DataNalysis/DataBase/turbine-cluster/inputData.py

import pandas as pd
import datetime, time
import re
import os
import pymysql
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/'

def readData(name):
    """
    读取数据
    :param name: 名字
    :return:
    """
    path =  r"./cache/data/" + name
    return pd.read_csv(path)


def saveData(name, data):
    """
    存放数据
    :param name: 名字
    :param data: 数据
    :return:
    """
    path =  r"./cache/data/" + 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, logger):
        self.begin = begin
        self.opt = opt
        # self.his_begin = self.begin - pd.Timedelta(hours=self.opt.Model["his_points"]/4)
        # self.end = end + pd.Timedelta(days=1) - pd.Timedelta(minutes=15)
        # self.begin_stamp = timestr_to_timestamp(str(begin))
        # self.his_begin_stamp = timestr_to_timestamp(str(self.his_begin))
        # self.end_stamp = timestr_to_timestamp(str(self.end))
        self.database = opt.database
        self.logger = logger
        # self.towerloc = self.opt.tower

    def clear_data(self):
        """
        删除所有csv
        :return:
        """
        # 设置文件夹路径
        import glob
        import os
        folder_path = 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)
        self.logger.info("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_NWP(self):
        """
        从数据库中获取NWP数据，并进行简单处理
        :param database:
        :return:
        """
        # NPW数据
        engine = self.create_database()
        sql_NWP = "select C_PRE_TIME,C_T,C_RH,C_PRESSURE, C_SWR," \
                  "C_DIFFUSE_RADIATION, C_DIRECT_RADIATION,  " \
                  "C_WD10,C_WD30,C_WD50,C_WD70,C_WD80,C_WD90,C_WD100,C_WD170," \
                  "C_WS10,C_WS30,C_WS50,C_WS70,C_WS80,C_WS90,C_WS100,C_WS170 from t_nwp" \
                  " where C_PRE_TIME between {} and {}".format(self.begin_stamp, self.end_stamp) # 风的NWP字段
        NWP = self.exec_sql(sql_NWP, engine)

        NWP['C_PRE_TIME'] = NWP['C_PRE_TIME'].apply(timestamp_to_datetime)

        NWP = NWP.rename(columns={'C_PRE_TIME': 'C_TIME'})
        # NWP['DT_TAG'] = NWP.apply(lambda x: dt_tag(x['C_TIME']), axis=1)
        NWP = cleaning(NWP, 'NWP')
        # NWP = self.split_time(NWP)
        NWP['C_TIME'] = NWP['C_TIME'].dt.strftime('%Y-%m-%d %H:%M:%S')
        saveData("NWP.csv", NWP)
        self.logger.info("导出nwp数据")
        return NWP

    def get_process_tower(self):
        """
        获取环境检测仪数据
        :param database:
        :return:
        """
        engine = self.create_database()
        self.logger.info("提取测风塔：{}".format(self.opt.towerloc))
        for i in [self.opt.towerloc]:
            # 删除没用的列
            drop_colmns = ["C_ID","C_DATA1","C_DATA2","C_DATA3","C_DATA4","C_DATA5","C_DATA6","C_DATA7","C_DATA8","C_DATA9","C_DATA10","C_IS_GENERATED","C_ABNORMAL_CODE"]

            get_colmns = []
            # # 查询表的所有列名
            result_set = self.exec_sql("SHOW COLUMNS FROM t_wind_tower_status_data", engine)

            tower = pd.read_csv("./cache/data/t_wind_tower_status_data.csv")
            tower.columns = list(result_set['Field'].values)

            for name in result_set.iloc[:,0]:
                if name not in drop_colmns:
                    get_colmns.append(name)

            all_columns_str = ", ".join([f'{col}' for col in get_colmns])
            # tower_sql = "select " + all_columns_str + " from t_wind_tower_status_data where C_EQUIPMENT_NO="+str(i) + " and C_TIME between '{}' and '{}'".format(self.his_begin, self.end)
            # tower = self.exec_sql(tower_sql, engine)
            tower = tower[all_columns_str.split(', ')]
            # tower.drop(columns=drop_colmns, inplace=True)
            tower['C_TIME'] = pd.to_datetime(tower['C_TIME'])
            saveData("/tower-{}.csv".format(i), tower)
            self.logger.info("测风塔{}导出数据".format(i))

