Log data quality evaluation and quantitative model method based on reinforcement learning

By adopting a reinforcement learning-based log data quality assessment and governance method, the problems of real-time performance and accuracy of log quality assessment in dynamic environments are solved. Adaptive updates and resource optimization are achieved, thereby improving the real-time performance of log quality assessment and the reliability of governance decisions.

CN121998661APending Publication Date: 2026-05-08CHONGQING UNIV OF POSTS & TELECOMM
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Patent Information

Application Number
CN202610004600.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-05
Publication Date
2026-05-08

AI Technical Summary

Technical Problem

Existing technologies struggle to continuously and quantitatively assess log quality and adaptively update it under dynamic business load and log distribution changes, and also struggle to control resource consumption while ensuring quality targets.

Method used

By employing a reinforcement learning-based approach, a state space and action space are constructed through data governance and log analysis platform modeling, entity recognition, quality indicator calculation and quantitative scoring, drift detection and dynamic updating. This enables real-time evaluation of log quality and learning of governance strategies, forming a closed-loop feedback mechanism.

Benefits of technology

It enables continuous quantitative assessment and adaptive updating of log quality in a dynamic environment, improving the real-time performance and accuracy of log quality assessment, reducing resource consumption, and enhancing the reliability of governance decisions.

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Abstract

The invention discloses a reinforcement learning-based log data quality evaluation and quantification model method, which comprises the following steps of: performing desensitization, deduplication, timestamp alignment and structured analysis on server logs to form a log sequence; outputting a quality score based on an analysis result, an entity extraction result and quality indexes such as missing and abnormal statistical calculation integrity, consistency and timeliness; setting a rapid scoring device and a reference scoring device, identifying distribution drift according to the scoring deviation of the rapid scoring device and the reference scoring device, and updating a scoring model; and constructing a state space and an action space, learning a governance action selection strategy by adopting Q-learning, re-scoring the governed log, and updating strategy parameters according to quality improvement and resource consumption. According to the method, the log data quality can be continuously evaluated, the quality problem can be found and positioned, and the balance between quality improvement and resource consumption is realized.
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