Open AccessResearch on observation data compression of hydraulic engineering based on swinging door trending algorithm
Author(s) -
Lihuan Liu,
Ceng Jiang,
C.D. Gu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1656/1/012012
Subject(s) - process (computing) , computer science , data compression , compression (physics) , data collection , compression ratio , algorithm , alarm , computer data storage , data acquisition , work (physics) , big data , data mining , real time computing , engineering , computer hardware , mechanical engineering , electrical engineering , mathematics , statistics , materials science , composite material , internal combustion engine , operating system
With the wide application of big data technology, there is a geometric series increase of the real-time data collection of water conservancy engineering, which causes a large number of storage units and memory. In order to make the huge amount of data easy to collect and process, an improved Swinging Door Trending algorithm is proposed to compress the data of water conservancy project based on the deep research of data compression algorithm. This work firstly analyzed the collected abnormal points, which identified and processed the points in time to realize the timely alarm of abnormal conditions according to the characteristics of water conservancy engineering data and acquisition requirements; then discussed the dynamic adjustment of tolerance, which is carried out according to the fluctuating state of water conservancy engineering data to store less real-time data; and finally put forward that the improved SDT algorithm has higher compression ratio and better compression efficiency than the traditional SDT method does by experiments.