摘要： 为满足卫星数据处理、车辆远程控制等快速响应和大范围覆盖的应用场景需求,聚焦于采用分层控制和人工智能技术的方法,设计一种空天地一体化算力网络资源调度机制。将空天地网络划分为3个域,分别部署域控制器,负责本地域的资源管理,同时通过卫星和无人机的覆盖范围进行地面区域划分,确保地面区域能够得到有效的服务保障,以实现高效的数据传输和任务处理。为了优化空天地算力网络资源利用率,引入多智能体强化学习算法,对不同场景下产生的计算任务进行实时处理,将每个域控制器视为具备任务调度和资源分配能力的智能体,在满足时延和能耗的约束下,通过协同学习和分布式决策实现计算任务智能调度和高效分配。实验结果表明,该机制能够有效提高资源利用率和缩短任务响应时间。

关键词: 空天地一体化网络, 算力网络, 任务调度, 资源分配, 多智能体强化学习

Abstract: In response to the increasing demand for fast response and large-scale coverage in application scenarios such as satellite data processing and vehicle remote control, this study focuses on utilizing hierarchical control and artificial intelligence technology to design a resource scheduling mechanism for space-air-ground integrated computing power networks. Air, space, and ground networks are divided into three domains, and domain controllers are deployed for resource management in the corresponding local domain. The areas are divided based on the coverage of satellites and drones to ensure that they can achieve effective service guarantees, efficient data transmission, and task processing. A multi-agent reinforcement learning-based scheduling algorithm is proposed to optimize resource utilization in space-air-ground integrated computing power networks, considering each domain controller is treated as an agent with task scheduling and resource allocation capabilities. Intelligent resource scheduling and efficient resource allocation for computing tasks are realized through collaborative learning and distributed decision-making with satisfactory delay and energy consumption constraints. Computing tasks are generated in different scenarios and processed in real time. Simulation results show that the proposed mechanism can effectively improve resource utilization and shorten task response time.

Key words: Space-Air-Ground Integrated Network (SAGIN), computing power network, task scheduling, resource allocation, multi-agent reinforcement learning

中图分类号: 

• TN929