Ground-mining-selection integrated ore body development method and system based on ore body gene characteristics

Abstract

The invention discloses a land-mining-selection integrated ore body development method and system based on ore body gene characteristics, and relates to the technical field of mining production. The method comprises the steps of obtaining geological data of a target mining area; based on the geological data, constructing a three-dimensional digital model for representing the gene characteristics of the ore body; the gene characteristics of the ore body are represented at least through the spatial geometric morphology of the ore body, element grade distribution and mineral composition spatial distribution; performing space mineralization domain division on the three-dimensional digital model according to mineral attributes and process mineralogy characteristics in ore body gene characteristics; aiming at each divided mineralization region, adapting to a beneficiation process flow and determining a corresponding beneficiation process constraint condition; on the basis of beneficiation process constraint conditions, a mining ore blending model is constructed; and making a mining and production plan according to the mining and ore blending model. According to the method, professional barriers among geological, mining and mineral separation links can be broken, and collaborative optimization and intelligent decision-making of the whole process are realized.

Description

Technical Field

[0001] This invention relates to the field of mining production technology, and in particular to an integrated method and system for ore body development based on the genetic characteristics of ore bodies, encompassing mining, beneficiation, and geological processes. Background Technology

[0002] Mineral resources, as a crucial material foundation for national economic and social development, have long faced severe challenges in their efficient development. Due to the deep burial and uneven distribution of ore bodies, geological information data is vast and complex, making effective integration and sharing throughout the development process difficult. In traditional development models, the three stages of geological exploration, mining, and mineral processing operate independently, creating professional barriers. This prevents the high-precision ore deposit models (such as grade and ore body morphology) obtained during the geological exploration stage from directly and effectively guiding subsequent mining design and beneficiation plant process optimization.

[0003] This disconnect between different stages has caused a series of chain problems in actual production. Ores of different grades and properties are often mined together, and high-grade and low-grade ores, as well as easily beneficiated and difficult-to-benefit ores, are not separated and processed. This not only significantly increases the difficulty and cost of subsequent beneficiation operations, but also leads to the abandonment of some low-grade resources due to poor economic viability, resulting in low comprehensive resource utilization. At the same time, mining operations lack real-time feedback on beneficiation process requirements, resulting in poor compatibility between the mined ore and beneficiation equipment in terms of particle size and composition; while beneficiation process parameters are often fixed and cannot predict or adapt to dynamic changes in the properties of the incoming ore.

[0004] This lack of coordination across the entire process ultimately leads to low resource recovery rates, large fluctuations in mineral processing indicators, unstable concentrate quality, and persistently high production costs. This not only severely restricts mining efficiency but also makes it difficult to meet the strategic needs of green mine construction and the development of deep and complex resources. Therefore, how to overcome information barriers between geology, mining, and mineral processing to achieve efficient coordination and dynamic optimization across the entire process has become a core technical problem that urgently needs to be solved in the field of mineral resource development. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides an integrated ore body development method and system based on the genetic characteristics of ore bodies, which can break down the professional barriers between geology, mining, and mineral processing, and achieve collaborative optimization and intelligent decision-making throughout the entire process.

[0006] To achieve the above objectives, this invention provides an integrated orebody development method based on orebody genetic characteristics, comprising: Obtain geological data of the target mining area; the geological data includes geological exploration data, engineering exposure data, and ore sample test and analysis data; Based on the geological data, a three-dimensional digital model is constructed to characterize the genetic characteristics of the ore body; the genetic characteristics of the ore body are characterized at least by the spatial geometry of the ore body, the distribution of elemental grades, and the spatial distribution of mineral composition. Based on the mineral properties and process mineralogical characteristics in the ore body's genetic features, the three-dimensional digital model is divided into spatial mineralization domains; For each identified mineralization zone, adapt the mineral processing flow and clarify the corresponding mineral processing constraints. Based on the constraints of the mineral processing technology, a mining and ore blending model is constructed; Based on the mining and ore blending model, a mining and production plan is formulated.

[0007] Optionally, based on the geological data, a three-dimensional digital model is constructed, including: The geological data is preprocessed; Based on the preprocessed data, a solid model of the spatial geometric morphology of the ore body is constructed. Using the spatial geometric morphology model of the ore body as a spatial constraint, a geostatistical method is used to construct an element grade distribution model; Based on the elemental grade distribution model and multidimensional feature data, a spatial distribution model of mineral composition is constructed; the multidimensional feature data includes process mineralogical identification test data and spectral analysis data. The spatial geometric morphology model, element grade distribution model, and mineral composition spatial distribution model of the ore body are integrated to form a three-dimensional digital model for characterizing the genetic properties of the ore body.

[0008] Optionally, for each identified mineralization zone, the following beneficiation process flow can be adapted: Based on the process mineralogical characteristics of each mineralized zone, mineral processing experiments were conducted. Based on the results of mineral processing experiments, an initial mineral processing flow was matched for each of the mineralization zones. Based on the spatial distribution of each mineralization domain, the mineralization domains that match the initial beneficiation process and are compatible with the beneficiation process are optimized by merging the process flow. Based on the results of the merging and optimization, the final beneficiation process flow adapted to each of the aforementioned mineralization domains is determined.

[0009] Optionally, based on the constraints of the mineral processing technology, a mining and ore blending model is constructed, including: The objective function is to maximize overall economic benefits. The boundary conditions are the spatial location of the mineralization zone, the constraints of mining capacity, and the constraints of the mineral processing technology. Based on the objective function and boundary conditions, a multi-objective optimization mathematical model is established; By solving the mathematical model, the optimal mining sequence and ore output ratio scheme are output.

