This invention relates to the field of
power grid inspection technology, and more particularly to a spatiotemporal dynamic path
planning method for power grids integrating GNN-UTVD-CBF. This method first constructs a
hybrid power grid environment model and calculates the safe time intervals for potential path edges based on equipment dynamics constraints. Then, it uses a graph neural network to predict edge expansion priorities and combines a unified temporal
visibility deformation criterion to construct a dynamically connected
visibility map, generating a candidate path set with multiple topological features. In
path search, a safety
verification mechanism based on a
linear quadratic regulator and a high-
order control barrier function is integrated to achieve efficient safety
verification of the quadratic
programming solution. Finally, a smooth trajectory is generated through spatial-temporal corridor expansion and B-spline curve optimization. When the environment changes dynamically, the
system quickly adjusts the path through local
pruning and incremental repair algorithms. This invention significantly improves the planning efficiency, safety, and robustness of
power grid inspection equipment in complex dynamic environments.