'''
暂时采用http方式获取历史数据。
预留：后期若改用通过访问数据库的形式进行数据的获取，则本文件负责数据库的连接，sql指令的执行，数据获取等功能。
'''
__author__ = 'lmstack'
import time
import datetime
import time
import pandas as pd
import numpy as np
import json
import requests
import pymysql
import pdb
class DBManager():
def __init__(self, host='', port='', auth='', db='', username='', password=''):
self.host = host
self.port = port
self.auth = auth
self.db = db
self.username = username
self.password = password
pass
def __enter__(self):
self.connect()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
def connect(self):
conn_success_flag = 0
while not conn_success_flag:
try:
self.conn = pymysql.connect(host=self.host, port=self.port, user=self.user, password=self.password, database=self.db)
except Exception as e:
conn_success_flag = 0
print("数据库连接失败 :{}".format(e))
time.sleep(5)
else:
conn_success_flag = 1
self.cursor = self.conn.cursor()
def close(self):
try:
self.conn.close()
except Exception as e:
print(e)
else:
print('数据库已断开连接')
def add(table, keyvalue):
fields_str = ''
values_str = ''
for k,v in keyvalue.items():
fields_str += k+' '
sql = 'insert into table {} ({}) values ({})'.format(table, fields_str, values_str)
# 以下各个函数实现 通过http方式获取数据
@staticmethod
def _get_var_name(cellnum,Tempnum,Othernum):
temp = []
for i in range(cellnum):
temp.append('单体电压'+str(i+1))
for i in range(Tempnum):
temp.append('单体温度'+str(i+1))
for i in range(Othernum):
temp.append('其他温度'+str(i+1))
return temp
@staticmethod
def _download_json_data(url):
'''
返回json数据的生成器，一次一行
'''
i = 0
while 1:
try:
r = requests.get(url, stream=True, timeout=(0.1, 1000), headers={'Connection':'keep-alive', 'Accept':'*/*', 'Accept-Encoding':'gzip,deflate,br'})
break
except requests.exceptions.RequestException as e:
if (i == 0):
print()
print('\r' + 'Server Error, retry {}......'.format(str(i)), end=" ")
time.sleep(0.1)
i+=1
# print(r.content)
# pdb.set_trace()
data = []
for line in r.iter_lines():
if line:
data.append(json.loads(line))
# yield json.loads(line)
return data
@staticmethod
def _convert_to_dataframe_bms(data, mode=0):
CellU = []
CellT = []
OtherT = []
CellU_Num = 0
CellT_Num = 0
OtherT_Num = 0
CellU_Num = len(data['ffBatteryStatus']['cellVoltageList'])
CellT_Num = len(data['ffBatteryStatus']['cellTempList'])
try:
OtherT_Num = len(data['ffBatteryStatus']['otherTempList'])
except:
OtherT_Num = 0
for i in range(CellU_Num):
CellU.append(data['ffBatteryStatus']['cellVoltageList'][i]*1000)
for i in range(CellT_Num):
CellU.append(data['ffBatteryStatus']['cellTempList'][i])
for i in range(OtherT_Num):
CellU.append(data['ffBatteryStatus']['otherTempList'][i])
if mode == 0:
data_len = 16
# data_block = np.array([data['info']['obdTime'],data['ffBatteryStatus']['rssi'],data['ffBatteryStatus']['errorLevel'],data['ffBatteryStatus']['errorCode']
# ,data['ffBatteryStatus']['current'],data['ffBatteryStatus']['voltageInner'],data['ffBatteryStatus']['voltageOutter'],
# data['ffBatteryStatus']['totalOutputState'],data['ffBatteryStatus']['lockedState'],
# data['ffBatteryStatus']['chargeState'],data['ffBatteryStatus']['heatState'],data['ffBatteryStatus']['cellVoltageDiff']
# ,data['ffBatteryStatus']['soc'],data['ffBatteryStatus']['soh'],data['ffBatteryStatus']['cellVolBalance']]).reshape(1,data_len)
data_block = np.array([data['info']['obdTime'],data['ffBatteryStatus'].get('rssi',None),data['ffBatteryStatus'].get('errorLevel', None),data['ffBatteryStatus'].get('errorCode', None),
data['ffBatteryStatus'].get('current',None),data['ffBatteryStatus'].get('voltageInner', None),data['ffBatteryStatus'].get('voltageOutter', None),
