针对无人机集群应急救援中信息高效交换的迫切需求，本文创新性地将空中计算（Over-the-Air Computation, OAC）技术与谱图理论相结合，提出一种面向低时延高一致性通信的集群拓扑优化策略。研究揭示了通信拓扑结构、时间同步精度与状态信息一致性收敛之间的内在关联机制，建立了三者的理论量化模型。在此基础上，设计了双层联合优化框架：上层基于谱图理论准则优化网络拓扑结构，通过调控节点度分布与环路特性，同步提升同步性能与收敛速率；下层自适应调整MAC层时帧长度，最小化端到端通信时延。所提出的拓扑设计准则突破了传统点对点通信范式局限，实现了状态信息一致性收敛条件与收敛速率的平衡优化。实验结果表明，与基于深度Q网络的强化学习方案相比，本文方法在信息协同效率上最大提升达37%，且算法执行时间大幅降低；相较于传统方案，本方法在大规模集群场景下展现出显著的时延优势，为无人机集群在灾害应急等高时效性应用场景中的高效协同提供了理论支撑。

To address the urgent need for efficient information exchange in UAV swarm emergency rescue scenarios, this paper combines Over-the-Air Computation (OAC) with spectral graph theory and proposes a topology optimization strategy for low-latency and high-consistency communication. The relationship among communication topology, time synchronization accuracy, and consensus convergence of state information is analyzed, and a theoretical model is established to characterize their interactions. A bi-level joint optimization framework is then developed. The upper layer optimizes the network topology based on spectral graph criteria to improve synchronization performance and convergence speed, while the lower layer adaptively adjusts the MAC-layer frame length to reduce end-to-end latency. Experimental results show that the proposed method improves information collaboration efficiency by up to 37% compared with a Deep Q-Network (DQN)-based reinforcement learning scheme, while significantly reducing algorithm execution time. In large-scale swarm scenarios, the proposed approach also achieves lower communication latency than conventional methods, demonstrating its effectiveness for time-critical applications such as disaster emergency response.