An alarm fingerprint intelligent merging method based on a memory matrix

By adopting an intelligent alarm fingerprint merging method based on a memory matrix, the problems of large computational load and results that do not meet user needs in existing technologies are solved, and efficient and customized alarm merging is achieved.

CN115269591BActive Publication Date: 2026-05-12BEIJING YUNJI ZHIZAO TECH CO LTD
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Patent Information

Application Number
CN202210822532.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-07-12
Publication Date
2026-05-12
Estimated Expiration
2042-07-12

AI Technical Summary

Technical Problem

In existing SLS alarm intelligent merging technology, the MinJoin clustering algorithm has a large computational load and high storage requirements, and the clustering results do not meet user needs, resulting in unsatisfactory noise reduction effect.

Method used

An intelligent alarm fingerprint merging method based on a memory matrix is ​​adopted. By extracting key alarm fields to generate hash values, storing the memory matrix in Redis, the alarm merging process is optimized, and the alarm category probability is calculated using the memory matrix to meet user expectations.

Benefits of technology

It reduces the cost of intelligent alarm merging, improves processing speed and customization, and the merging results are more in line with user needs.

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Abstract

The application discloses an alarm fingerprint intelligent merging method based on a memory matrix, which comprises the following stages: an alarm preprocessing stage, a fingerprint optimization stage and an alarm merging stage. The above technical scheme has the following advantages: 1) the cost of intelligent alarm merging is reduced; 2) the memory matrix mode is used in the merging stage, the customizability of alarm merging is improved while the user configuration cost is reduced, and the demand of users in actual business scenarios can be better met; and 3) the processing speed is faster, and compared with a large number of neural network calculations, matrix calculation can greatly improve the speed of merging calculation.
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