import pandas as pd import datetime, time import re import os import argparse from sqlalchemy import create_engine import pytz from data_cleaning import cleaning, rm_duplicated, key_field_row_cleaning # current_path = os.path.dirname(__file__) # dataloc = current_path + '/data/' station_id = '260' def readData(name): """ 读取数据 :param name: 名字 :return: """ path = rf"../../cluster/{station_id}/" + name return pd.read_csv(path) def saveData(name, data): """ 存放数据 :param name: 名字 :param data: 数据 :return: """ path = rf"../../cluster/{station_id}/" + name os.makedirs(os.path.dirname(path), exist_ok=True) data.to_csv(path, index=False) def timestamp_to_datetime(ts): local_timezone = pytz.timezone('Asia/Shanghai') if type(ts) is not int: raise ValueError("timestamp-时间格式必须是整型") if len(str(ts)) == 13: dt = datetime.datetime.fromtimestamp(ts/1000, tz=pytz.utc).astimezone(local_timezone) return dt elif len(str(ts)) == 10: dt = datetime.datetime.fromtimestamp(ts, tz=pytz.utc).astimezone(local_timezone) return dt else: raise ValueError("timestamp-时间格式错误") def dt_tag(dt): date = dt.replace(hour=0, minute=0, second=0) delta = (dt - date) / pd.Timedelta(minutes=15) return delta + 1 def timestr_to_timestamp(time_str): """ 将时间戳或时间字符串转换为datetime.datetime类型 :param time_data: int or str :return:datetime.datetime """ if isinstance(time_str, str): if len(time_str) == 10: dt = datetime.datetime.strptime(time_str, '%Y-%m-%d') return int(round(time.mktime(dt.timetuple())) * 1000) elif len(time_str) in {17, 18, 19}: dt = datetime.datetime.strptime(time_str, '%Y-%m-%d %H:%M:%S') # strptime字符串解析必须严格按照字符串中的格式 return int(round(time.mktime(dt.timetuple())) * 1000) # 转换成毫秒级的时间戳 else: raise ValueError("时间字符串长度不满足要求！") else: return time_str class DataBase(object): def __init__(self, begin, end, opt): self.begin = begin self.end = end self.opt = opt self.begin_stamp = timestr_to_timestamp(str(begin)) self.end_stamp = timestr_to_timestamp(str(end)) self.database = opt.database self.dataloc = opt.dataloc def clear_data(self): """ 删除所有csv :return: """ # 设置文件夹路径 import glob import os folder_path = self.dataloc # 使用 glob 获取所有的 .csv 文件路径 csv_files = glob.glob(os.path.join(folder_path, '**/*.csv'), recursive=True) # 遍历所有 .csv 文件并删除 for file_path in csv_files: os.remove(file_path) print("清除所有csv文件") def create_database(self): """ 创建数据库连接 :param database: 数据库地址 :return: """ engine = create_engine(self.database) return engine def exec_sql(self, sql, engine): """ 从数据库获取数据 :param sql: sql语句 :param engine: 数据库对象 :return: """ df = pd.read_sql_query(sql, engine) return df def get_process_power(self): """ 获取整体功率数据 :param database: :return: """ engine = self.create_database() sql_cap = "select C_CAPACITY from t_electric_field" cap = self.exec_sql(sql_cap, engine)['C_CAPACITY'] sql_power = "select C_TIME,C_REAL_VALUE, C_ABLE_VALUE, C_IS_RATIONING_BY_MANUAL_CONTROL, C_IS_RATIONING_BY_AUTO_CONTROL" \ " from t_power_station_status_data where C_TIME between '{}' and '{}'".format(self.begin, self.end) powers = self.exec_sql(sql_power, engine) powers['C_TIME'] = pd.to_datetime(powers['C_TIME']) mask1 = powers['C_REAL_VALUE'].astype(float) > float(cap) mask = powers['C_REAL_VALUE'] == -99 mask = mask | mask1 print("实际功率共{}条，要剔除功率有{}条".format(len(powers), mask.sum())) powers = powers[~mask] print("剔除完后还剩{}条".format(len(powers))) binary_map = {b'\x00': 0, b'\x01': 1} powers['C_IS_RATIONING_BY_AUTO_CONTROL'] = powers['C_IS_RATIONING_BY_AUTO_CONTROL'].map(binary_map) powers = rm_duplicated(powers) saveData("power.csv", powers) def get_process_turbine(self, output_dir): """ 从数据库中获取风头数据，并进行简单处理 :param database: :return: """ for number in self.opt.turbineloc: # 机头数据 engine = self.create_database() print("导出风机{}的数据".format(number)) sql_turbine = "select C_TIME, C_WS, C_WD, C_ACTIVE_POWER from t_wind_turbine_status_data " \ "WHERE C_EQUIPMENT_NO=" + str(number) + " and C_TIME between '{}' and '{}'".format(self.begin, self.end) # + " and C_WS>0 