Frequent natural disasters cause significant losses to human society, and timely, efficient collection of post-disaster environmental information is the foundation for effective rescue operations. Due to the extreme complexity of post-disaster environments, existing sensing technologies such as mobile crowdsensing suffer from weak environmental adaptability, insufficient professional sensing capabilities, and poor practicality of sensing solutions. Therefore, this paper explores a heterogeneous multi-agent online collaborative scheduling algorithm, HoCs-MPQ, to achieve efficient collection of post-disaster environmental information. HoCs-MPQ models collaboration and conflict relationships among multiple elements through weighted undirected graph construction, and iteratively solves the maximum weight independent set based on multi-priority queues, ultimately achieving collaborative sensing scheduling of time-dependent UA Vs, vehicles, and workers. Specifically, (1) HoCs-MPQ constructs weighted undirected graph nodes based on collaborative relationships among multiple elements and quantifies their weights, then models the weighted undirected graph based on conflict relationships between nodes; (2) HoCs-MPQ solves the maximum weight independent set based on iterated local search, and accelerates the solution process using multi-priority queues. Finally, we conducted detailed experiments based on extensive real-world and simulated data. The experiments show that, compared to baseline methods (e.g., HoCs-GREEDY, HoCs-K-WTA, HoCs-MADL, and HoCs-MARL), HoCs-MPQ improves task completion rates by an average of 54.13%, 23.82%, 14.12%, and 12.89% respectively, with computation time for single online autonomous scheduling decisions not exceeding 3 seconds.

翻译：频繁的自然灾害给人类社会造成重大损失，及时、高效地收集灾后环境信息是有效救援行动的基础。由于灾后环境极其复杂，现有感知技术（如移动众包感知）存在环境适应性弱、专业感知能力不足、感知方案实用性差等问题。为此，本文探索了一种异构多智能体在线协同调度算法HoCs-MPQ，以实现灾后环境信息的高效采集。HoCs-MPQ通过构建加权无向图对多要素间的协作与冲突关系进行建模，并基于多优先级队列迭代求解最大权重独立集，最终实现时间依赖型无人机、车辆与工作人员的协同感知调度。具体而言：（1）HoCs-MPQ基于多要素间的协作关系构建加权无向图节点并量化其权重，进而依据节点间的冲突关系建立加权无向图模型；（2）HoCs-MPQ基于迭代局部搜索求解最大权重独立集，并利用多优先级队列加速求解过程。最后，基于大量真实与模拟数据进行了详细实验。实验表明，相较于基线方法（如HoCs-GREEDY、HoCs-K-WTA、HoCs-MADL和HoCs-MARL），HoCs-MPQ的任务完成率平均分别提升了54.13%、23.82%、14.12%和12.89%，且单次在线自主调度决策的计算时间不超过3秒。