[0010] Optionally, based on the mining and ore blending model, a mining and production plan is formulated, including: Based on the mining sequence and ore output ratio scheme output by the mining and ore blending model, the mining sequence of the ore block is determined; Based on the ore extraction ratio scheme, formulate a multi-timescale ore extraction plan that meets the constraints of the mineral processing technology; The ore extraction plan is verified and balanced with the mine production system capacity to generate an executable production operation plan.

[0011] Optionally, the mineral properties include the content of main elements, valuable elements, associated valuable elements, and harmful elements; the process mineralogical characteristics include the grain size of the target mineral and the type and content of gangue minerals.

[0012] Optionally, the method further includes: Obtain actual production information of the target mining area during the mining process; The three-dimensional digital model is updated based on the actual production information. The updated three-dimensional digital model is used to simultaneously optimize the mining and ore blending model, and a new mining and production plan is formulated.

[0013] This invention also provides an integrated orebody development system based on orebody genetic characteristics, comprising: The data acquisition unit is used to acquire geological data of the target mining area; the geological data includes geological exploration data, engineering exposure data, and ore sample test and analysis data. A three-dimensional modeling unit is used to construct a three-dimensional digital model to characterize the genetic characteristics of the ore body based on the geological data; the genetic characteristics of the ore body are characterized at least by the spatial geometry of the ore body, the distribution of elemental grades, and the spatial distribution of mineral composition. The mineralization domain division unit is used to divide the three-dimensional digital model into spatial mineralization domains based on the mineral properties and process mineralogical characteristics in the ore body's genetic characteristics. The process adaptation unit is used to adapt the mineral processing flow to each of the defined mineralization zones and to clarify the corresponding mineral processing constraints. The ore blending model construction unit is used to construct an ore blending model based on the constraints of the ore beneficiation process. The production planning unit is used to formulate mining and production plans based on the mining and ore blending model.

[0014] Optionally, the system further includes: The data acquisition unit is updated to acquire actual production information of the target mining area during the mining process; Update unit, used for: The three-dimensional digital model is updated based on the actual production information. The updated three-dimensional digital model is used to simultaneously optimize the mining and ore blending model, and a new mining and production plan is formulated.

[0015] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: The orebody development method based on the orebody's genetic characteristics, integrating geological, mining, and beneficiation processes, provides this invention. By constructing and integrating a three-dimensional digital model characterizing the orebody's genetic characteristics, it breaks down the professional barriers between geology, mining, and beneficiation in the traditional model. It transforms geological exploration data into a unified digital platform that carries the core genetic characteristics of the orebody, such as its spatial geometry, elemental grade distribution, and mineral composition spatial distribution, achieving efficient integration and sharing of information throughout the entire process. By dividing spatial mineralization domains based on mineral attributes and process mineralogical characteristics within the orebody's genetic characteristics, and adapting beneficiation processes to each mineralization domain, it effectively solves the problem of mixed mining of ores with different properties in traditional development. This enables precise classification and differentiated development of ores based on the orebody's genetic characteristics, significantly reducing the difficulty and cost of subsequent beneficiation operations and improving the comprehensive utilization rate of mineral resources. By optimizing the relationship between ore body genetic characteristics and mineral processing technology, a mining and ore blending model was constructed and a production plan was formulated. This ensured the precise matching between mining operations and mineral processing technology requirements, realized intelligent transmission from ore body genetics to production decisions, enabled mineral processing parameters to proactively adapt to changes in ore properties, stabilized mineral processing indicators, improved concentrate product quality, and ultimately achieved the dual goals of increasing resource recovery rate and reducing production costs. Attached Figure Description

[0016] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0017] Figure 1 This is a schematic diagram of the method flow for an integrated ore body development method based on ore body genetic characteristics, according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating an integrated mining and beneficiation development and utilization method based on ore body genetic characteristics, as shown in an embodiment of the present invention. Figure 3 This is a schematic diagram of the spatial distribution of the principal element (Tfe) as shown in an embodiment of the present invention; Figure 4This is a schematic diagram illustrating an example of the spatial distribution of co-occurring valuable elements (Co) according to an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the spatial distribution of harmful element (S) according to an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the spatial distribution of prime magnetite (Mfe) according to an embodiment of the present invention. Figure 7 This is a schematic diagram illustrating the spatial distribution of hematite (Tfe-mfe) according to an embodiment of the present invention. Figure 8 The following is a diagram illustrating the spatial mineralization zones of the early, middle, and late stages of the ore body, as shown in the embodiments of the present invention; wherein, a represents the early mineralization distribution zone of the ore body; b represents the middle mineralization distribution zone of the ore body; c represents the late mineralization distribution zone of the ore body; and d represents a comprehensive display of the early, middle, and late mineralization distribution zones of the ore body. Figure 9 This is a schematic diagram of the modular structure of an integrated ore body development system based on the genetic characteristics of ore bodies, as shown in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 , Figure 1 This is a schematic diagram of the process flow for an integrated ore body development method based on the genetic characteristics of the ore body, encompassing mining, beneficiation, and geological processes.

[0020] An integrated orebody development method based on the genetic characteristics of ore bodies, encompassing geological mining and beneficiation, includes: S101: Obtain geological data of the target mining area.

[0021] The geological data includes geological exploration data, engineering exposure data, and ore sample testing and analysis data.

