## 1 Introduction  
Spatial Information Networks (SINs), including satellite platforms, ground stations, and other communication nodes, provide worldwide coverage and real-time information transmission services. Compared with traditional terrestrial communication systems, the advantages of satellite networks lie in their ability to efficiently and flexibly handle complex communication needs on a global scale, break through geographic constraints, and provide continuous and reliable communication safeguards in unexpected situations of extreme weather or natural disasters. Satellite networks will play an increasingly important role in the future development of 6G and next-generation communication technologies, and they will function alongside terrestrial networks to build a smarter and faster global communications infrastructure [1; 2]. Although satellite information networks have demonstrated advantages in providing global coverage and real-time information transmission, there are still many challenges in adapting data transmission to rapidly changing network conditions in highly dynamic and uncertain environments [3]. Factors such as the high mobility of satellites, orbital changes, and fluctuations in the quality of communication links make routing design particularly complex [4]. Real-time sensing and adjustment of data transmission methods, to ensure efficient system operation and prevent resource wastage, have become core issues in current research. Furthermore, with the rapid advancement in multi-antenna satellite communication systems, effective calibration techniques for onboard wide-band RF antennas become critical for maintaining system accuracy and reliability [5]. Excessive resource waste not only diminishes a system's overall effectiveness but may also lead to significant increases in operational costs and latency in critical tasks [6]. To address the challenges of resource allocation and task scheduling in highly dynamic and uncertain environments, researchers have primarily employed spatial-temporal models and machine learning methods for traffic prediction and scheduling. However, these approaches often rely on extensive historical data for training, making it difficult to adapt in real-time to frequent changes in network topology and traffic fluctuations. Additionally, their high computational complexity and slow convergence rates limit their efficiency in large-scale satellite networks. Excessive resource wastage not only diminishes the overall effectiveness of a system, but also leads to significant increases in operational costs and latency in critical tasks. Therefore, there is an urgent need for an innovative solution that can achieve maximum resource utilization and system stability in large-scale and high-dimensional environments. This paper propose a novel adaptive dynamic resource allocation method for satellite networks, designed to maximize resource utilization and ensure system stability in large-scale and high-dimensional environments. Building on the limitations of existing approaches that rely heavily on historical data and face high computational demands, our method models the competitive interactions among network participants to achieve fair and efficient resource distribution. By continuously monitoring network conditions and dynamically adjusting resource allocations, the proposed framework effectively responds to frequent changes in network topology and fluctuating traffic demands. This strategy addresses real-time adaptability and resource optimization challenges, thereby minimizing resource wastage and enhancing satellite networks' overall robustness and reliability. In this paper, our main contributions are as follows:  
1. We propose a novel resource allocation framework based on cooperative game theory, to address the complex challenges of resource scarcity and interference constraints in satellite networks.