关键词: 航空运输, 抗毁性, 复杂网络, 航线网络, 无人机

Abstract: To accurately quantify the impact of weather factors and node failures on the destructivity of low-altitude logistics route networks, this paper proposes an evaluation framework that integrates meteorological data spatial interpolation and complex network cascade failures. Using the published elevation data (DEM) as a covariate, the study uses the local thin disk smooth spline method to perform high-precision spatial interpolation of wind speed and rainfall data on low-altitude routes, which overcomes the sparsity of station data and accurately depicts the distribution of meteorological risks on the route. Based on six indicators, including the centrality of the degree of entropy weight method and the centrality of the intermediary, the M_si is introduced to identify the key nodes of the network. The comprehensive destructibility index of convergence network efficiency, maximum connected subgraph size, and network density is defined, H , and a cascading failure model considering node failure and load redistribution is developed. The low-altitude logistics route network built by 88 operating stations in Shenzhen is used as the object for simulation. The simulation results show that the comprehensive destructive resistance index, H can fully reflect the change of network destructivity, and the entropy-weighted node importance index M_si has a significant effect on identifying key nodes, and the removal of the first 10 nodes leads to a decrease of 80% in H and the network collapse. The quantitative analysis of meteorological impact shows that the constructed network effectively avoids the high wind speed area in Shenzhen, and the rainfall in 60% of the area is lower than the UAV operation threshold, and when the top 30 nodes affected by weather are removed, H only decreases by 28.3%, which proves that the impact of weather on the overall destructive resistance of the network is limited. The resulting destructive optimization scheme are adding 4 alternate landing fields near key nodes. Simulation verification shows that the destructive resistance of the network is significantly improved after optimization, and when the first 10 nodes are removed according to the degree value, H increases from 0.08 to 0.25 before optimization. This study provides safety evaluation tools and optimization strategies for the safe operation and destructive resistance improvement of low-altitude logistics networks in bad weather.

Key words: air transportation, invulnerability, complex networks, route network, drones