and C_ACTIVE_POWER>0" turbine = self.exec_sql(sql_turbine, engine) turbine = cleaning(turbine, 'turbine', cols=['C_WS', 'C_ACTIVE_POWER'], dup=False) turbine['C_TIME'] = pd.to_datetime(turbine['C_TIME']) turbine = turbine[turbine['C_TIME'].dt.strftime('%M').isin(['00', '15', '30', '45'])] # 直接导出所有数据 output_file = os.path.join(output_dir, f"turbine-{number}.csv") saveData(output_file, turbine) def process_csv_files(self, input_dir, output_dir, M, N): # MBD:没有考虑时间重复 if not os.path.exists(output_dir): os.makedirs(output_dir) for i in self.opt.turbineloc: input_file = os.path.join(input_dir, f"turbine-{i}.csv") output_file = os.path.join(output_dir, f"turbine-{i}.csv") # 读取csv文件 df = pd.read_csv(input_file) # 剔除异常值，并获取异常值统计信息 df_clean, count_abnormal1, count_abnormal2, total_removed, removed_continuous_values = self.remove_abnormal_values(df, N) # 输出异常值统计信息 print(f"处理文件：{input_file}") print(f"剔除 -99 点异常值数量：{count_abnormal1}") print(f"剔除连续异常值数量：{count_abnormal2}") print(f"总共剔除数据量：{total_removed}") print(f"剔除的连续异常值具体数值：{removed_continuous_values}\n") # 保存处理过的CSV文件 df_clean.to_csv(output_file, index=False) def remove_abnormal_values(self,df, N): # 标记C_ACTIVE_POWER为-99的行为异常值 abnormal_mask1 = df['C_ACTIVE_POWER'] == -99 count_abnormal1 = abnormal_mask1.sum() # 标记C_WS, A, B连续5行不变的行为异常值 columns = ['C_WS', 'C_WD', 'C_ACTIVE_POWER'] abnormal_mask2 = self.mark_abnormal_streaks(df, columns, N) count_abnormal2 = abnormal_mask2.sum() # 获得所有异常值的布尔掩码 abnormal_mask = abnormal_mask1 | abnormal_mask2 # 获取连续异常值具体数值 removed_continuous_values = {column: df.loc[abnormal_mask2, column].unique() for column in columns} # 剔除异常值 df_clean = df[~abnormal_mask] total_removed = abnormal_mask.sum() return df_clean, count_abnormal1, count_abnormal2, total_removed, removed_continuous_values # ——————————————————————————机头风速-99和连续异常值清洗代码—————————————————————————————— def mark_abnormal_streaks(self, df, columns, min_streak): abnormal_mask = pd.Series(False, index=df.index) streak_start = None for i in range(len(df)): if i == 0 or any(df.at[i - 1, col] != df.at[i, col] for col in columns): streak_start = i if i - streak_start >= min_streak - 1: abnormal_mask[i - min_streak + 1:i + 1] = True return abnormal_mask # ——————————————————————————风机单机时间对齐—————————————————————————————— def TimeMerge(self, input_dir, output_dir, M): # 读取所有CSV文件 files = [os.path.join(input_dir, f"turbine-{i}.csv") for i in self.opt.turbineloc] dataframes = [pd.read_csv(f) for f in files] # 获取C_TIME列的交集 c_time_intersection = set(dataframes[0]["C_TIME"]) for df in dataframes[1:]: c_time_intersection.intersection_update(df["C_TIME"]) # 只保留C_TIME交集中的数据 filtered_dataframes = [df[df["C_TIME"].isin(c_time_intersection)] for df in dataframes] # 将每个过滤后的DataFrame写入新的CSV文件 os.makedirs(output_dir, exist_ok=True) for (filtered_df, i) in zip(filtered_dataframes, self.opt.turbineloc): if i == 144: filtered_df['C_ACTIVE_POWER'] /= 1000 filtered_df.to_csv(os.path.join(output_dir, f"turbine-{i}.csv"), index=False) def data_process(self): """ 数据导出+初步处理的总操控代码 :param database: :return: """ self.clear_data() self.get_process_power() self.get_process_turbine(f'../../cluster/{station_id}') self.process_csv_files(f'../../cluster/{station_id}', f'../../cluster/{station_id}', 50, 5) self.TimeMerge(f'../../cluster/{station_id}', f'../../cluster/{station_id}', 50) # self.zone_powers('../cluster/data') if __name__ == '__main__': import pandas as pd turbineloc = [x for x in range(76, 151, 1)] c_names = ['G01', 'G02', 'G03', 'G04', 'G05', 'G06', 'G07', 'G08', 'G09', 'G10'] + ['G' + str(x) for x in range(11, 76)] id_names = {id: c_names[x] for x, id in enumerate(turbineloc)} args = {'database': 'mysql+pymysql://root:mysql_T7yN3E@192.168.12.10:19306/ipfcst_j00260_20250507161106', 'cap': 225, 'id_name': id_names, 'turbineloc': turbineloc, 'dataloc': '../cluster/data'} opt = argparse.Namespace(**args) db = DataBase(begin='2025-01-01', end='2025-05-01', opt=opt) db.data_process()