earticle

영어

Swing door trending (SDT) algorithm is a lossy compression algorithm that be applied on the real-time database and proposed by OSI software company of American; SDT is widely used to compress process data generated by process industry. Using straight line for a data section to linear fitting in traditional SDT algorithm. However, the data generated in the process of industrial production are slightly fluctuating with time. So, the use of linear fitting will lead to a large decompression error. In order to overcome the large decompression error generated by the traditional SDT, we proposed Controllable Curve Fitting Based Swing Door Trending (CCFSDT). The CCFSDT algorithm uses curve line for a data section to fitting, the data restored are closer to the true value. And in order to reduce the cost of curve fitting, it can be appropriate to reduce the total number of points of curve fitting. We filter noise point before fitting to void the impact on the reduction data and achieve better compression effect. The experimental results on simulated data and actual plant data show that: under the same conditions, the CCFSDT can well reduce the errors of decompression and achieve satisfactory performance.