A method and system for evaluating mineral resource reserves based on digital model

Abstract

The present invention relates to the field of mineral exploration technology, and in particular to a method and system for mineral resource reserve assessment based on a digital model. The method comprises the following steps: using geological survey, drilling and geophysical exploration technology to obtain multi-source geological data, and performing geological body expression and model construction based on implicit modeling technology based on the multi-source geological data, thereby obtaining a three-dimensional geological model of the ore deposit; extracting the geological characteristics of the ore deposit based on lithology, structure and mineralization information from the three-dimensional geological model of the ore deposit, thereby obtaining geological characteristic data; performing grade estimation based on the Kriging method based on the geological characteristic data, thereby generating a three-dimensional grade distribution model; performing uncertainty analysis on the three-dimensional grade distribution model, thereby obtaining a grade estimation error distribution map. The present invention utilizes advanced implicit modeling and Kriging methods to provide higher-precision grade estimation and geological models, greatly improving the reliability of reserve assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral exploration, and in particular to a method and system for evaluating mineral resource reserves based on a digital model. Background Art

[0002] Mineral resource reserve assessment refers to the process of scientifically calculating and analyzing the quantity, quality, mining conditions and economic feasibility of underground or surface mineral resources. This assessment aims to determine the total amount of mineral resources in a certain deposit or mining area, as well as the part of the reserves that can be reasonably mined technically and economically, so as to provide a basis for mineral development and investment decisions. Specifically, mineral resource reserve assessment includes the following key aspects: geological exploration of ore bodies, calculation of mineral resources, classification of reserves, analysis of ore quality, assessment of mining technical conditions, economic feasibility analysis and environmental impact assessment.

[0003] But what are the problems with traditional mineral resource reserve assessment methods? Mineral grade is a key parameter for assessing reserves, but due to the large grade variation within the deposit, it is difficult to accurately estimate the average grade of the entire deposit. The geological conditions of many deposits are very complex, including lithology changes, structural deformation, mineralization morphology, etc., which makes exploration very difficult and requires a large amount of drilling and geological survey data to build an accurate geological model. It is difficult to assess the feasibility of mining technology. The technical conditions such as the mining method and mining and dressing process of the ore body are closely related to the geological characteristics of the deposit, but this correlation is difficult to accurately assess, which affects the formulation of the mining plan. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a mineral resource reserve assessment method and system based on a digital model to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a mineral resource reserve assessment method based on a digital model includes the following steps:

[0006] Step S1: using geological survey, drilling and geophysical exploration technology to obtain multi-source geological data, and based on the multi-source geological data, perform geological body expression and model construction based on implicit modeling technology, so as to obtain a three-dimensional geological model of the ore deposit; extracting the geological characteristics of the ore deposit based on lithology, structure and mineralization information from the three-dimensional geological model of the ore deposit, so as to obtain geological characteristic data;

[0007] Step S2: performing grade estimation based on the Kriging method according to the geological characteristic data, thereby generating a three-dimensional grade distribution model; performing uncertainty analysis on the three-dimensional grade distribution model, thereby obtaining a grade estimation error distribution map;

[0008] Step S3: Calculate the resources under different grade boundary conditions according to the three-dimensional grade distribution model and the grade estimation error distribution map, and classify the resources based on the grade position confidence level, thereby generating a preliminary resource classification model, wherein the resource classification includes proven, controlled and inferred;

[0009] Step S4: obtaining a mining and mineral processing process numerical model; simulating the production process under different mining plans according to the preliminary resource classification model and the process numerical model, thereby obtaining production simulation data; conducting a technical feasibility assessment on the production simulation data, and optimizing the process parameters, thereby obtaining mining plan data;

[0010] Step S5: According to the mining plan data, the preliminary resource classification model is modified for mineable minerals, and reserves are converted based on the modified resource classification model to obtain recoverable reserve data; sensitivity analysis is performed on the recoverable reserve data, and risk assessment and uncertainty quantification are performed to obtain the final mineral resource reserve assessment report.

[0011] The data obtained by the present invention using geological survey, drilling and geophysical exploration technology is more comprehensive and can reflect the geological characteristics of the ore deposit from multiple angles; the implicit modeling technology can more accurately express the spatial morphology and mutual relationship of the geological body and improve the authenticity of the model; through the extraction of geological features, it can identify key geological information and provide accurate basic data for subsequent grade estimation and resource assessment. As a mature geostatistical method, Kriging can reasonably perform spatial interpolation on the grade of the ore deposit and generate a continuous three-dimensional grade distribution model; uncertainty analysis can evaluate the reliability of grade estimation, identify potential risks in the estimation process, and provide important reference for decision makers; the grade estimation error distribution map provides an error range for further resource assessment, increasing the transparency and credibility of the assessment. Calculating resources according to different grade boundary conditions can adapt to market changes and changes in mining costs, and provide a flexible solution for mine planning; the resource classification based on the level of grade confidence meets the international standards of geological exploration and improves the standardization and professionalism of resource assessment; the preliminary resource classification model provides basic data and classification basis for subsequent mining plan design and reserve assessment. Acquiring and applying the numerical models of mining and mineral processing can simulate various situations in the actual production process and improve the pertinence and practicality of the mining plan; the technical feasibility evaluation and process parameter optimization of production simulation data are helpful to improve the production efficiency and economic benefits of the mine, and the mining plan data provides process and technical support for the conversion of resources to recoverable reserves. The correction of recoverable minerals and the conversion of reserves take into account the actual mining conditions, making the reserve data more consistent with production practice and improving the practicality of reserve assessment; sensitivity analysis and risk assessment help identify the key factors affecting reserve assessment and provide a basis for risk management and decision-making; uncertainty quantification increases the credibility of the assessment results and provides solid data support for the long-term planning and investment of the mine; the final mineral resource reserve assessment report integrates multiple factors such as geology, technology, economy and risk, providing a comprehensive and reliable decision-making basis for mine development.

[0012] The present invention provides a mineral resource reserve assessment system based on a digital model, which is used to execute the above-mentioned mineral resource reserve assessment method based on a digital model. The mineral resource reserve assessment system based on a digital model comprises:

[0013] The geological modeling module is used to obtain multi-source geological data using geological surveys, drilling and geophysical exploration technologies, and to express geological bodies and construct models based on implicit modeling technology based on the multi-source geological data, thereby obtaining a three-dimensional geological model of the ore deposit; extracting the geological characteristics of the ore deposit based on lithology, structure and mineralization information from the three-dimensional geological model of the ore deposit, thereby obtaining geological characteristic data;

[0014] The grade estimation module is used to perform grade estimation based on the Kriging method according to geological feature data, thereby generating a three-dimensional grade distribution model; and to perform uncertainty analysis on the three-dimensional grade distribution model, thereby obtaining a grade estimation error distribution map;

[0015] The resource classification module is used to calculate the resource quantity under different grade boundary conditions based on the three-dimensional grade distribution model and the grade estimation error distribution map, and classify the resource quantity based on the grade position confidence level, thereby generating a preliminary resource quantity classification model, where the resource quantity classification includes proven, controlled and inferred;

[0016] The production simulation module is used to obtain the process numerical model of mining and ore dressing; simulate the production process under different mining plans according to the preliminary resource classification model and the process numerical model to obtain production simulation data; conduct technical feasibility assessment on the production simulation data and optimize the process parameters to obtain mining plan data;

[0017] The reserve assessment module is used to amend the preliminary resource classification model for mineable minerals according to the mining plan data, and to convert reserves based on the revised resource classification model to obtain recoverable reserve data; perform sensitivity analysis on the recoverable reserve data, and conduct risk assessment and uncertainty quantification to obtain the final mineral resource reserve assessment report.

[0018] The present invention uses data obtained from geological surveys, drilling and geophysical exploration techniques to provide comprehensive basic information for establishing geological models; the application of implicit modeling technology makes the expression of geological bodies more consistent with actual geological conditions, improving the accuracy and practicality of the model; the construction of a three-dimensional geological model helps to better understand the spatial structure and geological characteristics of the ore deposit, and provides an important basis for subsequent grade estimation and resource assessment. As a mature geostatistical method, Kriging can reasonably perform spatial interpolation on the grade of the ore deposit and generate a continuous three-dimensional grade distribution model; uncertainty analysis evaluates the reliability of grade estimation, identifies potential risks in the estimation process, and provides an important reference for decision makers. Calculating resources according to different grade boundary conditions can adapt to market changes and changes in mining costs, and provide flexible solutions for mine planning; resource classification based on grade position confidence level meets international standards for geological exploration, and improves the standardization and professionalism of resource assessment. The acquisition and application of process numerical models can simulate various situations in the actual production process, improve the pertinence and practicality of mining plans; technical feasibility evaluation of production simulation data and process parameter optimization help improve mine production efficiency and economic benefits. The correction of mineable minerals and the conversion of reserves take into account the actual mining conditions, making the reserve data more consistent with production reality and improving the practicality of reserve assessment; sensitivity analysis and risk assessment help identify the key factors affecting reserve assessment and provide a basis for risk management and decision-making. Combining the effects of these modules, the entire mineral resource assessment system can provide a solid geological and statistical foundation for mine planning, design, production and management. It can not only provide accurate resource data, but also assess the uncertainty and risks associated with grade, providing a scientific basis for investment decisions and mine management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0020] Figure 1 It is a schematic diagram of the steps of the mineral resource reserve assessment method based on the digital model of the present invention;

[0021] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0022] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0023] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0025] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for evaluating mineral resource reserves based on a digital model, the method comprising the following steps:

[0027] Step S1: using geological survey, drilling and geophysical exploration technology to obtain multi-source geological data, and based on the multi-source geological data, perform geological body expression and model construction based on implicit modeling technology, so as to obtain a three-dimensional geological model of the ore deposit; extracting the geological characteristics of the ore deposit based on lithology, structure and mineralization information from the three-dimensional geological model of the ore deposit, so as to obtain geological characteristic data;

[0028] Step S2: performing grade estimation based on the Kriging method according to the geological characteristic data, thereby generating a three-dimensional grade distribution model; performing uncertainty analysis on the three-dimensional grade distribution model, thereby obtaining a grade estimation error distribution map;

[0029] Step S3: Calculate the resources under different grade boundary conditions according to the three-dimensional grade distribution model and the grade estimation error distribution map, and classify the resources based on the grade position confidence level, thereby generating a preliminary resource classification model, wherein the resource classification includes proven, controlled and inferred;

[0030] Step S4: obtaining a mining and mineral processing process numerical model; simulating the production process under different mining plans according to the preliminary resource classification model and the process numerical model, thereby obtaining production simulation data; conducting a technical feasibility assessment on the production simulation data, and optimizing the process parameters, thereby obtaining mining plan data;

[0031] Step S5: According to the mining plan data, the preliminary resource classification model is modified for mineable minerals, and reserves are converted based on the modified resource classification model to obtain recoverable reserve data; sensitivity analysis is performed on the recoverable reserve data, and risk assessment and uncertainty quantification are performed to obtain the final mineral resource reserve assessment report.