    def get_process_power(self):
        """
        获取整体功率数据
        :param database:
        :return:
        """
        powers = pd.read_csv('./cache/data/t_power_station_status_data.csv')
        shouzu = pd.read_csv('./cache/data/全场_原始数据_2024-11-23_16-22-42.csv')
        shouzu.rename(columns={'时间':'C_TIME', '场外受阻发电量': 'SHOUZU'}, inplace=True)
        shouzu['C_TIME'] = pd.to_datetime(shouzu['C_TIME'])
        shouzu = shouzu[~(shouzu['SHOUZU'] > 0)]


        engine = self.create_database()
        sql_cap = "select C_CAPACITY from t_electric_field"
        cap = self.exec_sql(sql_cap, engine)['C_CAPACITY']
        self.opt.cap = float(cap)

        result_set = self.exec_sql("SHOW COLUMNS FROM t_power_station_status_data", engine)
        powers.columns = list(result_set.iloc[:, 0].values)
        powers['C_TIME'] = pd.to_datetime(powers['C_TIME'])
        powers = pd.merge(powers, shouzu, on='C_TIME')
        powers = powers[['C_TIME', 'C_REAL_VALUE', 'C_ABLE_VALUE', 'C_IS_RATIONING_BY_MANUAL_CONTROL', 'C_IS_RATIONING_BY_AUTO_CONTROL']]

        # if self.opt.usable_power["clean_power_by_signal"]:
        #     sql_power += " and C_IS_RATIONING_BY_MANUAL_CONTROL=0 and  C_IS_RATIONING_BY_AUTO_CONTROL=0"
        powers['C_TIME'] = pd.to_datetime(powers['C_TIME'])
        mask2 = powers['C_REAL_VALUE'] < 0
        mask1 = powers['C_REAL_VALUE'].astype(float) > float(cap)
        mask = powers['C_REAL_VALUE'] == -99

        mask = mask | mask1 | mask2
        self.logger.info("实际功率共{}条，要剔除功率有{}条".format(len(powers), mask.sum()))
        powers = powers[~mask]
        self.logger.info("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_filter4.csv", powers)

    def get_process_dq(self):
        """
        获取短期预测结果
        :param database:
        :return:
        """
        engine = self.create_database()
        sql_dq = "select C_FORECAST_TIME AS C_TIME, C_FP_VALUE from t_forecast_power_short_term " \
                 "where C_FORECAST_TIME between {} and {}".format(self.his_begin_stamp, self.end_stamp)
        dq = self.exec_sql(sql_dq, engine)
        # dq['C_TIME'] = pd.to_datetime(dq['C_TIME'], unit='ms')
        dq['C_TIME'] = dq['C_TIME'].apply(timestamp_to_datetime)
        # dq = dq[dq['C_FORECAST_HOW_LONG_AGO'] == 1]
        # dq.drop('C_FORECAST_HOW_LONG_AGO', axis=1, inplace=True)
        dq = cleaning(dq, 'dq', cols=['C_FP_VALUE'])
        dq['C_TIME'] = dq['C_TIME'].dt.strftime('%Y-%m-%d %H:%M:%S')
        saveData("dq.csv", dq)

    def indep_process(self):
        """
        进一步数据处理：时间统一处理等
        :return:
        """
        # 测风塔数据处理
        for i in [self.opt.towerloc]:
            tower = readData("/tower-{}.csv".format(i))
            tower = cleaning(tower, 'tower', [self.opt.usable_power["env"]])

            tower['C_TIME'] = pd.to_datetime(tower['C_TIME'])
            tower_ave = tower.resample('15T', on='C_TIME').mean().reset_index()
            tower_ave = tower_ave.dropna(subset=[self.opt.usable_power['env']])
            tower_ave.iloc[:, 1:] = tower_ave.iloc[:, 1:].round(2)
            saveData("/tower-{}-process.csv".format(i), tower_ave)

    def get_process_cdq(self):
        """
        获取超短期预测结果
        :param database:
        :return:
        """
        engine = self.create_database()
        sql_cdq = "select C_FORECAST_TIME AS C_TIME, C_ABLE_VALUE, C_FORECAST_HOW_LONG_AGO from t_forecast_power_ultra_short_term_his" \
                  " where C_FORECAST_TIME between {} and {}".format(self.begin_stamp, self.end_stamp)
        cdq = self.exec_sql(sql_cdq, engine)
        cdq['C_TIME'] = cdq['C_TIME'].apply(timestamp_to_datetime)
        cdq = cleaning(cdq, 'cdq', cols=['C_ABLE_VALUE'], dup=False)
        # cdq = cdq[cdq['C_FORECAST_HOW_LONG_AGO'] == int(str(self.opt.predict_point)[1:])]
        cdq['C_TIME'] = cdq['C_TIME'].dt.strftime('%Y-%m-%d %H:%M:%S')
        saveData("cdq.csv", cdq)

