针对无人机集群对时序任务分配中因跨队列影响导致的分配失效和冲突问题，提出一种基于时序耦合分析的任务分配方法（TCATA）。首先，建立考虑无人机载荷、任务需求和任务时序约束的任务分配模型，并从效能函数设计和效能有效性两个方面对市场机制下任务时序耦合约束的影响进行分析；其次，构造集群内各无人机局部调整方案集，并通过通讯一致性获取全局调整方案集；随后，各无人机基于各调整方案效能大小和冲突关系，将全局调整方案集的决策问题建模为最大加权团问题，并采用贪婪算法进行求解；最终确定任务的执行者并更新全局任务分配结果。仿真实验表明，在求解无人机数和任务数均过百的大规模时序任务分配问题时，TCATA在求解效率和指标上显著优于分布式遗传算法，和CBBA-TCC算法相比，求解指标基本一致，但求解时间低于CBBA-TCC的50%，验证了TCATA求解大规模时序任务分配问题的有效性。

To address the allocation failures and conflicts caused by cross-queue influences in temporal task allocation for UAV swarm, a Temporal Coupling Analysis-based Task Allocation method (TCATA) is proposed. Firstly, a task allo-cation model considering UAV payloads, task requirements, and task temporal constraints is established. The im-pact of task temporal coupling constraints under the market mechanism is analyzed from two aspects: performance function design and performance validity. Next, a local adjustment set is constructed for each UAV within the swarm, and a global adjustment set is obtained through communication consensus. Then, based on the performance mag-nitude and conflict relationships of each adjustment, the global adjustment scheme decision problem is modeled as a maximum weighted clique problem and solved by a greedy algorithm by each UAV individually. Finally, the execu-tors of the tasks are determined and the global task assignment result is updated. Simulation experiments demon-strate that in solving temporal task assignment problems with hundreds of UAVs and tasks, TCATA significantly out-performs distributed genetic algorithms in both efficiency and performance metrics. Compared with the CBBA-TCC algorithm, TCATA achieves comparable performance while reducing computation time by more than 50%, vali-dating its effectiveness in large-scale sequential task allocation.