A power equipment operation and maintenance scheduling system and method based on multi-source state awareness

By constructing a state transition graph and a health scoring model, the problem of low resource allocation efficiency in traditional power equipment operation and maintenance was solved, preventive collaborative scheduling was realized, and the resource utilization rate of power grid operation and maintenance was improved.

CN122134077BActive Publication Date: 2026-07-17国网山西省电力有限公司吕梁供电分公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
国网山西省电力有限公司吕梁供电分公司
Filing Date
2026-05-08
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Traditional power equipment operation and maintenance models rely on preventive testing and regular inspections with preset cycles. They lack correlation analysis of the continuous evolution trend of equipment status in all dimensions, and cannot proactively identify the gradual deterioration process and systemic chain risks within the equipment. This results in low resource allocation efficiency and an inability to effectively prevent the occurrence and spread of complex faults.

Method used

By collecting multi-source state perception data, constructing state transition maps and health scoring models, identifying potential failure modes and the timing of operation and maintenance needs, achieving preventive collaborative scheduling, and optimizing the scheduling priority of operation and maintenance tasks.

Benefits of technology

It has enabled a shift from passively responding to single device failures to proactively planning cluster collaborative maintenance, optimizing the batch scheduling and path planning of operation and maintenance tasks, significantly reducing repetitive work and resource waiting time, and improving resource utilization.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application provides a power equipment operation and maintenance scheduling system and method based on multi-source state awareness. It constructs a state transition graph between power equipment by leveraging state transition features and state association features among various state nodes, thereby obtaining confidence constraint features during power equipment operation. It acquires equipment operation data and extracts health scores and topological dependencies between each power equipment from this data. Based on the health scores and topological dependencies, it performs preventative identification of potential fault modes and operation and maintenance demand sequences for the power equipment, resulting in a preventative operation and maintenance task sequence for multi-equipment collaboration. It then adjusts the scheduling priority of operation and maintenance tasks in the power equipment based on the confidence constraint features and the preventative operation and maintenance task sequence. Based on this scheme, predictive collaborative scheduling based on state graphs and health scores can be achieved.
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