Abnormality detection method and device, equipment, storage medium and product

By applying deep learning anomaly detection method in container clusters, and using the classified anomaly detection model to detect and identify traffic timing indicators, the problems of inefficiency and poor adaptability in the existing technology are solved, and efficient and accurate abnormality detection and fault alarm are achieved.

CN119690715APending Publication Date: 2025-03-25CHINA MERCHANTS BANK
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Patent Information

Application Number
CN202411519960.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-29
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

The prior art is inefficient when processing traffic timing indicators of large-scale container clusters, and it is difficult to meet the needs of real-time monitoring. In addition, traditional methods rely on static thresholds or rules, making it difficult to adapt to dynamically changing cluster environments, resulting in false alarms and missed alarms occur frequently.

Method used

A method of abnormal detection based on deep learning is proposed. The traffic timing indicator data of the container cluster is obtained through preset time intervals, and a pre-constructed classification abnormality detection model is used for classification detection to obtain traffic classification detection results, and abnormal identification and fault alarm are performed based on this result.

Benefits of technology

It significantly improves the accuracy and efficiency of traffic timing indicator abnormal detection in container clusters with large data volumes, and can promptly identify and warn of abnormal conditions in container clusters, reduce false alarms and missed reports, and meet the needs of real-time monitoring.

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Abstract

The invention discloses an anomaly detection method and device, equipment, a storage medium and a product, and relates to the technical field, and the method comprises the steps: obtaining traffic time sequence index data of a target container cluster based on a preset time interval; performing classification detection on the traffic time sequence index data based on a pre-constructed classification anomaly detection model to obtain a traffic classification detection result; and performing anomaly identification and fault alarm based on the traffic classification detection result, through the above scheme, detecting the traffic time sequence index of the container cluster within the preset time, and classifying the traffic index data according to the specially constructed classification anomaly detection model to obtain the traffic trend category of the cluster traffic within a period of time. And then anomaly identification and fault alarm are carried out based on the trend category, so that the traffic trend is identified and alarmed after being classified, and the accuracy and efficiency of traffic time sequence index anomaly detection in a container cluster with a large data volume are remarkably improved.
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