Mineral reserve estimation modeling method, system and equipment for optimizing block division

Abstract

The invention relates to the technical field of geological three-dimensional modeling, and discloses a mineral reserve estimation modeling method, system and equipment for optimizing block division. The method comprises the following steps: delineating an ore body according to imported drilling data, and establishing an entity model for estimating the ore body; block division is carried out on the entity model based on earth grid division or space grid division; converting the drilling data into block data according to the size of block division, and carrying out statistics on the block data to obtain a data distribution characteristic graph; calculating a variation function based on the data distribution characteristic graph and performing cross validation; and estimating according to the variation function and the block data, and outputting a resource reserve estimation report. Earth grid division is introduced, and during block division, longitude and latitude reference earth grid division and space grid division adopting a digital storage octree index can be selected, so that the storage speed is increased, the memory space is saved, the method adapts to multi-scale index transformation, data integration sharing and hierarchical management are facilitated, and the efficiency is improved. And rapid construction of the ore body and block subdivision storage are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of geological three-dimensional modeling, and in particular to a mineral reserve valuation modeling method, system and equipment for optimizing block division. Background Art

[0002] There are abundant underground mineral resources, and their mining involves the development, processing and safety engineering of mineral resources. In order to ensure the rationality and scientificity of mine development, geologists need to carefully plan and design mines. In this process, it is crucial to rationally develop and scientifically mine geological mineral resources. The goal of geological exploration is to minimize damage to the surrounding geological environment while meeting mining needs. Therefore, in the preparation work before mining, estimating the reserves of underground ore bodies is a very critical step. Traditional methods for estimating mineral resources include geometric methods and geostatistical methods.

[0003] Geostatistics provides a mature theoretical framework, among which Kriging is an important interpolation technique, which is mainly used to estimate ore reserves and grades. The core of this method is to analyze the spatial geological structure of minerals. Based on the theory of regionalized variables, the spatial distribution of regionalized variables is analyzed through the variation function, aiming to estimate the total ore reserves through weighted calculation of grade values ​​under the premise of minimizing the estimated variance. In other words, geostatistics uses specific mathematical tools to accurately analyze the spatial distribution and grade of ore bodies in order to achieve accurate estimation of ore reserves.

[0004] Geometric methods, such as the cross-section method, geological block method, and triangle method, have been widely used in practice due to their strong operability, simple application, and convenient calculation. Geostatistical methods, such as the Kriging method and the random simulation method, can more accurately estimate the amount of mineral resources and quickly optimize the mining plan through the integration with three-dimensional visualization technology. The current common Kriging reserve valuation process can be simplified as follows: ① Import basic data such as drilling holes ② Preprocess the data ③ Calculate the variation function based on the data characteristics and establish a search ellipsoid ④ Divide the block into blocks and perform Kriging valuation on each block ⑤ Count all block information to obtain the reserve valuation result; At the same time, when performing Kriging valuation, eight-division strategies and other strategies can be used for constraints based on data distribution.

[0005] However, in the existing technical process, when the block is divided after the ore body data is imported, the traditional octree technology is often used for division. Although this method has strong adaptability and efficient spatial indexing, it often brings a large storage overhead, especially when facing large-scale data. At the same time, if the data distribution is more extreme, it will lead to performance degradation, and boundary processing cannot cope with all situations; secondly, when the block is kriged in the existing processing flow, it often only supports eight-partition constraints in the case of unevenly distributed data and constraints on the number of drill holes involved in the valuation to improve accuracy. In the face of complex situations, it cannot provide flexible constraints. At the same time, the existing technology can usually only perform one valuation, and one valuation often cannot achieve the desired effect, and the valuation needs to be set repeatedly, which has poor operability. Summary of the invention

[0006] The main purpose of the present invention is to provide a mineral reserve valuation modeling method, system and equipment for optimizing block division, aiming to solve at least one of the above-mentioned technical problems.

[0007] To achieve the above object, the present invention provides a mineral reserve valuation modeling method for optimizing block division, comprising:

[0008] Importing drilling data, delineating the ore body according to the drilling data, and establishing a physical model of the estimated ore body;

[0009] Based on earth grid division or space grid division, the entity model of the estimated ore body is divided into blocks;

[0010] Converting the drilling data into block data according to the size of the block division, and performing statistics based on the block data to obtain a data distribution characteristic diagram;

[0011] Calculating a variance function based on the data distribution characteristic diagram, and cross-validating the variance function;

[0012] The resource reserves are estimated according to the variation function and the block data, and a resource reserves estimation report is output.

[0013] In some embodiments, the importing of drilling data includes:

[0014] Obtain drilling data;

[0015] Establishing a borehole database, and creating a borehole basic information table, a borehole stratification information table, and a borehole inclinometer table in the borehole database;

[0016] The drilling data is subjected to error correction and integrity and logic checks.

[0017] In some embodiments, delineating the ore body according to the drilling data to establish a physical model of the estimated ore body includes:

[0018] establishing a three-dimensional model of the ore deposit based on the drilling data;

[0019] Delineating the boundaries of the ore body in the three-dimensional model according to industrial indicators and geological characteristics of the ore deposit;

[0020] A physical model of the estimated ore body is established based on the three-dimensional model and the ore body boundary.

[0021] In some embodiments, the block division of the entity model of the estimated ore body based on earth grid division or space grid division includes:

[0022] Importing the entity model of the estimated ore body, and dividing the entity model of the estimated ore body into blocks;

[0023] Select earth grid division or space grid division based on preset rules;

[0024] When selecting the earth grid division, the index division is performed according to the longitude and latitude;

[0025] When selecting the spatial grid division, the block index information is encoded using a binary order of 64-bit numbers to convert the spatial position information into digital codes;

[0026] The block index information is stored in a MongoDB database.

