def main():
"""
c_id = pd.DataFrame(np.random.randint(low=20, high=50, size=(5, 1)),
columns=['vin_code'])
df = create_bat_id(c_id['vin_code'])
df = df.join(c_id)
"""
config = {
's': '192.168.1.105',
'u': 'data',
'p': 'For2019&tomorrow',
'db': 'bsa',
'port': 3306
}
df = ioo.read_sql_data(config, 'vehicle_info')
df = df[['vin']]
df1 = create_bat_id(df['vin'])
df = df.join(df1)
config['db'] = 'bat_config'
save_bat_id(df, config)
config['db'] = 'evb_batterybase'
df = df = ioo.read_sql_data(config, 'bat_info')
df = df[['sys_id', 'vin_code']]
df = df.rename(columns={'vin_code': 'vin'})
config['db'] = 'bat_config'
save_bat_id(df, config)
def get_cell_rate_para(config, cell_info, fuzzy=False):
"""
#通过电池基础信息表获得电池基础参数
"""
bat_config = {'C_RATE': 210, 'V_RATE': 3.2, 'T_REFER': 20}
df = ioo.read_sql_data(config, '电池信息表')
cell_info_list = set(df['电池型号'].tolist())
if not fuzzy:
if cell_info in cell_info_list: #暂时先按每个电芯做匹配
bat_config['C_RATE'] = df[df['电池型号'] == cell_info]['额定容量'].iloc[0]
bat_config['V_RATE'] = df[df['电池型号'] == cell_info]['额定电压'].iloc[0]
bat_config['bat_type'] = df[df['电池型号'] ==
cell_info]['bat_type'].iloc[0]
bat_config['parallel'] = df[df['电池型号'] == cell_info]['并联数'].iloc[0]
bat_config['series'] = df[df['电池型号'] == cell_info]['串联数'].iloc[0]
else:
for cell_reg in cell_info_list:
if re.match(cell_reg, cell_info):
bat_config['C_RATE'] = df[df['电池型号'] ==
cell_reg]['额定容量'].iloc[0]
bat_config['V_RATE'] = df[df['电池型号'] ==
cell_reg]['额定电压'].iloc[0]
bat_config['bat_type'] = df[df['电池型号'] ==
cell_reg]['bat_type'].iloc[0]
bat_config['parallel'] = df[df['电池型号'] ==
cell_reg]['并联数'].iloc[0]
bat_config['series'] = df[df['电池型号'] ==
cell_reg]['串联数'].iloc[0]
break
return bat_config
def read_bat_data(para_dict, mode, bat_name, **kwg):
config = para_dict['config'][mode]
kwds = {}
if 'limit' in kwg:
kwds['limit'] = kwg['limit']
if 'start_time' in kwg and 'end_time' in kwg:
kwds['start_time'] = kwg['start_time']
kwds['end_time'] = kwg['end_time']
raw_data = ioo.read_sql_data(config, bat_name, **kwds)
return raw_data
def scale_data(ser, config, cell_no, score_key, start_kwd, end_kwd, nclip=10, no_scale=False):
score, start_time, end_time = get_info(ser, score_key, start_kwd, end_kwd)
cell_info = str(ser[cell_no])
data_dict = ioo.read_sql_data(config, cell_info, start_time=start_time, end_time=end_time, no_scale=no_scale, nclip=nclip)
return data_dict, score, cell_info
def get_vin_list(para_dict, mode):
config = para_dict['config'][mode]
vin_info = ioo.read_sql_data(config, para_dict['info_table_name'])
print(vin_info)
return vin_info
def find_sequence(table_name, showbar=None, total=100, t_keywords='stime', cur_keywords='current',
time_gap=300, charge_time=100, discharge_time=100, valid_cnt=100,
start_index=0, **kwg):
"""
#找到给定对数据中所有满足条件对充电1、静置0或放电2过程对应的数据起始与终止字段信息
#通用的处理是根据电流值来进行判断
#放电2：电流值<-1；充电1：>1:
#充电charge_time和放电discharge_time过程小于设定值则认为过程无效，直接丢弃，并且不影响前后过程的判断,默认值300秒
#充放电过程数据valid_cnt<100,无效，静置数据默认10
#两个数据间格time_gap大于设定值，也认为是新的一个过程,默认值300秒
#默认时间字段stime
#start_index代表pro的起始编号
"""
dis_current = -1
cha_current = 1
mode = kwg['run_mode']
config = kwg['config'][mode]
df = ioo.read_sql_data(config, table_name, limit=kwg['data_limit'][mode])
print(df.shape.__str__())
#filt samples on rows, if a row has too few none-nan value, drop it
DROPNA_THRESH = df.shape[1]//5
df = df.dropna(thresh=DROPNA_THRESH)
df = df.rename(columns={t_keywords: 'stime'})
df['stime'] = df['stime'].apply(str)
df['stime'] = df['stime'].apply(lambda x: parser.parse(x))
df = df.sort_values('stime')
#增加每条数据的充放电状态，并将不符合条件的数据丢弃
df['state'] = np.nan
df['state'][df['current'] > cha_current] = 1
df['state'][df['current'] < dis_current] = 2
df['state'] = df['state'].fillna(0)
#开始划分
pro_df = []
j_last = 0
start_index = start_index
df_len = len(df)
per = df_len // total + 1
if showbar != None:
next(showbar)
for j in range(1, df_len):
state_cur = df['state'].iloc[j]
state_last = df['state'].iloc[j - 1]
if j >= (df_len - 1) or (df.iloc[j]['stime'] - df.iloc[j - 1]['stime']).seconds > time_gap or state_last != state_cur:
cur_df = df.iloc[j_last:j]
valid_flag = True
cur_mean = cur_df[cur_keywords].mean(skipna=True)
if cur_mean < dis_current: #放电
cad_time = discharge_time
state = 2 #'discharge'
elif cur_mean > cha_current: #充电
cad_time = charge_time
state = 1 #'charge'
else:
valid_cnt = 1 #改写有效计数要求
cad_time = valid_cnt
state = 0 #'rest'
if state_last != state_cur and (j - cad_time) < j_last:#电流状态改变持续时间不够条件
valid_flag = False
if valid_flag:
tmp = get_process_info(cur_df, start_index, state, cad_time/10)
if tmp is not None:
pro_df.append(tmp)
start_index += 1 #只有满足条件了才改变
j_last = j
valid_flag = False
if showbar != None:
r = showbar.send(j//per)
pro_df = pd.concat(tuple(pro_df), axis=0)
return pro_df