Coal mine disaster risk monitoring and early warning field knowledge graph construction method

By standardizing the processing of multi-source heterogeneous data and using a deep learning fusion mechanism, combined with the collaborative storage of graph databases and relational databases, the problems of accuracy in extracting graph elements and data storage efficiency in the field of coal mine disaster risk monitoring and early warning have been solved. This has enabled the adaptive dynamic evolution of knowledge graphs and improved the accuracy and timeliness of disaster early warning.

CN122114102APending Publication Date: 2026-05-29CCTEG CHINA COAL RES INST
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CCTEG CHINA COAL RES INST
Filing Date
2026-01-09
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

In the field of coal mine disaster risk monitoring and early warning, existing technologies suffer from low accuracy in extracting map elements, low efficiency in storing and querying large-scale multi-source heterogeneous data, and difficulty in adaptive dynamic evolution of knowledge graphs, thus failing to effectively reveal the coupling and chain-like characteristics of disaster risks.

Method used

By adopting a standardized processing and deep learning fusion mechanism for multi-source heterogeneous data, combined with a collaborative storage strategy of graph databases and relational databases, and using deep learning models such as GeoMine-BERT-DPN, graph attention networks, and spatiotemporal causal reasoning graph convolutional networks for entity extraction and relation mining, a hybrid storage system is constructed. Furthermore, an incremental data association analysis and conflict verification mechanism is introduced to achieve adaptive dynamic evolution of the knowledge graph.

Benefits of technology

It improves the accuracy and timeliness of knowledge graphs in the field of coal mine disaster risk monitoring and early warning, ensures that the knowledge graph can reflect the evolution of disasters in real time, improves data access efficiency and query response speed, and avoids interference of logical conflicts on the quality of the graph.

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Abstract

The present application relates to the technical field of coal mine safety, and discloses a method for constructing a knowledge graph in the field of coal mine disaster risk monitoring and early warning, which comprises collecting physical perception data, managing business data and professional knowledge data to form an initial data set; cleaning and standardizing the initial data set, and classifying and extracting characteristic attributes; using a deep learning model and a rule base to perform graph element extraction and fusion, and constructing a complete knowledge graph; constructing a hybrid storage system that cooperates with a graph database and a relational database, and performing joint queries; monitoring incremental data for the hybrid storage system, performing update operations, and completing adaptive dynamic evolution. The present application determines entity labels by using a dynamic prototype network, distinguishes explicit and implicit attributes based on a graph attention network model, and introduces a causal mask matrix to aggregate spatiotemporal causal correlation information, thereby solving the problems of entity hierarchical nesting and complex semantic correlation, and improving the accuracy and completeness of the knowledge graph.
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