Topology-aware preconditioning for UAV logistics infrastructure and distributed swarm scheduling

In large-scale UAV logistics systems, distributed scheduling has emerged as a key approach to overcoming the scalability limitations of centralized architectures. However, existing studies primarily focus on improving optimization algorithms, while largely overlooking the fundamental constraints imposed by the underlying communication topology. In real urban environments, heterogeneous spatial distributions often lead to poorly connected networks, which in turn become a critical bottleneck limiting distributed coordination efficiency.This paper investigates how communication topology shapes distributed scheduling performance from the perspective of coupling transportation infrastructure and information interaction. Building upon algebraic graph theory and distributed optimization, we establish a quantitative relationship between network connectivity and the convergence rate of cooperative errors, and show that, under construction cost constraints, system performance is subject to an inherent structural upper bound. To address this limitation, we propose a topology optimization method that enhances connectivity under realistic spatial constraints, and further develop a topology-aware distributed scheduling framework that enables joint design of infrastructure and coordination mechanisms.Experiments based on real-world logistics networks demonstrate that modest topology enhancements can accelerate system convergence and reduce scheduling delays under high-load conditions. Ablation studies further confirm that network connectivity plays a key role in determining overall system performance.Overall, this work reveals the mechanism linking transportation infrastructure topology to distributed coordination efficiency, and provides a principled approach to upgrading infrastructure from passive physical assets to active computational topology resources, offering theoretical support for the integration of computing and networking in future intelligent transportation systems.