Monitoring data compression method suitable for operation and maintenance automation system

Abstract

The invention discloses a monitoring data compression method applicable to an operation and maintenance automation system. The method includes the following steps that 1, data preprocessing is conducted, wherein smoothing is conducted on original data to reduce interference with an SDT algorithm from noisy data; 2, preliminary data compression is conducted, wherein a control algorithm is adopted to compress data preliminarily; 3, further data compression is conducted, wherein the SDT algorithm is adopted for further compression; 4, SDT compression precision parameter matching is conducted, wherein after each time of data compression, compression precision parameters are matched with the characteristics of data fluctuation changes according to the data fluctuation changes, and compression precision parameters generated after matching are obtained; 5, adaptive adjustment of the SDT compression precision parameters is conducted, wherein the step 2, the step 3 and the step 4 are repeated according to the compression precision parameters generated after matching, and then the compression precision parameters are adaptively adjusted; 6, the step 2, the step 3 and the step 5 are repeated until the compression precision parameters are completely matched with the characteristics of the data fluctuation changes, the optimal compression precision parameters are obtained, and then the step 2 and the step 3 are repeated to complete data compression.

Description

technical field [0001] The invention relates to the technical field of data compression, in particular to a monitoring data compression method suitable for an operation and maintenance automation system. Background technique [0002] With the continuous advancement and improvement of enterprise informatization construction, the operation of computer software and hardware systems has become a common concern in various industries. A very important part of IT operation and maintenance work is to monitor and record information such as the running status of the host equipment and network load in real time, so as to realize the functions of timely alarming, fault diagnosis and data mining of abnormal conditions. Due to the large number of data collection points and the short collection interval, the amount of real-time monitoring data is very large, and some noise data in these data is not only useless for other monitoring data, but also affects the compression of other overall da...

AI Technical Summary

Problems solved by technology

However, the compression rate and information loss rate of the traditional SDT algorithm are greatly affected by the compression accuracy parameter ΔE, and the compression performance of this algorithm is relatively low when the data is noisy.

[0004] Although the existing technology has made some improvements to the SDT algorithm, or increased the compression rate, or reduced the compression error and compression time, they have not considered the influence of noise data, and have not solved the compression problem well. Selection of Accuracy Parameter ΔE

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