Objective Conventional UAV （unmanned aerial vehicle） inspections in urban rail transit control protection zones are limited by the 'take-off and return-to-base' mode, presenting issues such as significant power loss during return trips, a large number of deployed hangars, and high construction and O&M （operation and maintenance） costs. Therefore, it is necessary to study a network-level UAV inspection optimization scheme in complex urban environments.

Method Comprehensively considering urban airspace restrictions, line-laying methods, and network connectivity, a configuration optimization method for UAV inspection systems based on leapfrog strategy is proposed. In combination with the actual geographical and business environments of the fully operational lines of Jinan Rail Transit, a multi-objective joint optimization mathematical model for 'hangar siting–tidal scheduling–route planning' is established, aiming to minimize the number of deployed hangars and maximize inspection coverage. This optimization model is applied proactively to the network-level holistic planning of Jinan Rail Transit.

Result & Conclusion  The measured data from the pilot section of Jinan Rail Transit Line 3 demonstrate that the leapfrog strategy extends the one-way continuous UAV inspection mileage from the conventional 3.25 km to 6.50 km, and maintains a safety redundancy of over 30% for the battery state of charge （SOC） upon landing, successfully running through the control closed loop of 'flight–fast charge–relay.' The simulation results of the network-level holistic planning show that by extending the single-line hangar spacing and implementing 'cross-line sharing of hangars at transfer hubs,' the total number of hangars deployed across the entire network is reduced from 72 under conventional independent planning to 38. To a certain extent, this solves the engineering pain points of 'unaffordable construction and lack of land' for hangars within the rail network.