A natural language and neural network based approach for large-scale remote sensing satellite constellation management

With the development of natural language and neural network technology, intelligent satellite constellation management methods will have great research value. Traditional management methods rely heavily on the commanders' proficiency in the command system, and there are problems such as comprehension bias due to insufficient level of understanding, long time of task issuance, which bring many difficulties to the subsequent determination of requirements and collaborative planning, and have limitations such as high computation volume and slow response speed when dealing with large-scale constellations. Using natural language processing technology, a natural language processing based architecture is proposed for large-scale remote-sensing constellation management. On this basis, a neural network-based assisted decision-making algorithm is proposed to quickly provide the commander with selectable satellites, optimize the decisionmaking process and shorten the decision-making time, and assist the commander in monitoring the entire mission process. Simulation experiments show that the algorithm has high computational efficiency while the probability that the recommended satellite can actually perform the task reaches 98.52%, and the architecture proposed in this paper has good application prospects in the management of large-scale constellations.