[0027] In some embodiments, the converting the drilling data into block data according to the size of the block division, and performing statistics based on the block data to obtain a data distribution characteristic diagram includes:

[0028] According to the size of the block division, the drilling data is converted into block data by using an inverse distance power weighted method, a geometric mean method or a sample length average method;

[0029] The block data is imported, and statistics are performed on the block center point data to obtain a data distribution characteristic diagram; wherein the data distribution characteristic diagram includes a basic distribution diagram, a frequency histogram, a box plot, a correlation analysis diagram, a probability-probability diagram, and a quantization-quantization diagram.

[0030] In some embodiments, the calculating of the variance function based on the data distribution characteristic diagram and cross-validating the variance function includes:

[0031] Sectoring is performed according to the data characteristics of the data distribution characteristic diagram, and the search parameters are adjusted to obtain adjusted search parameters;

[0032] For each sector, calculating an initial variogram according to the adjusted search parameters;

[0033] Draw a variance map based on the data of the initial variance function;

[0034] Determine and derive the variance functions of the major axis, the minor axis, and the minor axis according to the variance diagram and the variance function curve;

[0035] Cross-validating the variogram;

[0036] If the Kriging variance ratio and error meet the preset verification requirements, the variance function is considered to meet the standards.

[0037] In some embodiments, performing resource reserve estimation according to the variation function and the block data and outputting a resource reserve estimation report includes:

[0038] When estimating resource reserves, different attribute fields are determined based on the multiple attributes of the block;

[0039] Setting a multi-attribute constraint based on the attribute field to constrain the combination of multiple attributes;

[0040] Based on the multi-attribute constraints, resource reserves estimation is performed through the variation function and the block data according to a preset estimation algorithm to obtain an estimation result;

[0041] Output a resource reserve estimation report based on the estimation results.

[0042] In some embodiments, the method further comprises:

[0043] Determine the valuation parameters according to the actual industrial situation; wherein the valuation parameters include: the search distance for each valuation, the maximum and minimum number of selected samples, whether to perform eight-partitioning, the maximum and minimum number of selected samples in the partition, whether to perform drilling constraints, and the maximum number of drilling selections;

[0044] In each valuation process, each partition is estimated in batches based on a preset estimation algorithm and the valuation parameters;

[0045] The results of the multiple valuations are combined to obtain a resource reserve estimate, which is used as the estimation result.

[0046] In addition, to achieve the above purpose, the present invention also proposes a mineral reserve valuation modeling system for optimizing block division, comprising:

[0047] A data import and model building module, used to import drilling data, delineate the ore body according to the drilling data, and build a physical model of the estimated ore body;

[0048] A block division module, used for performing block division on the entity model of the estimated ore body based on earth grid division or space grid division;

[0049] A data conversion and statistics module, used for converting the drilling data into block data according to the size of the block division, and performing statistics based on the block data to obtain a data distribution characteristic diagram;

[0050] A variation function calculation and verification module, used for calculating the variation function based on the data distribution characteristic diagram, and performing cross-validation on the variation function;

[0051] The resource estimation and output report module is used to estimate resource reserves based on the variation function and the block data, and output a resource reserve estimation report.

[0052] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, which includes: a memory, a processor, and a mineral reserve valuation modeling program for optimized block division stored in the memory and executable on the processor, wherein the mineral reserve valuation modeling program for optimized block division is configured to implement the mineral reserve valuation modeling method for optimized block division as described above.

[0053] The present invention provides a mineral reserve valuation modeling method for optimizing block division, including: importing drilling data, delineating the ore body according to the drilling data, so as to establish a physical model of the estimated ore body; performing block division on the physical model of the estimated ore body based on earth grid division or space grid division; converting the drilling data into block data according to the size of the block division, and performing statistics according to the block data to obtain a data distribution characteristic diagram; calculating a variation function based on the data distribution characteristic diagram, and cross-validating the variation function; performing resource reserve estimation according to the variation function and the block data, and outputting a resource reserve estimation report. In the present invention, earth grid division is introduced, and earth grid division based on longitude and latitude can be selected when dividing blocks, and a spatial grid division strategy using digital storage of octree index information can be used, which improves storage speed, saves memory space, can also adapt to multi-scale index transformation, and is more flexible in processing boundary conditions; earth grid division facilitates data integration and sharing, facilitates hierarchical management of spatial data, and is more suitable for geographic information systems, thereby realizing rapid construction of ore bodies, block segmentation storage, and multi-constraint multi-batch estimation of reserves. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of the structure of an electronic device in a hardware operating environment involved in an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of a process flow of an embodiment of a mineral reserve valuation modeling method for optimizing block division according to the present invention;

[0056] Figure 3 This is a schematic diagram of the drilling formation information involved in the embodiment of the present invention;

[0057] Figure 4 A schematic diagram of entity data involved in an embodiment of the present invention;

[0058] Figure 5 A schematic diagram of a block model involved in an embodiment of the present invention;

[0059] Figure 6 A schematic diagram of using MongoDb to store block information and visualize it in an embodiment of the present invention;

[0060] Figure 7 A schematic diagram of a drilling gridding method according to an embodiment of the present invention;

[0061] Figure 8 A schematic diagram of data space distribution involved in an embodiment of the present invention;

[0062] Fig. 9 A frequency histogram involved in the embodiment of the present invention;

[0063] Fig.10 A schematic diagram of sector division involved in an embodiment of the present invention;

[0064] Fig.11 A schematic diagram of a variation function curve involved in an embodiment of the present invention;

[0065] Fig.12 A variance diagram related to an embodiment of the present invention;

[0066] Fig.13 A schematic diagram of multiple attribute constraints involved in an embodiment of the present invention;

[0067] Fig.14 A schematic diagram of the multiple evaluation involved in the embodiment of the present invention;

[0068] Fig.15 A schematic diagram of the valuation results involved in the embodiment of the present invention;

[0069] Fig.16 This is a structural block diagram of an embodiment of a mineral reserve valuation modeling system for optimizing block division according to the present invention.

