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@@ -174,6 +174,142 @@ class DataPreProcess:
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df['data_split_by_status'] = status_id
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df['data_status'] = status_list
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return df
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+ def data_split_by_status_forMGMCUD02(self, dfin, drive_interval_threshold=120, charge_interval_threshold=300,
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+ drive_stand_threshold=120, charge_stand_threshold=300):
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+ '''
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+ # 数据预处理分段, 将原始数据段分为 charge、drive、stand、none段
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+ # 状态判断
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+ # 1、drive：(状态为2或3 且 存在电流>0 ) 或 (电流持续为0 且 持续时间<阈值 且 上一段数据为行车)
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+ # 2、charge：(状态为2或3 且 不存在电流>0 ) 或 (电流持续为0 且 持续时间<阈值 且 上一段数据为充电)
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+ # 3、stand：(电流持续为0 且 是数据段的第一段) 或 (电流持续为0 且 持续时间>阈值)
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+ # 4、none： 其他
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+
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+ --------------输入参数-------------：
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+ drive_interval_threshold: 行车段拼接阈值，如果两段行车的间隔时间小于该值，则两段行车合并。
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+ charge_interval_threshold: 充电段拼接阈值，如果两段充电的间隔时间小于该值，则两段充电合并。
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+ drive_stand_threshold: 静置段合并至行车段阈值，如果静置时间小于该值，则合并到上一段的行车中。
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+ charge_stand_threshold: 静置段合并至充电段阈值，如果静置时间小于该值，则合并到上一段的充电中。
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+
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+ --------------输出-----------------:
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+ 在原始数据后面，增加data_split_by_crnt， data_split_by_status, data_status 三列
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+ data_split_by_crnt: 按电流分段的序号
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+ data_split_by_status：按电流和状态分段的序号
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+ data_status： 状态标识
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+ '''
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+ # 首先根据电流是否为0 ，将数据分段
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+ df = dfin.copy()
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+ df['时间戳'] = pd.to_datetime(df['时间戳'])
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+
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+ crnt_zero_or_not = df['总电流[A]']==0
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+ last_crnt_flag = crnt_zero_or_not[0]
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+ temp = 1
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+ group_id = [temp]
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+ for cur_crnt_flag in crnt_zero_or_not[1:]:
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+ if last_crnt_flag ^ cur_crnt_flag:
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+ temp = temp + 1
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+ last_crnt_flag = cur_crnt_flag
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+ group_id.append(temp)
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+ df['data_split_by_crnt'] = group_id
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+
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+ # 然后判断每个段内的 充电状态及电流=0持续时长，决定当前状态
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+ temp = 1
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+ last_status = ""
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+ status_id = []
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+ status_list = []
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+ data_number_list = sorted(list(set(df['data_split_by_crnt'])))
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+
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+ for data_number in data_number_list:
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+ df_sel = df[df['data_split_by_crnt'] == data_number]
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+ origin_index = list(df_sel.index)
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+ df_sel = df_sel.reset_index(drop=True)
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+ temp_2 = 0
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+ # 如果当前数据段的电流非0，则可能分为charge、drive或none段
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+ if df_sel.loc[0,'总电流[A]'] != 0:
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+ # 电流 分段中可能存在状态变化的时刻， 内部根据状态进行分段.
