Key Technologies for UAV Inspection in Dense Distribution Networks: Grid-Deployed Nests and Nest-to-Nest Hop-Flight Coordination

Aiming at the inefficiency and high missed inspection rate of traditional methods, as well as technical bottlenecks of single-nest Unmanned Aerial Vehicle (UAV) inspection (limited coverage, insufficient endurance, multi-UAV conflicts) in dense distribution networks, this study proposes a multi-source data fusion-driven UAV inspection solution integrating grid-based nest deployment, nest-to-nest hop-flight, and intelligent algorithms. Multi-source data fusion involves five core data types (spatial distribution of distribution network facilities, topographic features, historical fault statistics, real-time meteorological data, UAV performance parameters) and a threestep logic: standardized preprocessing for dimensional consistency, demand-tailored weighted feature extraction, and dynamic updating of integrated datasets to support subsequent decision-making. Core designs include optimized grid nest layout via heterogeneous data standardization, a nest-to-nest hop-flight mechanism (path decision-making, collision avoidance, emergency response), and three key intelligent algorithms (inspection scheduling, multi-UAV anti-collision, global path planning). Empirical tests in three typical Anhui regions show that the scheme improves inspection efficiency by 200%, achieves 96% defect recognition accuracy, reduces blind spot rate by 70%, controls multi-UAV conflict probability below 5%, and attains 92% inspection completion rate under <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8 \mathrm{m} / \mathrm{s}$</tex> crosswind. This solution provides a technically feasible and cost-effective approach for unmanned inspection of dense distribution networks.