[0022] "Geological exploration, mining, and beneficiation" refers to the complete industrial process of mineral resources, from exploration and discovery (geology) to extraction (mining) and processing and purification (mineral beneficiation). The purpose of this invention is to achieve "integration," connecting these three traditionally relatively independent links into a collaborative and intelligent whole.

[0023] To achieve integrated orebody development based on its genetic characteristics, encompassing mining, processing, and beneficiation, the first step is to acquire geological data of the target mining area. This aims to construct a comprehensive and accurate multi-dimensional information database to provide reliable data support for subsequent processing. In traditional development models, high-precision ore deposit models obtained during the geological exploration phase have failed to effectively guide mining design and beneficiation plant optimization, resulting in fragmented processes and low resource utilization. Therefore, this invention ensures the integrity, standardization, and accuracy of geological data from the outset, laying the foundation for collaborative optimization across the entire process.

[0024] The acquired geological data mainly includes geological exploration data, engineering exposure data, and ore sample testing and analysis data. Geological exploration data forms the basis for revealing the macroscopic characteristics of the ore body, encompassing borehole data, geological profile data, surveying data, and trench and geophysical / geochemical data, systematically depicting the spatial distribution and geological structural background of the ore body. Engineering exposure data originates from the mine development and preparation process, including shaft logging data and stope exposure data, enabling dynamic verification and correction of exploration inferences, and providing precise information on ore body boundaries, ore types, and lithological variations. Ore sample testing and analysis data deeply characterizes the intrinsic properties of the ore, including not only the content data of major elements, associated valuable elements, and harmful elements obtained through chemical analysis, but also the process mineralogical characteristics data obtained through mineral microscopy and spectral analysis, such as the grain size of target minerals and the types and contents of gangue minerals. These serve as the basis for analyzing the genetic characteristics of the ore body.

[0025] S102: Based on geological data, construct a three-dimensional digital model to characterize the genetic properties of ore bodies.

[0026] The genetic characteristics of the ore body are characterized at least through its spatial geometry, elemental grade distribution, and spatial distribution of mineral composition. The constructed three-dimensional digital model aims to transform multi-source heterogeneous geological information into a unified, visualized, and quantifiable comprehensive expression that characterizes the genetic characteristics of the ore body, forming a digital foundation that runs through the entire process. This provides a precise decision-making basis for subsequent spatial mineralization domain division, beneficiation process adaptation, and mining blending.

[0027] The spatial geometry of the ore body precisely depicts its external outline, occurrence, and spatial relationship with the surrounding rocks, defining its physical boundaries and internal structure. This represents a digital representation of the spatial distribution characteristics within the ore body's genetic properties. Elemental grade distribution, through multivariate geostatistical methods, reveals the distribution patterns and variations of major, associated, and harmful elements in three-dimensional space, representing a digital representation of elemental endowment and symbiotic relationships within the ore body's genetic properties. The spatial distribution of mineral composition further reveals the spatial occurrence and enrichment patterns of different minerals based on the transformation relationship between elemental combinations and mineral phases, representing a digital representation of the mineral composition and embedding characteristics within the ore body's genetic properties. These three elements together constitute a complete cognitive system from macroscopic morphology to microscopic composition, achieving a synergistic digital analysis and expression of the multi-dimensional connotations of the ore body's genetic properties.

[0028] By constructing an integrated 3D visualization and quantitative model centered on the genetic characteristics of ore bodies, the consistency of multidimensional geological data in spatial dimensions can be effectively ensured. Traditionally fragmented geological exploration data, engineering exposure data, and ore sample testing and analysis data are deeply integrated and precisely located based on their reflected genetic characteristics. The resulting model can dynamically display the grade and mineral distribution in different areas, supports arbitrary profile cutting and reserve calculation, and provides precise spatial data support for subsequent development decisions based on the genetic characteristics of ore bodies. Thus, by establishing a digital twin that truly reflects and quantitatively characterizes the genetic characteristics of ore bodies, a technological foundation is laid for breaking down professional barriers and achieving collaborative optimization across the entire process.

[0029] In one embodiment, the above-mentioned construction of a three-dimensional digital model based on geological data includes: Preprocess the geological data; Based on the preprocessed data, a solid model of the spatial geometric morphology of the ore body is constructed. Using the spatial geometric morphology of the ore body as a spatial constraint, a geostatistical model of element grade distribution is constructed. Based on the elemental grade distribution model and multidimensional feature data, a spatial distribution model of mineral composition is constructed; the multidimensional feature data includes process mineralogical identification test data and spectral analysis data. The spatial geometric morphology model of the ore body, the element grade distribution model, and the spatial distribution model of mineral composition are integrated to form a three-dimensional digital model.

[0030] In the specific implementation of constructing a three-dimensional digital model, the acquired geological data can first undergo systematic preprocessing. This includes validating various types of collected data, identifying and correcting outliers, standardizing formats, and unifying coordinate systems to eliminate errors and improve data consistency, providing clean and usable input for subsequent analysis and characterization of the ore body's genetic characteristics. Based on the preprocessed data, combined with regional geological structural features and ore body occurrence patterns, the ore body is geologically layered, clarifying the boundaries between ore and rock layers and ore type zones. A three-dimensional wireframe model of the ore body is then constructed. By solidifying the three-dimensional wireframe model, a solid model representing the spatial geometric morphology of the ore body is finally formed, accurately depicting the physical boundaries, occurrence, and internal structure of the ore body.