[0032] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for evaluating mineral resource reserves based on a digital model of the present invention. In this example, the method for evaluating mineral resource reserves based on a digital model includes the following steps:

[0033] Step S1: using geological survey, drilling and geophysical exploration technology to obtain multi-source geological data, and based on the multi-source geological data, perform geological body expression and model construction based on implicit modeling technology, so as to obtain a three-dimensional geological model of the ore deposit; extracting the geological characteristics of the ore deposit based on lithology, structure and mineralization information from the three-dimensional geological model of the ore deposit, so as to obtain geological characteristic data;

[0034] The present invention uses geological surveys to obtain surface geological information of the mining area, collects borehole core samples through drilling technology, and further carries out geophysical exploration work, such as magnetic method, electrical method, etc., to obtain geological structure and physical property data underground in the mining area. These data will be standardized, unified coordinate system and unit to form multi-source geological data. Then, implicit modeling technology is used to express these multi-source geological data in three dimensions, and a three-dimensional geological model of the ore deposit is constructed through interpolation algorithm and grid processing. The model reflects the main strata, structure and lithology units of the mining area. Then, the geological characteristics of the ore deposit are extracted based on the lithology, structure and mineralization information in the model. For example, important geological features such as the main lithology contact zone, fault line and mineralization zone are extracted, and finally geological characteristic data that can describe the lithology, structure and mineralization information of the mining area are formed. In actual application scenarios, the grid spacing parameter can be set to 25 meters to capture the detailed changes of the ore deposit.

[0035] Step S2: performing grade estimation based on the Kriging method according to the geological characteristic data, thereby generating a three-dimensional grade distribution model; performing uncertainty analysis on the three-dimensional grade distribution model, thereby obtaining a grade estimation error distribution map;

[0036] After obtaining the geological characteristic data, the embodiment of the present invention uses the Kriging method to perform grade estimation. First, based on the geological characteristic data, a suitable estimation range is selected, and the area is divided into grid units, and the grid size is usually set to 50 meters × 50 meters. On this basis, a variogram analysis is performed to determine the spatial correlation structure of the grade in the mining area, and a variogram model is generated. Then, the model is combined with the Kriging method to estimate the grade of each grid unit to generate a three-dimensional grade distribution model. In order to analyze the reliability of the grade estimation, conditional simulation technology is further used to generate multiple grade simulation results, the differences between these results are counted, and the grade estimation error of each grid unit is calculated based on the simulation results, and finally a grade estimation error distribution map is generated. In practical applications, 100 conditional simulation results are used to calculate the error.

[0037] Step S3: Calculate the resources under different grade boundary conditions according to the three-dimensional grade distribution model and the grade estimation error distribution map, and classify the resources based on the grade position confidence level, thereby generating a preliminary resource classification model, wherein the resource classification includes proven, controlled and inferred;

[0038] The embodiment of the present invention sets multiple grade boundary conditions (such as boundary grades of 0.3%, 0.5% and 0.7%) based on the three-dimensional grade distribution model and the grade estimation error distribution map, and determines the corresponding resource data by calculating the volume under each grade boundary condition. Then, the grade position confidence is calculated for each grid unit using the grade estimation error distribution map, and the resources are classified into three categories: proven, controlled and inferred according to the preset confidence thresholds (such as confidence levels of 70%, 80% and 90%). Through this process, a preliminary resource classification model is formed. In practical applications, a copper deposit may have a proven resource of 5 million tons, a controlled resource of 3 million tons, and an inferred resource of 2 million tons at a boundary grade of 0.5%.

[0039] Step S4: obtaining a mining and mineral processing process numerical model; simulating the production process under different mining plans according to the preliminary resource classification model and the process numerical model, thereby obtaining production simulation data; conducting a technical feasibility assessment on the production simulation data, and optimizing the process parameters, thereby obtaining mining plan data;

[0040] According to the preliminary resource classification model, the embodiment of the present invention collects mining and mineral processing parameters, including recovery rate, depletion rate, mineral processing recovery rate, etc., and constructs a mining numerical model and a mineral processing numerical model. Then, multiple mining plans are designed, including different mining sequences (such as from the east area to the west area), mining methods (such as a combination of open-pit mining and underground mining), and production scales (annual production capacity of 2 million tons or 3 million tons). Using these parameters, the mining and mineral processing processes of each plan are numerically simulated to obtain production simulation data for each plan. By analyzing the production simulation data, evaluating the technical feasibility, optimizing the process parameters (such as increasing the mineral processing recovery rate from 85% to 88%), the optimal mining plan is selected and the mining plan data is generated.

[0041] Step S5: According to the mining plan data, the preliminary resource classification model is modified for mineable minerals, and reserves are converted based on the modified resource classification model to obtain recoverable reserve data; sensitivity analysis is performed on the recoverable reserve data, and risk assessment and uncertainty quantification are performed to obtain the final mineral resource reserve assessment report.

[0042] The embodiment of the present invention evaluates mining losses and depletion based on the final mining plan data, amends the preliminary resource classification model, and amends the proven, controlled and inferred resources. For example, according to the model correction, the proven resources may be reduced by 10% and the controlled resources by 5%. At the same time, the resource data is further corrected considering factors that restrict mining (such as environmental protection areas). According to the revised resource data, the resource is converted into recoverable reserves according to the NI 43-101 mineral resource reserve classification standard. For recoverable reserve data, a single factor sensitivity analysis is performed, such as analyzing metal prices, mining costs, and mineral processing recovery rates under different values, combining the Monte Carlo simulation method, evaluating the uncertainty of different factors on reserves, and finally obtaining the expected value of reserves, confidence intervals and other indicators. Finally, a mineral resource reserve assessment report is generated in combination with sensitivity analysis and risk assessment. In actual copper mine projects, recoverable reserves may fluctuate within a range of 3%-5%.

[0043] The data obtained by the present invention using geological survey, drilling and geophysical exploration technology is more comprehensive and can reflect the geological characteristics of the ore deposit from multiple angles; the implicit modeling technology can more accurately express the spatial morphology and mutual relationship of the geological body and improve the authenticity of the model; through the extraction of geological features, it can identify key geological information and provide accurate basic data for subsequent grade estimation and resource assessment. As a mature geostatistical method, Kriging can reasonably perform spatial interpolation on the grade of the ore deposit and generate a continuous three-dimensional grade distribution model; uncertainty analysis can evaluate the reliability of grade estimation, identify potential risks in the estimation process, and provide important reference for decision makers; the grade estimation error distribution map provides an error range for further resource assessment, increasing the transparency and credibility of the assessment. Calculating resources according to different grade boundary conditions can adapt to market changes and changes in mining costs, and provide a flexible solution for mine planning; the resource classification based on the level of grade confidence meets the international standards of geological exploration and improves the standardization and professionalism of resource assessment; the preliminary resource classification model provides basic data and classification basis for subsequent mining plan design and reserve assessment. Acquiring and applying the numerical models of mining and mineral processing can simulate various situations in the actual production process and improve the pertinence and practicality of the mining plan; the technical feasibility evaluation and process parameter optimization of production simulation data are helpful to improve the production efficiency and economic benefits of the mine, and the mining plan data provides process and technical support for the conversion of resources to recoverable reserves. The correction of recoverable minerals and the conversion of reserves take into account the actual mining conditions, making the reserve data more consistent with production practice and improving the practicality of reserve assessment; sensitivity analysis and risk assessment help identify the key factors affecting reserve assessment and provide a basis for risk management and decision-making; uncertainty quantification increases the credibility of the assessment results and provides solid data support for the long-term planning and investment of the mine; the final mineral resource reserve assessment report integrates multiple factors such as geology, technology, economy and risk, providing a comprehensive and reliable decision-making basis for mine development.

[0044] Preferably, step S1 comprises the following steps:

[0045] Step S11: Conducting geological survey on the target mineral to obtain surface geological information data;

[0046] Step S12: Performing drilling operations on the target minerals to obtain drill core data; and conducting geophysical exploration work on the target minerals to obtain geographic exploration data;

[0047] Step S13: performing data standardization processing on surface geological information data, drilling core data and geographic exploration data, and unifying the coordinate system and units, thereby obtaining multi-source geological data;

[0048] Step S14: performing data interpolation and gridding processing on multi-source geological data to obtain gridded geological data;

[0049] Step S15: performing geological body expression and model construction based on implicit modeling technology according to the gridded geological data, thereby obtaining a three-dimensional geological model of the ore deposit;

[0050] Step S16: extracting geological characteristics of the ore deposit based on lithology, structure and mineralization information from the 3D ore deposit model to obtain geological characteristic data.

[0051] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0052] Step S11: Conducting geological survey on the target mineral to obtain surface geological information data;

[0053] The embodiment of the present invention uses field geological survey methods to obtain surface geological information data for the target mineral area. This includes using satellite images and geological mapping technology to identify the stratum distribution, rock types, fault structures and other geological features of the mining area. The elevation data of the mining area can also be obtained through topographic surveying as the surface basis of the three-dimensional model. In practical applications, a geological map can be drawn at a scale of 1:5000 to accurately reflect the surface geological information of the mining area.

[0054] Step S12: Performing drilling operations on the target minerals to obtain drill core data; and conducting geophysical exploration work on the target minerals to obtain geographic exploration data;

[0055] The embodiment of the present invention carries out drilling operations in the target mineral area, usually using diamond drilling technology, and the drilling depth is set according to the geological characteristics of the mining area, and the general depth ranges from 500 to 1000 meters. During the drilling process, drill core samples are collected, and the lithology, structure, mineralization and other information of each core are recorded. At the same time, geophysical exploration work is carried out, such as magnetic, electrical or gravity methods, to obtain underground structure and physical property data. For example, the magnetic method is used to detect the distribution of magnetic deposits, while the electrical method can reveal the differences in electrical conductivity underground and indicate mineralized zones. In specific applications, the survey line spacing of magnetic geophysical exploration can be set to 50 meters to ensure the accuracy of the data.

