Risk conduction association map optimization method and device and computer device

Abstract

The invention relates to the technical field of big data, and provides a risk conduction association map optimization method and device, a computer device and a storage medium. The method comprises the following steps of obtaining the risk nodes in an initial risk conduction association map and the user risk data corresponding to the risk nodes, according to the device label information corresponding to the risk nodes, determining an device label risk weight parameter of the risk nodes, according to the correlation factors between the directly-correlated risk nodes and the corresponding user risk data, determining a reference weight parameter of the correlation factor; according to an association factor combination between the indirectly associated risk nodes, and the correlation levels of the correlation factors in the combination, combining the reference weight parameters, determining the risk conduction weight parameters of the correlation factors under the correlation levels, updating the parameters of the initial risk conduction correlation map, optimizing the risk conduction correlation map, and fully mining the deep relation between the nodes of the correlation map, so that the optimization of the initial risk conduction correlation map is achieved, and the risk analysis efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of big data, in particular to a method, device, computer equipment and storage medium for optimizing a risk conduction correlation map. Background technique [0002] The knowledge graph, also known as the scientific knowledge graph, is a series of different graphs showing the knowledge development process and structural relationship, using visualization technology to describe knowledge resources and their carriers, mining, analyzing, constructing, drawing and displaying knowledge and the relationship between them of mutual connection. In a general sense, the main goal of the knowledge graph is to describe various entities and concepts that exist in the real world, as well as the relationships between them. [0003] The traditional knowledge map can be constructed through entities and the relationship between entities, which can be used for risk transmission between entities. In some application sc...

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Problems solved by technology

[0004] However, due to the diversity of node data dimensions in the knowledge graph constructed in the traditional way, to a certain extent, it leads to the intricate relationship of the risk transmission correlation graph

On the one hand, the traditional risk transmission association map has huge node basic data, which affects the calculation complexity of relationship transmission to a certain extent. It is calculated, which leads to the problem that the data analysis efficiency of this risk transmission correlation map is not high when conducting risk transmission analysis, and this risk transmission correlation map needs to be optimized urgently

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