摘要：

随着巨型星座逐步成为空天地一体化网络的核心基础设施, 其资源调度正面临高维约束、动态任务分配与多目标优化等多重挑战。针对这一领域的智能调度方法, 可归纳为数学模型驱动、基于启发式算法以及深度学习与强化学习(RL)方法3类。数学模型驱动方法借助混合整数规划、图论建模等工具构建优化模型, 通过精确的数学推演描述资源调度中的约束条件与目标函数, 在静态场景下能够提供理论最优解, 但其计算复杂度会随问题规模呈指数级增长, 难以应对大规模动态调度需求。基于启发式算法的方法依托仿生机制快速生成近似解, 在处理中等规模问题时展现出较高的效率与灵活性, 不过解的质量易受参数设置影响, 且无法保证全局最优性。深度学习与强化学习方法凭借数据驱动和交互学习机制, 能够从海量调度数据中挖掘隐含规律, 通过智能体与环境的持续交互优化决策策略, 在动态拓扑、突发任务等复杂场景中表现出独特优势, 但其对训练数据的依赖性较强, 且决策过程的可解释性仍有待提升。当前研究在跨层协同调度、鲁棒性优化、异构资源融合等方面仍存在不足, 未来需进一步探索多模态学习与自适应决策机制, 推动巨型星座资源调度向智能化、高效化、可靠化方向发展, 为空天地一体化网络的大规模部署与应用提供关键技术支撑。

关键词: 巨型星座, 资源调度, 数学模型, 启发式算法, 机器学习, 强化学习

Abstract:

As mega-constellations gradually become the core infrastructure of space—air—ground integrated networks, their resource scheduling faces multiple challenges, including high-dimensional constraints, dynamic task allocation, and multi-objective optimization. Intelligent scheduling methods can be broadly classified into three categories: model-driven methods, heuristic algorithms-based methods, and deep learning and Reinforcement Learning (RL) methods. Model-driven methods leverage tools, such as mixed-integer programming and graph-theoretical modeling, to construct optimization models and describe the constraints and objective functions of resource scheduling through precise mathematical formulations. These methods can provide theoretically optimal solutions in static scenarios. However, computational complexity increases exponentially as the problem size increases, posing difficulties for their application to large-scale dynamic scheduling. Heuristic algorithms-based methods, which are inspired by biological mechanisms, can rapidly generate approximate solutions and demonstrate high efficiency and flexibility in handling medium-scale problems. However, the quality of solutions is sensitive to parameter settings, and global optimality is not guaranteed. Deep learning and RL methods, which are driven by data and interactive learning mechanisms, can extract hidden patterns from massive scheduling datasets and continuously optimize decision strategies through agent-environment interactions. These approaches exhibit unique advantages in complex scenarios such as dynamic topologies and burst tasks. However, they are highly dependent on training data, and the interpretability of their decision-making processes remains limited. Research progress in areas such as cross-layer collaborative scheduling, robustness optimization, and heterogeneous resource integration remains limited. Future efforts need to further explore multimodal learning and adaptive decision-making mechanisms for driving mega-constellation resource scheduling toward greater intelligence, efficiency, and reliability and providing technological support for the large-scale deployment and application of space-air-ground integrated networks.

Key words: mega-constellation, resource scheduling, mathematical model, heuristic algorithm, machine learning, Reinforcement Learning (RL)