[0070] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0073] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0074] Reference Figure 1 , Figure 1 The figure is a schematic diagram of the structure of an electronic device of a hardware operating environment involved in an embodiment of the present invention.

[0075] like Figure 1As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM memory) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0076] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0077] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a mineral reserve estimation modeling program for optimizing block division.

[0078] exist Figure 1 In the electronic device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device, and the electronic device calls the mineral reserve valuation modeling program for optimized block division stored in the memory 1005 through the processor 1001, and executes the mineral reserve valuation modeling method for optimized block division provided in the embodiment of the present invention.

[0079] The present invention provides a mineral reserve valuation modeling method, system and equipment for optimizing block division.

[0080] The embodiment of the present invention provides a mineral reserve valuation modeling method for optimizing block division, referring to Figure 2 , Figure 2 A schematic flow chart of an embodiment of a mineral reserve valuation modeling method for optimizing block division according to the present invention.

[0081] like Figure 2 As shown, the mineral reserve valuation modeling method of optimizing block division includes:

[0082] Step S100: importing drilling data, delineating the ore body according to the drilling data, and establishing a physical model of the estimated ore body;

[0083] Step S200: performing block division on the entity model of the estimated ore body based on earth grid division or space grid division;

[0084] Step S300: converting the drilling data into block data according to the size of the block division, and performing statistics based on the block data to obtain a data distribution characteristic diagram;

[0085] Step S400: calculating a variance function based on the data distribution characteristic diagram, and performing cross-validation on the variance function;

[0086] Step S500: perform resource reserve estimation based on the variation function and the block data, and output a resource reserve estimation report.

[0087] It should be noted that the execution subject in this embodiment may be an electronic device, which may be a computer device with data processing functions, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment, a computer device is used as an example for explanation.

[0088] It is understandable that this embodiment is explained by taking geological mineral reserve valuation as an example, and is specifically applied to the field of geological mineral reserve valuation modeling. In view of the problems that the existing block division has large storage capacity and performance needs to be improved, the method described in this embodiment achieves accurate estimation and visualization of mineral resources through block space division optimization and mineral reserve valuation modeling that supports multi-constraint and multi-batch valuation.

[0089] Specifically, the method described in this embodiment proposes an optimized block partitioning scheme, which can support both earth grid partitioning and space grid partitioning. Earth grid partitioning does not use traditional index coordinates, but uses longitude and latitude for partitioning, which improves accuracy without increasing storage pressure. Space grid partitioning optimizes the traditional octree method, making the partitioning more flexible, and making it more convenient to handle boundary conditions and store attributes. For the reserve estimation step, a method that supports multiple constraints and multiple batches of valuation is provided to cope with more situations. The method described in this embodiment is described below in conjunction with specific steps.

[0090] In one embodiment, importing drilling data includes: acquiring drilling data; establishing a drilling database, and creating a drilling basic information table, a drilling stratification information table, and a drilling inclinometer table in the drilling database; and performing error correction, integrity, and logic checks on the drilling data.

[0091] Specifically, (1) Figure 3 As shown in the figure, the drilling data is imported, the drilling database is established, the basic information table of drilling, the stratified information table of drilling, the inclinometer table of drilling, etc. are established, and the drilling data is corrected and the integrity and logic are checked. Through the above steps, the accuracy and reliability of the drilling data can be ensured, providing a solid foundation for subsequent data analysis and application.

[0092] For example, to import drilling data: prepare electronic files and database software for existing drilling data, and determine the method for data import. Arrange the drilling data in the electronic file into a standard format, ensure that each field has a clear name and data type, use a database import tool or write an import script to import the sorted data into the database, and set the properties of the database table fields according to the data type and length requirements.

[0093] For example, a drilling database is established: a database instance is created and named "drilling database"; the following tables are created in the database: drilling basic information table (including fields such as drilling number, drilling name, drilling location, drilling depth, drilling time, etc.), drilling layer information table (including fields such as drilling number, layer number, layer name, layer depth, layer thickness, layer property, etc.), drilling inclinometer table (including fields such as drilling number, inclinometer point number, inclinometer depth, inclinometer angle, inclinometer direction, etc.). Primary key and foreign key constraints are set for each table to ensure the association between data tables, and indexes are set for key fields to improve query efficiency.

[0094] Exemplarily, data error correction: perform a preliminary check on the imported drilling data to eliminate obvious errors, such as illegal characters, null values, duplicate records, etc.; use database query statements or write scripts to check data integrity, such as checking whether the drilling number exists in all tables, whether the drilling depth is greater than the sum of the layered depths, etc. Among them, the integrity check includes but is not limited to: checking whether the key fields in each table are empty; checking whether the foreign key constraints are satisfied, such as whether the drilling number in the drilling basic information table exists in the drilling layered information table and the drilling inclinometer table. Logical checks include but are not limited to: checking whether the drilling depth is consistent with the sum of the layered depths; checking whether the inclinometer data is within the drilling depth range; checking whether the layer thickness is a positive number; checking whether the inclinometer angle is within a reasonable range.

[0095] Exemplarily, data cleaning and optimization: correct or delete the problematic data found; optimize the data table according to the actual situation, such as adding statistical data, establishing views, etc.

[0096] In one embodiment, the ore body is delineated based on the drilling data to establish a physical model of the estimated ore body, including: establishing a three-dimensional model of the ore bed based on the drilling data; delineating the ore body boundary in the three-dimensional model based on industrial indicators and geological characteristics of the ore bed; and establishing a physical model of the estimated ore body based on the three-dimensional model and the ore body boundary.

