A device running state detection method and device and storage medium

By integrating multi-source data to construct a time-series feature extraction and dynamic state assessment model, a heat map of equipment operating status is generated and the fault risk level is determined in real time. This solves the problems of response lag and insufficient detection accuracy in existing technologies, and realizes dynamic optimization and reliability of equipment operating status.

CN122219306APending Publication Date: 2026-06-16SHANXI YUNJING TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANXI YUNJING TECHNOLOGY CO LTD
Filing Date
2026-01-27
Publication Date
2026-06-16

AI Technical Summary

Technical Problem

Existing equipment operation status detection technologies suffer from problems such as slow response, limited detection accuracy, and weak adaptive capability, making it difficult to meet the reliable operation requirements of equipment under complex working conditions.

Method used

By integrating multi-source data such as real-time equipment operating parameters, environmental impact parameters, historical operating status, and fault characteristics, a time-series feature extraction model and a dynamic status assessment model are constructed. This generates a heat map of equipment operating status and determines the fault risk level in real time. Combined with targeted early warning and adjustment strategies, the model parameter weights are continuously updated.

Benefits of technology

It significantly improves detection accuracy and adaptability, reduces operation and maintenance costs and failure losses, and achieves dynamic optimization and reliability of equipment operation status.

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Abstract

The present application belongs to the technical field of equipment monitoring, and particularly relates to an equipment operation state detection method and device and a storage medium, wherein real-time operation data, environmental influence parameter data, historical operation state data and historical fault data of equipment are acquired; a time sequence feature extraction model is constructed according to the historical fault data and the real-time operation data of the equipment to predict the change trend of the equipment operation parameters; a dynamic state evaluation model is constructed based on the historical operation state data and the environmental influence parameter data, the change trend of the equipment operation parameters is combined to generate an equipment operation state thermal map and to determine the equipment fault risk level in real time, and an equipment state detection result is output; according to the equipment state detection result, a targeted early warning and adjustment strategy are generated and a response operation is performed, equipment response data and state feedback data are synchronously acquired, a state evaluation report is generated, and the parameter weight of the dynamic state evaluation model is updated. Thus, the problems of response lag and limited detection accuracy in the prior art are solved.
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