CN-121980103-A - Mineral resource three-dimensional quantitative evaluation method and system based on data driving

CN121980103ACN 121980103 ACN121980103 ACN 121980103ACN-121980103-A

Abstract

The invention discloses a three-dimensional quantitative evaluation method and system for mineral resources based on data driving, which belong to the technical field of mineral resource evaluation analysis, and specifically comprise the steps of carrying out vector grid integrated modeling on a research area, customizing and designing a multi-attribute vector grid model integrated storage scheme in combination with the characteristics of mineral resource data, publishing an analysis result model into data service which can be accessed by a browser end through a cache after multi-factor analysis and calculation, and finally realizing high-efficiency visualization and analysis of high-precision evaluation model data based on WebGL and GPU rendering technology. The invention solves the problems of insufficient multi-source data fusion capability, missing dynamic evaluation capability, poor adaptability to complex geological conditions and imperfect evaluation result reliability verification mechanism in the quantitative evaluation technology of mineral resources in the prior art.

Inventors

YANG BO
WANG XIANG
BAI RUYU
WANG YIN
WEI GUOHUI
SHI KE
Guan Houchun
LIANG YUHUI

Assignees

安徽省地质调查院(安徽省地质科学研究所)

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (9)

1. A mineral resource three-dimensional quantitative evaluation method based on data driving is characterized by comprising the following steps: S1, constructing a vector grid integrated model, namely integrating a grid data structure and a vector data structure to form the vector grid integrated model, constructing a regional three-dimensional geological structure model based on geological drilling, geological map, geological profile and interrupt map data, dividing the three-dimensional geological structure model into three-dimensional grid models, giving geological attribute parameters to grid units, and generating a vector grid integrated three-dimensional geological attribute model; S2, a multi-attribute data model and storage management are carried out, a storage thought of separating a geometric grid from attributes is adopted, a three-layer data structure comprising geometric grid data, multi-dimensional attribute data and metadata is designed, a set of lightweight structure of geometric and multi-dimensional attributes is formed, and the storage management of the space grid structure and the attribute data is realized by utilizing a MongoDB document database; S3, mineral resource evaluation calculation, namely establishing a mineral resource three-dimensional evaluation index library of multi-source factors, carrying out multi-factor weight analysis and comprehensive evaluation by adopting a weighted superposition model, and supporting a user to dynamically select an evaluation factor according to needs, wherein a weight calculation module system provides a calculation method comprising an analytic hierarchy process, a random forest process and an evidence weight process; s4, data light-weight processing, namely constructing a LOD model with a pyramid structure based on a three-dimensional tile technology, and publishing the three-dimensional grid model and a dynamic evaluation result into M3D data service through a service publishing tool; s5, web end visualization and analysis, wherein a WebGL visualization system is built based on Cesium engines, and efficient rendering and analysis of the web end are realized through dynamic scheduling, asynchronous calling, data caching and a GPU/CPU hybrid architecture load balancing strategy of a hardware layer.
2. The method for three-dimensional quantitative evaluation of mineral resources based on data driving of claim 1, wherein the three-dimensional geological structure model of the region in S1 is constructed by adopting a Kriging interpolation or discrete smooth interpolation or distance inverse weighting algorithm, and a mesh subdivision technology is adopted for subdividing the three-dimensional geological structure model.
3. The method for three-dimensional quantitative evaluation of mineral resources based on data driving according to claim 2, wherein in the step S1, under the constraint of a three-dimensional geological structure model, the multi-source factor attribute parameters of geological exploration data, geophysical prospecting data, chemical prospecting data and remote sensing interpretation data are assigned to grid cells by adopting a Kriging interpolation or a collaborative Kriging interpolation spatial interpolation method.
4. The method for three-dimensional quantitative evaluation of mineral resources based on data driving according to claim 1, wherein the geometric network data in S2 stores coordinates and topological connection relations of all grid nodes, attribute data is used as independent documents to store attribute names and corresponding attribute value arrays, and grids i, j and k are used as unique codes for associating geometric grids with a plurality of attribute sets.
5. The method for three-dimensional quantitative evaluation of mineral resources based on data driving of claim 1, wherein the system in S3 provides a hierarchical analysis method, a random forest method or an evidence weight method, and according to a weight calculation method and a scoring standard set by a user, the system automatically calls attribute data of each factor from a database to perform grid algebra operation.
6. The method for three-dimensional quantitative evaluation of mineral resources based on data driving of claim 1, wherein the step S4 is characterized in that a tiling technology is adopted to cut a three-dimensional network model to form a LOD model with a pyramid structure, and a service interface supports query according to space range, level and attribute conditions.
7. The method for three-dimensional quantitative evaluation of mineral resources based on data driving of claim 6, wherein the method is characterized in that Draco network compression algorithm is used for efficiently encoding vertex coordinates and topology information in the step S4, and differential encoding and GZIP compression algorithm are adopted for attribute data.
8. The method for three-dimensional quantitative evaluation of mineral resources based on data driving according to claim 1, wherein in the step S5, loading and rendering are performed on a web end in a mode of dynamic adjustment strategy or asynchronous calling strategy or data caching strategy of a software layer and GPU or CPU hybrid architecture load balancing strategy of a hardware layer, and a diversified analysis algorithm of data statistics, attribute filtering or model sectioning is provided.
9. A three-dimensional quantitative evaluation system based on data driving comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and is characterized in that the processor executes the computer program to realize the three-dimensional quantitative evaluation method based on the data driving of the mineral resources according to any one of claims 1-8.