[0031] Using the constructed spatial geometric model of the ore body as spatial boundary constraints, a multivariate geostatistical method is employed to analyze and construct a model characterizing the genetic characteristics of elemental grade distribution. By analyzing the genetic characteristics of major elements, associated elements, and harmful elements, as well as the spatial correlations between elements, a collaborative regionalization model is established. Kriging interpolation is then performed within the ore body boundary to construct a multivariate collaborative grade model integrating major, associated, and harmful elements, accurately depicting the distribution patterns of elemental endowments in three-dimensional space. Furthermore, based on multidimensional characteristic data such as mineral microscopic identification and spectral analysis, and the aforementioned elemental grade model, according to the quantitative transformation and spatial correlation rules between "elemental assemblage-mineral phases," a model characterizing the spatial distribution genetic characteristics of mineral composition is analyzed and constructed, clearly demonstrating the occurrence state and enrichment patterns of different minerals in three-dimensional space.

[0032] Among them, the process mineralogical identification test data can be obtained through mineral dissociation analysis (MLA), scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), or optical microscopy, and is used to quantitatively characterize key process mineralogical parameters such as the type, content, embedment size, symbiotic relationship, and degree of liberation of target minerals and gangue minerals in the ore; the spectral analysis data includes X-ray diffraction (XRD), infrared spectroscopy, and other analytical results, used to determine the type and relative content of mineral phases and to assist in the identification of similar minerals. These multidimensional characteristic data, combined with the elemental grade model, together constitute the key input information foundation for constructing a spatial distribution model of mineral composition and analyzing the characteristics of mineral composition and embedment genes.

[0033] Finally, the models representing three types of genetic characteristics—spatial geometry, elemental grade distribution, and spatial distribution of mineral composition—are integrated to form a unified three-dimensional digital model representing the genetic characteristics of ore bodies. This integrated model can dynamically display the grade distribution and mineral composition of different regions, supports arbitrary profile cutting and reserve calculation, and provides a complete digital genetic foundation for subsequent mineralization domain division and beneficiation process design based on the genetic characteristics of ore bodies, achieving a multi-dimensional unified expression from geometric morphology to chemical composition and mineral composition.

[0034] S103: Based on the mineral properties and process mineralogical characteristics in the ore body's genetic characteristics, spatial mineralization domains are divided into three-dimensional digital models.

[0035] After constructing the three-dimensional digital model, spatial mineralization domains are further divided based on mineral properties and process mineralogical characteristics. This process aims to deconstruct the geologically continuous ore body into several spatial units with relatively homogeneous internal genetic characteristics and clear process response features, using the ore body genetic characteristics uniformly represented by the three-dimensional digital model as the direct basis. This addresses the problems of large fluctuations in beneficiation indicators and low resource utilization caused by the mixed mining of ores of different properties in traditional development from the source.

[0036] Based on a three-dimensional digital model characterizing the genetic characteristics of ore bodies, scientific zoning is performed according to the spatial heterogeneity and combination patterns of these genetic characteristics. This zoning is an active analysis and classification of the spatial pattern of the genetic characteristics of ore bodies. The specific zoning criteria comprehensively consider two aspects: mineral properties and process mineralogical characteristics. Mineral properties include the content and spatial distribution of major elements, valuable elements, associated valuable elements, and harmful elements; process mineralogical characteristics include key parameters that determine beneficiation efficiency, such as the grain size of target minerals and the types and contents of gangue minerals. Through systematic analysis of these genetic characteristics of each spatial unit in the three-dimensional digital model, areas with similar property characteristics and spatially adjacent areas are grouped into the same mineralization domain, forming spatial distribution zones reflecting different mineralization intensities, such as early, middle, and late stages.

[0037] Through this refined spatial division based on genetic characteristics, the originally complex and heterogeneous ore bodies are transformed into a series of development units with clear boundaries and controllable genetic characteristics. This division not only enables accurate prediction and classification of ore properties, but more importantly, it provides a direct basis for adapting specific mineral processing procedures to mineralization domains with different genetic characteristics and designing differentiated mining schemes.

[0038] S104: For each mineralized zone, adapt the mineral processing flow and clarify the corresponding mineral processing constraints.

[0039] After completing the spatial mineralization domain division, it is necessary to adapt the mineral processing technology to each domain and clarify the corresponding mineral processing constraints. The aim is to transform the geological model into an executable mineral processing production plan, and to achieve precise matching and optimization of the mineral processing technology based on the differences in the genetic characteristics of each mineralization domain, thereby solving the problems of large fluctuations in ore properties and poor process adaptability in traditional models.

[0040] Specifically, firstly, based on the process mineralogical characteristics of each mineralization zone, mineral processing experiments are conducted to deeply analyze the process mineralogical properties of ores within different mineralization zones and reveal the key factors affecting the mineral processing technology. Through systematic experimental research, the most suitable initial mineral processing flow is matched for each mineralization zone. This matching process comprehensively considers the spatial distribution characteristics and process compatibility of the mineralization zone, and formulates differentiated process schemes based on key parameters such as the content thresholds of major elements, harmful elements, and valuable elements.

[0041] While adapting the mineral processing technology, it is essential to define the corresponding process constraints. These constraints constitute the boundary rules for the stable and efficient operation of the mineral processing process, typically expressed as content indicators of key chemical components, such as the grade range of major elements, the upper limit of harmful elements, and the recovery requirements of valuable elements. By setting these mineral processing constraints, clear inputs are provided for subsequent mining and ore blending models, ensuring that the ore produced from mining operations meets the requirements of the mineral processing technology.