[0056] Step S13: performing data standardization processing on surface geological information data, drilling core data and geographic exploration data, and unifying the coordinate system and units, thereby obtaining multi-source geological data;

[0057] The embodiment of the present invention standardizes surface geological information data, drill core data, and geographic exploration data. First, the coordinate system of various data is unified, such as the WGS84 coordinate system; second, the units are unified, and the units of depth, distance, grade, etc. are unified into standard units such as meters and grams per ton. For drilling data, the drilling database system is used to enter detailed records of the stratigraphic distribution and mineralization of the core, and the geophysical data is processed by professional software to generate profiles or contour maps. Through these steps, various types of data are integrated to form a complete multi-source geological data set.

[0058] Step S14: performing data interpolation and gridding processing on multi-source geological data to obtain gridded geological data;

[0059] The embodiment of the present invention interpolates and grids multi-source geological data. Interpolation algorithms such as inverse distance weighted (IDW) or Kriging are used to fill discrete drilling and geophysical data into the three-dimensional space of the mining area. In this process, the entire mining area is divided into a number of grid units, with a common grid size of 25 meters × 25 meters, which can maintain the details of the geological information without making the calculation too cumbersome. The interpolated data can reflect the continuous distribution of geological information in space, which is convenient for subsequent model construction. Step S15: According to the gridded geological data, the geological body expression and model construction based on implicit modeling technology are performed to obtain a three-dimensional geological model of the ore deposit;

[0060] The embodiment of the present invention is based on gridded geological data and uses implicit modeling technology to construct a three-dimensional geological model of the ore deposit. Implicit modeling technology directly generates a three-dimensional geological body through mathematical functions without depicting geological boundaries layer by layer. First, the main stratigraphic model of the ore deposit is constructed using geological data, including the spatial distribution and lithological division of each stratigraphic unit. Then, combined with geophysical and drilling data, structural features such as faults and folds are extracted to form a structural model. Finally, these models are integrated to generate a three-dimensional geological model of the ore deposit. In practical applications, the resolution of implicit modeling can be set to 50 meters to ensure the fineness of the model.

[0061] Step S16: extracting geological characteristics of the ore deposit based on lithology, structure and mineralization information from the 3D ore deposit model to obtain geological characteristic data.

[0062] After the embodiment of the present invention completes the three-dimensional geological model, the lithology, structure and mineralization characteristics of the mining area are extracted based on the model. For lithological characteristics, the spatial distribution of different rock units is identified, and the main contact relationships are marked. The extraction of structural information includes identifying the spatial position and extension direction of faults and folds. The extraction of mineralization characteristics focuses on analyzing the distribution range of mineralized zones and alteration zones. For example, the mineralized zone of a mining area may show a northeast distribution and be maximized at a depth of 500 meters. Finally, these data are integrated into a complete geological characteristic data set for subsequent grade estimation and resource classification.

[0063] The geological survey of the present invention provides geological information on the surface, including strata, rock types, geological structures, etc., laying the foundation for understanding the surface geological characteristics of the ore deposit; surface geological information data helps to identify key geological elements such as mineralized alteration zones and fault zones, and has guiding significance for determining the drilling location and direction. The drill core data obtained from the drilling operation is the most direct evidence for evaluating mineral resources, and can intuitively reflect the scale, shape and ore quality of the ore body; the geographical exploration data obtained from the geophysical work can reveal the physical properties such as electrical, magnetic, and density of the underground, which helps to identify hidden ore bodies and geological structures. Data standardization processing ensures the consistency and comparability of data from different sources and types in subsequent processing and analysis; the unification of coordinate systems and units provides the necessary premise for the integration of multi-source geological data and the construction of three-dimensional geological models. Data interpolation and gridding processing convert discrete geological data into continuous grid models, improving the spatial resolution and expression ability of the data; gridded geological data provides the basis for subsequent implicit modeling and geological body expression, and enhances the accuracy and reliability of the model. Implicit modeling technology can construct a three-dimensional geological model that reflects the actual morphology of the geological body based on geological data, improving the spatial expression ability of the geological structure of the ore deposit; the three-dimensional geological model provides an intuitive and accurate geological framework for mineral resource assessment, which helps to better understand the spatial distribution characteristics of the ore deposit. Geological feature extraction can identify key geological elements in the ore deposit, such as lithological units, structural lines, mineralized zones, etc., providing key information for grade estimation and resource assessment; the extraction of geological feature data helps to analyze the mineralization laws and mineralization control factors of the ore deposit, and provides a scientific basis for ore deposit evaluation and exploration. In summary, the effect of these steps is reflected in providing comprehensive, accurate and standardized geological data for mineral resource assessment, constructing a three-dimensional geological model that reflects the actual geological characteristics of the ore deposit, and extracting key geological feature information, laying a solid foundation for subsequent grade estimation and resource assessment.

[0064] Preferably, step S15 comprises the following steps:

[0065] Step S151: Determine the spatial distribution of the main stratigraphic units based on the gridded geological data, thereby constructing a ore deposit stratigraphic model;

[0066] The embodiment of the present invention determines the spatial distribution of the main stratigraphic units in the mining area based on the gridded geological data. The drilling data and geophysical data are used to analyze the vertical changes of different strata and their geographical locations. The thickness, inclination and depth distribution of different strata are determined by analyzing the stratigraphic columnar diagram of the borehole. For example, in a certain mining area, the shallow part is the Upper Cretaceous sandstone layer, and the deep part is the Jurassic limestone layer, and there is a significant contact relationship between the strata. In this way, the geological software is used to perform spatial interpolation on these stratigraphic information to construct a three-dimensional stratigraphic model. The stratigraphic type and depth range corresponding to each unit in the model can lay the foundation for the subsequent structural feature analysis.

[0067] Step S152: extracting main structural features based on discontinuous features based on gridded geological data, thereby establishing a mineral deposit structural model;

[0068] In the construction of the structural model of the embodiment of the present invention, the discontinuous features in the gridded geological data are first analyzed, which include geological structures such as faults and folds. Structural clues are identified through geophysical data (such as magnetic and seismic data), and the strike and dip of the fault are confirmed in combination with the drilling data. In actual operation, the discrete element method is used to analyze the structural position to accurately locate the extension and range of the fault. For example, in a mining area, there may be a NE-SW-trending reverse fault with an extension length of several thousand meters and a dip of 45 degrees. By integrating these data points into linear features, a three-dimensional structural model is established in the software to mark the specific location and geometry of the faults and folds.

[0069] Step S153: dividing the distribution ranges of different lithological units according to the gridded geological data, thereby obtaining a lithological unit model of the ore deposit;

[0070] The embodiment of the present invention spatially divides different lithologic units based on gridded geological data. First, the lithologic characteristics of different core samples are analyzed to identify the main rock types in the mining area, such as sandstone, shale, granite, etc. Through spatial interpolation of lithologic data, these rock types are expanded in the mining area and their distribution ranges are divided. In a typical application scenario, there may be a variety of lithologic units in the mining area, such as granite bodies distributed in the western part of the mining area, and interlayers of shale and sandstone in the eastern part. These lithologic information are input into the geological modeling software to generate spatial models of different lithologic units, and combined with the structural model to provide detailed lithologic basis for the final three-dimensional geological model.

[0071] Step S154: using implicit modeling technology to convert the ore deposit stratigraphic model, ore deposit structural model and ore deposit lithological unit model into mathematical functions, and constructing implicit function fields of lithology, structure and mineralization, thereby obtaining implicit function field data;

[0072] The embodiment of the present invention utilizes implicit modeling technology to convert previously constructed stratigraphic models, structural models, and lithologic unit models into mathematical functions. The core of implicit modeling is to represent the boundaries and internal structures of different geological units in space through implicit function fields. In specific operations, each geological unit (such as different strata, faults, and lithologic units) is expressed as a continuous function, and the geological characteristic field of the entire mining area can be generated by solving these functions. For example, using interpolation algorithms and multivariate regression analysis, lithologic changes, fault locations, and stratum thicknesses are expressed as implicit functions, so that even in areas where data are relatively sparse, reasonable inferences can be made through mathematical models to obtain continuous implicit function field data.

[0073] Step S155: Generate a three-dimensional geological body based on an interpolation algorithm according to the implicit function field data, thereby obtaining a three-dimensional geological model of the ore deposit.

[0074] The embodiment of the present invention generates a three-dimensional geological model of the ore deposit using an interpolation algorithm based on the implicit function field data. Interpolation algorithms (such as Kriging or spline interpolation) can spatially smooth discrete geological data to ensure the continuity and accuracy of the model throughout the mining area. For example, the Kriging method is used to interpolate the grade of the drill hole data to generate a three-dimensional morphology of the mineralized zone in the mining area. In practical applications, the grid size is usually set to 50 meters × 50 meters × 10 meters to ensure the details and computational efficiency of the model. Through the interpolation algorithm, the three-dimensional geological model finally generated can accurately reflect the structure, lithology distribution and mineralization characteristics of the ore deposit, providing a reliable basis for subsequent resource estimation.

[0075] The spatial distribution of the main stratigraphic units determined by the present invention is helpful to understand the geological age and stratigraphic sequence of the ore deposit, and provides important information for the geological background and metallogenic environment of the ore deposit; the construction of the ore deposit stratigraphic model can clarify the contact relationship and rock type of the strata, and provide a basis for the positioning and exploration direction of the ore body. Extracting the main structural features, such as discontinuous geological structures such as faults and folds, helps to identify key structures that may affect the integrity and continuity of the ore body. Establishing a ore deposit structural model can reveal the control of structure on the distribution of mineralization, and provide important guidance for the spatial positioning and exploration of the ore body. Dividing the distribution range of different lithological units helps to identify rock types with high mineralization potential, such as mineralized granite, volcanic rock, etc.; the lithological unit model of the ore deposit provides a basis for understanding the relationship between mineralization and lithology, and helps to predict the spatial distribution characteristics of mineralization. The stratum, structure and lithological unit models are converted into mathematical functions using implicit modeling technology, realizing the digitalization and parameterized expression of the geological model; the construction of implicit function field data provides a continuous mathematical description for the spatial distribution of the geological body, enhancing the accuracy and applicability of the model. The generation of three-dimensional geological bodies based on interpolation algorithms can generate continuous and smooth three-dimensional geological models based on implicit function field data, which improves the visual effect and analysis ability of the model; obtaining a three-dimensional geological model of a mineral deposit is a key step in the entire mineral evaluation process, providing an accurate geological framework for subsequent grade estimation, resource calculation and mining plan design. In summary, the effect of these steps is reflected in the conversion of geological information into accurate three-dimensional geological models through digitalization and parameterization technology, providing scientific, intuitive and reliable basic data and analysis tools for the exploration, evaluation and development of mineral resources.

