This paper investigates the joint scheduling problem of unmanned aerial vehicles (UAVs) and trucks for community logistics, where UAVs act as service providers for last-mile delivery and trucks serve as mobile storage platforms for drone deployment. To address the complexity of decision variables, this paper proposes a three-stage solution scheme that divides the problem into the following: (1) UAV mission set generation via clustering, (2) truck-drone route planning, and (3) collaborative scheduling via a Mixed-Integer Linear Programming (MILP) model. The MILP model, solved exactly using Gurobi, optimizes truck movements and drone operations to minimize total delivery time, representing the core contribution. In the experimental section, to verify the correctness and effectiveness of the proposed Mixed-Integer Linear Programming (MILP) model, comparative experiments were conducted against a heuristic algorithm based on empirical intuitive decision-making. The solution results of experiments with different scales indicate that the joint scheduling model outperforms the scheduling strategies based on empirical experience across various scenario sizes. Additionally, multiple experiments conducted under different parameter settings within the same scenario successfully demonstrated that the model can stably be solved without deteriorating results when parameters change. Furthermore, this study observed that the relationship between the increase in the number of drones and the decrease in the total consumed time is not a simple linear relationship. This phenomenon is speculated to be due to the periodic patterns exhibited by the drone scheduling sequence, which align with the average duration of individual tasks.

Risk assessment in air traffic control (ATC) operations is a core element in ensuring flight safety. Addressing the limitations of current assessment techniques in deeply identifying critical risks within ATC procedures, this study proposes a method that integrates text mining with causal analysis for comprehensive risk identification. First, the BERTopic model is applied to conduct deep semantic analysis of ATC-related safety occurrence reports, extracting key risk themes and their representative keywords with enhanced semantic coherence and diversity. Second, by incorporating domain-specific operational knowledge and extracted keywords, a structured Risk Factor Representation Framework is established. Event chains are annotated to construct a Causal Network for ATC Operational Safety. Finally, topological analysis (including PageRank and betweenness centrality) and vulnerability analysis are employed to identify critical risk nodes within the network. Using incident reports from China's civil aviation ATC operations in 2021 as a case study, the results demonstrate that the proposed method significantly improves the accuracy of risk theme identification and highlights the prominent role of nodes with high PageRank and centrality in system safety. By transforming fragmented textual information into a structured risk association network, this study enables a shift from scattered factor identification to systematic causal analysis. It offers a novel approach for precise hazard detection and timely risk mitigation, contributing to enhanced civil aviation safety governance.