Abnormal log detection method and device, storage medium and program product

By filtering potential abnormal log templates at the log template level and combining them with a deep learning model for semantic judgment, the problem of low efficiency and insufficient accuracy in abnormal log detection in existing technologies is solved. This achieves efficient and real-time abnormal log detection, adapts to log format changes in different software systems, and reduces operation and maintenance costs.

CN121301128APending Publication Date: 2026-01-09BEIJING ZITIAO NETWORK TECH CO LTD +1
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Patent Information

Application Number
CN202511439793.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-09
Publication Date
2026-01-09

AI Technical Summary

Technical Problem

Existing abnormal log detection methods are inefficient and inaccurate in complex and ever-changing production environments. They are unable to cope with inconsistent log formats and model aging and failure caused by system pattern evolution, resulting in high operation and maintenance costs and poor detection effects.

Method used

By filtering potentially abnormal log templates at the log template level, then performing semantic judgment on the original logs, and using deep learning models such as large language models to extract keywords for semantic comparison, an adaptive detection model is constructed, reducing computational and storage overhead and minimizing false positives and false negatives.

Benefits of technology

It achieves efficient and real-time anomaly log detection, improves detection accuracy and recall, reduces operation and maintenance costs, adapts to log format changes of different software systems, and ensures the long-term effectiveness of the detection model.

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Abstract

Embodiments of the invention provide an abnormal log detection method and device, a storage medium and a program product. The method comprises the steps of obtaining a first log template set of a first log set; determining potential abnormal log templates in the first log template set according to the first log template set and a second log template set, wherein the second log template set comprises a second log template constructed according to a stable second log set; determining a first log matched with the potential abnormal log template from the first log set, and screening a second log related to the semantics of the first log from the second log set; and performing semantic comparison on the first log and the second log through the first model, and determining whether the first log is an abnormal log or not according to a comparison result. By means of the method, efficient and real-time abnormal log detection can be achieved, calculation and storage expenses are remarkably reduced, the accuracy rate and recall rate of abnormal detection are improved, the method can adapt to different log formats and styles between different software systems and assemblies, and the operation and maintenance cost is greatly reduced.
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