arXiv:2605.19892cs.DCcs.AI2026-05

在轨处理卫星数据,缓解地面传输压力

Deep Tech to Space: Space Data Centers and AI Revolution at the Edge

论文配图:Deep Tech to Space: Space Data Centers and AI Revolution at the Edge
图 1 · 摘自论文原文
  • 构建低轨卫星星座,在轨运行人工智能服务
  • 可减少90%以上地面传输数据量,降低延迟
  • 适合遥感、探月等需要实时处理的场景

私营部门创新带来的成本大幅下降,推动了在轨卫星数量激增和空间数据产量飙升。随着这一趋势持续,将大量数据传回地球处理可能面临链路拥堵和延迟上升的挑战。传统地面站网络也受限于容量、调度复杂性和可见窗口少等问题,难以应对日益增长的数据流。空间数据中心(SDCs)——基于软件、支持多租户、具备人工智能能力的在轨数据处理平台——为解决此问题提供了新思路。本文提出低轨SDC卫星星座架构,涵盖轨道设计、星间链路与网络拓扑、计算资源组织及软件服务编排,并结合技术路线图预测模型分析其技术可行性和经济可行性。通过地球观测和月球探测的应用案例验证该概念。

原文摘要 · Abstract (English)

Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresponding surge in space-generated data. As this trend continues, transmitting large volumes of data to Earth for processing may become increasingly costly and challenging due to potential space-to-Earth link congestion and increased latency. Moreover, traditional ground station networks may face difficulties accommodating growing data flows and workloads because of capacity constraints, complex scheduling logistics, and restricted visibility windows, which can limit scalability. Space Data Centers (SDCs) -- software-driven, multi-tenant artificial intelligence-based service platforms capable of processing data in orbit to generate actionable insights for client satellites and ground users -- represent a promising approach to address these challenges. This article presents the architecture of a Low Earth Orbit SDC satellite constellation, considering orbital design, inter-satellite links and network topology, computational resource organization, and software service orchestration. We analyze the potential technical feasibility and economic viability of SDCs using forecasting models informed by technology roadmaps and illustrate the concept through Earth observation and lunar exploration use cases.

空间计算在轨处理卫星星座人工智能

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