Data and knowledge fusion driven integrated modeling method and system for mine

By constructing a mine knowledge graph and performing deep mapping optimization between geometric models and semantic rules, the problem of lack of geological semantic constraints in existing 3D mine modeling is solved, realizing a high-precision and logically consistent integrated 3D mine model that supports dynamic analysis and intelligent decision-making in complex environments.

CN121482282BActive Publication Date: 2026-07-03SOUTHWEST PETROLEUM UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SOUTHWEST PETROLEUM UNIV
Filing Date
2025-12-08
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing 3D modeling technologies for mines lack geological semantic constraints, leading to spatial topological errors in areas with sparse or conflicting data. They cannot balance high geometric accuracy with geological logical consistency, making it difficult to support dynamic evolution simulation and intelligent decision-making in complex mining environments.

Method used

By constructing a mine knowledge graph and combining multi-source heterogeneous data, we perform deep mapping and optimization of geometric models and semantic rules to ensure logical consistency of the models and achieve dynamic synchronization through unique identifier mapping.

Benefits of technology

It has achieved a leap from geometric reconstruction to high-fidelity expression of both semantics and logic in 3D mining scenes, eliminated modeling errors that violate geological common sense, and provided a digital twin foundation with interpretability, logical completeness and dynamic evolution capabilities, thereby improving the accuracy of spatial analysis and intelligent decision support.

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Abstract

This invention relates to the field of environmental model construction technology, and in particular to a data and knowledge fusion-driven integrated mine modeling method and system. The method first preprocesses multi-source heterogeneous data to establish a unified dataset, and then constructs a mine knowledge graph based on physical spatial information, linking objects, attributes, and the environment. Next, initial geometric models of the surface, ore body, and tunnels are constructed, and a mapping is established between the models and the knowledge graph. The semantic rules in the knowledge graph are used to enforce geometric constraints and logical optimization on the initial models, automatically correcting spatial conflicts. Finally, multi-element spatial integration is achieved under a unified coordinate system. This method achieves deep integration of geometric reconstruction and geological semantics, effectively solving the problem of missing model logic, and constructing a high-precision three-dimensional model with dynamic evolution capabilities and logical self-consistency, providing reliable support for intelligent mine management.
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