[0076] Preferably, step S16 comprises the following steps:

[0077] Step S161: extracting lithology distribution characteristics from the three-dimensional geological model of the ore deposit, and identifying the main lithology units and their contact relationships, thereby obtaining lithology characteristic data;

[0078] The embodiment of the present invention extracts the distribution characteristics of lithology in the three-dimensional geological model of the ore deposit. This process uses the lithology information in the drilling data and geophysical exploration results to analyze the distribution of different lithology units through spatial interpolation technology. In the specific operation, a lithology distribution analysis module based on geological software (such as GOCAD or Leapfrog Geo) is used to spatially identify and classify different lithologies (such as sandstone, shale, and granite). At the same time, the contact relationship of the main lithology units is identified, such as the contact interface between granite and the surrounding rock, the transition zone between sandstone and shale, etc. In the application scenario, it may be found that there is an obvious intrusive contact between granite and sandstone. This contact relationship helps to infer the location of mineralization and the complexity of the mineralization process. Through this extraction method, accurate lithology characteristic data is finally obtained.

[0079] Step S162: extracting structural information from the three-dimensional geological model of the ore deposit, and determining the spatial distribution of the main structural lines and surfaces, thereby obtaining structural characteristic data;

[0080] In the structural information extraction stage, the embodiment of the present invention uses structural units (such as faults, folds, etc.) in the three-dimensional geological model for analysis. In specific operations, based on the spatial distribution of structural lines and structural surfaces, fault tracking algorithms and structural linear analysis techniques are used to determine the strike, dip, and extension range of major faults. For example, the fault positions revealed by drilling are compared with the high-gradient areas of geophysical data to identify NE-SW-trending faults in a certain area. Through continuous tracking of faults, it is determined that their extension direction may span several kilometers across the mining area, and the intersection relationship between structural surfaces and strata is displayed through three-dimensional visualization, thereby obtaining complete structural feature data.

[0081] Step S163: Analyze the mineralization characteristics of the three-dimensional geological model of the ore deposit, divide the distribution range of the mineralization zone and the alteration zone, and thus generate mineralization characteristic data;

[0082] When analyzing the mineralization characteristics in the embodiment of the present invention, the distribution areas of the mineralized zones and alteration zones are first identified in the three-dimensional geological model. Using the known grade data of the mineralized samples, the mineralization degree of different drilling positions is expanded by spatial interpolation methods (such as Kriging method) to divide the areas with different mineralization intensities. Then, combined with the rock alteration data, the distribution of the alteration zones is identified by multivariate analysis, such as the superposition relationship between the pyrite alteration zone and the quartz vein mineralization zone. By analyzing the interaction between different mineralization types and the spatial distribution characteristics of these areas, three-dimensional mineralization characteristic data is finally generated. In a certain mining area application, the mineralized zone may be distributed along the main fault trend, while the alteration zone is distributed on the periphery of the mineralized zone. This relationship helps to determine the best mining location.

[0083] Step S164: integrating the lithology characteristic data, structural characteristic data and mineralization characteristic data based on spatial relationships, thereby obtaining geological characteristic data.

[0084] In the process of integrating geological characteristic data, the embodiment of the present invention first spatially matches and associates the lithological characteristic data, structural characteristic data, and mineralization characteristic data. A spatial relationship analysis algorithm is used to identify the mutual influence between different characteristic data, such as the spatial overlap between certain lithological units and faults, and the concentrated mineralization areas near the faults. In specific operations, the spatial data integration tool of the geological modeling software is used to superimpose different data layers together, and comprehensive geological characteristic data is generated through weighted summation or joint interpolation technology. In application scenarios, it may be found that areas with complex structures are often accompanied by more concentrated mineralized belts, and the changes in lithological units also show a strong correlation with the mineralization intensity. The integrated geological characteristic data can provide a scientific basis for subsequent resource estimation and deposit evaluation.

[0085] The lithology distribution feature extraction of the present invention helps to identify different rock types and their spatial distribution in the ore deposit, and provides data support for understanding the petrological characteristics of the ore deposit; identifying the main lithology units and their contact relationships can reveal the geological age and genetic relationship of the rock units, which is crucial for the location of the ore body and the evaluation of the mineralization potential. Structural information extraction can clarify the main structural lines and surfaces in the ore deposit, such as faults, folds, etc., which often control the spatial distribution of mineralization. Determining the spatial distribution of structures helps to understand the structural evolution of the ore deposit and provide important information for the exploration and stability assessment of the ore body. Mineralization feature analysis helps to identify mineralized zones and alteration zones, which are usually closely related to the formation of the ore body; dividing the distribution range of mineralized zones and alteration zones can provide direct targets for the exploration and evaluation of the ore body and increase the probability of discovering economic ore bodies. Data integration based on spatial relationships comprehensively considers lithology, structure and mineralization feature data, improves the comprehensiveness and practicality of geological feature data; the obtained geological feature data provides a comprehensive and accurate geological basis for subsequent geological interpretation, resource assessment and mining plan design. Through these steps, a framework that comprehensively reflects the geological characteristics of the deposit can be constructed, providing a solid geological basis for the exploration, evaluation and development of mineral resources. These data not only help to improve the accuracy of the geological model, but also enhance the understanding of the geological processes of the deposit, thereby better guiding the formulation of exploration strategies and mining plans.

[0086] Preferably, step S2 comprises the following steps:

[0087] Step S21: spatially analyzing the geological characteristic data and determining the spatial range and grid size for grade estimation, thereby obtaining estimated grid parameter data;

[0088] Step S22: performing a variogram analysis based on the geological feature data and determining a spatial correlation structure, thereby obtaining a variogram model;

[0089] Step S23: using the Kriging method to perform grade estimation based on the estimated grid parameter data and the variogram model, and perform geological constraint processing to generate a three-dimensional grade distribution model;

[0090] Step S24: using conditional simulation technology to generate multiple equally probable grade realizations according to the three-dimensional grade distribution model, thereby obtaining a grade simulation set;

[0091] Step S25: Calculate the grade estimation error of each grid cell based on the grade simulation set, so as to obtain a grade estimation error distribution map.

[0092] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0093] Step S21: spatially analyzing the geological characteristic data and determining the spatial range and grid size for grade estimation, thereby obtaining estimated grid parameter data;

[0094] The embodiment of the present invention performs a detailed spatial analysis of the geological feature data to determine the spatial range involved in the grade estimation and the grid size during the estimation. During the operation, GIS or three-dimensional geological modeling software (such as Surfer or GOCAD) is used to perform spatial distribution analysis of geological data, focusing on the scope of the mineralized area and the morphology of the ore body. For example, based on the geological characteristics of the mining area, the spatial boundary of the ore body is determined to be 1000 meters × 800 meters, and extends 200 meters in the vertical direction. Then, according to the geological and mining requirements, a suitable estimation grid is divided, and the commonly used grid size can be set to 50 meters × 50 meters × 20 meters, which can retain sufficient spatial details and ensure estimation efficiency. In actual application scenarios, if the mineralized area presents a more complex spatial distribution, a smaller grid may be required to capture details, especially in areas with dense faults. Through the above operations, the grid parameter data for grade estimation is obtained, which provides a basis for subsequent grade estimation.

[0095] Step S22: performing a variogram analysis based on the geological feature data and determining a spatial correlation structure, thereby obtaining a variogram model;

[0096] The embodiment of the present invention performs a variogram analysis based on the geological characteristic data before the grade estimation. First, a suitable spatial variogram model is selected, such as a spherical, exponential or Gaussian model. Common analysis software such as SGeMS or ISATIS can be used for calculation. The experimental variogram is drawn by analyzing the spatial correlation between the sample grade data. For example, the variogram is calculated along the strike, dip and vertical directions of the ore body to analyze the spatial variation of the grade. If the variability of the grade data is low in the strike direction, indicating that the spatial correlation in this direction is strong, the spherical model fitting is selected. Combined with the actual situation of the geological characteristic data, the spatial correlation structure is determined, such as a variogram model with a range of 300 meters and a base of 0.5. In the application of a specific mining area, if the ore body extends along the fault for a long time, a longer range may be required to match the spatial characteristics of the grade data. Finally, a variogram model for grade estimation is obtained.

[0097] Step S23: using the Kriging method to perform grade estimation based on the estimated grid parameter data and the variogram model, and perform geological constraint processing to generate a three-dimensional grade distribution model;

[0098] The embodiment of the present invention uses the Kriging method to perform grade estimation based on the previously determined estimation grid parameter data and the variogram model. In the specific operation, the geological characteristic data of the mining area is first input into the estimation software, such as Micromine or Surpac, and the ordinary Kriging method or the indicator Kriging method is selected for interpolation estimation. The software will perform spatial interpolation based on the geological characteristic data to generate a grade estimate for each grid unit. In this process, geological constraint processing is performed, that is, the distribution range of the grade is limited according to geological information such as lithology and structure. For example, in some specific lithology units, mineralization may be more concentrated, so the grade estimation of these units is limited through geological constraint processing. In an actual application scenario, in this way, the grade estimation results can be made more in line with geological laws, and finally a three-dimensional grade distribution model of the ore deposit is generated.

