针对森林火灾场景下通用航空器救援飞行存在的任务调度与航迹规划协同性不足、作业效率低下等问题，提出了一种融合改进合同网协议与蚁群优化的多机任务调度与航迹规划方法。首先，构建动态更新的熟人库机制，基于航空器属性与任务能力预筛选投标节点，减少协商开销；通过引入任务栅格重要性量化模型，将火场面积映射为重要性权重，指导调度优先级决策；设计公共消息黑板机制，实现救援成员主动感知全局任务状态，突破被动响应局限。其次，提出CNP-ACO融合算法：通过CNP分配灭火任务栅格后采用ACO优化多机航迹，结合改进的状态转移概率与信息素更新策略生成最小化总航程的最优路径。最后，以川西某地区真实山火场景为例进行仿真验证，对比分析CBBA、CBGA算法以及提出的基于改进合同网协议的CNP-ACO任务分配与航迹规划算法的计算结果。实验结果表明了改进算法的有效性，且优化后的方案任务栅格分配成功率为98.5%，较CBBA、CBGA算法分别提高11%和10.8%；调度方案平均生成时间较CBBA算法减少12.4%、较CBGA算法减少14.7%。所提算法能够有效解决任务调度与航迹规划之间的耦合问题,提升了森林火灾中多机救援任务调度的整体效率与资源利用率，具有重要的现实意义。

Aiming at the problems of insufficient coordination between task scheduling and flight trajectory planning, as well as low operational efficiency, in general aviation aircraft rescue operations for forest fire scenarios, this paper designs a collaborative optimization model for multi-aircraft task scheduling and flight trajectory planning. Firstly, an improved contract net protocol is utilized to achieve optimized task scheduling based on real-time fire conditions, aircraft status, and task priorities. Secondly, for complex fire environments, autonomous flight trajectory planning is implemented using the ant colony optimization, ensuring orderly and efficient execution of rescue tasks by multiple aircraft within the fire zone. Finally, the improved algorithm is validated through simulation experiments. The results demonstrate that the proposed algorithm effectively addresses the coupling problem between task scheduling and trajectory planning, thereby enhancing the overall efficiency of multi-aircraft rescue task scheduling and resource utilization rate.