Abnormality detection method and device, electronic equipment and storage medium

By receiving product parameters and using the abnormal cause estimation model to determine the estimated abnormal cause, combined with incremental training to update the model and threshold range, the existing industrial abnormality detection methods are solved, and efficient and accurate automated abnormality detection is achieved.

CN119961835APending Publication Date: 2025-05-09BOE TECHNOLOGY GROUP CO LTD
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Patent Information

Application Number
CN202510053480.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-13
Publication Date
2025-05-09

AI Technical Summary

Technical Problem

The existing industrial anomaly detection methods mainly rely on manual inspection, which are inefficient, costly, and are difficult to adapt to changes in different process flows and production batches.

Method used

Provide an abnormality detection method, by receiving product parameters, using the abnormal cause estimation model to determine the estimated cause of the abnormality, and present product parameters and abnormal cause. At the same time, incremental training is carried out based on the actual abnormality cause, and the abnormality cause estimation model and threshold range are updated to adapt to changes in different process stages and production batches.

Benefits of technology

It realizes automated abnormality detection, improves detection efficiency and accuracy, is highly adaptable, can be widely used in various industrial scenarios, and reduces production costs.

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Abstract

The invention provides an anomaly detection method and device, electronic equipment and a storage medium. The method comprises the following steps: receiving product parameters of a product; under the condition that the state of the product parameter is abnormal, based on an abnormal reason estimation model, an estimated abnormal reason corresponding to the product parameter is determined, the product parameter and the estimated abnormal reason are presented, and the state comprises normal and abnormal states; and under the condition that an actual abnormal reason indicated by a user for the product parameter is received, performing incremental training on the abnormal reason estimation model based on the product parameter and the actual abnormal reason. According to the embodiment of the invention, the problems of low efficiency and relatively high cost of a manual method are solved, and the defects of high deployment cost, poor adaptability to different technological processes, difficulty in maintenance and the like of a computer anomaly monitoring system are also overcome, so that the automatic anomaly detection method can be widely applied to various industrial scenes, and the working efficiency of technicians is greatly improved.
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