A log exception priority dynamic calculation method, device, equipment and medium

By matching the process affiliation and assigning cycle time constraint labels to the production logs, and combining this with the standard scheduling execution path graph, the spread and priority of anomaly impacts are calculated. This addresses the shortcomings of traditional log anomaly handling in monitoring, enabling precise location and dynamic optimization of production anomalies.

CN122412759APending Publication Date: 2026-07-17BEIJING YOUTEJIE INFORMATION TECH

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING YOUTEJIE INFORMATION TECH
Filing Date
2026-04-20
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies cannot accurately map logs to production cycle time when handling production log anomalies, and lack assessment and priority calculation of the chain reaction impact of anomalies, resulting in wasted production resources and delivery delays.

Method used

By continuously collecting production logs, matching process affiliations and assigning cycle time constraint labels, mapping them to the standard scheduling execution path map, calculating the actual completion cycle and the spread of abnormal impacts, combining multi-factor fusion calculations to determine priority ranking, and dynamically updating the abnormal handling priority.

Benefits of technology

It achieves a complete closed loop from log data to on-site actionable measures, quickly locates anomalies, ensures stable production operation, adapts to the needs of different production scenarios, and improves the pertinence and operability of anomaly handling.

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Abstract

本发明公开了一种日志异常优先级动态计算方法、装置、设备及介质。方法包括:按照预设观测时间窗口对采集的生产日志进行工序归属匹配以及节拍约束标签赋值后,进行调度路径节点分配;筛选受节拍约束日志,构建时序日志组并计算实际完成周期;与工序与标准节拍区段基准表匹配,定位异常源节点;计算异常源节点的异常连锁影响系数;对标准化计算因子进行加权计算,生成异常处理优先级排序结果;进入新的观测时间窗口时,筛选出新增受节拍约束日志,重新计算实际完成周期;与工序与标准节拍区段基准表重新匹配后,得到新的异常处理优先级排序结果。本发明通过日志与调度节拍深度融合及多维度量化计算,实现对生产日志异常的精准判别与合理排序。
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