[0042] In one embodiment, the above-mentioned adaptation of the mineral processing flow to each delineated mineralization zone includes: Based on the process mineralogical characteristics of each mineralized zone, mineral processing experiments were conducted. Based on the results of mineral processing experiments, initial mineral processing procedures were matched for each mineralization zone. Based on the spatial distribution of each mineralization domain, the mineralization domains that match the initial beneficiation process and are compatible with the beneficiation process are optimized by merging the process flow. Based on the results of the merger and optimization, the final beneficiation process flow adapted to each mineralization zone was determined.

[0043] In application, mineral processing experiments are first conducted based on the process mineralogical characteristics of each mineralization zone. Through systematic mineral processing tests on representative ore samples from different mineralization zones, the mineralogical characteristics of associated valuable, harmful, and beneficial elements affecting the mineral processing process are analyzed in depth, and key mineral processing parameters and process adaptability data are obtained. Based on the results of the mineral processing experiments, a corresponding initial mineral processing flow is matched for each mineralization zone. This matching process comprehensively considers the content characteristics of major elements, valuable elements, associated valuable elements, and harmful elements in each mineralization zone, as well as the process mineralogical characteristics, ensuring that the initial process flow can be specifically adapted to the ore characteristics of each mineralization zone.

[0044] Based on the spatial distribution characteristics of each mineralization zone, adjacent mineralization zones that match the initial beneficiation process and are compatible with the beneficiation process are merged and optimized. This optimization process analyzes the spatial relationship and process compatibility of mineralization zones, and merges mineralization zones with similar process characteristics and close spatial locations to form development units of appropriate scale and unified processes, thereby improving production efficiency and equipment utilization.

[0045] Based on the results of the merger and optimization, a beneficiation process flow adapted to each mineralization zone was finally determined. This optimized process flow not only retains the process adaptability to the characteristics of each mineralization zone, but also achieves economies of scale through reasonable merger and optimization, providing a technical basis for the subsequent formulation of economically reasonable production plans, and laying the foundation for the stable operation of the beneficiation process and the continuous optimization of beneficiation indicators.

[0046] S105: Construct a mining and ore blending model based on mineral processing constraints.

[0047] Mining blending models, serving as quantitative decision-making tools connecting geological understanding, beneficiation requirements, and mining practices, aim to accurately translate the technological demands of beneficiation into feasible solutions for mining, thereby overcoming the resource waste and increased costs caused by the disconnect between mining and beneficiation in traditional models. The construction of mining blending models follows the aforementioned beneficiation process constraints, while comprehensively considering the process mineralogical characteristics and spatial distribution of each mineralization zone. By coordinating the property variation patterns of ore bodies in three-dimensional space with the boundary requirements of beneficiation processes, a mathematical model capable of optimizing resource allocation is established. This modeling process ensures the continuity of mining within the same mineralization zone and the stability of process mineralogical characteristics, preventing the arbitrary mixing of ores with different properties from the outset, and providing homogeneous and stable feed ore for the beneficiation stage.

[0048] The establishment of this mining and ore blending model transforms abstract technological constraints and complex geological information into concrete and feasible mining guidance schemes. Through model optimization calculations, the optimal mining sequence and ore output ratio can be determined, enabling mining activities to precisely meet the requirements of the mineral processing technology.

[0049] In one embodiment, the above-mentioned construction of a mining and ore blending model based on mineral processing constraints includes: The objective function is to maximize overall economic benefits. The boundary conditions are the spatial location of the mineralized domain, the constraints of mining capacity, and the constraints of mineral processing technology. A multi-objective optimization mathematical model is established based on the objective function and boundary conditions; By solving the mathematical model, the optimal mining sequence and ore output ratio scheme are output.

[0050] The process of constructing a mining and ore blending model based on mineral processing constraints is a mathematical optimization process that transforms complex geological and technological constraints into quantifiable decision-making schemes. First, the objective function is to maximize overall economic benefits, ensuring that all decisions revolve around the final economic gains, achieving the goal of maximizing resource recovery and optimizing economic efficiency. To achieve this goal, multi-dimensional boundary conditions can be set, mainly including the spatial location of mineralization domains, mining capacity constraints, and mineral processing constraints. These boundary conditions collectively ensure a balance between technical feasibility and economic rationality in the optimized scheme. Specifically, the spatial location of mineralization domains and the spatial distribution of ore body genes constitute the physical basis of the model; mining capacity constraints consider the limitations of actual production conditions; and mineral processing constraints serve as key technical thresholds, ensuring that the mined ore meets the requirements of subsequent mineral processing operations.

[0051] Based on the aforementioned objective function and boundary conditions, a multi-objective optimization mathematical model is established. This model comprehensively considers multiple factors, including the spatial distribution of ore body genes, mining sequence, and beneficiation process constraints, through mathematical modeling methods, and employs optimization algorithms for a comprehensive solution. During the model's solution process, the algorithm iteratively calculates and seeks the optimal solution that maximizes overall economic benefits while satisfying all constraints.

[0052] Finally, by solving the mathematical model, the optimal mining sequence and ore output ratio scheme are output. This scheme precisely specifies the mining sequence of each mineralization zone and its proportion in the total ore output, ensuring both the continuity of mining operations and the rational proportion of ores with different properties. This provides a scientific basis for subsequent specific production planning and lays the foundation for achieving optimal resource allocation and maximizing economic benefits.

