Wildfire monitoring demands timely data collection and processing for early detection and rapid response. UAV-assisted edge computing is a promising approach, but jointly minimizing end-to-end service response time while satisfying energy, revisit time, and capacity constraints remains challenging. We propose an integrated framework that co-optimizes UAV route planning, fleet sizing, and edge service provisioning for wildfire monitoring. The framework combines fire-history-weighted clustering to prioritize high-risk areas, Quality of Service (QoS)-aware edge assignment balancing proximity and computational load, 2-opt route optimization with adaptive fleet sizing, and a dynamic emergency rerouting mechanism. The key insight is that these subproblems are interdependent: clustering decisions simultaneously shape patrol efficiency and edge workloads, while capacity constraints feed back into feasible configurations. Experiments show that the proposed framework reduces average response time by 70.6--84.2%, energy consumption by 73.8--88.4%, and fleet size by 26.7--42.1% compared to GA, PSO, and greedy baselines. The emergency mechanism responds within 233 seconds, well under the 300-second deadline, with negligible impact on normal operations.

翻译：野火监测需要及时的数据收集与处理以实现早期预警和快速响应。无人机辅助的边缘计算是一种有前景的解决方案，但如何在满足能量、重访时间和容量约束的同时，联合最小化端到端服务响应时间仍然具有挑战性。本文提出一个集成框架，针对野火监测任务，协同优化无人机路径规划、机队规模配置和边缘服务供给。该框架融合了基于火灾历史加权的聚类方法以优先关注高风险区域、兼顾邻近性与计算负载的服务质量（QoS）感知边缘节点分配、结合自适应机队规模调整的2-opt路径优化算法，以及动态应急重路由机制。核心洞见在于这些子问题相互关联：聚类决策同时影响巡逻效率和边缘工作负载，而容量约束又反馈到可行的配置方案中。实验表明，与遗传算法（GA）、粒子群优化（PSO）及贪婪基线方法相比，所提框架将平均响应时间降低了70.6%至84.2%，能耗降低了73.8%至88.4%，机队规模减少了26.7%至42.1%。应急机制可在233秒内完成响应，远低于300秒的截止时限，且对正常运营的影响可忽略不计。