Mineral resource reserve intelligent management method and system based on big data

Through big data intelligent management methods, mineral resource data is cleaned and standardized, and three-dimensional geological maps are constructed using Transformer and GNN graph neural networks. Reserve reports are generated and encrypted, which solves the problems of insufficient data accuracy and security in traditional methods and realizes efficient and reliable resource management and data sharing.

CN120724481AActive Publication Date: 2025-09-30河北省水文工程地质勘查院(河北省遥感中心)
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Patent Information

Application Number
CN202510948838.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-10
Publication Date
2025-09-30
Estimated Expiration
2045-07-10

AI Technical Summary

Technical Problem

Traditional mineral resource data management methods are unable to process massive amounts of data, resulting in insufficient accuracy and reliability in resource reserve calculations, isolated and lacking integration of multi-source information, and low data security and trustworthiness.

Method used

A big data-based intelligent management method for mineral resource reserves is adopted. By obtaining multi-source heterogeneous data, cleaning and standardizing them, the self-attention mechanism of Transformer is used to mine data modal associations, and a three-dimensional geological map is constructed by combining GNN graph neural network and InSAR technology. A reserve report is generated and encrypted and uploaded to the blockchain network.

Benefits of technology

It has achieved a more accurate and comprehensive reflection of mineral resource conditions, improved the scientific nature of reserve calculations and the rationality of mining plans, ensured data security and traceability, and enhanced the transparency and collaborative efficiency of resource management.

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Abstract

The invention discloses an intelligent mineral resource reserve management method and system based on big data, and the method comprises the steps: obtaining a mineral resource data set, mining the potential correlation between different data modes in the mineral resource data set through a self-attention mechanism, and visualizing key features through an attention weight; based on the cross-modal mineral feature data, introducing a GNN graph neural network to be combined with an InSAR technology, constructing a dynamic three-dimensional geological map, and modeling an ore body, a fault and a rock stratum into graph nodes and edges to represent a spatial topological relation; and generating a reserve report and a mining plan according to the ore body space structure map, encrypting the reserve report and the mining plan by using a Hash algorithm, and uploading the encrypted reserve report and mining plan to a block chain network for storage. The actual situation of mineral resources can be reflected more accurately and comprehensively, and the resource mining efficiency is improved.
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