With the rapid growth of application demands and the real-time change of environmental situations, the defects of the UAV task network in adaptability, flexibility, and resilience are becoming more and more prominent. The current network architecture that the junction of points and lines is fixed cannot dynamically provide capacity requirements in real-time due to the failure nodes encountered in the Unmanned Aerial Vehicle (UAV) task scheduling process. To address this challenging issue, this paper proposes a flexible network architecture supporting dynamic fault-tolerant task scheduling model (DSM-FNA) for the UAV cluster. To be specific this paper resorts to super network theory, combining the management theory of flexible network and resilience network to carry out the organizational calculation on the model, and also draw upon linear transformation function to weight and stratify the capability value according to the ability requirement required by the task. Then, a flexible network architecture dynamic scheduling algorithm (FDSA) is proposed, and the substitution strategy is designed for the failure point, to realize the capability and dynamically adapt to the task. Finally, compared with the classical Max-Min algorithm and other algorithms, it is verified that the FDSA algorithm performs better dynamic adjustment for quick response in case of UAV cluster emergencies.

中文翻译：

---

基于灵活网络架构的无人机集群动态任务调度模型

随着应用需求的快速增长和环境情况的实时变化，无人机任务网络在适应性、灵活性和弹性方面的缺陷越来越突出。由于无人飞行器（UAV）任务调度过程中遇到的故障节点，当前点线连接固定的网络架构无法实时动态提供容量需求。为了解决这个具有挑战性的问题，本文提出了一种灵活的网络架构，支持无人机集群的动态容错任务调度模型（DSM-FNA）。具体而言，本文借助超级网络理论，结合柔性网络和弹性网络的管理理论，对模型进行组织计算，并根据任务所需的能力要求，利用线性变换函数对能力值进行加权和分层。然后，提出了一种灵活的网络架构动态调度算法（FDSA），并针对故障点设计了替代策略，实现了能力的实现和任务的动态适应。最后，与经典的 Max-Min 算法和其他算法相比，验证了 FDSA 算法在无人机集群突发事件的情况下能够进行更好的动态调整以快速响应。实现能力并动态适应任务。最后，与经典的 Max-Min 算法和其他算法相比，验证了 FDSA 算法在无人机集群突发事件的情况下能够进行更好的动态调整以快速响应。实现能力并动态适应任务。最后，与经典的 Max-Min 算法和其他算法相比，验证了 FDSA 算法在无人机集群突发事件的情况下能够进行更好的动态调整以快速响应。