一种基于孪生图谱与强化学习的AGV全生命周期智能运维方法
By constructing a dynamic twin graph and using closed-loop management based on reinforcement learning, the problem of time lag in AGV equipment operation and maintenance was solved, enabling real-time health assessment and economically optimal maintenance of AGV equipment, thereby improving production line utilization and operation and maintenance efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- MASCH TECH DEV CO LTD
- Filing Date
- 2025-09-25
- Publication Date
- 2026-07-17
AI Technical Summary
The existing AGV equipment operation and maintenance methods suffer from a time lag between risk assessment and maintenance actions, resulting in untimely equipment operation and maintenance and affecting production line utilization.
By employing a twin graph-based approach and reinforcement learning, real-time data of key AGV components is collected to construct a dynamic twin graph. Risks are assessed through a graph neural network, and a reinforcement learning agent is trained to generate an economically optimal maintenance strategy, thereby achieving closed-loop management of equipment health assessment and maintenance.
It enables precise control of spare parts inventory without interrupting production, reduces downtime, increases production line utilization, and enhances the real-time nature and portability of operations and maintenance.
Smart Images
Figure CN121526552B_ABST