Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit. However, these constellations often rely on bent-pipe architecture, resulting in high communication costs. Existing onboard inference architectures suffer from limitations in terms of low accuracy and inflexibility in the deployment and management of in-orbit applications. To address these challenges, we propose a cloud-native-based satellite design specifically tailored for Earth Observation tasks, enabling diverse computing paradigms. In this work, we present a case study of a satellite-ground collaborative inference system deployed in the Tiansuan constellation, demonstrating a remarkable 50\% accuracy improvement and a substantial 90\% data reduction. Our work sheds light on in-orbit energy, where in-orbit computing accounts for 17\% of the total onboard energy consumption. Our approach represents a significant advancement of cloud-native satellite, aiming to enhance the accuracy of in-orbit computing while simultaneously reducing communication cost.

翻译：近年来，卫星技术的进步与太空访问成本的降低推动了低地球轨道大型卫星星座的涌现。然而，这些星座常采用弯管架构，导致通信成本高昂。现有星上推理架构存在精度低、在轨应用部署与管理灵活性不足等局限性。为解决上述挑战，我们提出一种专为地球观测任务设计的云原生卫星架构，能够支持多种计算范式。本研究以"天算"星座中部署的星地协同推理系统为案例，展示了其50%的精度提升与90%的数据压缩效果。研究揭示了在轨能量分配特性——在轨计算仅占星上总能耗的17%。本方法代表了云原生卫星技术的重大突破，旨在提升在轨计算精度的同时降低通信成本。