A Methodology for Situation Assessing of Space-Based Information Networks
Sai Xu, Jun Liu, Jiawei Tang, Xiangjun Liu, Zhi Li · Applied Sciences · 2025
This paper proposes a cloud-edge collaborative method for operational situation assessment to ensure the efficient and reliable operation of space-based information networks. By analyzing time-varying network topology characteristics, we establish a 14-dimensional assessment factor system that can characterize the operational situation of space-based information networks. Considering the resource constraints of satellites, traditional on-orbit assessment methods often lead to high latency and excessive resource consumption. A cloud-edge collaborative situation assessment method is introduced to enhance assessment efficiency. The proposed method first applies principal component analysis for dimensionality reduction, followed by pre-labeling situational factor data using an improved K-means clustering algorithm. The on-orbit assessment of individual satellites is then performed using a particle swarm optimization-support vector machine algorithm. Finally, a fusion assessment of the space-based information networks is conducted at the ground cloud center, incorporating situation weighting factors. Experimental results demonstrate that the proposed cloud-edge collaborative method improves assessment accuracy by 13% compared to baseline methods, significantly reduces average completion time, and maintains stable performance in large-scale satellite constellations.