| Issue |
ITM Web Conf.
Volume 11, 2017
2017 International Conference on Information Science and Technology (IST 2017)
|
|
|---|---|---|
| Article Number | 01009 | |
| Number of page(s) | 7 | |
| Section | Session I: Computational Intelligence | |
| DOI | https://doi.org/10.1051/itmconf/20171101009 | |
| Published online | 23 May 2017 | |
The Research and Improvement of SDT Algorithm for Historical Data in SCADA
Faulty of Information Technology, Beijing University of Technology, Beijing, 100124, China
a Corresponding author: 958591548@qq.com
With the rapid development of Internet of things and big data technology, the amount of data collected by SCADA(Supervisory Control And Data Acquisition)system is growing exponentially, which the traditional SDT algorithm can not meet the requirements of SCADA system for historical data compression. In this paper, ASDT(Advanced SDT) algorithm based on SDT algorithm is proposed and implemented in the Java language, which is based on the deep research of the data compression method, especially the Swing Door Trending. ASDT algorithm through the sine curve fitting data to achieve data compression, compared with the performance of the traditional SDT algorithm, which it can achieve better compression results. The experimental results show that compared with the traditional SDT algorithm, the ASDT algorithm can improve the compression ratio in the case of no significant increase in the compression error, and the compression radio is increased by nearly 50%.
© Owned by the authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.