[0097] Specifically, (2) ore body delineation: Based on information such as industrial indicators and geological characteristics of the ore deposit, a physical model of the estimated ore body (e.g. Figure 4 Through ore body delineation, an accurate ore body entity model can be established, providing a scientific basis for the exploration, development and management of the deposit.

[0098] Exemplarily, geological data of the ore deposit are collected, including but not limited to geological exploration reports, geological maps, drilling data, core samples, etc. According to the type of ore deposit, ore quality, mining technical conditions, etc., the industrial indicators of the ore deposit, such as boundary grade, minimum industrial grade, thickness of interlayer stone removal, etc., are determined. The drilling data (such as the location, depth, lithology, grade, etc. of the drilling hole) are sorted out, the geological structure, ore body morphology, mineralization law, etc. of the ore deposit are studied, and the geological characteristics are analyzed. A three-dimensional model of the ore deposit is established using professional software (such as Surpac, Leapfrog, Micromine, etc.), the drilling data is imported into the software, and the grade data is spatially interpolated using interpolation methods (such as Kriging method, inverse distance power method, etc.) to construct an ore body grade distribution model.

[0099] For example, ore body delineation: Delineate the boundaries of the ore body in a three-dimensional model based on industrial indicators. For complex ore body morphology, it is necessary to combine geological boundaries, ore-controlling structures, mineralization characteristics and other factors for comprehensive delineation. The accuracy of ore body delineation can be verified using known drilling data and exploration line profiles, and the ore body boundaries can be adjusted to ensure that the ore body delineation is in line with the actual situation.

[0100] In one embodiment, the physical model of the estimated ore body is divided into blocks based on the earth grid division or the space grid division, including: importing the physical model of the estimated ore body and dividing the physical model of the estimated ore body into blocks; selecting the earth grid division or the space grid division based on preset rules; when selecting the earth grid division, indexing is performed according to longitude and latitude; when selecting the space grid division, encoding the block index information using a 64-bit binary sort to convert the spatial position information into a digital code; and storing the block index information in a MongoDB database.

[0101] Specifically, (3) Figure 5 As shown, building blocks: import entity data, divide entities into blocks, select earth grid division or space grid division according to needs, earth grid division is indexed according to longitude and latitude, and space grid division hides block index information in a 64-bit binary sort, converts the index into a number as a code, and can support conversion between multiple levels and store it through MongoDB (such as Figure 6 As shown in Figure 1, MongoDB is a NoSql database with high performance and flexibility, which is very suitable for storing block information. Through the above steps, a large amount of ore body data can be effectively managed and processed, providing strong data support for ore body estimation, resource assessment and mining design.

[0102] Exemplarily, importing physical data: collecting and processing physical data related to the ore body, such as geological exploration data, drilling data, core sample data, etc., importing these data into data processing software or directly into a database, such as using GIS software, geological modeling software or MongoDB, etc. Block division: determining the block division scheme according to the geometric shape and size of the ore body, where the block division can be regular or irregular, and the appropriate division method can be selected according to actual needs.

[0103] Exemplarily, earth grid division: If earth grid division is selected, the area where the ore body is located is divided according to longitude and latitude, the resolution of the grid is determined, that is, the longitude and latitude interval of each grid unit, and the ore body data is mapped to the corresponding grid unit to achieve index division according to longitude and latitude. Space grid division: If space grid division is selected, a three-dimensional space grid is established according to the three-dimensional shape of the ore body. The space grid can be a cube or other polygonal unit. The appropriate grid shape and size are selected according to the complexity of the ore body, and each block in the space grid is assigned a unique index.

[0104] Exemplarily, block index encoding: For spatial grid division, the block index information is encoded using a binary order of 64-bit numbers, and the spatial position information is converted into a digital code, which allows for easy comparison and search between blocks. Support for hierarchical conversion: A hierarchical system for block models is designed to allow conversion between different resolutions. For example, large blocks can be subdivided or small blocks can be merged according to the required level of refinement of the ore body. Data storage: This embodiment selects MongoDB as the storage solution. MongoDB is a NoSql-type database with good performance and flexibility. Block data and related index and attribute information can be stored in MongoDB. The powerful query capabilities of MongoDB can be used to quickly locate specific blocks and their attribute information.

[0105] In one embodiment, the drilling data is converted into block data according to the size of the block division, and statistics are performed based on the block data to obtain a data distribution characteristic diagram, including: according to the size of the block division, the drilling data is converted into block data using an inverse distance power weighted method, a geometric mean method, or a sample length average method; the block data is imported, and statistics are performed on the block center point data to obtain a data distribution characteristic diagram; wherein the data distribution characteristic diagram includes a basic distribution diagram, a frequency histogram, a box plot, a correlation analysis diagram, a probability-probability diagram, and a quantization-quantization diagram.

[0106] Specifically, (4) the drilling data is gridded, and the drilling data is converted into block data according to the size of the block division. Figure 7 As shown, theoretical methods can choose to use the inverse distance power weighted method, geometric mean method, sample length average method, etc. The gridded block data is used for subsequent analysis work such as resource estimation, ore body modeling, and mining design.

[0107] Exemplarily, according to the three-dimensional model of the ore body and the requirements of block division, the spatial position and volume of each block are determined, and a block index system is established to facilitate subsequent data processing. Select a theoretical method, the inverse distance power weighted method: according to the distance from the drill hole data point to the center of the block, the grade value is weighted averaged. The closer the distance, the greater the contribution of the drill hole data to the block grade. Geometric mean method: All drill hole data points that affect the block grade are geometrically averaged, which is suitable for situations where the grade change is not particularly large. Sample length average method: According to the sample length of the drill hole data point in the block, the grade value is weighted averaged. The longer the sample length, the greater the contribution to the block grade. Here, according to the characteristics of the ore body and the data distribution, a suitable gridding method is selected. Gridding: The drill hole data is distributed to the corresponding blocks to ensure that the data of each drill hole is only distributed to the blocks it affects. For each block, the selected theoretical method is used to estimate the grade.

