关键词: 复杂网络, 无人机(UAV), 物流配送, 安全风险, 级联失效, 演化模型

Abstract:

To enhance the safety risk management level of UAV logistics distribution, a cascading failure evolution model for UAV logistics distribution safety risks was constructed based on complex network theory. Through simulated evolution experiments, the risk evolution characteristics in specific scenarios of UAV logistics distribution were revealed. By analyzing 63 interview transcripts, safety risk factors and their interrelationships were identified, and the credibility of these findings was verified by comparing them with relevant literature analysis results. The safety risk factors were categorized into five types: personnel, machinery, materials, methods, and environment. A directed weighted complex network with 69 nodes and 469 edges was then constructed. Using the Gephi platform, the overall topological structure characteristics of the complex network were calculated and analyzed, confirming the necessity of dynamic risk evolution analysis. The importance levels of nodes in the complex network were classified as high, medium, and general, and the development stages were classified as latent, diffusion, and occurrence, which laid the foundation for the construction of the dynamic cascading failure evolution model. Risk propagation probability and risk load redistribution rules were defined to quantitatively characterize risk evolution. A Python program was designed to conduct simulation evolution experiments. Key experimental groups were defined, and the evolution network of UAV logistics distribution safety risks was constructed. The simulated evolution results were analyzed from dimensions such as key evolution nodes and key evolution paths. The results show that in the evolution network, nodes s15, s17, and s18 in the machinery category are the most important, and the key evolution paths starting from this category of factors are the most abundant. The loss of control of nodes s54 and s57 in the environment category is the primary cause of control failure in the machinery category nodes. Therefore, strategies should be formulated to focus on controlling factors in the environment and machinery categories, such as immature external supervision and equipment signal issues, to achieve forward shifting of safety risk control.

Key words: complex network, unmanned aerial vehicle (UAV), logistics distribution, safety risk, cascading failure, evolution model