A coal mine safety risk inference method, system, device and storage medium

By optimizing the storage and inference of knowledge graphs through blockchain networks and low-rank entity embedding technology, the problems of insufficient storage security, data traceability and throughput of knowledge graphs in coal mine safety risk inference systems are solved, and efficient and secure coal mine safety risk inference is achieved.

CN118297158BActive Publication Date: 2026-08-07UNIV OF SCI & TECH OF CHINA
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Patent Information

Application Number
CN202410286098.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-03-13
Publication Date
2026-08-07
Estimated Expiration
2044-03-13

AI Technical Summary

Technical Problem

Existing technologies have shortcomings in terms of storage security, data traceability, throughput, and inference efficiency of knowledge graphs, which limits their effective application in coal mine safety risk inference systems.

Method used

A blockchain network is used to store a knowledge graph in the field of coal mine safety. Entity classification is performed through low-rank entity embedding and a self-supervised entity classification model, and inference is performed by combining graph neural networks to optimize the storage and inference process of the knowledge graph.

Benefits of technology

It improves the storage security and data traceability of knowledge graphs, increases data processing throughput and inference efficiency, and enhances the stability and response speed of coal mine safety risk inference systems.

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Abstract

The application discloses a coal mine safety risk inference method, system, device and storage medium, which are one-to-one corresponding solutions, and the solutions aim to improve the safety of knowledge graph storage, the traceability of data, the maximum throughput of data, and the efficiency of inference operation, specifically: the knowledge graph storage and optimized transaction processing scheme based on the blockchain can improve the safety of the knowledge graph storage, the traceability of data, and the maximum throughput of data; the entity embedding decomposition scheme can reduce parameter redundancy, improve reasoning efficiency by using refined type perception reasoning, and combine a self-supervised entity classification model and an efficient graph neural network processing to optimize the construction and query processing of the knowledge graph, so that efficient reasoning and application of a large-scale knowledge graph are realized.
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