[0108] Exemplarily, for the inverse distance power weighted method: calculate the distance from each drill hole data point to the center of the block; calculate the weight of each drill hole data point based on the distance and a pre-set power; and perform weighted averaging on all drill hole data affecting the block to obtain the estimated grade of the block.

[0109] Exemplarily, for the geometric mean method: the geometric mean of all the grade values ​​affecting the drill holes in each block is calculated; and the result of the geometric mean is used as the estimated grade of the block.

[0110] Exemplarily, for the sample length averaging method: determine the sample length of each drill hole in the block; perform weighted averaging on the grade values ​​according to the sample length to obtain the estimated grade of the block.

[0111] Specifically, (5) perform basic statistics on known data. Import the block-based drilling data and perform statistics on the block center point data, such as Figure 8 and Fig. 9 As shown, you can view the basic distribution, frequency histogram, box plot, correlation analysis, PP diagram, QQ diagram, and view its data distribution characteristics. You can conduct a comprehensive statistical analysis of the block center point data to understand its distribution characteristics and provide a basis for subsequent data analysis and ore body modeling.

[0112] Exemplarily, block-based drilling data is imported from a database or storage system. Basic distribution statistics: Calculate the statistics of the block center point data, including mean, median, standard deviation, skewness, kurtosis, etc. Frequency histogram: Use statistical software or programming language to draw a frequency histogram to visualize the shape of the data distribution and determine the interval width of the histogram to better display the distribution characteristics of the data. Box plot: Draw a box plot to view the distribution range, median, quartiles, and potential outliers of the data. The box plot can provide an intuitive impression of the data distribution, especially the degree of dispersion and symmetry of the data. Correlation analysis: If there are multiple indicators (such as grade, thickness, etc.), perform correlation analysis to evaluate the relationship between these indicators. Correlation coefficients (such as Pearson correlation coefficient and Spearman rank correlation coefficient) can be used to quantify the linear relationship between indicators. PP plot and QQ plot: PP plot (probability-probability plot): checks the degree of fit between the data distribution and the theoretical distribution (such as the normal distribution); QQ plot (quantitative-quantitative plot): also used to check the degree of fit between the data distribution and the theoretical distribution, by comparing the quantiles of the data with the quantiles of the theoretical distribution, and drawing the PP plot and QQ plot. If the data points are close to a straight line, it indicates that the data distribution is close to the selected theoretical distribution.

[0113] In one embodiment, a variance function is calculated based on the data distribution characteristic diagram, and the variance function is cross-validated, including: sectoring according to the data features of the data distribution characteristic diagram, and adjusting the search parameters to obtain adjusted search parameters; for each sector, calculating an initial variance function according to the adjusted search parameters; drawing a variance map based on the data of the initial variance function; determining and deriving the variance functions of the major axis, minor axis, and short axis based on the variance map and the variance function curve; cross-validating the variance function; if the Kriging variance ratio and error meet the preset validation requirements, the variance function is deemed to meet the standards.

[0114] Specifically, (6) calculates the variance function. Fig.10As shown, according to the statistical and observed data characteristics, sector division is performed and relevant search parameters are adjusted. By analyzing the variogram models of different sectors, the variability of the ore body in different directions can be understood, and the variogram model can be used for spatial interpolation, such as Kriging interpolation, to predict data values ​​at unknown locations.

[0115] Exemplarily, calculating the variogram is an important step in geostatistics, which is used to describe the variability of spatial data, including sector division and adjustment of search parameters: according to the data type (such as grade, thickness, etc.), select the corresponding data for variogram calculation. The variogram is a function that describes the difference between variables Z(i) and Z(j) at spatial positions i and j. Sector division: The study area is divided into several sectors according to the geological characteristics of the ore body and the distribution of the data. Sector division can be based on the direction of the drill hole or the main trend of the ore body to ensure that the data in each sector has similar spatial distribution characteristics. Sectors are usually divided into four main directions: northeast (NE), southeast (SE), southwest (SW) and northwest (NW). Adjustment of search parameters: Determine the radius, angle and direction of the search ellipsoid; adjust the size and shape of the search ellipsoid to adapt to the data characteristics in different sectors. For example, for some sectors, it may be necessary to lengthen or shorten one or more axes of the ellipsoid. Calculation of variogram: For each sector, use the adjusted search parameters to calculate the variogram value. Kriging software (such as GS+, Isatis, GSLIB, etc.) or custom programming scripts can be used for calculations. Fitting of the variogram model: According to the calculated variogram value, select a suitable theoretical model for fitting, such as a Gaussian model, exponential model, spherical model, etc. During the fitting process, the parameters of the model (such as the sill value, range, partial sill value, etc.) can be adjusted to minimize the difference between the model and the actual data. Model validation: This embodiment uses a cross-validation method, such as leave-one-out validation, to evaluate the accuracy of the variogram model, check the difference between the predicted value and the actual value of the model, and adjust the model if necessary.

[0116] Specifically, (7) Fig.11 and Fig.12 As shown in the figure, according to the variance map and the experimental variogram curve, the variograms of the major axis, minor axis and short axis are determined and exported. They are used for subsequent spatial analysis. The similarities and differences of the three-axis variograms are analyzed to understand the spatial variability of the ore body in different directions. These variograms are used for Kriging interpolation or other spatial prediction methods to predict the data values ​​of the unsampled areas of the ore body.

