大规模遥感卫星智能任务调度方法研究进展

杜永浩; 张本奎; 吴健; 陈盈果; 闫东磊; 于海琰; 邢立宁; 白保存

doi:10.11999/JEIT251038

大规模遥感卫星智能任务调度方法研究进展

doi: 10.11999/JEIT251038 cstr: 32379.14.JEIT251038

杜永浩¹,
张本奎²,
吴健^3, ,,
陈盈果¹,
闫东磊^{4, 5},
于海琰^{5, 8},
邢立宁⁶,
白保存⁷

1.
国防科技大学系统工程学院长沙 410073
2.
中国科学院空天信息创新研究院北京 100094
3.
西安电子科技大学杭州研究院杭州 311231
4.
航天恒星科技有限公司北京 100086
5.
中国空间技术研究院北京 100094
6.
江苏理工学院电气信息工程学院常州 213001
7.
装备项目管理中心北京 100000
8.
钱学森空间技术实验室北京 100094

基金项目: 国家自然科学基金(72201272, 72501214, 72201273)，中国科协青年人才托举工程(2023-JCJQ-QT-042)，湖南省科技创新计划(2025RC3111)

详细信息

作者简介:
杜永浩：男，副教授，研究方向为智能优化方法与卫星调度应用

张本奎：男，高级工程师，研究方向为目标认知与智能筹算

吴健：男，助理研究员，研究方向为数据驱动优化算法理论、方法与设计等

陈盈果：男，副教授，研究方向系统体系设计和卫星任务调度等

闫东磊：男，高级工程师，研究方向为卫星任务调度

于海琰：男，高级工程师，研究方向为航天器体系仿真和任务规划等

邢立宁：男，教授，研究方向为复杂系统仿真优化与调度

白保存：男，高级工程师，研究方向为航天系统工程、装备项目管理

通讯作者:
吴健　jianwwwu@126.com

中图分类号: TN209; TP11
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- 文章访问数: 45
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- 被引次数: 0
出版历程
- 收稿日期: 2025-09-30
- 修回日期: 2026-01-09
- 网络出版日期: 2026-01-13

Survey on Intelligent Methods for Large-scale Remote Sensing Satellite Scheduling

DU Yonghao¹,
ZHANG Benkui²,
WU Jian^{3
, ,},
CHEN Yingguo¹,
YAN Donglei^{4, 5},
YU Haiyan^{5, 8},
XING Lining⁶,
BAI Baocun⁷

1.
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
2.
Aerospace Information Innovation Institute, Chinese Academy of Sciences, Beijing 100094, China
3.
Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China
4.
Space Star Technology Co., Ltd., Beijing 100086, China
5.
China Academy of Space Technology, Beijing 100094, China
6.
School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
7.
Project Management Center, Beijing 100000, China
8.
Qian Xuesen Space Technology Laboratory, Beijing 100094, China

Funds: The National Natural Science Foundation of China (72201272,72501214,72201273), Young Elite Scientists Sponsorship Program by CAST(2023-JCJQ-QT-042), The Science and Technology Innovation Program of Hunan Province (2025RC3111)

摘要

摘要: 针对遥感卫星任务调度大规模、复杂化的发展趋势和星群协同、即时服务的常态要求，依据自顶向下的原则，该文相继综述了其任务调度框架、模型与算法的发展现状。首先，基于集中式调度框架、分布式调度框架和集中-分布式调度框架，阐明了各调度框架的典型流程和适用场景。其次, 按照发源时间与建模特点的不同，从经典运筹学模型、约束满足优化模型和基于神经网络的决策模型3个角度出发, 探讨了不同卫星任务调度模型的描述方式和适用性。在此基础上,介绍了精确求解、元启发式和机器学习类等3类卫星任务调度主流算法, 揭示了各算法运行原理与优劣势。最后, 指出了规模化、订单化改造调度框架，发展混合式调度模型以及机器学习、大模型交融背景下算法工程化等未来研究新方向。
- 遥感卫星任务调度 /
- 任务调度框架 /
- 任务调度模型 /
- 任务调度算法 /
- 智能优化方法
Abstract: Significance Satellite task scheduling is an operational optimization technique. It constructs combinatorial optimization models for space–ground resources and applies operations research and computational intelligence methods to generate task plans, resolve task conflicts and constraints, and maximize satellite utilization efficiency. With the development of large-scale constellations, satellite task scheduling faces several new challenges. (1) The rapid increase in the number of satellites and tasks leads to a combinatorial explosion of the solution space. (2) Satellite applications are shifting from planned operations to on-demand services, which require response times to be reduced from hours to minutes or even seconds. (3) Advances in satellite payload capabilities enable onboard autonomous decision making and in-orbit collaboration, which support interactive and swarm-intelligence-based management of large-scale remote sensing constellations. Progress To address large-scale complexity, constellation collaboration, and on-demand service requirements in satellite task scheduling, recent research developments are reviewed from the perspectives of task scheduling frameworks, task scheduling models, and task scheduling algorithms, following a top-down approach. First, centralized scheduling frameworks, distributed scheduling frameworks, and hybrid centralized–distributed scheduling frameworks are described, and their control paradigms and application scenarios are clarified. Second, task scheduling models are examined according to their theoretical foundations and applicable solution methods, including classical operations research models, constraint satisfaction optimization models, and artificial neural network-based decision-making models. Their modeling approaches and application scopes are discussed in detail. Subsequently, three major classes of task scheduling algorithms are summarized, including exact algorithms, metaheuristic algorithms, and machine learning-based algorithms. Their decision-making mechanisms, advantages, and limitations are analyzed. Finally, future research directions are identified, including the reconstruction of large-scale and order-oriented task scheduling frameworks, the development of novel task scheduling models, and the innovative integration of different task scheduling algorithms. Conclusions and prospects At the framework level, task scheduling frameworks for constellations consisting of more than one thousand satellites have not yet been reported. Existing task scheduling frameworks mainly address problems with fewer than 100 satellites, which remains insufficient for large-scale remote sensing constellations with thousands or even tens of thousands of satellites. The hybrid centralized–distributed task scheduling framework combines the advantages of centralized scheduling frameworks and distributed scheduling frameworks and is consistent with the hierarchical construction and management characteristics of satellite constellations. It can further adapt to satellite scale expansion and order-based process mechanisms. At the model level, constraint satisfaction optimization models focus on detailed representations of optimization attributes and elements and are suitable for small-scale and medium-scale satellite task scheduling problems. In contrast, artificial neural network-based decision-making models emphasize classification and decision-making characteristics and support online and on-demand scheduling, which makes them suitable for large-scale satellite task scheduling scenarios. These two types of task scheduling models can therefore be coordinated to characterize different stages of large-scale constellation task scheduling. At the algorithm level, the integration of metaheuristic algorithms and machine learning-based algorithms has become an important technical approach for solving satellite task scheduling problems. This integrated approach supports hybrid centralized–distributed task scheduling frameworks and complements both constraint satisfaction optimization models and artificial neural network-based decision-making models.
- Remote sensing satellite task scheduling /
- Task scheduling framework /
- Task scheduling model /
- Task scheduling algorithm /
- Intelligent optimization method