data['ffBatteryStatus'].get('totalOutputState', None),data['ffBatteryStatus'].get('lockedState', None),
data['ffBatteryStatus'].get('chargeState', None),data['ffBatteryStatus'].get('heatState', None),data['ffBatteryStatus'].get('cellVoltageDiff', None)
,data['ffBatteryStatus'].get('soc', None),data['ffBatteryStatus'].get('soh', None),data['ffBatteryStatus'].get('cellVolBalance', None),data['ffBatteryStatus'].get('insResis', None)]).reshape(1,data_len)
elif mode == 1:
data_len = 12
data_block = np.array([data['info']['obdTime'],data['ffBatteryStatus'].get('rssi',None)
,data['ffBatteryStatus'].get('errorLevel', None),data['ffBatteryStatus'].get('errorCode', None),data['ffBatteryStatus'].get('switchState', None)
,data['ffBatteryStatus'].get('current',None),data['ffBatteryStatus'].get('voltageInner', None),data['ffBatteryStatus'].get('chargeState', None),
data['ffBatteryStatus'].get('cellVoltageDiff', None),data['ffBatteryStatus'].get('soc', None),data['ffBatteryStatus'].get('soh', None),data['ffBatteryStatus'].get('insResis', None)]).reshape(1,data_len)
data_block = np.append(data_block,CellU)
data_block = np.append(data_block,CellT)
data_block = np.append(data_block,OtherT)
data_block = data_block.reshape(1,len(data_block))
return data_block,CellU_Num,CellT_Num,OtherT_Num
@staticmethod
def _convert_to_dataframe_gps(data, mode=0):
if mode == 0:
if data['info']['subType'] == 1:
data_block = np.array([data['info']['obdTime'],data['ffGps']['locationType'], data['ffGps']['satellites'],
data['ffGps']['latitude'],data['ffGps']['longitude'],data['ffGps']['speed'],
data['ffGps']['altitude'], data['ffGps']['direction']]).reshape(1,8)
df = pd.DataFrame(
columns=['时间戳','定位类型', '卫星数','纬度','经度','速度[km/h]','海拔','航向'],data=data_block)
elif data['info']['subType'] == 2:
df = pd.DataFrame(
columns=['时间戳','定位类型', '卫星数','纬度','经度','速度[km/h]','海拔','航向'])
if mode == 1:
data_block = np.array([data['info']['obdTime'],data['ffGps']['locationType'],data['ffGps']['latitude'],data['ffGps']['longitude']
,data['ffGps']['speed'], data['ffGps']['isValid']]).reshape(1,6)
df = pd.DataFrame(
columns=['时间戳','定位类型', '纬度','经度','速度[km/h]','有效位'],data=data_block)
return df
@staticmethod
def _convert_to_dataframe_system(data, mode=0):
if mode == 0:
data_block = np.array([data['info'].get('obdTime', None),data['ffSystemInfo'].get('heatTargetTemp', None), data['ffSystemInfo'].get('heatTimeout',None),
time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(int(data['ffSystemInfo'].get('rentalStartTime'))/1000)),
data['ffSystemInfo'].get('rentalPeriodDays',None),data['ffSystemInfo'].get('bmsInterval',None),
data['ffSystemInfo'].get('gpsInterval', None)]).reshape(1,7)
df = pd.DataFrame(
columns=['时间戳','加热目标温度', '加热超时','租赁开始时间','租赁天数','bms上传周期','gps上传周期'],data=data_block)
if mode == 1:
df = pd.DataFrame()
return df
@staticmethod
def _convert_to_dataframe_accum(data, mode=0):
if mode == 0:
data_block = np.array([data['info'].get('obdTime',None),data['ffBatteryAccum'].get('SOH_AlgUnexTime',None), data['ffBatteryAccum'].get('CHG_AHaccum',None),
data['ffBatteryAccum'].get('CHG_PHaccum',None), data['ffBatteryAccum'].get('DSG_AHaccum',None),
data['ffBatteryAccum'].get('DSG_PHaccum',None),data['ffBatteryAccum'].get('OverTemp_CHG_AHaccum',None),
data['ffBatteryAccum'].get('OverTemp_CHG_PHaccum',None)]).reshape(1,8)
df = pd.DataFrame(
columns=['时间戳','SOH未标定时间', '累计充电电量','累计充电能量','累计放电电量','累计放电能量',
'累计高温充电电量', '累计高温充电能量'],data=data_block)
if mode == 1:
data_block = np.array([data['info'].get('obdTime',None), data['ffBatteryAccum'].get('CHG_AHaccum',None),
