Safety production risk identification method and system based on knowledge graph

Through graph neural network analysis based on knowledge graphs, multi-dimensional feature vectors are extracted and risk propagation paths are dynamically tracked, which solves the problem of difficult identification of risk patterns in complex systems and achieves high-precision identification of production safety risks.

CN120744408AActive Publication Date: 2025-10-03BAIYIN POWER SUPPLY COMPANY STATE GRID GANSU ELECTRIC POWER
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Patent Information

Application Number
CN202511251822.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-03
Publication Date
2025-10-03
Estimated Expiration
2045-09-03

AI Technical Summary

Technical Problem

Existing technologies have difficulty capturing hidden risk patterns in the interaction between equipment and human behavior in complex systems, and lack a comprehensive analysis of risk transmission paths, resulting in untimely discovery of potential hidden dangers and insufficient early warning capabilities.

Method used

A knowledge graph-based method is adopted to extract multi-dimensional feature vectors through graph neural networks, and the weight distribution between entity nodes is calculated in combination with the attention mechanism to generate risk propagation sequences. The risk propagation paths are identified by dynamically updating entity states and integrating environmental factors.

Benefits of technology

It achieves high-precision risk identification of the interactive impact of equipment status, personnel behavior and environmental factors in complex industrial scenarios, significantly improving operational safety and management efficiency.

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Abstract

The invention discloses a safety production risk identification method and system based on a knowledge graph, and relates to the technical field of safety production risk identification. Entity nodes and relation edge data of the knowledge graph are obtained, feature vectors are extracted, and embedded representation is generated by adopting a graph neural network; calculating a node weight by using an attention mechanism to determine a risk mode, traversing an association path to generate a risk propagation sequence, fusing time sequence features to update an entity state and determine a dynamic propagation path, extracting a key node sub-graph to adjust an edge weight to optimize the risk mode, and finally integrating environment features through iterative query to identify a complete risk propagation chain. According to the invention, dynamic tracking of equipment, personnel and environment network risks and cross-dimension cascade risk identification are realized.
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