A preferred method and system for metal mine mining

Abstract

This invention provides a method and system for optimizing mining methods in metal mines, relating to the field of mining method optimization technology. The method first constructs a candidate mining method library and an evaluation index system for mining method optimization; secondly, it sets multi-level quality grades for mining methods and establishes grading standards for the evaluation indicators; further, it constructs a relationship network of evaluation indicators and uses graph theory analysis and breadth-first search algorithms to achieve objective weighting of the evaluation indicators; it constructs a TOPSIS evaluation model to calculate the proximity of each mining scheme to its ideal optimal value; it constructs a cloud model to calculate the membership matrix of each mining method; finally, it obtains the final score through a joint probability function and selects the mining method with the highest score. This invention can reduce the influence of subjective human weighting on the evaluation results, improve the accuracy of the evaluation results, and enable the evaluation process to present more diverse information, providing a quantitative basis for scientific decision-making in mining methods.

Description

Technical Field

[0001] This invention relates to the field of preferred mining methods, specifically to a preferred method and system for mining metal mines. Background Technology

[0002] As a crucial cornerstone of the national industrial system, the mining of metallic mineral resources is developing towards deeper, larger-scale, and more intensive operations. The selection of mining methods is a core element, directly determining the mine's infrastructure investment, production capacity, ore recovery rate, dilution rate, and production safety. Due to the extremely complex geological conditions of metallic deposits (such as variable ore body morphology, varying thickness, and significant differences in surrounding rock stability), and the constraints imposed by multiple factors including technology, economics, and the environment, how to scientifically and rationally select the optimal mining method has always been a key technical challenge in the field of mining engineering.

[0003] Traditional mining methods primarily rely on empirical analogies and qualitative analysis, using geological data as a foundation, referencing similar mine experiences, and making preliminary selections based on personal intuition and experience. This approach has significant limitations: it is highly subjective, easily leading to decision-making biases due to differences in the experience of designers; it lacks quantitative support, making it impossible to accurately assess core indicators; and it is inefficient, struggling to adapt to complex evaluation scenarios with multiple schemes and indicators, thus hindering mine design progress. In recent years, to overcome these traditional bottlenecks, mathematical tools such as fuzzy mathematics, the Analytic Hierarchy Process (AHP), and artificial neural networks have been introduced, driving the quantitative development of mining optimization methods. However, three major pain points remain in practical implementation: First, the computational models are complex and lack practical applicability. Existing multi-algorithm coupled models require specialized software support, involve complex calculations, and place extremely high demands on frontline technical personnel. However, the limited professional capabilities of frontline personnel in deep mines make it difficult to apply advanced models in the field. Second, the weight allocation is unreasonable and lacks scientific rigor. Both single subjective and objective weighting methods have shortcomings, and while combined weighting methods can balance both, the calculations are cumbersome and not adapted to the special conditions of deep mines. Third, the calculation method is highly subjective, ignoring the coupling of factors and leading to distorted decision-making. The multi-dimensional factors in mining optimization exhibit significant coupling relationships. Existing methods often assume factors are independent, considering only the relationship between a single factor and the solution, resulting in evaluation results that deviate from reality. Chinese invention patent CN117052394A (publication date 2023.11.14) proposes an optimization scheme for underground phosphate mining methods. It constructs an indicator system encompassing economy, safety, and productivity, analyzes stope safety using FLAC3D software, calculates subjective and objective weights using the AHP and CRITIC methods respectively, optimizes the combined weights based on game theory, and finally selects the optimal solution using the TOPSIS method. This method has two shortcomings: first, the combined weighting process is complex (AHP + CRITIC + game theory), affecting evaluation efficiency; second, the TOPSIS method relies excessively on ideal solution calculations, neglecting the uncertainty and ambiguity of the indicators themselves. Another Chinese invention patent, CN117313865A (publication date 2023.12.29), proposes a multi-factor structural weight analysis method for multiple alternative selection. It characterizes the overall nature of an alternative by relating the value range of a factor to the entire value range of that factor in the alternative, and characterizes the distinguishability by relating the value ranges of the same factor in different alternatives. It defines overall degree and distinguishability to form structural weights. While this method is suitable for scenarios lacking implementation results, it assumes that each influencing factor is independent and calculates weights only based on the structural relationship between a single factor and the alternative, ignoring potential correlations or coupling relationships between factors, thus leading to a lack of scientific rigor in the evaluation results.