[0099] Step S24: using conditional simulation technology to generate multiple equally probable grade realizations according to the three-dimensional grade distribution model, thereby obtaining a grade simulation set;

[0100] The embodiment of the present invention utilizes conditional simulation technology to generate multiple equally probable grade realizations based on a three-dimensional grade distribution model to perform uncertainty analysis. In specific operations, simulation techniques such as Turning Bands or Sequential Gaussian Simulation (SGS) are used to generate multiple possible grade distributions. Each simulation is based on the same input data and variance function, but due to the randomness in the simulation, each realization represents a possible distribution of grade. During operation, 50 to 100 equally probable grade realizations can be generated as a set of grade simulations. In the application of a specific mining area, if the geological conditions of the mining area are complex, the differences between different simulations may be large, which indicates that the uncertainty of the ore body grade is high. Through this simulation, more basis can be provided for risk assessment.

[0101] Step S25: Calculate the grade estimation error of each grid cell based on the grade simulation set, so as to obtain a grade estimation error distribution map.

[0102] The embodiment of the present invention calculates the grade estimation error of each grid cell based on the grade simulation set. In specific operations, statistical software such as the geostatistical module in R or Python is used to analyze all grade realizations of each grid cell, and the standard deviation or confidence interval is calculated to evaluate the estimation error. For example, the standard deviation of 50 grade realizations in a grid cell is calculated. If the standard deviation in some areas is large, it indicates that the uncertainty of grade estimation in this area is high. Based on the error results of all grids, a grade estimation error distribution map is drawn. In practical applications, the grade estimation error distribution map can help identify areas with large grade uncertainty in the mining area. These areas may require more drilling or further geological verification in mining planning to reduce production risks.

[0103] The spatial analysis of the present invention ensures that the spatial range and grid size of grade estimation are adapted to the geological characteristics, and improves the accuracy and efficiency of estimation; determining the estimated grid parameter data provides an accurate geographical and spatial framework for subsequent grade estimation. Variogram analysis reveals the spatial correlation structure of geological variables and provides the necessary theoretical basis for Kriging; determining the variogram model helps to understand the spatial dependence of data, so as to make more accurate grade estimation. Kriging combines geological constraint processing to generate a more accurate three-dimensional grade distribution model; the application of geostatistics not only improves the reliability of estimation, but also provides a quantitative means for predicting uncertainty. The multiple equiprobable grade realizations generated by conditional simulation technology increase the stability of grade estimation and the diversity of prediction; the generation of grade simulation sets provides the possibility of evaluating the potential of mineral resources under different geological scenarios. The grade estimation error of each grid unit is calculated to provide a quantitative indicator for evaluating the reliability of prediction, and the grade estimation error distribution map provides an important basis for risk management and decision-making, which helps to identify uncertainty and outliers in the data. Through these steps, the mineral resource reserve assessment method can more accurately reflect the grade distribution of the deposit, assess the resource potential, and quantify the uncertainty in the assessment process. This provides a solid geological and statistical basis for mine planning, design and operations.

[0104] Preferably, step S3 comprises the following steps:

[0105] Step S31: setting a plurality of grade boundary conditions according to the three-dimensional grade distribution model, thereby obtaining a grade boundary parameter set;

[0106] The embodiment of the present invention sets a plurality of different grade boundary conditions according to the three-dimensional grade distribution model. In the specific operation, the economic grade threshold of the mining area is first analyzed, and a plurality of grade boundary values ​​are determined according to different mining conditions. For example, the low-grade boundary is set to 0.5g / t, the medium-grade boundary is set to 1.0g / t, and the high-grade boundary is set to 1.5g / t. Then, using the ore deposit model software (such as Datamine or Surpac), these boundary conditions are applied to the three-dimensional grade distribution model to generate a grade boundary parameter set. In actual application scenarios, the grade boundaries of different types of ores may vary greatly. Copper mines may use a copper grade of 0.2%-1.0% as the boundary, while the boundary conditions of gold mines may be set between 0.5-2.0g / t. By setting multiple grade boundaries, a grade boundary parameter set is formed to provide a basis for subsequent resource calculations.

[0107] Step S32: performing volume calculation on the three-dimensional grade distribution model based on the grade boundary parameter set, thereby obtaining resource quantity data under different grade boundary conditions;

[0108] The embodiment of the present invention calculates the volume of the three-dimensional grade distribution model based on the grade boundary parameter set in step S31. In specific operations, geological modeling software (such as Micromine or Leapfrog) is used to calculate the volume of each grade interval according to different grade boundaries. The software will divide the ore deposit into spaces according to the set grade threshold and calculate the volumes of different grade areas. For example, at the low-grade boundary (0.5g / t), the total volume of the ore body is 500,000 cubic meters, at the medium-grade boundary (1.0g / t), the volume of the ore body may be reduced to 300,000 cubic meters, and at the high-grade boundary (1.5g / t), the volume of the ore body is further reduced to 100,000 cubic meters. In this way, resource data under different grade boundary conditions are obtained for evaluating the economic potential of the ore deposit.

[0109] Step S33: calculating the grade position confidence level of each grid unit according to the grade estimation error distribution map, thereby obtaining a grade position confidence distribution model;

[0110] The embodiment of the present invention calculates the grade position confidence level of each grid unit according to the grade estimation error distribution map. In the specific operation, a statistical method (such as Bootstrap or Monte Carlo simulation) is used to calculate the confidence of each grid unit. For example, for some grid units, the error range achieved by counting 50 equally probable grades is used to calculate its 95% confidence interval. If the grade position confidence of a certain grid unit is high (such as above 95%), it can be considered that the grade estimation of this area is more reliable; while the area with lower confidence reflects that the uncertainty of the estimate is higher. In practical applications, the level of grade position confidence directly affects the development decision of the mining area. After the calculation is completed, a grade position confidence distribution model is generated to clarify the estimated credibility of each grid unit.

[0111] Step S34: spatially divide the resources into three categories: proven, controlled and inferred, according to the preset confidence threshold and the product location confidence distribution model, so as to generate a preliminary resource classification model.

[0112] The embodiment of the present invention divides the resources spatially according to the product location confidence distribution model generated in step S33 and in combination with a preset confidence threshold (such as 80%, 90%, etc.). In specific operation, the area where the product location confidence is higher than a certain threshold is divided into proven mineral resources, the area with a higher confidence but insufficient to be completely determined is divided into controlled mineral resources, and the area with a relatively low confidence but with a certain mining potential is divided into inferred mineral resources. For example, the area with a confidence greater than 90% can be divided into proven resources, the area between 80%-90% is divided into controlled resources, and the area below 80% is divided into inferred resources. In practical applications, only a part of the area in a mining area may meet the standard of proven resources, and most of the area may only be divided into controlled or inferred resources. Finally, based on this spatial division, a preliminary resource classification model is generated, which provides an important basis for mineral reserve assessment and mining planning.

[0113] The present invention sets multiple grade boundary conditions to more finely distinguish the different values ​​and mining feasibility of mineral resources, and provides diversified options for mine design and mining plans; the determination of the grade boundary parameter set lays the foundation for the accurate calculation of resource volume, which helps to more reasonably evaluate the economic value of mineral resources. The volume calculation based on the grade boundary parameter set can obtain resource volume data under different grade conditions, which helps to evaluate the resource potential and value under different mining conditions; the acquisition of resource volume data is crucial for the economic benefit analysis, risk management and mining strategy formulation of the mine. The calculation of the grade position confidence level can evaluate the reliability of the grade estimation of each grid unit, and provide quality control for the accurate evaluation of the resource volume; the grade position confidence distribution model helps to identify the uncertainty and risk in the data, and provides important reference information for decision makers. According to the spatial division of the confidence threshold and the grade position confidence distribution model, the resource volume can be divided into three categories: proven, controlled and inferred, which helps to manage and utilize the resource volume according to different geological credibility; the generation of the preliminary resource volume classification model provides a scientific basis for the feasibility study, investment decision and development plan of the mine. Through these steps, the mineral resource reserve estimation method can not only provide accurate resource quantity data, but also assess the uncertainty and risks associated with grade, thereby providing a solid foundation for the long-term planning and operation of the mine.

[0114] Preferably, step S4 comprises the following steps:

[0115] Step S41: collecting mining and mineral processing parameters, and establishing a mining numerical model and a mineral processing numerical model, thereby obtaining a process numerical model;

[0116] The process of collecting mining and mineral processing parameters in the embodiment of the present invention includes a comprehensive investigation of relevant literature, technical specifications and industry standards to determine the parameter range applicable to the target ore deposit. For example, for the mining process of copper ore, the main parameters include mining methods (such as open-pit or underground mining), recovery rate (usually between 85%-95%), equipment type (such as excavators, transport vehicles, etc.) and crushing, grinding and flotation process parameters (such as flotation agent dosage, grinding fineness, etc.) in the mineral processing process. On this basis, a mining numerical model and a mineral processing numerical model are established, and professional software (such as GEOVIA Whittle or Simul8) is used for simulation modeling to obtain a comprehensive process numerical model, which reflects the production potential and technical feasibility under different mining and mineral processing conditions.

[0117] Step S42: designing multiple mining plans according to the preliminary resource classification model, thereby obtaining a mining plan set, wherein the mining plan includes different mining sequences, mining methods and production scales;

[0118] When designing multiple mining plans based on the preliminary resource classification model, the embodiment of the present invention first needs to analyze the characteristics of different grade areas, and formulate multiple mining strategies based on market demand, economic benefits and technical conditions. For example, plan A can be designed to give priority to mining high-grade areas, plan B can be a mixed mode of layered mining, and plan C can be a mining method suitable for low-grade ore bodies. Each plan also needs to define in detail the mining sequence (such as mining the east side first and then the west side), the mining method (such as the longwall method for underground mining or the stripping method for open-pit mining), and the production scale (such as the average daily ore processing volume of 500 tons or 1,000 tons). Through the design of these plans, a mining plan set is generated for subsequent simulation and analysis.

[0119] Step S43: numerically simulate the mining and ore dressing process of the mining plan set according to the process numerical model, so as to obtain production simulation data;

[0120] When numerically simulating the mining and mineral processing processes of the mining scheme set, the embodiment of the present invention uses a process numerical model to simulate the entire process through professional simulation software (such as MODSIM or Aspen Plus). The specific operation includes inputting the parameters of each scheme, such as mining volume, ore grade, mineral processing process conditions, etc. Based on these parameters, the system simulates the flow, loss and recovery of ore in the entire production process and generates production simulation data. These data usually include the output, processing volume, loss, etc. of each stage. For example, scheme A may show an average daily mining volume of 600 tons and a mineral processing recovery rate of 90% in the simulation, while the mineral processing recovery rate of scheme B under the same conditions may be 85%.