[0117] Exemplarily, ensure that the initial variogram has been calculated and there are enough sample data pairs to support the estimation of the variogram. Using the initial variogram data, draw a variance map (semivariogram). The variance map shows the sample variance or semivariance at different lag distances. According to the shape of the initial variogram curve, select a suitable theoretical variogram model for fitting, such as a spherical model, an exponential model, or a Gaussian model. Determine the three axes of the variogram: Major Axis: This is the maximum range direction of the variogram, which is usually consistent with the main geological structure direction of the ore body. Minor Axis: This is the secondary range direction perpendicular to the major axis, reflecting the variability of the ore body in this direction. Short Axis: This is the third range direction perpendicular to the major axis and the minor axis, usually with the smallest range, reflecting the maximum variability of the ore body in this direction. Fit the three-axis variogram: Fit the variogram for each axis separately to obtain the respective variogram expressions. According to the fitting results, derive the variogram expression for each axis. The accuracy of the three-axis variogram can be verified using cross-validation or other statistical methods. If the fitting results are not ideal, adjust the model according to the verification results.

[0118] Specifically, (8) cross validation: The above variogram parameters are cross validated. If the kriging variance ratio is close to 1 and the error is close to 0, the parameters are considered to meet the standards.

[0119] Exemplarily, cross-validation is an important step in evaluating the accuracy of the variance function parameters. An example process of cross-validation: Divide the data set into two parts: one part is used for modeling (training set), and the other part is used for validation (test set). Using the training set data, modeling is performed based on the variance function model of the major axis, minor axis, and minor axis previously determined, the variance function model is fitted, and the model parameters (such as sill value, range, partial sill value, etc.) are determined. Remove a data point from the training set, and use the remaining data points to estimate the value of the removed point. Repeat the above steps, removing different data points each time, until all data points have been verified.

[0120] In one example, cross validation: randomly select a data point (or in a certain order) and remove it from the training set; use the variogram model established by the remaining data points to predict the value of the removed point; calculate the error between the predicted value and the actual value, such as absolute error, relative error, etc.; record the results of each validation, including the predicted value, actual value and error; calculate the Kriging variance ratio and error, the Kriging variance ratio is an indicator of the degree of fit between the predicted value and the actual value, and the error can be the mean absolute error (MAE), mean square error (MSE), etc.; if the Kriging variance ratio is close to 1 and the error is close to 0, it can be considered that the variogram parameters meet the standards; if the results do not meet the standards, it is necessary to return to the variogram model establishment step, readjust the variogram model parameters, and conduct a new round of cross validation. Through the above steps, the accuracy of the variogram parameters can be evaluated to ensure that the model can reliably predict the unsampled areas of the ore body.

[0121] In one embodiment, resource reserves are estimated based on the variation function and the block data, and a resource reserve estimation report is output, including: when performing resource reserve estimation, different attribute fields are determined based on multiple attributes of the block; multiple attribute constraints are set based on the attribute fields to perform combined constraints on multiple attributes; based on the multiple attribute constraints, resource reserves are estimated through the variation function and the block data according to a preset estimation algorithm to obtain an estimation result; and a resource reserve estimation report is output based on the estimation result.

[0122] In one embodiment, the method further includes: determining valuation parameters according to actual industrial conditions; wherein the valuation parameters include: the search distance for each valuation, the maximum and minimum number of selected samples, whether to divide into eight zones, the maximum and minimum number of selected samples within a zone, whether to perform drilling constraints, and the maximum number of drilling selections; in each valuation process, each zone is valued in batches based on a preset estimation algorithm and the valuation parameters; the results of the batch valuations are merged to obtain a resource reserve estimation value, and the resource reserve estimation value is used as the estimation result.

[0123] Specifically, (9) resource reserve estimation. Fig.13 As shown, when making an estimate, multiple attribute constraints can be performed as needed based on the multiple attributes of the block, different attribute fields can be selected, and multiple attributes can be combined and constrained to select a valuation effect that is more in line with the ideal situation.

[0124] Exemplarily, resource reserve estimation: ensure that block data is prepared and each block has a series of attributes, such as grade, thickness, density, etc., and import these attributes into resource reserve estimation software or a custom estimation program; select appropriate attribute fields for estimation based on the characteristics of the ore body and the purpose of estimation. These fields may include grade (the content of valuable components in the ore), thickness (vertical thickness of the ore body), density (density of the ore, used to calculate the quality of the ore), and mineralization intensity (an indicator reflecting the degree of mineralization); set multi-attribute constraint rules, which define how to combine different attributes for estimation. For example, a grade threshold can be set. Only when the grade of the block is higher than this threshold, the block is considered to be ore. For the combination constraints of multiple attributes, logical operators (such as AND, OR) can be used to define whether the block meets the conditions; select an appropriate preset estimation algorithm such as direct method, Kriging method, Monte Carlo simulation, etc. based on the characteristics of the ore body and the data situation; use the selected estimation algorithm to estimate the resource reserves for each block. The estimate can also be verified by comparison with known resource data or by using statistical methods to assess the accuracy of the estimate.

[0125] In one example, multi-attribute constraints: according to the constraint rules, different attribute fields are combined to form an estimation model; according to the multi-attribute constraints, blocks that meet the estimation conditions are screened out; for the screened out blocks, their resource volume is calculated, and the calculation formula may be: Q = V × D × C; where Q is the resource volume, V is the block volume, D is the density, and C is the grade or other attribute value.

[0126] Specifically, (10) fractional valuation: Fig.14 As shown, according to the actual industrial situation, the search distance used for each valuation, the maximum and minimum number of selected samples, whether to perform eight-partitioning, the maximum and minimum number of selected samples within the partition, whether to perform drilling constraints, the maximum number of drilling selections, etc. are determined.

