2results about How to "Offset negative effects" patented technology

A method for dynamic beam optimization in satellite communication and a satellite communication terminal are disclosed. The terminal has a locally pre-installed hybrid architecture deep learning model. The specific steps are as follows: The terminal collects spatiotemporal feature data such as space, time, orbit, interference, and position in real time and stores it in a local historical database; it calculates the instantaneous phase angle of the satellite based on the position features and satellite ephemeris, identifies the scene type, and adapts the interference features to obtain optimized features; it extracts historical multi-frame spatiotemporal position features that match the current phase angle, combines them with the optimized features to construct a multi-dimensional feature matrix, inputs it into the model, and outputs spatial and temporal feature scores; it calculates dynamic weights by combining real-time interference and scene, balances the spatiotemporal score decision ratio, integrates historical reputation scores to calculate a comprehensive beam score, selects the optimal beam, and stores the decision result back into the local historical database. This invention integrates spatiotemporal multi-dimensional features, scene-based signal processing, and local deep learning inference to achieve beam optimization, improving the reliability of satellite communication and the efficiency of beam decision-making.

This invention relates to the field of intelligent connected vehicle technology, addressing the shortcomings of existing intelligent connected vehicle communication network trust assessment and consistency coordination. Specifically, it relates to a collaborative control method for trusted interaction among intelligent connected vehicle groups under a zero-trust architecture. By fusing direct and indirect trust functions to obtain a dynamic comprehensive trust assessment function, it achieves real-time fusion of the global communication network and the comprehensive trust calculation model, accurately quantifying the degree of interaction between vehicles and significantly improving the safety performance of ICV group interaction. A distributed collaborative control algorithm based on state feedback adapts to differences in vehicle trust levels, achieving the goal of consistent coordination among vehicle queues and ensuring the stability of collaborative control in dynamic communication scenarios for the ICV group. The integrated design of zero-trust security protection and vehicle communication network allows the security of communication relationships in the ICV group system to be updated in real time, adapting to the communication topology changes under dynamic vehicle movement.