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 inspection 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 constraints. 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 scanning 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.