时间序列模型 加速演算时间

时间序列模型 加速演算时间

将一天的数据量加速跑完, 取历史数据， 不要等待.......

def start():
    now_temp = datetime.now()
    t = datetime(year=now_temp.year, month=now_temp.month, day=now_temp.day, hour=now_temp.hour,
                 minute=now_temp.minute)

    # 测试t0 时刻 预测值
    t0 = datetime(t.year, t.month, t.day, config.t0_hour, config.t0_minute) - relativedelta(days=1)   # 调整到的时间节点
    t1 = datetime(t0.year, t0.month, t0.day, config.t1_hour, config.t1_minute)
    t2 = t0 + relativedelta(days=1) - relativedelta(minutes=1)
    # t2 = t0 + relativedelta(minutes=3)
    print('时间： ', t0, t1, t2)

    # Q_inflow_forecast = 6000.
    # up_load_flow(t0, t2, tz_now, Q_inflow_forecast)

    print('时间： ', t0, t1, t2)
    time_lst = []


    # 定截止时间
    t_end = t0 + relativedelta(days=1) - relativedelta(minutes=1)   # 跑到晚上24点
    t_key = int((t_end - t0).seconds/60) + 1
    print('执行时间查看', time_lst)
    t = t0     # 实时时间调整为 t
    for i in range(t_key):
        time_lst.append(t)
        t += relativedelta(minutes=1)
        print('=========================================================================== 实时执行时间：', t0, t1, t2, t)

        # 热启动
        if number == 0:
            Q_inflow_forecast = hotstart(config, t0, t, t1, t2, tz_now)
            target_time = t
            number = 1

        # 上传实时SCADA数据
        up_load_data(client, t0, t, tz_now)

        # 预测t0点的流速
        if t.hour == t0.hour and t.minute == t0.minute:
            raw_water_flow_volume_t0(config, t0, t1, tz_now)
        elif t.hour == t1.hour and t.minute == t1.minute:
            raw_water_flow_volume_t1(config, t0, t1, t2, tz_now)

        print('实时期望流速: +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++', Q_inflow_forecast)
        # 在t0到t1之间的调流触发
        if t.hour < t1.hour or (t.hour == t1.hour and t.minute < t1.minute):
            print('在t0到t1之间的调流触发')
            water_volume_t0_t1, Q_inflow_t0_t1 = raw_water_volume_t0_t2(target_time, t, t1, tz_now,Q_inflow_forecast,t0)
            up_load_flow(t, t1, tz_now, Q_inflow_forecast)
            if abs(water_volume_t0_t1) > config.Q1:
                Q_inflow_forecast = Q_inflow_t0_t1
                target_time = t
                # up_load_flow(t, t1, tz_now, Q_inflow_forecast)

        # 当在t1和t2之间触发调流时
        elif t.hour > t1.hour or (t.hour == t1.hour and t.minute >= t1.minute):
            print('在t1到t2之间的调流触发')
            water_volume_t0_t1, Q_inflow_t0_t1 = raw_water_volume_t0_t2(target_time, t, t2, tz_now, Q_inflow_forecast,t0)
            up_load_flow(t, t2, tz_now, Q_inflow_forecast)
            if abs(water_volume_t0_t1) > config.Q1:
                Q_inflow_forecast = Q_inflow_t0_t1
                target_time = t

传入连续pandas数据

idx = pd.period_range(start=t, end=target_time,
                          freq=str(interval) + "min").to_timestamp().tz_localize(tz_now)

rawwater_forecast_series = pd.Series(inflow_discharge, index=idx[1:])