In this paper, we propose a UAV path planning algorithm that takes into account real-time weather conditions. In recent years, numerous researchers have focused on optimisation algorithms for optimal path planning. Federated learning architectures allow distributed UAV nodes to share critical path features and local optimisation experience for collaborative knowledge accumulation without compromising private data. The ant colony algorithm, with its bionic optimality seeking mechanism, emulates ant behaviour in terms of pheromone release and path optimisation, thereby initially delineating feasible routes for drones. However, existing algorithms are deficient in their inability to incorporate real-time weather conditions into the path planning process, a shortcoming that significantly limits their practical application. To address this shortcoming, this paper proposes a weather-based adaptive heuristic ant colony optimisation (ACO) UAV circuit inspection planning algorithm (WACA). The algorithm is based on the original ACO algorithm and incorporates real-time weather conditions in the inspection area. Experimental results show that the proposed method improves the practical feasibility and versatility of route planning while minimising the time cost.