[0121] Step S44: Performing technical index analysis on the production simulation data to obtain technical index evaluation data, wherein the technical indexes include recovery rate, depletion rate and mineral processing recovery rate;

[0122] When the embodiment of the present invention performs technical index analysis on the production simulation data, key indexes such as recovery rate, depletion rate and mineral processing recovery rate are focused on. The specific analysis steps include statistics and comparison of the simulation results. The recovery rate can be calculated by the ratio of the actual mining volume to the exploitable resource volume, and the depletion rate is evaluated by comparing the grade changes of the original ore and the selected ore. The index data table of each scheme is generated using professional software (such as Excel or Minitab), which is convenient for intuitively comparing the technical and economic feasibility of different schemes. For example, the recovery rate of scheme A is 92% and the depletion rate is 3%, while the recovery rate of scheme B is 90% and the depletion rate is 5%. Through these technical indicators, decision makers are helped to evaluate the feasibility of each scheme.

[0123] Step S45: optimizing the mining and beneficiation process parameters of the production simulation data based on the technical indicator evaluation data, thereby obtaining an optimized process parameter set;

[0124] When optimizing mining and mineral processing parameters based on technical indicator evaluation data, the embodiment of the present invention adopts an optimization algorithm such as response surface method (RSM) or genetic algorithm (GA) to achieve the best production effect by adjusting process parameters (such as grinding fineness, flotation agent dosage, etc.). In specific operations, the optimization goal is first defined, such as maximizing the mineral processing recovery rate or minimizing the production cost. Then, through multiple simulations, the effects of different parameter combinations are evaluated, and the best parameter set is selected. For example, during the optimization process, it was found that when the grinding fineness was adjusted to -200 mesh, the mineral processing recovery rate was increased to 92%, while the production cost was reduced by 5%. Finally, the optimized process parameter set is generated to provide data support for subsequent simulations and decisions.

[0125] Step S46: performing a secondary simulation of the production process according to the optimized process parameter set, and conducting a feasibility assessment based on geological conditions and engineering difficulty technology, thereby obtaining a technical feasibility assessment report;

[0126] When the embodiment of the present invention performs secondary simulation of the production process according to the optimized process parameter set, an advanced simulation tool (such as Arena or FlexSim) is used to perform detailed process simulation. The operation steps include inputting the optimized parameters, performing a comprehensive production process simulation, and analyzing the production feasibility under different geological conditions and engineering difficulties. This step is intended to evaluate the actual performance of the optimization scheme under a real mining environment. For example, it was found in the secondary simulation that due to changes in geological conditions, the production recovery rate may drop to 88%, while the corresponding mineral processing recovery rate remains above 90%. Combining these results, a technical feasibility evaluation report is generated and provided to the decision-making team for comprehensive evaluation.

[0127] Step S47: Select the best mining plan for the optimized process parameter set according to the technical feasibility assessment report, so as to obtain mining plan data.

[0128] The embodiment of the present invention comprehensively considers multiple factors such as economic benefits, technical feasibility and environmental impact when selecting the optimal mining plan based on the technical feasibility evaluation report. In the specific operation, an expert team is organized to discuss and score each plan, focusing on analyzing the rate of return, risks and potential problems of each plan. For example, although Plan A has a higher recovery rate, it has a lower final score due to an unsatisfactory environmental impact assessment; although Plan B has a slightly lower recovery rate, its economic benefits and environmental impact assessment are relatively ideal. Therefore, after a comprehensive evaluation, Plan B is selected as the final mining plan, and the final mining plan data is generated to ensure the efficiency and sustainability of the mining process.

[0129] The present invention collects and establishes numerical models of mining and mineral processing parameters, which helps to digitize and model the actual process, and provides an accurate theoretical basis for subsequent simulation and optimization; the establishment of process numerical models can simulate the influence of different process conditions on production effects, and provide a basis for process improvement and technological innovation. Designing multiple mining plans can compare the advantages and disadvantages of different plans, and provide decision makers with diversified choices; the formulation of mining plan sets helps to evaluate the comprehensive effects of different mining strategies on resource utilization, cost control and environmental impact. Through numerical simulation of mining and mineral processing processes, production results can be predicted before actual production, reducing uncertainty and risks in actual production; production simulation data provides a comprehensive evaluation of different mining plans, which helps to identify potential problems and improvement points. Technical indicator analysis provides a quantitative evaluation of mining and mineral processing efficiency, helping decision makers understand the current process level and improvement direction; technical indicator evaluation data is an important reference for optimizing process parameters, improving resource utilization and reducing costs. Optimizing process parameters based on evaluation data can improve production efficiency and product quality, and reduce production costs; optimizing process parameter sets helps to achieve efficient resource utilization and environmental protection goals. Secondary simulation and feasibility assessment can verify the actual application effect of the optimized process parameter set and ensure the reliability and effectiveness of the technical solution; the technical feasibility assessment report provides key technical support and decision-making basis for the final selection of the mining solution. Selecting the optimal mining solution based on the technical feasibility assessment report helps ensure the economy and sustainability of mine development; the determination of mining solution data provides clear guidance and planning for the actual mining activities of the mine. Through these steps, the mineral resource reserve assessment method can not only accurately assess the amount of resources, but also provide scientific and reasonable process design and technical solutions for the actual mining activities of the mine, thereby improving the efficiency and benefits of mine development and reducing risks and costs.

[0130] Preferably, step S5 comprises the following steps:

[0131] Step S51: conducting mining loss and dilution assessment according to the mining plan data, and revising the proven, controlled and inferred resources in the preliminary resource classification model, thereby obtaining revised resource data;

[0132] When the embodiment of the present invention evaluates mining losses and dilution according to mining scheme data, it is first necessary to define specific standards for losses and dilution. For example, mining losses can be defined as the amount of ore that cannot be mined due to technical or operational errors during the mining process, and dilution refers to the situation where valuable components in the ore are lost during the beneficiation process. By analyzing the preliminary resource classification model, the resource quantities of different categories (proven, controlled, and inferred) are calculated, and software tools (such as Surpac or MineSight) are used for modeling and simulation to quantitatively evaluate the loss and dilution ratio of each area. In this process, for each resource category, it is assumed that the proven resource quantity is 1 million tons, the controlled resource quantity is 800,000 tons, and the inferred resource quantity is 500,000 tons. According to the evaluation results, it is assumed that the loss rate is 5% and the dilution rate is 10%, and finally the revised resource quantity data is obtained, for example, the proven resource quantity is revised to 950,000 tons, the controlled resource quantity is revised to 720,000 tons, and the inferred resource quantity is revised to 450,000 tons.

[0133] Step S52: performing restricted mining resource correction on the resource quantity data according to the pre-acquired restricted mining data, thereby obtaining a resource quantity classification model;

[0134] When the embodiment of the present invention corrects the restricted mining resources of the resource quantity data according to the pre-acquired restricted mining data, it is necessary to first obtain relevant restrictive conditions, such as laws and regulations, environmental protection requirements and mining rights restrictions. Then, these restrictive conditions are input into the resource quantity classification model. For example, suppose that a certain area is restricted from mining due to environmental protection regulations, resulting in a 20% reduction in the exploitable area. Using geological modeling software for spatial analysis, the revised resource quantity data needs to be re-evaluated according to the restrictive conditions, and finally an updated resource quantity classification model is generated, which may result in a further reduction in the proven resources to 850,000 tons, the controlled resources to 650,000 tons, and the inferred resources remaining unchanged.

[0135] Step S53: extracting proven reserves and possible reserves from the resource classification model according to the mineral resource reserve classification standard NI 43-101, thereby converting it into a recoverable reserve model;

[0136] In the embodiment of the present invention, when the proven reserves and possible reserves are extracted from the resource classification model according to the mineral resource reserve classification standard NI 43-101, the resources need to be strictly classified according to the specific requirements of the standard. First, the revised resource data is compared with the NI 43-101 standard to determine whether it meets the conditions of proven and possible reserves. Proven reserves require sufficient geological information, economic feasibility and mining feasibility, while possible reserves require relatively low geological certainty. Assume that after comparison, it is found that 850,000 tons of proven reserves meet the standards, and the possible reserves are 600,000 tons. Use special resource assessment software (such as Gemcom) to convert data and finally obtain a recoverable reserve model.

[0137] Step S54: determining the boundary conditions of recoverable reserves according to the recoverable reserves model, and generating recoverable reserves data;

[0138] When the embodiment of the present invention determines the boundary conditions of recoverable reserves based on the recoverable reserves model, it is first necessary to analyze the model in detail to confirm the spatial distribution and boundaries of various types of resources. Using the geological information system (GIS) software, the spatial data of the recoverable reserves model is input, and spatial analysis is performed to generate the boundary conditions of recoverable reserves. For example, it is assumed that the boundaries of recoverable reserves in a certain area are determined to be an ore body range of 100 meters on the east side, 50 meters on the west side, 75 meters on the south side, and 30 meters on the north side. By establishing a three-dimensional model, recoverable reserves data is finally generated, showing that the recoverable reserves in the area are 800,000 tons.

[0139] Step S55: Conduct sensitivity analysis on the recoverable reserve data, and perform risk assessment and uncertainty quantification to obtain the final mineral resource reserve assessment report.

[0140] When the embodiment of the present invention performs sensitivity analysis on recoverable reserves data, key influencing factors such as ore grade, recovery rate, mining cost, etc. are first determined. A sensitivity analysis method (such as stepwise change method or Monte Carlo simulation) is used to systematically analyze these parameters. The impact of changes in each parameter is simulated and its impact on recoverable reserves is recorded. For example, it is assumed that the impact of a grade fluctuation of ±5% on recoverable reserves is ±100,000 tons, and the impact of changes in recovery rate on recoverable reserves is ±80,000 tons. Finally, risk assessment and uncertainty quantification are performed in combination with these analysis results to form a final mineral resource reserve assessment report, which describes in detail the degree of impact of each factor and its risk level to provide a basis for decision-making.