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+ # 该数据段内部，根据bms状态信号进行二次分段
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+ status_drive_or_not = df_sel['充电状态']==3
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+ last_status_flag = status_drive_or_not[0]
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+ temp_2 = 0
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+ group_id_2 = [temp_2]
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+ for cur_status_flag in status_drive_or_not[1:]:
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+ if last_status_flag ^ cur_status_flag:
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+ temp_2 = temp_2 + 1
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+ last_status_flag = cur_status_flag
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+ group_id_2.append(temp_2)
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+
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+ # 遍历二次状态分段
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+ temp_2 = 0
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+ last_status_2 = last_status
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+ df_sel['index'] = group_id_2
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+ data_number_list_2 = sorted(list(set(group_id_2)))
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+ for data_number_2 in data_number_list_2:
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+
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+ df_sel_2 = df_sel[df_sel['index'] == data_number_2]
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+ df_sel_2 = df_sel_2.reset_index(drop=True)
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+
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+ # 根据bms状态 及 电流符号决定是charge还是drive
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+ # 如果状态为2或3， 且电流均<=0 则记为充电
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+ if df_sel_2.loc[0, '充电状态'] in [2, 3] and len(df_sel_2[df_sel_2['总电流[A]'] > 0]) == 0:
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+ cur_status = 'charge'
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+ # 如果状态为2或3，且存在电流>0 则记为行车
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+ elif df_sel_2.loc[0, '充电状态'] in [2, 3] and len(df_sel_2[df_sel_2['总电流[A]'] > 0]) > 0:
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+ cur_status = 'drive'
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+ # 否则 记为none
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+ else:
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+ cur_status = 'none'
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+ status_list.extend([cur_status] * len(df_sel_2))
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+
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+ # 状态id号与前面电流为0的相同状态进行合并, 均判断应不应该与上一段合并
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+ if origin_index[0] == 0: # 如果是所有数据的起始段数据，则直接赋值id号
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+ status_id.extend([temp + temp_2]*len(df_sel_2))
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+
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+ else: # 判断是否与上一段数据合并
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+ deltaT = (df.loc[origin_index[0], '时间戳'] - df.loc[origin_index[0]-1, '时间戳']).total_seconds()
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+ # 如果 状态一致， 且 间隔时间小于阈值，则合并
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+ if last_status_2 == 'drive' and cur_status == last_status_2 and deltaT < drive_interval_threshold:
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+ temp_2 = temp_2 - 1
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+ status_id.extend([temp + temp_2]*len(df_sel_2))
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+ # 如果状态一致， 且 间隔时间小于阈值，则合并
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+ elif last_status_2 == 'charge' and cur_status == last_status_2 and deltaT < charge_interval_threshold:
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+ temp_2 = temp_2 - 1
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+ status_id.extend([temp + temp_2]*len(df_sel_2))
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+ else:
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+ status_id.extend([temp + temp_2]*len(df_sel_2))
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+ temp_2 = temp_2 + 1
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+ last_status_2 = status_list[-1]
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+ temp_2 = temp_2 - 1
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+ else:
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+ # 如果当前数据段的电流为0，则可能分为stand，charge、drive或none段
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+ if origin_index[0] == 0: # 如果是数据的起始，则无论长短，都认为是stand
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+ status_id.extend([temp]*len(df_sel))
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+ status_list.extend(['stand'] * len(df_sel))
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+ else: # 不是数据的起始
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+ cur_deltaT = (df.loc[origin_index[-1], '时间戳'] - df.loc[origin_index[0], '时间戳']).total_seconds()
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+ if last_status == 'charge': # 如果上一个状态为充电
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+ if cur_deltaT < charge_stand_threshold: # 如果本次电流为0的持续时间小于 阈值，则合并
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+ status_list.extend(['charge'] * len(df_sel))
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+ temp = temp - 1
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+ status_id.extend([temp]*len(df_sel))
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+ else: # 否则超过了阈值，记为stand
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+ status_id.extend([temp]*len(df_sel))
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+ status_list.extend(['stand'] * len(df_sel))
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+ elif last_status == 'drive': # 如果上一个状态为行车
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+ if cur_deltaT < drive_stand_threshold: # 如果本次电流为0的持续时间小于 阈值，则合并
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+ status_list.extend(['drive'] * len(df_sel))
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+ temp = temp - 1
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+ status_id.extend([temp]*len(df_sel))
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+ else: # 否则超过了阈值，记为stand
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+ status_id.extend([temp]*len(df_sel))
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+ status_list.extend(['stand'] * len(df_sel))
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+ elif last_status == 'none': # 如果上一个状态未知
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+ status_id.extend([temp] * len(df_sel))
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+ status_list.extend(['stand'] * len(df_sel))
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+ temp = temp + temp_2 + 1
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+ last_status = status_list[-1] # 上一组状态
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+ df['data_split_by_status'] = status_id
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+ df['data_status'] = status_list
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+ return df
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+
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def data_split_by_time(self, dfin, default_time_threshold = 300, drive_time_threshold=300, charge_time_threshold=300,
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stand_time_threshold = 1800):
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'''
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@@ -488,6 +624,8 @@ class DataPreProcess:
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res_record['charge'] = (res_record['charge'])/len(set(df_bms[df_bms['data_status_after_combine']=='charge']['data_split_by_status_after_combine']))
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return df_bms, df_gps, res_record
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+
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+
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'''
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为故障数据打标签
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sn: 电池编码
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