Dynamic operation and maintenance resource scheduling method and device based on reinforcement learning and electronic equipment

By adopting a dynamic operation and maintenance resource scheduling method based on reinforcement learning, and combining geographic grid information and global skill collaboration knowledge, the problems of resource mismatch and low efficiency in the operation and maintenance resource scheduling of battery swapping stations are solved, and efficient matching and long-term balance between work orders and resources are achieved.

CN121860368BActive Publication Date: 2026-05-29QINGDAO TIEQI NETWORK TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
QINGDAO TIEQI NETWORK TECH CO LTD
Filing Date
2026-03-18
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

The existing operation and maintenance scheduling scheme for battery swapping stations lacks a holistic optimization approach, resulting in resource misallocation, low efficiency, and an imbalance between long-term and short-term benefits. It also fails to effectively match the urgency of work orders, personnel skills, and battery reserve levels.

Method used

A dynamic operation and maintenance resource scheduling method based on reinforcement learning is adopted. By acquiring geographic grid information and combining global geographic skill collaborative knowledge, the modal factors of work orders are determined, resource scheduling decisions are optimized, and multi-dimensional adaptation of work orders and resources is achieved.

Benefits of technology

It improves the matching efficiency and accuracy of operation and maintenance resources, ensures short-term efficiency and long-term balance, enhances the stability and intelligence level of the operation and maintenance system, and reduces resource waste and operating costs.

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Abstract

The application provides a dynamic operation and maintenance resource scheduling method and device based on reinforcement learning and electronic equipment, relates to the technical field of artificial intelligence, determines the space-time preference of a to-be-processed work order based on geographic grid information of an operation and maintenance area, and can clearly determine the comprehensive adaptation of the work order in the dimensions of geography, skills, battery reserves and the like. Further based on the quantification of the work order demand by a work order modal factor, the final target geographic grid and the corresponding operation and maintenance resource are determined in combination with global geographic skill coordination knowledge, the optimal scheduling scheme that takes into account short-term efficiency and long-term balance can be obtained by overall planning response efficiency, skill matching degree, resource load balancing and battery reserve level. In summary, the application can fundamentally overcome the defects of local decision-making and short-sighted scheduling of the prior art, solve problems such as resource mismatch, low efficiency, high cost and uneven load, and improve the stability and intelligent level of the operation and maintenance system.
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