SCADA中历史数据的SDT算法研究与改进

随着物联网和大数据技术的快速发展，数据采集与监视控制SCADA系统每天采集的数据量呈几何级数增长，传统的数据压缩算法--旋转门算法SDT 已经不能满足SCADA系统对历史数据压缩的要求。在深入研究了数据压缩方法尤其是旋转门SDT算法的基础上，提出了一种改进的ASDT 算法,并用Java语言加以实现。ASDT算法通过正弦曲线拟合数据以实现数据压缩，与传统SDT算法的性能相比，ASDT算法能取得更好的压缩效果。实验数据结果表明，相对于传统SDT算法，ASDT算法可以在不显著增加压缩误差的前提下，有效地提高压缩比。

SDT algorithm and its improvement for historical data in SCADA

With the rapid development of Internet of things and big data，the amount of data collected by the supervisory control and data acquisition (SCADA) system is growing exponentially, resulting in that the traditional swing door trending (SDT) algorithm can no longer meet the needs of the SCADA system for historical data compression. We propose an advanced swing door trending (ASDT) algorithm and implement it in Java language based on the deep research on the data compression method and the SDT algorithm particularly. The ASDT algorithm which uses sine curve fitting data to achieve data compression has better compression results in comparison with the traditional SDT algorithm. Experimental results show that compared with the traditional SDT algorithm，the ASDT algorithm can improve the compression ratio without significant increase in compression error.