[0141] The assessment of mining losses and dilution in the present invention helps to optimize mining plans, reduce resource waste, and improve resource utilization; the correction of the preliminary resource classification model makes the resource data closer to the actual mining conditions, and enhances the accuracy and practicality of the assessment. The application of restricted mining data ensures that the resource data meets the actual mining conditions and avoids over-assessment; the generation of the resource classification model provides an accurate data basis for subsequent reserve conversion and enhances the reliability of the assessment. The classification of resources according to the NI 43-101 standard ensures the international recognition of the assessment results and facilitates cross-border transactions and investments; the establishment of the recoverable reserve model provides clear guidance for the actual mining activities of the mine and helps to improve mining efficiency and economic benefits. Determining the boundary conditions of recoverable reserves and generating data helps to clarify the scale and scope of mine development and provide a basis for mine design and planning; the generation of recoverable reserve data provides key information for the economic benefit analysis and risk assessment of the mine. Sensitivity analysis and risk assessment help to identify and quantify uncertainties and potential risks in the assessment process; the quantification of uncertainty provides decision makers with in-depth insights into the reliability of resource reserves and enhances the transparency and credibility of the assessment report. Through these steps, the mineral resource reserve assessment method can provide comprehensive and accurate data support for mine development, while identifying and quantifying risks, and providing a scientific basis for investment decisions and mine management.

[0142] Preferably, step S55 includes the following steps:

[0143] Step S551: using a single factor sensitivity analysis method to perform parameter changes on recoverable reserves data based on metal prices, mining costs, ore dressing recovery rates, and cut-off grades, and calculating the degree of influence of the parameters of the changing factors on recoverable reserves one by one, thereby obtaining sensitivity analysis data;

[0144] When the embodiment of the present invention uses a single factor sensitivity analysis method to analyze recoverable reserves data, it is first necessary to determine the key factors affecting recoverable reserves, including metal prices, mining costs, ore dressing recovery rates and cut-off grades. The range of each factor needs to be set according to market data or historical data. For example, assuming that the metal price range is 1,500 to 2,000 U.S. dollars / ton, the mining cost changes to 50 to 100 U.S. dollars / ton, the ore dressing recovery rate is from 70% to 90%, and the cut-off grade is set to 0.5 to 1.0 grams / ton. Then, calculate the impact of each factor on recoverable reserves one by one, use Excel or professional sensitivity analysis tools to gradually adjust each parameter, and record the corresponding recoverable reserves changes. For example, when the metal price is raised to 1,800 U.S. dollars / ton, the recoverable reserves increase by 100,000 tons, and the sensitivity analysis data of each factor is obtained after completion.

[0145] Step S552: setting the probability distribution of metal price, mining cost, ore dressing recovery rate and cut-off grade by Monte Carlo simulation method, and obtaining the probability distribution characteristics of recoverable reserves by a large number of random sampling calculations;

[0146] The embodiment of the present invention uses the Monte Carlo simulation method to set the probability distribution of metal prices, mining costs, mineral processing recovery rates and cut-off grades. First, select a suitable probability distribution model, such as normal distribution or triangular distribution, and set the distribution parameters of each factor. For example, the metal price can be set to a normal distribution with a mean of $1,750 / ton and a standard deviation of $100 / ton; the mining cost is a mean of $75 / ton and a standard deviation of $15 / ton; the mineral processing recovery rate is a triangular distribution of 70% to 90%; the cut-off grade is a normal distribution with a mean of 0.75 g / ton and a standard deviation of 0.1 g / ton. Monte Carlo simulation software is used to perform a large number of (e.g., 10,000) random sampling calculations to generate the recoverable reserves results for each sampling, and finally obtain a probability distribution characteristic diagram of recoverable reserves.

[0147] Step S553: ​​Calculate the expected value, standard deviation, and confidence interval of recoverable reserves according to the probability distribution characteristics, and quantify them into uncertainty data of recoverable reserves;

[0148] The embodiment of the present invention calculates statistical indicators based on the probability distribution characteristics of recoverable reserves obtained from the Monte Carlo simulation. The simulation results are analyzed using statistical software (such as the pandas library of R or Python) to calculate the expected value and standard deviation of recoverable reserves. For example, the expected value of recoverable reserves calculated from the simulation results is 1.2 million tons, and the standard deviation is 150,000 tons. At the same time, the confidence interval is determined, such as the 95% confidence interval is [1.05 million tons, 1.35 million tons]. These statistical indicators will be quantified as uncertainty data of recoverable reserves, providing a basis for subsequent decision-making.

[0149] Step S554: Determine the main risk factors based on the sensitivity analysis data, and conduct a project feasibility impact assessment on the main risk factors to obtain a risk assessment list;

[0150] When determining the main risk factors based on the sensitivity analysis data, the embodiment of the present invention first analyzes the sensitivity analysis results obtained previously to identify the factors that have the greatest impact on recoverable reserves, such as metal price fluctuations and changes in mining costs. Next, a project feasibility impact assessment is conducted for these main risk factors. For example, a scenario analysis is conducted to assess the changes in recoverable reserves under low metal prices (such as $1,400 / ton) and high mining costs (such as $90 / ton), and a risk assessment list is formed, listing the degree of impact and recommended countermeasures, such as cost control or adjustment of mining strategies.

[0151] Step S555: Prepare a report on the sensitivity analysis data, uncertainty data and risk assessment list to obtain the final mineral resource reserve assessment report.

[0152] When the embodiment of the present invention prepares a comprehensive report on sensitivity analysis data, uncertainty data and risk assessment checklist, it first summarizes the previous data and analysis results and organizes them into a systematic report structure, including introduction, methods, results and discussion, conclusions and suggestions. The report describes the implementation method and results of each step in detail, with charts and data support. For example, the report includes a probability distribution diagram of recoverable reserves, a sensitivity analysis table and a risk assessment checklist, and explains each data. Finally, a complete mineral resource reserve assessment report is formed to provide detailed information and decision support for stakeholders.

[0153] The single factor sensitivity analysis of the present invention can identify the factors that have the greatest impact on recoverable reserves, such as metal prices, mining costs, etc., thereby providing key reference information for decision makers; by analyzing the changes in each factor one by one, we can better understand how these factors affect the economic feasibility of the project and enhance the ability to adapt to future changes. Monte Carlo simulation provides a method for prediction under uncertain conditions, simulating possible results through a large number of random sampling, increasing the reliability of predictions; this method can reveal the interaction between different factors and their combined impact on recoverable reserves, providing a powerful tool for risk management. Calculating statistical indicators such as expected value, standard deviation and confidence interval provides a quantitative measure for the uncertainty of recoverable reserves; these statistical data help assess the risk level of the project and provide important risk assessment information for investment decisions. Determining the main risk factors and conducting project feasibility impact assessments on them helps identify key risk points that may be encountered during project implementation; the risk assessment checklist provides project managers with a systematic risk identification and management method, which helps reduce the impact of potential risks on project success. The sensitivity analysis data, uncertainty data and risk assessment list are compiled into a comprehensive report to provide a comprehensive assessment result for all stakeholders; the final mineral resource reserve assessment report not only includes quantitative analysis results, but also provides in-depth insights into the potential risks of the project, providing a solid foundation for project decision-making and planning. Through these steps, the mineral resource reserve assessment method can not only provide accurate reserve data, but also assess the uncertainty and risks associated with reserves, providing a scientific basis for investment decisions and mine management.

[0154] The present invention provides a mineral resource reserve assessment system based on a digital model, which is used to execute the above-mentioned mineral resource reserve assessment method based on a digital model. The mineral resource reserve assessment system based on a digital model comprises:

[0155] The geological modeling module is used to obtain multi-source geological data using geological surveys, drilling and geophysical exploration technologies, and to express geological bodies and construct models based on implicit modeling technology based on the multi-source geological data, thereby obtaining a three-dimensional geological model of the ore deposit; extracting the geological characteristics of the ore deposit based on lithology, structure and mineralization information from the three-dimensional geological model of the ore deposit, thereby obtaining geological characteristic data;

[0156] The grade estimation module is used to perform grade estimation based on the Kriging method according to geological feature data, thereby generating a three-dimensional grade distribution model; and to perform uncertainty analysis on the three-dimensional grade distribution model, thereby obtaining a grade estimation error distribution map;

[0157] The resource classification module is used to calculate the resource quantity under different grade boundary conditions based on the three-dimensional grade distribution model and the grade estimation error distribution map, and classify the resource quantity based on the grade position confidence level, thereby generating a preliminary resource quantity classification model, where the resource quantity classification includes proven, controlled and inferred;

[0158] The production simulation module is used to obtain the process numerical model of mining and ore dressing; simulate the production process under different mining plans according to the preliminary resource classification model and the process numerical model to obtain production simulation data; conduct technical feasibility assessment on the production simulation data and optimize the process parameters to obtain mining plan data;

[0159] The reserve assessment module is used to amend the preliminary resource classification model for mineable minerals according to the mining plan data, and to convert reserves based on the revised resource classification model to obtain recoverable reserve data; perform sensitivity analysis on the recoverable reserve data, and conduct risk assessment and uncertainty quantification to obtain the final mineral resource reserve assessment report.

[0160] The present invention uses data obtained from geological surveys, drilling and geophysical exploration techniques to provide comprehensive basic information for establishing geological models; the application of implicit modeling technology makes the expression of geological bodies more consistent with actual geological conditions, improving the accuracy and practicality of the model; the construction of a three-dimensional geological model helps to better understand the spatial structure and geological characteristics of the ore deposit, and provides an important basis for subsequent grade estimation and resource assessment. As a mature geostatistical method, Kriging can reasonably perform spatial interpolation on the grade of the ore deposit and generate a continuous three-dimensional grade distribution model; uncertainty analysis evaluates the reliability of grade estimation, identifies potential risks in the estimation process, and provides an important reference for decision makers. Calculating resources according to different grade boundary conditions can adapt to market changes and changes in mining costs, and provide flexible solutions for mine planning; resource classification based on grade position confidence level meets international standards for geological exploration, and improves the standardization and professionalism of resource assessment. The acquisition and application of process numerical models can simulate various situations in the actual production process, improve the pertinence and practicality of mining plans; technical feasibility evaluation of production simulation data and process parameter optimization help improve mine production efficiency and economic benefits. The correction of mineable minerals and the conversion of reserves take into account the actual mining conditions, making the reserve data more consistent with production reality and improving the practicality of reserve assessment; sensitivity analysis and risk assessment help identify the key factors affecting reserve assessment and provide a basis for risk management and decision-making. Combining the effects of these modules, the entire mineral resource assessment system can provide a solid geological and statistical foundation for mine planning, design, production and management. It can not only provide accurate resource data, but also assess the uncertainty and risks associated with grade, providing a scientific basis for investment decisions and mine management.

