Research On Resource Optimization Technology Of Aerospace TT&C Network Based On Knowledge Graph

After entering the 21st century,the resource optimization technology of the TT&C has always been a technology that our country is committed to developing.In recent years,the rapid development of Internet technology has brought about a step-by-step progress of artificial intelligence technology.With the increasingly scarce space resources in the future,such as the implementation of 6G technology and the proposed "star chain" plan of mask,the combination of artificial intelligence technology and space measurement and control technology has been accelerated.In the complex artificial intelligence technology,knowledge map technology relies on the strong data association ability and adaptability,and successfully displays its advantages in various fields.It is a research topic in this paper to use knowledge map to improve the intelligent level of space measurement and control network.Considering the characteristics of knowledge mapping technology and its advantages in decision-making field,in the third chapter,a space TT&C decision-making system based on knowledge mapping is proposed to optimize TT&C resources.Firstly,the construction process of TT&C knowledge map is described,and the main structure of TT&C knowledge map is described from six aspects:TT&C communication scene,main parameter set,partial interference set,partial fading set,available means set and decision result set.Then,a text vectorization method based on dynamic keyword table is proposed.The keyword table is obtained by knowledge map,and it changes dynamically with the content of knowledge map.The threshold division of data is more accurate by adding keyword calculation.Finally,it is the rule setting of decision system,considering the logic rules of multi-site and multi-channel joint reception,including the unique map route rules brought by the knowledge map structure,and solving the uncertainty problem in the decision-making process by setting the triple ring rules of map route.Finally,the advantages of the system function compared with the traditional system are demonstrated through the experimental results.In Chapter 4,a collaborative filtering algorithm for path loss is proposed.The concept of user is introduced into space TT&C system,and the current user’s position is described by longitude,latitude and altitude.We treat the problems encountered in the map as projects,and consider different situations that require decision-making.The problems encountered in the atlas are regarded as projects,and the different situations that need decision-making are regarded as project recommendations for users.Based on the path loss formula for the free propagation of radio waves,the normalization technology is used to derive the path loss similarity factor,and obtain the similarity calculation formula of the TT&C communication network.The simulation results show that the TT&C decision-making system based on the knowledge graph can provide an intelligent decision-making plan for the problems encountered in communication.At the same time,the system is more accurate,error-correcting,expandable and decoupling than the traditional case-based system.The personalized improved collaborative filtering algorithm fused with path loss is more suitable for user recommendation in the TT&C scenarios.From the results of multi-angle comparison,it can also be seen that the improved algorithm is superior to the traditional algorithm in recall and accuracy.