Fuzzy Logic Intelligent Space Routing Convolutional Decision and Optimization Scheme

This study mainly addresses the problem of spatial information network link congestion and how to ensure the quality of user experience, while improving the accuracy and interpretability of decision-making, making it more suitable for dealing with complex network environments. It proposes an intelligent spatial routing strategy on the ground of convolutional neural networks and fuzzy logic. Firstly, a time-varying graph model is established to describe the time-varying characteristics of satellite nodes and inter satellite links, and the processed physical and state data is used as input to the model. The results showed that the research strategy performed well in terms of processing power, recognition accuracy, and time complexity. The cost gap between the methods proposed by the research institute and intrusion detection systems is gradually becoming apparent and expanding. Specifically, the growth rate of the methods proposed in the study is significantly slower than that of intrusion detection systems, at 50MB, 60MB, 78MB, 82MB, and 98MB, respectively. In network performance analysis, the research strategy shows excellent network traffic balancing ability by minimizing network congestion values. Meanwhile, the research strategy performs excellently in resource utilization efficiency, is not affected by changes in network topology, and has unique advantages. From this, it can be seen that research strategies have shown excellent performance in handling large-scale tasks, network congestion management, resource utilization efficiency, computing speed and scalability, and controlling signaling overhead; This demonstrates its important application value in the integrated communication system of heaven and earth.