data['ffBatteryAccum'].get('CHG_PHaccum',None), data['ffBatteryAccum'].get('DSG_AHaccum',None),
data['ffBatteryAccum'].get('DSG_PHaccum',None),data['ffBatteryAccum'].get('totalMileage',None)]).reshape(1,6)
df = pd.DataFrame(
columns=['时间戳','累计充电电量','累计充电能量','累计放电电量','累计放电能量', '累积里程'],data=data_block)
return df
@staticmethod
def _get_data(urls,type_name,mode=0):
data = DBManager._download_json_data(urls)
if type_name == 'bms':
if mode == 0:
name_const = ['时间戳','GSM信号','故障等级','故障代码','总电流[A]','总电压[V]', '外电压', '总输出状态', '上锁状态', '充电状态','加热状态',
'单体压差', 'SOC[%]','SOH[%]','单体均衡状态', '绝缘电阻']
elif mode == 1:
name_const = ['时间戳','GSM信号','故障等级', '故障代码','开关状态', '总电流[A]','总电压[V]','充电状态', '单体压差', 'SOC[%]','SOH[%]', '绝缘电阻']
i=0
CellUNum = 0
CellTNum = 0
OtherTNumm = 0
st = time.time()
for line in data:
et = time.time()
try:
if i==0:
data_blocks,CellUNum,CellTNum,OtherTNumm = DBManager._convert_to_dataframe_bms(line, mode)
i+=1
continue
except Exception as e:
print(str(e))
i = 0
try:
data_block,CellUNum,CellTNum,OtherTNumm = DBManager._convert_to_dataframe_bms(line, mode)
except Exception as e:
print(str(e))
continue
try:
data_blocks = np.concatenate((data_blocks,data_block),axis=0)
except Exception as e:
if 'all the input array dimensions for the concatenation axis must match exactly' in str(e) or \
'all the input array dimensions except for the concatenation axis must match exactly' in str(e):
pass
else:
raise e
# print('\r'+str(i),end=" ")
# print(data_block)
# print(urls)
# print(time.time()-et)
i+=1
name_var = DBManager._get_var_name(CellUNum,CellTNum,OtherTNumm)
name_const.extend(name_var)
columns_name = name_const
if i==0:
data_blocks = []
df_all = pd.DataFrame(columns=columns_name,data=data_blocks)
if not df_all.empty:
df_all.loc[:,'时间戳'] = df_all.loc[:,'时间戳'].apply(lambda x:time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(int(x)/1000)))
return df_all
elif type_name =='gps':
if mode == 0:
df_all = pd.DataFrame(columns=['时间戳','定位类型', '卫星数','纬度','经度','速度[km/h]','海拔','航向'])
elif mode == 1:
df_all = pd.DataFrame(columns=['时间戳','定位类型', '纬度','经度','速度[km/h]','有效位'])
for line in data:
df_add = DBManager._convert_to_dataframe_gps(line, mode)
df_all = df_all.append(df_add,ignore_index=True)
if not df_all.empty:
df_all.loc[:,'时间戳'] = df_all.loc[:,'时间戳'].apply(lambda x:time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(int(x)/1000)))
return df_all
elif type_name =='system':
if mode == 0:
df_all = pd.DataFrame(columns=['时间戳','加热目标温度', '加热超时','租赁开始时间','租赁天数','bms上传周期','gps上传周期'])
elif mode == 1:
df_all = pd.DataFrame()
for line in data:
df_add = DBManager._convert_to_dataframe_system(line, mode)
df_all = df_all.append(df_add,ignore_index=True)
if not df_all.empty:
df_all.loc[:,'时间戳'] = df_all.loc[:,'时间戳'].apply(lambda x:time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(int(x)/1000)))
return df_all
elif type_name =='accum':
if mode == 0:
df_all = pd.DataFrame(columns=['时间戳','SOH未标定时间', '累计充电电量','累计充电能量','累计放电电量','累计放电能量',
'累计高温充电电量', '累计高温充电能量'])
elif mode == 1:
df_all = pd.DataFrame(columns=['时间戳','累计充电电量','累计充电能量','累计放电电量','累计放电能量', '累积里程'])
for line in data:
df_add = DBManager._convert_to_dataframe_accum(line, mode)
df_all = df_all.append(df_add,ignore_index=True)
if not df_all.empty:
df_all.loc[:,'时间戳'] = df_all.loc[:,'时间戳'].apply(lambda x:time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(int(x)/1000)))
return df_all
def get_data(self, url='http://172.16.121.236/store/load?dataType={}&limit=0&sn={}', sn='', start_time='', end_time='',
data_groups=['bms', 'gps']):
'''
获取指定 sn 和起止日期的bms和gps数据.
