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 in this field can be broadly classified into three categories: model-driven approaches, heuristic algorithms, and methods based on deep learning and reinforcement learning. Model-driven approaches leverage tools such as mixed-integer programming and graph-theoretical modeling to construct optimization models, describing the constraints and objective functions of resource scheduling through precise mathematical formulations. These methods can provide theoretically optimal solutions in static scenarios but suffer from exponential growth in computational complexity as the problem size increases, making them difficult to apply to large-scale dynamic scheduling. Heuristic algorithms, inspired by bio-inspired 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 they generally lack guarantees of global optimality. Deep learning and reinforcement learning methods, driven by data and interactive learning mechanisms, can extract hidden patterns from massive scheduling datasets and continuously optimize decision strategies through agent–environment interaction. These approaches exhibit unique advantages in complex scenarios such as dynamic topologies and unexpected tasks. Nevertheless, they are highly dependent on training data, and the interpretability of their decision processes remains limited. Current research still falls short in areas such as cross-layer collaborative scheduling, robustness optimization, and heterogeneous resource integration. Future efforts should further explore multimodal learning and adaptive decision-making mechanisms, 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.

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，可归纳为三类：基于数学模型驱动、基于启发式算法以及基于深度学习与强化学习的方法。其中，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。启发式算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，且缺乏全局最优性保证。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。