With the concept of "mosaic warfare," a novel combat style that involves constructing "kill webs" with unmanned aerial vehicle (UAV) swarms has emerged. However, little research has focused on this specific task scenario, particularly concerning the self-organization and adaptive collaboration of heterogeneous combat units in dynamic contested environments. Considering the scales and highly dynamic natures of such swarms, an adaptive communication network mechanism is developed based on the Molloy-Reed criterion. In contrast with common offline/noncombat task scenarios, the self-organization process is refined through agent-based modeling, and a combat effectiveness evaluation is introduced to provide enhanced task execution incentives. The proposed dynamic consensus-based coalition algorithm (DCBCA) addresses UAV intelligence defects such as "confusion," "forgetfulness," and "recklessness" during the dynamic target selection process, enabling effective bottom-up kill webs construction. Extensive simulation results demonstrate that the algorithmic system outlined in this paper can support the efficient and resilient operations of large-scale heterogeneous UAV swarms. The DCBCA outperforms the dynamically improved consensus-based grouping algorithm (CBGA) and the consensus-based timetable algorithm (CBTA) in terms of performance and convergence speed.

随着“马赛克战争”的概念出现，一种涉及用无人机 （UAV） 集群构建“杀伤网”的新型战斗方式已经出现。然而，很少有研究关注这种特定的任务场景，特别是关于动态竞争环境中异构作战单位的自组织和适应性协作。考虑到这种群体的规模和高动态性，基于 Molloy-Reed 准则开发了一种自适应通信网络机制。与常见的离线/非战斗任务场景相比，通过基于智能体的建模来细化自组织过程，并引入战斗效果评估以提供增强的任务执行激励。所提出的基于动态共识的联盟算法 （DCBCA） 解决了动态目标选择过程中的“混淆”、“健忘”和“鲁莽”等无人机智能缺陷，从而实现了有效的自下而上的杀伤网构建。广泛的仿真结果表明，本文概述的算法系统可以支持大规模异构无人机集群的高效和弹性运行。DCBCA 在性能和收敛速度方面优于动态改进的基于共识的分组算法 （CBGA） 和基于共识的时间表算法 （CBTA）。