DBManager.DBManager()
df_bms, df_gps = dbManager.get_data(sn='PK50201A000002005', start_time='2021-04-01 00:00:00', 
    end_time='2021-04-05 00:00:00', gps_switch=True, mode=0)

# 下载数据 7255
base_path = 'F:\work\QX\data_analyze_platform'
CONF_PATH = base_path + '\CONFIGURE\'
import sys
sys.path.append(CONF_PATH)
import PathSetting
sys.path.append(PathSetting.backend_path)
import Tools

tools = Tools.Tools()
write_path = r'D:\Platform\Users\CJH\Download\data'
st = '2021-06-01 00:00:00'
et = '2021-06-03 00:00:00'
tools.data_download(write_path=write_path, sn='UD02030118B4C0010', start_time=st, 
    end_time=et, mode=1)

# 下载数据 非7255
base_path = 'F:\work\QX\data_analyze_platform'
CONF_PATH = base_path + '\CONFIGURE\'
import sys
sys.path.append(CONF_PATH)
import PathSetting
sys.path.append(PathSetting.backend_path)
import Tools

tools = Tools.Tools()
write_path = r'D:\Platform\Users\WLM\data_ana\Data_Files'
tools.data_download(write_path=write_path, sn='PK504B00100004017', start_time='2021-04-01 00:00:00', 
    end_time='2021-04-05 00:00:00', gps_switch=True, mode=0)

# 数据预处理
base_path = 'F:\work\QX\data_analyze_platform'
CONF_PATH = base_path + '\CONFIGURE\'
import sys
sys.path.append(CONF_PATH)
import PathSetting
sys.path.append(PathSetting.backend_path)
import DataPreProcess
importlib.reload(DataPreProcess)
dataPrePro = DataPreProcess.DataPreProcess()

# 时间完全相同的数据仅保留一行
df_bms_pro, df_gps_pro = dataPrePro.time_filter(df_bms, df_gps)

# 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）
df_bms_pro = dataPrePro.data_split_by_status(df_bms_pro)
df_bms_pro = dataPrePro.data_split_by_time(df_bms_pro)

# bms数据将两次充电间的状态合并
df_bms_pro = dataPrePro.combine_drive_stand(df_bms_pro)
# bms 数据计算行车和充电开始前后的静置时间
df_bms_pro = dataPrePro.cal_stand_time(df_bms_pro)
# 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)
df_bms_pro, df_gps_pro, res_record= dataPrePro.gps_data_judge(df_bms_pro, df_gps_pro)
# 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)
df_bms_pro, df_gps_pro, res_record= dataPrePro.data_gps_judge_after_combine(df_bms_pro, df_gps_pro)



# 单cycle指标统计
base_path = 'F:\work\QX\data_analyze_platform'
CONF_PATH = base_path + '\CONFIGURE\'
import sys
sys.path.append(CONF_PATH)
import PathSetting
sys.path.append(PathSetting.middle_path)
#import IndexStaByOneCycle
import importlib
importlib.reload(IndexStaByOneCycle)

indexSta = IndexStaByOneCycle.IndexStaByOneCycle()

data_number_list = sorted(list(set(df_bms[(df_bms['data_status'].isin(['drive']))]['data_split_by_status'])))
for data_number in data_number_list[:]:
    df_sel_bms = df_bms[df_bms['data_split_by_status'] == data_number]
    df_sel_bms = df_sel_bms.reset_index(drop=True)
    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'])]
    df_sel_gps = df_sel_gps.reset_index(drop=True)

    print(indexSta.odo_sta(np.array(df_sel_gps['odo'])))
    print(indexSta.capacity_sta(40, np.array(df_sel_bms['SOC[%]']), np.array(df_sel_bms['SOH[%]'])))
    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]'])))
    print(indexSta.acc_time_sta(np.array(df_sel_bms['时间戳'])))
    print(indexSta.mean_temp_sta(np.array(df_sel_bms['单体温度1'])))
    print(indexSta.temp_change_rate_sta(np.array(df_sel_bms['时间戳']), np.array(df_sel_bms['单体温度1'])))
    print(indexSta.dischrg_max_pwr_sta(np.array(df_sel_bms['总电压[V]']), np.array(df_sel_bms['总电流[A]'])))
    print(indexSta.chrg_max_pwr_sta(np.array(df_sel_bms['总电压[V]']), np.array(df_sel_bms['总电流[A]'])))
    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'])))
    break