[0127] For example, the valuation is carried out in stages, and the search distance, number of samples, zoning method, drilling constraints and other parameters are determined according to the actual industrial situation: the basic requirements for valuation are determined according to the industrial indicators of the ore deposit, such as boundary grade, minimum industrial grade, thickness of interlayer removal, etc.; the search distance for each valuation is determined according to the geological characteristics of the ore body and the distribution of data. The search distance should be large enough to ensure that there are enough data points for estimation, but not too large to avoid introducing too much spatial correlation; the maximum and minimum number of samples used in each valuation are set. The minimum number of samples should ensure that there are enough data points for reliable estimation, and the maximum number of samples should avoid excessive smoothing of the estimation results due to too many data points; according to the geometric shape and geological structure of the ore body, determine whether Partitioning is required, and the partitioning can be based on geological boundaries, drilling locations, mineralized zones and other features; if partitioning is performed, set the maximum and minimum number of selected samples for each partition; determine whether it is necessary to constrain the drilling according to the distribution of geological exploration data, and drilling constraints can ensure that the data points from the drilling are mainly considered in the estimation process; if drilling is constrained, set the maximum number of drillings used in each partition; select the appropriate valuation method according to the characteristics and data of the ore body; according to the selected valuation method and parameters, perform multiple valuations for each partition, and in each valuation process, select and estimate samples according to the set search distance, number of samples, drilling constraints and other parameters; merge the results of the multiple valuations to obtain the final resource reserve estimate. The valuation results can also be verified to ensure that the results are in line with industrial indicators and actual conditions. If necessary, the valuation parameters can be optimized and adjusted to improve the accuracy and reliability of the estimate.

[0128] Specifically, (11) the valuation is statistically analyzed and a report (resource reserve estimation report) is output.

[0129] For example, reference Fig.15 The resource reserve estimation report is prepared based on the resource reserve estimation results of the multiple valuations. The report may include estimation methods, parameter settings, zoning conditions, constraints, estimation results and their uncertainty analysis, verification, etc. Based on the resource reserve estimation results, subsequent work such as mine planning, mining design, and economic evaluation is carried out.

[0130] It should be noted that, compared with the traditional technology, this embodiment can select the earth grid based on longitude and latitude when dividing the block and adopt the spatial grid division strategy of digitally storing octree index information, which improves the storage speed, saves memory space, and can also adapt to multi-scale index transformation, and handle boundary conditions more flexibly; earth grid division facilitates data integration and sharing, facilitates hierarchical management of spatial data, and is more suitable for geographic information systems. For the reserve valuation process, this embodiment adds support for multi-attribute combination constraints and multi-batch continuous valuation steps to the traditional process in the past, and more custom factors can be added during estimation, which is more open to meet the needs of different situations, save manpower, and improve operability.

[0131] In this embodiment, the above method is used to realize the rapid construction of the ore body, block segmentation storage and multi-constraint multi-batch estimation of reserves. The key technical point is the introduction of two strategies, earth grid segmentation and optimized octree space division, for block segmentation and storage, and the use of NoSql database MongoDB to store block information. At the same time, the difference from the previous reserve valuation process is that the combination of multiple attributes of the block is added to constrain the valuation, and the valuation parameters can be set multiple times at one time for automatic valuation, rather than a single valuation.

[0132] The present embodiment provides a mineral reserve valuation modeling method for optimizing block division, including: importing drilling data, delineating the ore body according to the drilling data, so as to establish a physical model of the estimated ore body; performing block division on the physical model of the estimated ore body based on earth grid division or space grid division; converting the drilling data into block data according to the size of the block division, and performing statistics according to the block data to obtain a data distribution characteristic diagram; calculating the variation function based on the data distribution characteristic diagram, and cross-validating the variation function; performing resource reserve estimation according to the variation function and the block data, and outputting a resource reserve estimation report. In the present embodiment, earth grid division is introduced, and earth grid division based on longitude and latitude can be selected when dividing the block, and a spatial grid division strategy using digital storage of octree index information can be used, which improves the storage speed, saves memory space, can also adapt to multi-scale index transformation, and is more flexible in processing boundary conditions; earth grid division facilitates data integration and sharing, facilitates hierarchical management of spatial data, and is more suitable for geographic information systems, thereby realizing rapid construction of ore bodies, block segmentation storage, and multi-constraint and multi-batch estimation of reserves.

[0133] In addition, an embodiment of the present invention also proposes a storage medium, on which is stored a mineral reserve valuation modeling program for optimized block division, and when the mineral reserve valuation modeling program for optimized block division is executed by a processor, the steps of the mineral reserve valuation modeling method for optimized block division as described above are implemented.

[0134] Reference Fig.16 , Fig.16 This is a structural block diagram of an embodiment of a mineral reserve valuation modeling system for optimizing block division according to the present invention.

[0135] like Fig.16 As shown, the mineral reserve valuation modeling system for optimizing block division includes:

[0136] The data import and model building module 10 is used to import the drilling data, delineate the ore body according to the drilling data, and build a physical model of the estimated ore body;

[0137] A block division module 20, used for performing block division on the entity model of the estimated ore body based on earth grid division or space grid division;

[0138] A data conversion and statistics module 30 is used to convert the drilling data into block data according to the size of the block division, and to perform statistics based on the block data to obtain a data distribution characteristic diagram;

[0139] A variation function calculation and verification module 40, used to calculate the variation function based on the data distribution characteristic diagram, and to perform cross-validation on the variation function;

[0140] The resource estimation and output report module 50 is used to perform resource reserve estimation based on the variation function and the block data, and output a resource reserve estimation report.