[0053] S106: Develop mining and production plans based on the mining and ore blending model.

[0054] In application, the mining and ore blending model serves as the direct basis, refining the optimal mining sequence and ore blending scheme output by the model into a series of specific mining activity arrangements, thus guiding mining operations. During the planning process, it is necessary to translate macro-level optimization strategies into practically executable production instructions. For example, determining the specific mining sequence for each ore block, formulating an ore extraction plan that meets the constraints of the beneficiation process, and comprehensively balancing these plans with the overall production capacity of the mine. Through system verification and coordination, an executable production operation plan is formed, clearly defining specific operational details such as the mining scope, ore extraction rhythm, and resource allocation.

[0055] In one embodiment, the above-mentioned formulation of a mining and production plan based on a mining and ore blending model includes: Based on the mining sequence and ore output ratio scheme output by the mining and ore blending model, the mining sequence of the ore block is determined; Based on the ore extraction ratio scheme, formulate a multi-timescale ore extraction plan that meets the constraints of the mineral processing technology; The mining plan is verified and balanced with the mine's production system capacity to generate an executable production operation plan.

[0056] In application, the specific mining sequence for each ore block is first determined based on the mining sequence and ore blending scheme output by the model. This ensures the continuity of mining within the same mineralization zone and the stability of process mineralogical properties, preventing the arbitrary mixing of ores with different properties from the outset. Subsequently, a multi-timescale ore blending plan is developed based on the ore blending scheme to meet the constraints of the mineral processing technology. This plan is then implemented in production scheduling across different time dimensions, ensuring that the ore delivered to the processing plant remains within the stable range required by the process in terms of grade and harmful element content. Finally, the ore blending plan is systematically verified and balanced against the mine's production system capacity. By coordinating production factors such as mining equipment capacity and transportation system capacity, an executable production operation plan is generated that clearly defines the mining scope, ore blending pace, and resource allocation scheme. This complete process ensures the accurate implementation of the optimized scheme, providing a scientific action guide for mining operations and ultimately achieving the goals of efficient resource recovery and stable production operation.

[0057] In one embodiment, the above method further includes: Obtain actual production information of the target mining area during the mining process; The 3D digital model is updated based on actual production information; The updated 3D digital model was used to simultaneously optimize the mining and ore blending model, and the mining and production plan was revised.

[0058] Furthermore, this invention can also feature a dynamically optimized closed-loop feedback process, enabling development decisions to continuously adapt to the actual conditions during the mining process. Specifically, it first acquires new geological information dynamically generated as mining operations progress through methods such as underground logging, borehole sampling, and real-time monitoring. This information includes the content and process mineralogical characteristics of major valuable elements, associated valuable elements, and harmful elements, as well as the actual mineral processing mineralogical indicators of different ore bodies in their spatial locations.

[0059] Based on this actual production information, the 3D digital model is systematically updated. This update process involves periodically comparing and analyzing the model's simulated data with actual production data to verify the model's rationality. When data deviations exceed preset thresholds or special circumstances arise that affect mineral processing indicators, the model is dynamically corrected through data calibration and algorithm iteration to ensure that it always accurately reflects the true state of the ore body.

[0060] Using the updated 3D digital model, the spatial mineralization domain division, mineral processing flow adaptation, and mining ore blending model are optimized in a coordinated manner, and mining and production plans are revised accordingly. The updated model is fed back to the mining planning stage in real time, guiding the re-optimization of mining layout and ore blending strategies. Specific measures may include revising the mining scope, adjusting the mining plan and ore extraction rhythm, and optimizing resource allocation. This closed-loop feedback mechanism ensures that mining operations are always matched with the real-time characteristics of the ore body, transforming geological uncertainties into controllable variables, ultimately forming a virtuous cycle of collaborative optimization and achieving continuous improvement in resource recovery rate and production efficiency.

[0061] To more clearly illustrate the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0062] See Figure 2 As shown below, taking a certain iron mine as an example, 31 geological profile maps were collected, including 15 geological exploration profiles such as line 0, line 0-1, line 1 to line 6-7, and 16 supplementary exploration profiles such as line BB, line CC, line DD to line PP, 1 comprehensive geological map (with work deployment, including topographic, geological and geological profile location information) and other relevant data. A total of 111 boreholes were extracted from the collected geological profile maps as the data basis for the detailed explanation of the process of this invention.

[0063] The first step was the construction of a three-dimensional digital model. The research objects were geologically explored ore bodies and those generated through supplementary exploration. Basic data, including borehole data from geological exploration lines, supplementary exploration borehole data, and sample point data, were compiled into TXT format borehole tables and inclination tables to represent the spatial location and morphology of the boreholes. The data from the exploration line profiles and supplementary exploration line profiles were interpreted to determine the approximate spatial location of the explored and supplementary ore bodies and the grades of mineral gene elements, including total iron (Tfe), magnetic iron (Mfe), sulfur (S), and cobalt (Co). The grades of the mineral gene elements in the ore bodies were interpolated to obtain a spatial distribution model of each element's grade, as well as the spatial distribution characteristics of the main mineral components, magnetite and hematite. The spatial distribution of the main element total iron (Tfe) is shown in [reference needed]. Figure 3 For the spatial distribution of the associated valuable element cobalt (Co), please refer to [reference needed]. Figure 4 For the spatial distribution of harmful element (S), please refer to [reference needed]. Figure 5 For the spatial distribution of prime magnetite (Mfe), see [reference needed]. Figure 6 For the spatial distribution of hematite (Tfe-mfe), please refer to [reference needed]. Figure 7 ; Figures 3 to 7 The numerical range in the legend represents the grade (content) of the corresponding element or mineral. The higher the value, the higher the enrichment of the element or mineral at that spatial location.

