Intelligent aggregation method and system for data center alarms based on multi-dimensional feature fusion

By using a multi-dimensional feature fusion method, the problem of multi-dimensional correlation in data center alarm aggregation is solved, achieving intelligent alarm aggregation with high accuracy and low false aggregation rate, thereby improving operation and maintenance efficiency and system stability.

CN122241587APending Publication Date: 2026-06-19HEFEI CITY COULD DATA CENT
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HEFEI CITY COULD DATA CENT
Filing Date
2026-03-20
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies are unable to effectively solve the multi-dimensional correlation problem of alarm aggregation in data centers, resulting in low alarm compression rate, high false aggregation rate, and high missed aggregation rate, which affects fault location efficiency and operation and maintenance costs.

Method used

A multi-dimensional feature fusion-based approach is adopted, which uses time, space and semantic similarity modeling, combined with hierarchical clustering and incremental aggregation to extract the time, space and semantic features of alarms, and performs weighted fusion and adaptive adjustment to achieve intelligent aggregation of alarms.

Benefits of technology

It improves the accuracy and stability of alarm aggregation, reduces the rate of false and missed aggregation, improves operation and maintenance efficiency, and reduces the cognitive burden and processing costs of operation and maintenance personnel.

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Abstract

This invention relates to a method and system for intelligent aggregation of data center alarms based on multi-dimensional feature fusion, which solves the problem of difficulty in achieving alarm aggregation in data centers compared with existing technologies. The invention includes the following steps: alarm reception and preprocessing; temporal feature extraction; spatial feature extraction; semantic feature extraction; multi-dimensional feature fusion; and obtaining clustering decision results. This invention introduces an alarm similarity modeling mechanism based on multiple dimensions of time, space, and semantics, and performs fusion analysis on the similarity of each dimension, thereby achieving a refined and systematic characterization of the correlation between alarms.
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