Intelligent maintenance decision-making method and system based on knowledge graph

Through the intelligent maintenance decision-making method based on knowledge graph, a correlation model between equipment operating status and maintenance behavior is constructed, maintenance paths and resource allocation are optimized, and maintenance time windows are dynamically adjusted. This solves the problem of insufficient adaptability of traditional maintenance methods in complex scenarios and realizes efficient and scientific equipment maintenance decisions.

CN120634531AActive Publication Date: 2025-09-12BEIJING UNITED MEDIA TECH CO LTD
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Patent Information

Application Number
CN202511136965.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-14
Publication Date
2025-09-12
Estimated Expiration
2045-08-14

AI Technical Summary

Technical Problem

Existing technologies lack in-depth correlation analysis of multi-source data in industrial equipment maintenance, resulting in extended complex fault handling time and increased downtime losses. Traditional maintenance methods are not adaptable enough in complex scenarios.

Method used

Based on the knowledge graph, an association relationship model for industrial equipment is constructed. By obtaining equipment operating status data and historical maintenance data, the optimal maintenance path is generated. Task allocation is optimized based on maintenance resource constraints, and the maintenance time window is dynamically adjusted. Finally, multi-objective optimization iterative analysis is performed to obtain the final maintenance decision.

Benefits of technology

It improves the scientificity and accuracy of industrial equipment maintenance decisions, reduces troubleshooting time, rationally allocates maintenance resources, reduces maintenance costs, and ensures stable and efficient operation of equipment.

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Abstract

The invention relates to an intelligent maintenance decision method and system based on a knowledge graph, and relates to the technical field of intelligent operation and maintenance and decision support and information, and the method comprises the steps: obtaining the operation state data and historical maintenance data of industrial equipment; constructing an industrial knowledge graph and generating an incidence relation model; an optimal maintenance path is obtained based on the analysis of the relationship between the fault type and the maintenance path; optimizing a maintenance task allocation scheme through the optimal maintenance path, the incidence relation model and the maintenance resource constraint condition; carrying out maintenance time window dynamic adjustment analysis to obtain an optimal maintenance strategy; and finally, performing multi-objective optimization iterative analysis to obtain a final maintenance decision. According to the scheme, various factors can be comprehensively considered, and the scientificity and the accuracy of maintenance decision making of the industrial equipment are improved.
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