[0064] Further steps include evaluating the applicability of mineral processing technologies and optimizing these technologies. Based on the spatial distribution characteristics of the ore body's genetic elements, a spatial mineralization domain division based on ore properties (such as...) is established. Figure 8 As shown in Table 1, the mineral processing technology (including crushing, grinding, and detailed mineral processing flow) and mineral processing indicators are adapted accordingly. Mineral processing constraints are set as shown in Table 1. Then, an intelligent decision-making scheme and economic indicator plan for the ore body mineral processing technology are designed.

[0065] Table 1

[0066] Next is the adaptation of ore blending and mining processes under the constraints of the mineral processing technology. Mining schemes are designed based on the set mineral processing technology constraints and the spatial location of the mineralization zone, and then production plans are formulated based on the ore blending model and mineral processing technology constraints.

[0067] Finally, there is model optimization. In practical applications, the established mining and production plan is implemented, and spatial information data of production is collected in real time. This real-time data is compared and analyzed with the initial model to identify and quantify model deviations. The original model is then partially or entirely corrected and updated. The updated model is immediately fed back to the production planning department to optimize subsequent mining design and ore blending schemes, thereby achieving efficient and intelligent integrated development and utilization of mineral resources. This dynamic process greatly improves resource recovery rates, reduces mining risks, and ensures the sustainability and economic benefits of mine production.

[0068] Corresponding to the aforementioned application function implementation method embodiments, the present invention also provides an integrated ore body development system based on ore body genetic characteristics, including a geological-mining-beneficiation system and corresponding embodiments.

[0069] Please see Figure 9 , Figure 9 This is a schematic diagram of the modular structure of an integrated ore body development system based on the genetic characteristics of the ore body, encompassing mining, beneficiation, and geological processes.

[0070] An integrated orebody development system based on the genetic characteristics of ore bodies, encompassing geological mining and beneficiation, includes: Data acquisition unit 91 is used to acquire geological data of the target mining area; the geological data includes geological exploration data, engineering exposure data and ore sample test and analysis data. The 3D modeling unit 92 is used to construct a 3D digital model based on geological data to characterize the genetic characteristics of the ore body; the genetic characteristics of the ore body are characterized at least by the spatial geometry of the ore body, the distribution of elemental grades, and the spatial distribution of mineral composition. Mineralization domain division unit 93 is used to divide the three-dimensional digital model into spatial mineralization domains based on the mineral properties and process mineralogical characteristics in the ore body gene characteristics. The process adaptation unit 94 is used to adapt the mineral processing flow to each of the divided mineralization zones and to clarify the corresponding mineral processing constraints. The ore blending model construction unit 95 is used to construct an ore blending model for mining based on the constraints of the mineral processing process. Production planning unit 96 is used to formulate mining and production plans based on the mining and ore blending model.

[0071] In one embodiment, when constructing a three-dimensional digital model based on geological data, the three-dimensional modeling unit 92 is specifically used for: Preprocess the geological data; Based on the preprocessed data, a solid model of the spatial geometric morphology of the ore body is constructed. Using the spatial geometric morphology of the ore body as a spatial constraint, a geostatistical model of element grade distribution is constructed. Based on the elemental grade distribution model and multidimensional feature data, a spatial distribution model of mineral composition is constructed; the multidimensional feature data includes process mineralogical identification test data and spectral analysis data. The spatial geometric morphology model of the ore body, the element grade distribution model, and the spatial distribution model of mineral composition are integrated to form a three-dimensional digital model.

[0072] In one embodiment, when adapting the mineral processing flow to each defined mineralization zone, the process adaptation unit 94 is specifically used for: Based on the process mineralogical characteristics of each mineralized zone, mineral processing experiments were conducted. Based on the results of mineral processing experiments, initial mineral processing procedures were matched for each mineralization zone. Based on the spatial distribution of each mineralization domain, the mineralization domains that match the initial beneficiation process and are compatible with the beneficiation process are optimized by merging the process flow. Based on the results of the merger and optimization, the final beneficiation process flow adapted to each mineralization zone was determined.

[0073] In one embodiment, when constructing a mining blending model based on mineral processing constraints, the blending model construction unit 95 is specifically used for: The objective function is to maximize overall economic benefits. The boundary conditions are the spatial location of the mineralized domain, the constraints of mining capacity, and the constraints of mineral processing technology. A multi-objective optimization mathematical model is established based on the objective function and boundary conditions; By solving the mathematical model, the optimal mining sequence and ore output ratio scheme are output.

[0074] In one embodiment, when formulating a mining and production plan based on a mining and ore blending model, the production planning unit 96 is specifically used for: Based on the mining sequence and ore output ratio scheme output by the mining and ore blending model, the mining sequence of the ore block is determined; Based on the ore extraction ratio scheme, formulate a multi-timescale ore extraction plan that meets the constraints of the mineral processing technology; The mining plan is verified and balanced with the mine's production system capacity to generate an executable production operation plan.

[0075] In one embodiment, the system further includes: Update the data acquisition unit to obtain actual production information of the target mining area during the mining process; Update unit, used for: The 3D digital model is updated based on actual production information; The updated 3D digital model was used to simultaneously optimize the mining and ore blending model, and the mining and production plan was revised.