[0161] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0162] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for evaluating mineral resource reserves based on a digital model, characterized in that: The following steps are involved: Step S1: using geological survey, drilling and geophysical exploration technology to obtain multi-source geological data, and based on the multi-source geological data, perform geological body expression and model construction based on implicit modeling technology to obtain a three-dimensional geological model of the ore deposit; Extracting the geological characteristics of the ore deposit based on lithology, structure and mineralization information from the 3D ore deposit model to obtain geological characteristic data, step S1 includes: Step S11: Conducting geological survey on the target mineral to obtain surface geological information data; Step S12: Performing drilling operations on the target minerals to obtain drill core data; and conducting geophysical exploration work on the target minerals to obtain geographic exploration data; Step S13: performing data standardization processing on surface geological information data, drilling core data and geographic exploration data, and unifying the coordinate system and units, thereby obtaining multi-source geological data; Step S14: performing data interpolation and gridding processing on multi-source geological data to obtain gridded geological data; Step S15: Performing geological body expression and model construction based on implicit modeling technology according to the gridded geological data, thereby obtaining a three-dimensional geological model of the ore deposit. Step S15 includes: Step S151: Determine the spatial distribution of the main stratigraphic units based on the gridded geological data, thereby constructing a ore deposit stratigraphic model; Step S152: extracting main structural features based on discontinuous features based on gridded geological data, thereby establishing a mineral deposit structural model; Step S153: dividing the distribution ranges of different lithological units according to the gridded geological data, thereby obtaining a lithological unit model of the ore deposit; Step S154: using implicit modeling technology to convert the ore deposit stratigraphic model, ore deposit structural model and ore deposit lithological unit model into mathematical functions, and constructing implicit function fields of lithology, structure and mineralization, thereby obtaining implicit function field data; Step S155: generating a three-dimensional geological body based on an interpolation algorithm according to the implicit function field data, thereby obtaining a three-dimensional geological model of the ore deposit; Step S16: extracting geological features of the ore deposit based on lithology, structure and mineralization information from the 3D ore deposit model to obtain geological feature data. Step S16 includes: Step S161: extracting lithology distribution characteristics from the three-dimensional geological model of the ore deposit, and identifying the main lithology units and their contact relationships, thereby obtaining lithology characteristic data; Step S162: extracting structural information from the three-dimensional geological model of the ore deposit, and determining the spatial distribution of the main structural lines and surfaces, thereby obtaining structural characteristic data; Step S163: Analyze the mineralization characteristics of the three-dimensional geological model of the ore deposit, divide the distribution range of the mineralization zone and the alteration zone, and thus generate mineralization characteristic data; Step S164: integrating the lithology characteristic data, the structural characteristic data and the mineralization characteristic data based on spatial relationships, thereby obtaining geological characteristic data; Step S2: performing grade estimation based on the Kriging method according to the geological characteristic data, thereby generating a three-dimensional grade distribution model; performing uncertainty analysis on the three-dimensional grade distribution model, thereby obtaining a grade estimation error distribution map; Step S3: Calculate the resources under different grade boundary conditions according to the three-dimensional grade distribution model and the grade estimation error distribution map, and classify the resources based on the grade position confidence level, thereby generating a preliminary resource classification model, wherein the resource classification includes proven, controlled and inferred; Step S4: obtaining a mining and mineral processing process numerical model; simulating the production process under different mining plans according to the preliminary resource classification model and the process numerical model, thereby obtaining production simulation data; conducting a technical feasibility assessment on the production simulation data, and optimizing the process parameters, thereby obtaining mining plan data; Step S5: According to the mining plan data, the preliminary resource classification model is modified for mineable minerals, and reserves are converted based on the modified resource classification model to obtain recoverable reserve data; sensitivity analysis is performed on the recoverable reserve data, and risk assessment and uncertainty quantification are performed to obtain the final mineral resource reserve assessment report.

2. The method for evaluating mineral resource reserves based on a digital model according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: spatially analyzing the geological characteristic data and determining the spatial range and grid size for grade estimation, thereby obtaining estimated grid parameter data; Step S22: performing a variogram analysis based on the geological feature data and determining a spatial correlation structure, thereby obtaining a variogram model; Step S23: using the Kriging method to perform grade estimation based on the estimated grid parameter data and the variogram model, and perform geological constraint processing to generate a three-dimensional grade distribution model; Step S24: using conditional simulation technology to generate multiple equally probable grade realizations according to the three-dimensional grade distribution model, thereby obtaining a grade simulation set; Step S25: Calculate the grade estimation error of each grid cell based on the grade simulation set, so as to obtain a grade estimation error distribution map.

3. The method for evaluating mineral resource reserves based on a digital model according to claim 2, characterized in that: Step S3 includes the following steps: Step S31: setting a plurality of grade boundary conditions according to the three-dimensional grade distribution model, thereby obtaining a grade boundary parameter set; Step S32: performing volume calculation on the three-dimensional grade distribution model based on the grade boundary parameter set, thereby obtaining resource quantity data under different grade boundary conditions; Step S33: calculating the grade position confidence level of each grid unit according to the grade estimation error distribution map, thereby obtaining a grade position confidence distribution model; Step S34: spatially divide the resources into three categories: proven, controlled and inferred, according to the preset confidence threshold and the product location confidence distribution model, so as to generate a preliminary resource classification model.

4. The method for evaluating mineral resource reserves based on a digital model according to claim 3, characterized in that: Step S4 includes the following steps: Step S41: collecting mining and mineral processing parameters, and establishing a mining numerical model and a mineral processing numerical model, thereby obtaining a process numerical model; Step S42: designing multiple mining plans according to the preliminary resource classification model, thereby obtaining a mining plan set, wherein the mining plan includes different mining sequences, mining methods and production scales; Step S43: numerically simulate the mining and ore dressing process of the mining plan set according to the process numerical model, so as to obtain production simulation data; Step S44: Performing technical index analysis on the production simulation data to obtain technical index evaluation data, wherein the technical indexes include recovery rate, depletion rate and mineral processing recovery rate; Step S45: optimizing the mining and beneficiation process parameters of the production simulation data based on the technical indicator evaluation data, thereby obtaining an optimized process parameter set; Step S46: performing a secondary simulation of the production process according to the optimized process parameter set, and conducting a feasibility assessment based on geological conditions and engineering difficulty technology, thereby obtaining a technical feasibility assessment report; Step S47: Select the best mining plan for the optimized process parameter set according to the technical feasibility assessment report, so as to obtain mining plan data.

5. The method for evaluating mineral resource reserves based on a digital model according to claim 4, characterized in that: Step S5 includes the following steps: Step S51: conducting mining loss and dilution assessment according to the mining plan data, and revising the proven, controlled and inferred resources in the preliminary resource classification model, thereby obtaining revised resource data; Step S52: performing restricted mining resource correction on the resource quantity data according to the pre-acquired restricted mining data, thereby obtaining a resource quantity classification model; Step S53: extracting proven reserves and possible reserves from the resource classification model according to the mineral resource reserve classification standard NI 43-101, thereby converting it into a recoverable reserve model; Step S54: determining the boundary conditions of recoverable reserves according to the recoverable reserves model, and generating recoverable reserves data; Step S55: Conduct sensitivity analysis on the recoverable reserve data, and perform risk assessment and uncertainty quantification to obtain the final mineral resource reserve assessment report.

6. The method for evaluating mineral resource reserves based on a digital model according to claim 5, characterized in that: Step S55 includes the following steps: Step S551: using a single factor sensitivity analysis method to perform parameter changes on recoverable reserves data based on metal prices, mining costs, ore dressing recovery rates, and cut-off grades, and calculating the degree of influence of the parameters of the changing factors on recoverable reserves one by one, thereby obtaining sensitivity analysis data; Step S552: setting the probability distribution of metal price, mining cost, ore dressing recovery rate and cut-off grade by Monte Carlo simulation method, and obtaining the probability distribution characteristics of recoverable reserves by a large number of random sampling calculations; Step S553: ​​Calculate the expected value, standard deviation, and confidence interval of recoverable reserves according to the probability distribution characteristics, and quantify them into uncertainty data of recoverable reserves; Step S554: Determine the main risk factors based on the sensitivity analysis data, and conduct a project feasibility impact assessment on the main risk factors to obtain a risk assessment list; Step S555: Prepare a report on the sensitivity analysis data, uncertainty data and risk assessment list to obtain the final mineral resource reserve assessment report.

7. A mineral resource reserve assessment system based on a digital model, characterized in that: Used to execute the mineral resource reserve assessment method based on the digital model as claimed in claim 1, the mineral resource reserve assessment system based on the digital model comprises: The geological modeling module is used to obtain multi-source geological data using geological surveys, drilling and geophysical exploration technologies, and to express geological bodies and construct models based on implicit modeling technology based on the multi-source geological data, thereby obtaining a three-dimensional geological model of the ore deposit; extracting the geological characteristics of the ore deposit based on lithology, structure and mineralization information from the three-dimensional geological model of the ore deposit, thereby obtaining geological characteristic data; The grade estimation module is used to perform grade estimation based on the Kriging method according to geological feature data, thereby generating a three-dimensional grade distribution model; and to perform uncertainty analysis on the three-dimensional grade distribution model, thereby obtaining a grade estimation error distribution map; The resource classification module is used to calculate the resource quantity under different grade boundary conditions based on the three-dimensional grade distribution model and the grade estimation error distribution map, and classify the resource quantity based on the grade position confidence level, thereby generating a preliminary resource quantity classification model, where the resource quantity classification includes proven, controlled and inferred; The production simulation module is used to obtain the process numerical model of mining and ore dressing; simulate the production process under different mining plans according to the preliminary resource classification model and the process numerical model to obtain production simulation data; conduct technical feasibility assessment on the production simulation data and optimize the process parameters to obtain mining plan data; The reserve assessment module is used to amend the preliminary resource classification model for mineable minerals according to the mining plan data, and to convert reserves based on the revised resource classification model to obtain recoverable reserve data; perform sensitivity analysis on the recoverable reserve data, and conduct risk assessment and uncertainty quantification to obtain the final mineral resource reserve assessment report.

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