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

Abstract

The invention discloses a wireless network protocol knowledge map construction and analysis method, system, device and medium. The method includes defining entities, relationships between entities and triples according to endogenous factors of the wireless communication network protocol, and constructing them through triples. The knowledge map of the endogenous factors of the wireless communication network protocol; the sparse representation vector of the node is obtained by calculating the random state weight of the node, and the cosine similarity between the sparse representation vectors is used to quantify and replace the edge connection relationship in the knowledge map, and then calculate the association vector of the node and feature vectors. The present invention establishes a knowledge graph of endogenous factors in the wireless communication network protocol, introduces a node sparse representation algorithm based on the graph structure of the knowledge graph, cosine similarity calculation and node association analysis method, and deeply excavates the graph structure information contained in the knowledge graph, Quantitative feature representation is performed on the connection relationship between knowledge graph nodes and edges, and the analysis and mining of the relationship between nodes is 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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