物理-知识双约束下的电力具身智能作业决策技术OA

This paper proposes a physics-knowledge dual-constraint decision-making technology for power-embodied intelligent operations,targeting the prominent problems in practical scenarios.Sudden state changes of equipment during power distribution cabinet sequential control easily cause skip-level operations,and trajectory deviation in UAV line in-spection may lead to insufficient safety distance.In addition,existing methods mostly implement safety specification verification in a post-event manner,which fails to realize full-process proactive safety prevention.The proposed method transforms spatial geometry,mechanical balance,and electrical safety requirements into computable physical consistency constraints,and converts operation timing,compliance criteria,and equipment limits into rigid safety specification con-straints.A closed-loop decision loop consisting of constraint verification,disturbance classification,local replanning,action pre-verification,and state confirmation is constructed.Combined with dynamic constraint prompt engineering,adaptive action correction is achieved without model retraining.Verified by Isaac Sim,AirSim,and field experiments,the method obtains a correct response rate of 94.5％under 200 random disturbances in cabinet control scenarios and achieves a task completion rate of 98.11％and an image availability rate of 92％in 53 UAV field tests,without point cloud scan-ning or teaching flights.The proposed method pre-embeds physical feasibility and operational compliance into action generation and execution,providing verifiable safety decision support for power-embodied intelligent operations.