An Efficient and Stable Knowledge Service Framework for Satellite-Ground Collaboration

Abstract

The rapid expansion of Low Earth Orbit (LEO) satellite constellations presents immense potential for in-orbit services. However, the large-scale and dynamic nature of LEO constellations creates unstable communication environments, where traditional methods struggle to ensure the efficiency and stability of onboard inference services. This highlights the need for an advanced knowledge service framework capable of both inference and root cause analysis of service disruptions. To address this, we propose a novel knowledge service framework that integrates data-driven and knowledge-driven models through satellite-ground collaboration. The framework leverages lightweight onboard models for real-time data processing and ground-based knowledge graphs for advanced inference and cause analysis. To further enhance stability within complex interconnected onboard systems, we propose a prediction-based algorithm for LEO satellite networks that uses joint spatio-temporal modeling to achieve accurate link prediction. Additionally, we formulate an optimization problem aimed at minimizing path distance variance and maximizing path stability across LEO topologies, and we propose a heuristic path selection strategy to ensure efficient inter-satellite routing. Extensive in-orbit deployments and simulation experiments demonstrate the feasibility and effectiveness of the proposed framework. Satellite-ground verification on the BUPT-1 satellite shows its ability to provide real-time services, while inter-satellite simulations using real constellation data indicate significant improvements in response latency and path stability. Compared with baseline methods, our proposed method significantly reduces path jitter by up to 62.6% and improves path availability by up to 17.3% across various LEO constellations.