添加了重试机制。
--------------输入参数------------
url：数据获取url， 可采用默认值
sn: str, 电池sn号
start_time: str, 开始时间
end_time: str, 结束时间
data_groups: 选择需要获取的数据组，可填入多个字符串（默认只获取bms和gps数据）
bms: bms数据
gps：gps数据
system：system数据
accum：accum数据
--------------输出参数------------
df_data: {'bms':dataframe, 'gps':dataframe, 'system':dataframe, ;accum':dataframe}
'''
if len(set(data_groups) - (set(data_groups) and set(['bms', 'gps', 'system', 'accum']))) > 0:
raise Exception("data_groups 参数错误")
# mode: 0：正常取数； 1：7255 取数
if sn[0:2] == 'UD' or sn[0:2] == 'MG':
mode = 1
else:
mode = 0
bms_all_data = pd.DataFrame()
gps_all_data = pd.DataFrame()
system_all_data = pd.DataFrame()
accum_all_data = pd.DataFrame()
maxnum = (datetime.datetime.strptime(end_time, "%Y-%m-%d %H:%M:%S") - datetime.datetime.strptime(start_time, "%Y-%m-%d %H:%M:%S")).days +1
print("### start to get data {} from {} to {}".format(sn, start_time, end_time))
# 为避免chunkEncodingError错误，数据每天获取一次，然后将每天的数据合并，得到最终的数据
for j in range(int(maxnum)):
timefrom = datetime.datetime.strptime(start_time, "%Y-%m-%d %H:%M:%S")+ datetime.timedelta(days=j)
timeto = datetime.datetime.strptime(start_time, "%Y-%m-%d %H:%M:%S")+ datetime.timedelta(days=j+1)
#滴滴的数据sub=0
if timefrom.strftime('%Y-%m-%d %H:%M:%S') >= end_time:
break
elif timeto.strftime('%Y-%m-%d %H:%M:%S') > end_time:
timeto = datetime.datetime.strptime(end_time, '%Y-%m-%d %H:%M:%S')
#print('{}_{}_----getting data----'.format(sn, timefrom))
bms_data = pd.DataFrame()
gps_data = pd.DataFrame()
system_data = pd.DataFrame()
accum_data = pd.DataFrame()
while True:
try:
print('\r' + "# get data from {} to {}.........".format(str(timefrom), str(timeto)), end=" ")
for data_group in data_groups:
if data_group == 'bms':
file_url = url.format(12, sn) + "&from="+timefrom.strftime('%Y-%m-%d %H:%M:%S')+"&to="+timeto.strftime('%Y-%m-%d %H:%M:%S')
bms_data = DBManager._get_data(file_url,'bms',mode)
if data_group == 'gps':
file_url = url.format(16, sn) + "&from="+timefrom.strftime('%Y-%m-%d %H:%M:%S')+"&to="+timeto.strftime('%Y-%m-%d %H:%M:%S')
gps_data = DBManager._get_data(file_url,'gps',mode)
if data_group == 'system':
file_url = url.format(13, sn) + "&from="+timefrom.strftime('%Y-%m-%d %H:%M:%S')+"&to="+timeto.strftime('%Y-%m-%d %H:%M:%S')
system_data = DBManager._get_data(file_url,'system',mode)
if data_group == 'accum':
file_url = url.format(23, sn) + "&from="+timefrom.strftime('%Y-%m-%d %H:%M:%S')+"&to="+timeto.strftime('%Y-%m-%d %H:%M:%S')
accum_data = DBManager._get_data(file_url,'accum',mode)
except Exception as e:
if 'Connection broken' in str(e):
continue
else:
raise Exception
else:
bms_all_data = pd.concat([bms_all_data, bms_data], ignore_index=True)
gps_all_data = pd.concat([gps_all_data, gps_data], ignore_index=True)
system_all_data = pd.concat([system_all_data, system_data], ignore_index=True)
accum_all_data = pd.concat([accum_all_data, accum_data], ignore_index=True)
break
bms_all_data = bms_all_data.reset_index(drop=True)
gps_all_data = gps_all_data.reset_index(drop=True)
system_all_data = system_all_data.reset_index(drop=True)
accum_all_data = accum_all_data.reset_index(drop=True)
print('\nall data-getting done, bms_count is {}, gps_count is {}, system_count is {}, accum_count is {} \n'.format(
str(len(bms_all_data)), str(len(gps_all_data)), str(len(system_all_data)), str(len(accum_all_data))))
return {'bms':bms_all_data, 'gps':gps_all_data, 'system':system_all_data, 'accum':accum_all_data}