## 6 Conclusions  
The dynamic programming approach for air-ground collaboration presented in this paper assigns trucks the dual role of facilitating primary road transportation and functioning as mobile base stations for unmanned aerial vehicles (UAVs). UAVs are tasked with covering nodes that have experienced failures. By implementing effective location selection for dynamic collaborative nodes and employing strategic task path planning, this method successfully addresses the challenges of task distribution interruptions or delays that arise from vulnerabilities within the road network. The proposed enhanced evolutionary algorithm for the air-ground collaborative scheduling problem demonstrates significant efficacy. Its adaptive weight adjustment mechanism allows for flexible modifications to optimization objectives based on varying scenario requirements, thereby improving the system's adaptability and robustness.  
Experiments indicate that the algorithm effectively achieves efficient delivery within a road network environment characterized by node failures. Notably, when the node failure rate reaches 70%, the algorithm can still maintain a task completion rate of 90%, significantly surpassing that of traditional delivery methods. Furthermore, in the context of multi-objective optimization, the algorithm strives to balance the following three critical dimensions: demand coverage, delivery cost, and delivery time. This results in a well-defined Pareto front and offers a variety of solutions. Additionally, the algorithm exhibits a commendable convergence performance; it reaches a stable state more rapidly than traditional algorithms and yields superior solutions. However, the algorithm is not without its limitations. Firstly, in extremely adverse road network conditions--specifically when the node failure rate exceeds 80%--the task completion rate of the algorithm experiences a notable decline. Secondly, the algorithm does not adequately account for the energy constraints of drones, which may result in insufficient drone endurance during practical applications. Lastly, there remains potential for optimization regarding the computational complexity of the algorithm, particularly in large-scale delivery networks, where solution times may increase significantly.  
Future research can be enhanced in several key areas. Firstly, it is essential to develop a more sophisticated model for drone energy consumption that takes into account the effects of variables such as payload and wind speed on flight distance. Secondly, the establishment of a distributed computing framework is necessary to increase the efficiency of solutions for large-scale problems. Thirdly, integrating machine learning techniques to analyze historical data could facilitate the prediction of changes in road network vulnerability, thereby enabling optimized preventive scheduling. Fourthly, the collaborative operation of various types of drones should be explored to further improve the system's adaptability in complex environments. By implementing these enhancements, it is anticipated that the algorithm will significantly contribute to addressing the challenges posed by more intricate urban logistics and distribution scenarios.  
Conceptualization, W.Q.; methodology, W.Q.; software, A.L.; validation, W.Q., A.L. and H.Z.; formal analysis, W.Q.; investigation, A.L.; resources, H.Z.; data curation, W.Q.; writing--original draft preparation, W.Q.; writing--review and editing, A.L.; visualization, A.L.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author (accurately indicate status).  
The authors declare no conflict of interest.  
The moment when the drone path arrives at node i  
The moment when the vehicle leaves node i