[0076] Regarding the system in the above embodiments, the specific manner in which each unit module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0077] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for integrated geological-mining-beneficiation orebody development based on orebody genetic characteristics, characterized in that, include: Obtain geological data of the target mining area; the geological data includes geological exploration data, engineering exposure data, and ore sample test and analysis data; Based on the geological data, a three-dimensional digital model is constructed to characterize the genetic characteristics of the ore body; the genetic characteristics of the ore body are characterized at least by the spatial geometry of the ore body, the distribution of elemental grades, and the spatial distribution of mineral composition. Based on the mineral properties and process mineralogical characteristics in the ore body's genetic features, the three-dimensional digital model is divided into spatial mineralization domains; For each identified mineralization zone, adapt the mineral processing flow and clarify the corresponding mineral processing constraints. Based on the constraints of the mineral processing technology, a mining and ore blending model is constructed; Based on the mining and ore blending model, a mining and production plan is formulated.

2. The integrated mining-beneficiation method for ore body development based on ore body genetic characteristics according to claim 1, characterized in that, Based on the geological data, a three-dimensional digital model is constructed, including: The geological data is preprocessed; Based on the preprocessed data, a solid model of the spatial geometric morphology of the ore body is constructed. Using the spatial geometric morphology model of the ore body as a spatial constraint, a geostatistical method is used to construct an element grade distribution model; Based on the elemental grade distribution model and multidimensional feature data, a spatial distribution model of mineral composition is constructed; the multidimensional feature data includes process mineralogical identification test data and spectral analysis data. The spatial geometric morphology model, element grade distribution model, and mineral composition spatial distribution model of the ore body are integrated to form a three-dimensional digital model for characterizing the genetic properties of the ore body.

3. The integrated mining-beneficiation method for ore body development based on ore body genetic characteristics according to claim 1, characterized in that, For each identified mineralization zone, an appropriate mineral processing flow is adopted, including: Based on the process mineralogical characteristics of each mineralized zone, mineral processing experiments were conducted. Based on the results of mineral processing experiments, an initial mineral processing flow was matched for each of the mineralization zones. Based on the spatial distribution of each mineralization domain, the mineralization domains that match the initial beneficiation process and are compatible with the beneficiation process are optimized by merging the process flow. Based on the results of the merging and optimization, the final beneficiation process flow adapted to each of the aforementioned mineralization domains is determined.

4. The integrated mining-beneficiation method for ore body development based on ore body genetic characteristics according to claim 1, characterized in that, Based on the aforementioned mineral processing constraints, a mining and ore blending model is constructed, including: The objective function is to maximize overall economic benefits. The boundary conditions are the spatial location of the mineralization zone, the constraints of mining capacity, and the constraints of the mineral processing technology. Based on the objective function and boundary conditions, a multi-objective optimization mathematical model is established; By solving the mathematical model, the optimal mining sequence and ore output ratio scheme are output.

5. The integrated mining-beneficiation method for ore body development based on ore body genetic characteristics according to claim 1, characterized in that, Based on the aforementioned ore blending model, a mining and production plan is formulated, including: Based on the mining sequence and ore output ratio scheme output by the mining and ore blending model, the mining sequence of the ore block is determined; Based on the ore extraction ratio scheme, formulate a multi-timescale ore extraction plan that meets the constraints of the mineral processing technology; The ore extraction plan is verified and balanced with the mine production system capacity to generate an executable production operation plan.

6. The integrated mining-beneficiation method for ore body development based on ore body genetic characteristics according to claim 1, characterized in that, The mineral properties include the content of main elements, valuable elements, associated valuable elements, and harmful elements; the process mineralogy characteristics include the grain size of the target mineral and the types and contents of gangue minerals.

7. The integrated mining-beneficiation method for ore body development based on ore body genetic characteristics according to claim 1, characterized in that, The method further includes: Obtain actual production information of the target mining area during the mining process; The three-dimensional digital model is updated based on the actual production information. The updated three-dimensional digital model is used to simultaneously optimize the mining and ore blending model, and a new mining and production plan is formulated.

8. An integrated orebody development system based on orebody genetic characteristics, characterized in that, include: The data acquisition unit is used to acquire geological data of the target mining area; the geological data includes geological exploration data, engineering exposure data, and ore sample test and analysis data. A three-dimensional modeling unit is used to construct a three-dimensional digital model to characterize the genetic properties of the ore body based on the geological data. The genetic characteristics of the ore body are characterized at least by the spatial geometry of the ore body, the distribution of element grades, and the spatial distribution of mineral composition. The mineralization domain division unit is used to divide the three-dimensional digital model into spatial mineralization domains based on the mineral properties and process mineralogical characteristics in the ore body's genetic characteristics. The process adaptation unit is used to adapt the mineral processing flow to each of the defined mineralization zones and to define the corresponding mineral processing constraints. The ore blending model construction unit is used to construct an ore blending model based on the constraints of the ore beneficiation process. The production planning unit is used to formulate mining and production plans based on the mining and ore blending model.

9. A mineral body development system based on the genetic characteristics of ore bodies, integrating mining and beneficiation, as described in claim 8, characterized in that... The system also includes: The data acquisition unit is updated to acquire actual production information of the target mining area during the mining process; Update unit, used for: The three-dimensional digital model is updated based on the actual production information. The updated three-dimensional digital model is used to simultaneously optimize the mining and ore blending model, and a new mining and production plan is formulated.

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