Early warning processing method and device based on dynamic threshold, equipment and storage medium

Abstract

The embodiment of the invention discloses an early warning processing method and device based on a dynamic threshold, equipment and a storage medium. According to the embodiment of the invention, theearly warning threshold value of the index data in the next index period is calculated and obtained according to the historical data of the index data in the plurality of historical year-on-year indexperiods, so that the problem that the dynamic threshold value determined by using a sliding window cannot refer to the same-period change of the plurality of historical periods in the prior art can be avoided. Furthermore, according to the embodiment of the invention, the early warning threshold value in the next index period is directly calculated; the index data newly generated in the next index period is directly pre-warned according to the calculated pre-warning threshold, so that the pre-warning processing method based on the dynamic threshold provided by the embodiment of the inventionis stronger in real-time performance, higher in efficiency and suitable for real-time effective detection and pre-warning of the index data under a large-scale instance cluster.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to an early warning processing method, device, equipment and storage medium based on a dynamic threshold. Background technique [0002] At present, in order to judge whether an indicator is abnormal in the operation and maintenance system, it is necessary to set a normal range threshold for the indicator, and an early warning will be generated if the threshold exceeds the set threshold. The threshold for anomaly detection is generally defined based on the personal experience of the operation and maintenance engineer, or use a sliding window to take historical data to calculate the average and standard deviation, and use the average value to fluctuate three times the standard deviation as the dynamic threshold range. [0003] However, the current threshold determination method has the following problems: [0004] It is difficult to accurately warn each indicator by using a...

AI Technical Summary

Problems solved by technology

[0004] It is difficult to accurately warn each indicator by using a single static threshold manually defined, and setting a corresponding reasonable threshold for each indicator requires a lot of statistics and configuration work by experienced operation and maintenance engineers

In addition, the dynamic threshold determined by using the sliding window can only reflect recent changes, making it susceptible to the influence of recent interference points, and because the sliding window is used to calculate the threshold range of the next point, the use of large-scale instance clusters It is difficult to achieve real-time and effective detection and early warning

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