[0141] The present embodiment provides a mineral reserve valuation modeling system that optimizes block division. In the present embodiment, earth grid division is introduced. When dividing blocks, earth grid division based on longitude and latitude can be selected, and a spatial grid division strategy that digitally stores octree index information can be used. This improves storage speed, saves memory space, and can also adapt to multi-scale index transformations and process boundary conditions more flexibly. Earth grid division facilitates data integration and sharing, facilitates hierarchical management of spatial data, and is more suitable for geographic information systems, thereby realizing rapid construction of ore bodies, block segmentation storage, and multi-constraint and multi-batch estimation of reserves.

[0142] It should be noted that the technical details that are not described in detail in the embodiment of the mineral reserve valuation modeling system for optimized block division can be found in the mineral reserve valuation modeling method for optimized block division as described above provided in any embodiment of the present invention, and will not be repeated here.

[0143] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0144] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0145] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0146] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0147] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0148] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A mineral reserve valuation modeling method for optimizing block division, characterized in that: include: Importing drilling data, delineating the ore body according to the drilling data, and establishing a physical model of the estimated ore body; Based on earth grid division or space grid division, the entity model of the estimated ore body is divided into blocks; Converting the drilling data into block data according to the size of the block division, and performing statistics based on the block data to obtain a data distribution characteristic diagram; Calculating a variance function based on the data distribution characteristic diagram, and cross-validating the variance function; The resource reserves are estimated according to the variation function and the block data, and a resource reserves estimation report is output.

2. The method according to claim 1, characterized in that The importing of drilling data comprises: Obtain drilling data; Establishing a borehole database, and creating a borehole basic information table, a borehole stratification information table, and a borehole inclinometer table in the borehole database; The drilling data is subjected to error correction and integrity and logic checks.

3. The method according to claim 1, characterized in that Delineating the ore body according to the drilling data to establish a physical model for estimating the ore body includes: establishing a three-dimensional model of the ore deposit based on the drilling data; Delineating the boundaries of the ore body in the three-dimensional model according to industrial indicators and geological characteristics of the ore deposit; A physical model of the estimated ore body is established based on the three-dimensional model and the ore body boundary.

4. The method according to claim 1, characterized in that The block division of the entity model of the estimated ore body based on earth grid division or space grid division includes: Importing the entity model of the estimated ore body, and dividing the entity model of the estimated ore body into blocks; Select earth grid division or space grid division based on preset rules; When selecting the earth grid division, the index division is performed according to the longitude and latitude; When selecting the spatial grid division, the block index information is encoded using a binary order of 64-bit numbers to convert the spatial position information into digital codes; The block index information is stored in a MongoDB database.

5. The method according to claim 1, characterized in that The method converts the drilling data into block data according to the size of the block division, and performs statistics based on the block data to obtain a data distribution characteristic diagram, including: According to the size of the block division, the drilling data is converted into block data by using an inverse distance power weighted method, a geometric mean method or a sample length average method; The block data is imported, and statistics are performed on the block center point data to obtain a data distribution characteristic diagram; wherein the data distribution characteristic diagram includes a basic distribution diagram, a frequency histogram, a box plot, a correlation analysis diagram, a probability-probability diagram, and a quantization-quantization diagram.

6. The method according to claim 1, characterized in that The calculating the variance function based on the data distribution characteristic diagram and cross-validating the variance function includes: Sectoring is performed according to the data characteristics of the data distribution characteristic diagram, and the search parameters are adjusted to obtain adjusted search parameters; For each sector, calculating an initial variogram according to the adjusted search parameters; Draw a variance map based on the data of the initial variance function; Determine and derive the variance functions of the major axis, the minor axis, and the minor axis according to the variance diagram and the variance function curve; Cross-validating the variogram; If the Kriging variance ratio and error meet the preset verification requirements, the variance function is considered to meet the standards.

7. The method according to claim 1, characterized in that The resource reserve estimation is performed according to the variation function and the block data, and a resource reserve estimation report is output, including: When estimating resource reserves, different attribute fields are determined based on the multiple attributes of the block; Setting a multi-attribute constraint based on the attribute field to constrain the combination of multiple attributes; Based on the multi-attribute constraints, resource reserves estimation is performed through the variation function and the block data according to a preset estimation algorithm to obtain an estimation result; Output a resource reserve estimation report based on the estimation results.

8. The method according to claim 7, characterized in that The method further comprises: Determine the valuation parameters according to the actual industrial situation; wherein the valuation parameters include: the search distance for each valuation, the maximum and minimum number of selected samples, whether to perform eight-partitioning, the maximum and minimum number of selected samples in the partition, whether to perform drilling constraints, and the maximum number of drilling selections; In each valuation process, each partition is estimated in batches based on a preset estimation algorithm and the valuation parameters; The results of the multiple valuations are combined to obtain a resource reserve estimate, which is used as the estimation result.

9. A mineral reserve valuation modeling system for optimizing block division, characterized in that: include: A data import and model building module, used to import drilling data, delineate the ore body according to the drilling data, and build a physical model of the estimated ore body; A block division module, used for performing block division on the entity model of the estimated ore body based on earth grid division or space grid division; A data conversion and statistics module, used for converting the drilling data into block data according to the size of the block division, and performing statistics based on the block data to obtain a data distribution characteristic diagram; A variation function calculation and verification module, used for calculating the variation function based on the data distribution characteristic diagram, and performing cross-validation on the variation function; The resource estimation and output report module is used to estimate resource reserves based on the variation function and the block data, and output a resource reserve estimation report.

10. An electronic device, characterized in that: The electronic device comprises: a memory, a processor and a mineral reserve valuation modeling program for optimized block partitioning stored in the memory and executable on the processor, wherein the mineral reserve valuation modeling program for optimized block partitioning is configured to implement the mineral reserve valuation modeling method for optimized block partitioning as described in any one of claims 1 to 8.

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