To address the current issues in the risk assessment of unmanned aerial vehicle (UAV) power inspection
which is mostly limited to single dimensions such as time and economy
and the lack of dynamic path adjustment capabilities based on task logic in path planning
this paper proposes a multi-objective intelligent inspection path planning method based on knowledge graphs. Firstly
considering the aging degree
failure probability and remaining life of the equipment
a risk assessment model is constructed. Then
taking into account the performance constraints such as the flight speed
climb rate and endurance of the UAV
a multi-objective optimization function integrating time cost
safety
economy and coverage completeness is constructed. Finally
an improved A* algorithm is proposed to transform the three-dimensional path planning problem into a multi-objective optimization problem and solve the optimal path. The experimental results show that compared with the traditional methods
the scheme in this paper reduces the obstacle conflict rate from the highest 3.5% to 1.2%
and the energy consumption per tower inspection from the highest 0.30kW?h/base to 0.26kW?h/base. Moreover
there are also significant improvements in algorithm convergence speed
path coverage rate and inspection compliance. The scheme in this paper provides a feasible technical path for the intelligent inspection of power systems and low-altitude economic applications.