Task-Driven Satellite Cluster Self-Organization Method Based on Linear Groups in Finite Fields
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摘要: 随着空间技术和卫星网络的迅速发展，大规模卫星智能体集群已成为执行复杂任务的重要手段。然而，传统的集中式任务分配方法在应对卫星数量增加和任务需求复杂化时，表现出实时性和鲁棒性不足的局限性。为此，本文提出了一种基于有限域线性群的卫星智能体认知建模方法，引入有限域和一般线性群的高级代数理论，将卫星智能体的能力和认知状态映射为有限域上的矩阵和向量，能力矩阵反映了智能体的不同能力水平以及能力之间的相互作用，认知向量表示智能体对任务需求的认知程度，利用有限域线性群的封闭性和可逆性，确保智能体间信息交换和协同决策的准确性和一致性。基于以上方法，本文设计了一种基于能力与认知模型的自主任务分配算法，卫星智能体通过与邻星的交互和自身能力的迭代更新，实现对任务需求的自主匹配和决策。该算法在有限域上运行，具有良好的收敛性和鲁棒性，适用于动态环境和不完备通信条件下的大规模卫星集群。同时，本文开发了基于能力视图的交互式卫星能力发布订购系统，通过高维数据融合和降维技术，将复杂的卫星集群能力信息直观地呈现给用户，支持多用户、多优先级的资源订购和管理，用户可以通过能力视图实时查看卫星的能力状态，并根据实际需求灵活订购所需资源。2022年4月在齐鲁一号上开展了在轨验证实验，实验结果表明所提方法具有可行性，为卫星集群系统的任务驱动自主化提供了理论和技术支持。
关键词 卫星集群；自主任务分配；有限域线性群；智能体认知；分布式算法；能力建模Abstract: With the rapid development of space technology and satellite networks, large-scale satellite clusters have become crucial means for executing complex tasks. However, traditional centralized task allocation methods exhibit limitations in real-time responsiveness and robustness as the number of satellites increases and task demands become more complex. To address these challenges, this paper proposes a capability modeling and cognitive state representation method for satellite agents based on finite field linear groups and designs an autonomous task allocation algorithm accordingly. By mapping the capabilities and cognitive states of agents to matrices and vectors over finite fields and leveraging the algebraic properties of finite field linear groups, efficient information exchange and collaborative decision-making among agents are achieved. Additionally, we develop an interactive satellite capability publishing and subscription system based on capability views. Preliminary simulation and on-orbit verification results demonstrate the feasibility of the proposed method, providing theoretical and technical support for task-driven autonomy in satellite cluster systems. -
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