针对大规模非均衡场景下多无人机任务分配存在的资源配置失衡、多目标优化难以协同的问题，提出融合改进聚类与联盟形成博弈的一体化集中-分布式混合分配框架。首先将资源、路径长度及能耗等优化目标建模为效用函数，接着基于密度的带噪声应用空间聚类（DBSCAN）算法通过k-距离图拐点识别实现邻域参数自适应改进，并采用近簇匹配的噪声点分层处理，生成空间紧凑的任务簇以降低问题规模；随后基于任务簇构建贪心联盟形成博弈模型，引入资源匹配与续航约束的初始化机制、无人机多策略决策，以效用函数为量化依据、功利主义顺序为决策准则，引导系统收敛至纳什均衡，实现资源动态均衡分配。最后仿真结果表明，该框架在核心优化目标上的综合表现显著优于对比算法，为非均衡场景下多无人机协同任务分配提供了兼顾多目标综合优化效果与稳定性的高效技术路径。

To address the problems of imbalanced resource allocation and difficulty in collaborative multi-objective optimization for multi-Unmanned Aerial Vehicle (UAV) task assignment in large-scale unbalanced scenarios, an integrated hybrid centralized-distributed assignment framework that combines improved clustering and coalition formation game is proposed. First, the optimization objectives including resource utilization, total path length and energy consumption are modeled as a utility function. Subsequently, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is improved to achieve adaptive adjustment of neighborhood parameters through inflection point identification of the k-distance graph, and hierarchical processing of noise points is implemented via near-cluster matching, so as to generate spatially compact task clusters and reduce the scale of the problem. On this basis, a greedy coalition formation game model is constructed based on the obtained task clusters. The initialization mechanism constrained by resource matching and endurance range, as well as the multi-strategy decision-making of UAVs are introduced into the model, which takes the utility function as the quantitative basis and the utilitarian order as the decision criterion to guide the system to converge to the Nash equilibrium, thus realizing dynamic and balanced resource allocation. Finally, simulation results demonstrate that the comprehensive performance of the proposed framework on core optimization objectives is significantly superior to that of the benchmark comparison algorithms. It provides an efficient technical approach that balances comprehensive multi-objective optimization performance and stability for multi-UAV cooperative task assignment in unbalanced scenarios.