关键词: 入库流量;数据平滑;梯度自适应迭代平滑算法;数据序列光滑度;

Abstract: In order to solve the problem that the jump of time series data such as inflow affects the judgment of the data change trend, we proved the limitation of "jump" in inflow based on the principle of water balance, proposed a smooth gradient adaptive iteration algorithm (SGAIA), designed a calculation method for evaluating the smoothness of data series, and expounded its detailed calculation principle and calculation step. Taking the actual inflow calculation of the Three Gorges Reservoir as an example, we compared the smoothing results of SGAIA with other methods, including 3/5/7-point linear smoothing algorithm, 5/7-point quadratic function fitting smoothing algorithm, and 5/7-point cubic function fitting smoothing algorithm. The results showed that SGAIA has better performance in the smoothing of inflow data. Then we execute smooth calculation on two more representative data series of hourly calculated inflow of the Three Gorges Reservoir in February and April, and the average smoothness of the data series after smoothing calculation reached 0.9584 and 0.9803, respectively. SGAIA can be well adapted to data series smoothing calculation in case of poor original data smoothness.

Key words: reservoir inflow; data smoothing; gradient adaptive iterative smoothing algorithm; smoothness of the data series