    def get_process_turbine(self):
        """
        从数据库中获取风头数据，并进行简单处理
        :param database:
        :return:
        """
        cids = [x for x in range(33, 65, 1)]
        c_names = ['F01', 'F02', 'F03', 'F04', 'F05', 'F06', 'F07', 'F08', 'F09', 'F10']+['F'+str(x) for x in range(10, 32)]
        id_names = {id: c_names[x] for x, id in enumerate(cids)}
        turbines = pd.read_csv('./cache/data/turbines.csv')
        turbines_ori = pd.read_csv('./cache/data/全场_原始数据_2024-08-01_19-40-25.csv')
        turbines_ori['风机'] = turbines_ori['风机'].apply(lambda x: re.sub(r'\(.*?\)|\[.*?\]|\{.*?\}', '', x))
        turbines_ori.rename(columns={"时间": "C_TIME"}, inplace=True)
        turbines_ori = turbines_ori[['C_TIME', '风机', '低位首触机组运行状态', '高位首触机组运行状态']]
        turbines_ori['C_TIME'] = pd.to_datetime(turbines_ori['C_TIME'])
        turbines['C_TIME'] = pd.to_datetime(turbines['C_TIME'])
        for number in self.opt.turbineloc:
            # number = self.opt.usable_power['turbine_id']
            # 机头数据
            turbine = turbines[turbines['C_EQUIPMENT_NO'] == number]
            turbine_ori = turbines_ori[(turbines_ori['风机'] == id_names[number]) & (turbines_ori['低位首触机组运行状态'] <=3) & (turbines_ori['高位首触机组运行状态'] <=3)]
            turbine = pd.merge(turbine, turbine_ori.loc[:, ["C_TIME", "风机"]], on="C_TIME")
            turbine.drop(columns=['风机'], inplace=True)
            turbine = key_field_row_cleaning(turbine, cols=['C_WS', 'C_ACTIVE_POWER'])
            turbine = turbine[turbine['C_TIME'].dt.strftime('%M').isin(['00', '15', '30', '45'])]
            # 直接导出所有数据
            saveData("turbine-{}.csv".format(number), 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
            self.logger.info(f"处理文件：{input_file}")
            self.logger.info(f"剔除 -99 点异常值数量：{count_abnormal1}")
            self.logger.info(f"剔除连续异常值数量：{count_abnormal2}")
            self.logger.info(f"总共剔除数据量：{total_removed}")
            self.logger.info(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

    # ——————————————————————————对分区的风机进行发电功率累加——————————————————————————————
    def zone_powers(self, input_dir):
        z_power = {}
        for zone, turbines in self.opt.zone.items():
            dfs = [pd.read_csv(os.path.join(input_dir, f"turbine-{z}.csv")) for z in self.opt.turbineloc if z in turbines]
            z_power['C_TIME'] = dfs[0]['C_TIME']
            sum_power = pd.concat([df['C_ACTIVE_POWER'] for df in dfs], ignore_index=True, axis=1).sum(axis=1)
            z_power[zone] = sum_power
        z_power = pd.DataFrame(z_power)
        z_power.iloc[:, 1:] = z_power.iloc[:, 1:].round(2)
        saveData("z-power-t.csv", z_power)



    # ——————————————————————————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_dq()
        # self.get_process_cdq()
        # self.get_process_NWP()
        # self.get_process_tower()
        # self.indep_process()
        self.get_process_turbine()
        self.process_csv_files('./cache/data', './cache/data', 50, 5)
        self.TimeMerge('./cache/data', './cache/data', 50)
        self.zone_powers('./cache/data')

if __name__ == '__main__':
    from logs import Log
    from config import myargparse
    import matplotlib.pyplot as plt
    import matplotlib.colors as mcolors
    import pandas as pd

    args = myargparse(discription="场站端配置", add_help=False)
    opt = args.parse_args_and_yaml()
    log = Log().logger
    db = DataBase(begin='', end='', opt=opt, logger=log)
    db.data_process()