计算机工程 ›› 2008, Vol. 34 ›› Issue (14): 58-59.doi: 10.3969/j.issn.1000-3428.2008.14.021

• 软件技术与数据库 • 上一篇    下一篇

基于SQL数据库的过程数据压缩方法

赵利强,于 涛,王建林   

  1. (北京化工大学信息科学与技术学院,北京 100029)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-07-20 发布日期:2008-07-20

Process Data Compression Method Based on SQL Database

ZHAO Li-qiang, YU Tao, WANG Jian-lin   

  1. ( College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-07-20 Published:2008-07-20

摘要: 利用SQL Server数据库存储过程历史数据,提出一种增量型的SDT压缩算法,有效地实现了过程历史数据的实时存储。对于通过OPC方式从化工过程底层读取的实时数据,利用增量型的SDT算法进行实时压缩存储,利用LZW算法对存储的值进行二次无损压缩。实验测试表明,该方法能够实时处理大量的过程数据,同时尽最大可能对过程数据进行压缩,降低存储成本。在容差为1%、测点为 1 000个时,该算法使平均压缩率达到约85%。

关键词: 过程数据压缩, 增量型SDT算法, LZW算法

Abstract: This paper presents incremental Swinging Door Trending(SDT) algorithm of historical data based on SQL Server database. For the real-time process data obtained by the OLE for Process Control(OPC), it is compressed by using the incremental SDT algorithm, and the LZW (Lempel-Ziv-Welch) algorithm is adopted for the storage value in the second-level lossless compression. Experimental results show that it can deal with the large process data on line, and at the same time it can reach the utmost compression ratio for the process history data. This storage method can also reduce the storage cost. For the 1 000 data points in one petrochemical factory, the average compression ratio can reach 85% with incremental SDT when the compression tolerance is set to 1%.

Key words: process data compression, incremental SDT algorithm, LZW algorithm

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