HTML全文

图 1 我国近10年在轨卫星规模变化及未来趋势一览

下载: 全尺寸图片幻灯片

图 2 滴滴打车、美团外卖及大规模遥感星群调度等订单式调度模式(以手机平台为例)

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图 3 典型遥感卫星任务调度框架示意图

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表 1 典型卫星任务调度框架（详情见批注）

框架种类	主要优缺点
集中式框架	√ 主→从式框架 √ 集中考虑全部要素，依赖主中心全局优化 × 未降低问题规模 × 至多适用100颗卫星，不适合大规模调度
分布式框架	√ 分布式，去中心化 √ 局部优化、协商交互 √ 问题降维、加速求解 × 耗费星上计算与通信资源，不易收敛 × 仅适用于理想情况或小范围独立星座
集中-分布式框架	√ 主$\leftrightarrow $分$\leftrightarrow $从式框架 √ 兼具集中式与分布式优势，更加灵活 √ 符合巨星座多级分层建设及管理特点 × 流程机理复杂度高 × 设计难度大

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表 2 典型卫星任务调度模型

模型种类	主要优缺点
经典运筹学模型 (VRP,JSP等)	√ 最早应用 √ 简明、直观、易用 √ 可借鉴经典运筹学的算法配套求解 × 需大幅简化、抽象问题 × 仅适用数十颗小规模的卫星调度
约束满足优化模型	√ 当前最常用 √ 客观描述组合优化变量关系及非线性约束 √ 适用于真实、复杂的卫星任务调度 √ 可适用上百颗卫星的大规模调度 × 复杂度与求解难度大 × 对框架及算法要求高
基于神经网络的决策模型	√ 研究热点 √ 突出分类、决策特点 √ 适用单星在线调度或多星任务分配环节 √ 可嵌入大规模订单式卫星任务调度流程 × 缺乏全局性建模考虑

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表 3 典型卫星任务调度算法

算法种类	典型算法	主要优缺点
精确求解算法	线性规划	√ 可求解理论最优解 × 仅适用于单星、单轨等小规模卫星调度 × 难处理复杂的非线性约束及优化目标 × 求解效率不可控
	2次规划
	动态规划
	数学规划求解器等
元启发式算法	进化算法	√ 全局寻优强，常用 × 局部寻优弱，收敛慢
	邻域搜索算法	√ 局部寻优强，收敛快 × 全局寻优弱，常借助自适应策略改进
	模因算法	√ 二者结合，兼具全局、局部寻优能力优势 √ 多融合自适应、自学习机制，求解能力强
机器学习类算法	改进元启发式方面	√ 算法研究热点 √ 赋能传统元启发式，提升大规模调度效能 × 机制设计复杂
机器学习类算法	训练ANN实现分类或决策方面	√ 与框架模型有机融合 √ 可实现“端到端”调度 × 易陷短视、局部最优

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表 4 部分参考文献中所解决的卫星任务调度问题规模一览

资源数量	部分参考文献(综述类除外)
1$ \sim $3	文献[7–10, 12–17, 25, 29, 33, 38, 39, 42–50, 61–65, 67, 76, 92, 98]
4$ \sim $10	文献 [6, 19, 20, 24, 26, 28, 31, 32, 36, 37, 40, 41, 53, 66, 86, 87, 94]
11$ \sim $20	文献 [22, 27, 60, 68]
21$ \sim $50	文献 [11, 69, 88, 97]
51$ \sim $100	文献 [23, 30, 70]
＞100	文献 [4, 18, 21, 96, 99]

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参考文献(100)

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