Wireless network protocol knowledge graph construction and analysis method and system, equipment and medium

Abstract

The invention discloses a wireless network protocol knowledge graph construction analysis method and system, equipment and a medium, and the method comprises the steps: defining entities, the relation between the entities and a triad according to the endogenous factors of a wireless communication network protocol, and constructing a knowledge graph of the endogenous factors of the wireless communication network protocol through the triad; obtaining sparse representation vectors of the nodes through node random state weight calculation, using cosine similarity among the sparse representation vectors for quantifying and replacing edge connection relations in the knowledge graph, and then calculating association vectors and feature vectors of the nodes. According to the method, the knowledge graph of the wireless communication network protocol endogenous factors is established, a node sparse representation algorithm based on a knowledge graph structure and a cosine similarity calculation and node association analysis method are introduced, and graph structure information contained in the knowledge graph is deeply mined; quantitative feature representation is carried out on the knowledge graph node and edge connection relation, and analysis and mining of the incidence relation between the nodes are completed.

Description

technical field [0001] The present invention relates to the technical field of intelligent wireless communication networks, in particular to a method, system, device and medium for constructing and analyzing wireless network protocol knowledge graphs. Background technique [0002] The network structure, terminal type, terminal behavior, data service requirements, and system resources of the future wireless communication network are all characterized by high dynamics, strong timeliness, and mutual coupling. Effectively clarify the relationship between these characteristics and the endogenous factors of the communication network protocol. It is helpful for efficient management of wireless communication network resources, network maintenance and collaboration, and theoretical research on intelligent wireless communication. Due to the multi-source heterogeneity and loose organizational structure of wireless communication data, the characteristics of the coupling of hierarchical ...

AI Technical Summary

Problems solved by technology

The relationship between various endogenous factors in the existing communication network architecture is usually a "black core", and it is difficult for researchers to dynamically understand the internal process of the network, network dynamic behavior, etc. in a visualized, hierarchical, and structured way.

In addition, the existing learning wireless networks focus on learning at a certain layer, such as the physical layer, data link layer, and layers above the core network, and the relationship between layers and the relationship between elements of the same layer are not well visualized.

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