Community gas system dynamic risk assessment method and device based on graph neural network

Abstract

The embodiment of the invention provides a community gas system dynamic risk assessment method and device based on a graph neural network. The method comprises the following steps: constructing a community gas system knowledge graph based on a community gas system risk assessment index system; inputting the community gas system knowledge graph containing the entities, the entity characteristics and the relationship between the entities into a community gas system dynamic risk assessment model so as to obtain the risk level of the target entity and carry out reason reasoning and accident consequence dynamic prediction on the community gas accident, wherein the community gas system dynamic risk assessment model comprises a first entity-level attention layer, a first semantic-level attentionlayer, a second entity-level attention layer, a second semantic-level attention layer, an entity classification layer and a relationship prediction layer. According to the embodiment of the invention,by obtaining the risk level of the target entity and carrying out reason reasoning and accident result dynamic prediction on the community gas accident, dynamic risk assessment of the community gas system is realized, and the accuracy of risk assessment is improved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a graph neural network-based dynamic risk assessment method and device for a community gas system. Background technique [0002] In recent years, my country's gas industry has made great progress. The popularization and application of gas has played an extremely important role in optimizing the energy structure, improving environmental quality, and improving people's living standards. Gas equipment and facilities are an important part of the community security system, which is related to the safety of people's lives and property, the safety of energy supply, and the safe development of cities. As the popularity of civil gas becomes wider and wider, accidents such as explosions, fires, and poisonings caused by gas leakage occur frequently every year, causing a large number of casualties and property losses, and the safe use of gas has gradually attracted people's ...

AI Technical Summary

Problems solved by technology

[0004] Most of the traditional risk assessment methods for gas systems are static analysis methods based on expert experience and knowledge, which lack dynamics. For example, the risk assessment of gas systems is realized by using Delphi method, AHP and fault tree methods.

Moreover, these methods generally only model individual components in the community gas system. For example, building a risk assessment model only for the gas pipeline network or indoor gas system, the model is generally relatively simple, ignoring the coupling of various components in the community gas system. complex connections

Due to the complexity of the community gas system, once a gas accident occurs, it is often difficult to determine the cause of the accident

The existing gas system risk assessment methods cannot accurately and comprehensively cover such complex scenarios as the community gas system, nor can they establish a dynamic risk assessment model for the multi-type components of the community gas system and the relationship between them for risk assessment

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