Description

Mineral resource three-dimensional quantitative evaluation method and system based on data driving Technical Field The invention relates to the technical field of mineral resource evaluation analysis, in particular to a data-driven mineral resource three-dimensional quantitative evaluation method and system. Background The accurate control of mineral resource reserves, quality and distribution characteristics is a core precondition for reasonable resource development, planning layout and risk management and control. The mineral resource quantitative evaluation technology is a multidisciplinary technology system based on geological exploration theory and integrating mathematical modeling, computer technology and big data analysis, the existing technology path can be roughly divided into three categories of traditional statistical evaluation methods, geological modeling driving methods and intelligent evaluation methods, and various methods show differential characteristics in technical principles, applicable scenes and evaluation accuracy. However, in the practical application process, the problems that the fusion capability of the primary and the multi-source data is insufficient, mineral resource evaluation relates to exploration data such as drilling, geophysical prospecting data (such as seismic waves and gravitational field data), chemical prospecting data (such as element content data), remote sensing data and other multi-type data, and the dimension, precision and format of different data are large. The method is characterized in that the method is mainly used for evaluating single data or simply spliced multiple data, the depth fusion and the feature extraction of the multiple source data are lacked, so that effective information in the data is not fully utilized, evaluation errors are easily caused by data one-sidedness especially under complex geological conditions, the dynamic evaluation capability is lost, the existing evaluation method is mainly used for carrying out 'one-time' evaluation based on static investigation data in a certain period, and the dynamic update requirement of the data in the mineral resource development process is difficult to adapt. In different stages of mineral resource exploration and development, new exploration engineering and exploitation data are continuously generated, the data can reflect actual distribution and taste change of a mineral body, the prior art lacks a real-time receiving, updating and model iteration mechanism for dynamic data, so that an evaluation result cannot be corrected in time, dynamic rules and risk management and control requirements in the resource development process are difficult to meet, and three-dimensional and grade distribution of the mineral body presents strong nonlinearity and heterogeneity characteristics under complex geological scenes such as fracture structure development, frequent mineral body phase change, multi-mineral body symbiosis and the like. The traditional method relies on simplified assumption of ore body morphology, is difficult to accurately describe complex ore body boundaries, can construct a three-dimensional model by using a geological modeling method, but has the problem that model distortion is easy to occur in complex parts such as ore body pinch out, branches and the like due to insufficient data constraint, and the used intelligent method has nonlinear fitting capability, but the existing model is mostly based on sample data training of a single type of ore body, lacks generalization capability for complex geological conditions, and remarkably reduces model prediction accuracy when being applied to a new complex ore region. Disclosure of Invention The invention aims to provide a three-dimensional quantitative evaluation method and system for mineral resources based on data driving, which are used for solving the problems of insufficient data fusion capability, missing dynamic evaluation capability, poor adaptability of complex geological conditions and imperfect reliability verification mechanism of evaluation results in the quantitative evaluation technology for the mineral resources in the prior art. In order to achieve the purpose, the invention provides the following technical scheme that the mineral resource three-dimensional quantitative evaluation method based on data driving comprises the following steps: S1, constructing a vector grid integrated model, namely integrating a grid data structure and a vector data structure to form the vector grid integrated model, constructing a regional three-dimensional geological structure model based on geological drilling, geological map, geological profile and interrupt map data, dividing the three-dimensional geological structure model into three-dimensional grid models, giving geological attribute parameters to grid units, and generating a vector grid integrated three-dimensional geological attribute model; S2, a multi-attribute data model and storage manage