[0004] In view of this, it is necessary to study a mining method optimization scheme that is computationally simple, has reasonable weights, and takes into account the coupling of factors, so as to ensure the safe supply of mineral resources. Summary of the Invention

[0005] In view of the technical problems existing in the background art, the present invention provides a preferred method and system for metal mine mining methods. Based on graph theory and breadth-first search objective weighting, TOPSIS and cloud model dual-mode evaluation, and joint probability function coupled decision-making, the present invention can reduce the influence of subjective human weighting on the evaluation results, strengthen the role of safety factors in the selection of mining methods, and improve the accuracy of the evaluation results. This allows the evaluation process to present more diverse information, providing a quantitative basis for scientific decision-making in mining methods.

[0006] In a first aspect, embodiments of the present invention provide a preferred method for mining metal mines, comprising the following steps: S1. Based on the ore body occurrence characteristics, technical conditions and mining requirements, preliminary screening of candidate mining methods is conducted to establish a mining method candidate pool. S2. Based on the pool of alternative mining methods, evaluation indicators are selected from three dimensions: technical feasibility, safety assurance, and economic rationality, to construct an evaluation indicator system for the optimal selection of mining methods. S3, set the quality level of mining methods, and establish a graded evaluation standard corresponding to the quality level for each evaluation indicator; S4, construct the evaluation indicators as network nodes, construct the evaluation indicator relationship network based on the correlation between the evaluation indicators, and calculate the weight of each evaluation indicator using graph theory centrality analysis; S5. Based on the weights of the evaluation indicators, a TOPSIS evaluation model is constructed to calculate the closeness of each mining method to the optimal ideal solution. S6. Based on the aforementioned grading evaluation criteria, the membership degree of each mining method under the excellence level is calculated using a cloud model. S7. Construct a multidimensional random variable relationship model based on the joint probability function, couple the proximity degree with the membership degree, and obtain the comprehensive evaluation value of each mining method; select the mining method with the highest score as the final preferred solution.

[0007] As a further improvement of the present invention, in step S7, based on the membership degree calculated by the cloud model and the closeness calculated by the TOPSIS evaluation model, a weighted geometric mean model is used to construct a joint probability function, taking into account both equilibrium and nonlinear effects. The multidimensional random variable relationship model is as follows: ; In the formula, P is the final score of the coupled model. The higher the score, the more suitable the evaluation method is for the target mine; X 1 Membership degree; For closeness; Weights for each dimension; is the sensitivity index; k is the kth model coupled to the joint probability function; n is the number of different models coupled to the joint probability function.

[0008] As a further improvement of the present invention Since there are two dimensions, and they are equally important, both are set to 0.5; It is usually set to 1.

[0009] As a further improvement of the present invention, in step S5, a TOPSIS evaluation model is constructed. By setting the ideal optimal value and the ideal worst value, the closeness of each mining method to the optimal ideal solution is calculated. The specific process is as follows: S51. Collect and standardize the evaluation index data of each mining method to obtain the data standardization matrix. The standardization method is as follows: For positive indicators: ; For negative indicators: ; In the formula, Standardized evaluation indicators; The maximum value of the indicator; The minimum value of the indicator; The index value; S52, calculate the difference between each evaluation index and the ideal optimal value and the ideal worst value, expressed as: ; In the formula, The difference between each evaluation indicator and the ideal optimal value; The difference between each evaluation indicator and the ideal worst value; The weight of index j; The ideal optimal value for index j; The ideal worst value for index j; Step S53: Calculate the closeness between the mining method and the optimal ideal solution, expressed as: In the formula, This represents the closeness of the mining method to the optimal ideal solution.

[0010] As a further improvement of the present invention, in step S3, the grading evaluation criteria corresponding to the excellence level of the mining method include: Level 1 indicates excellent, signifying a high degree of compatibility between the mining method and the target mine; Level 2 indicates good, meaning the mining method is applicable but the implementation effect is average; Level 3 indicates passing grade, signifying poor compatibility between the mining method and the target mine; By referring to industry standards and the importance of each evaluation indicator to the target mine, the range of values ​​for each evaluation indicator under different quality levels is determined. The ideal optimal value refers to all indicators taking the optimal value within the Level 1 range, while the ideal worst value refers to all indicators taking the worst value within the Level 3 range.

[0011] As a further improvement of the present invention, in step S6, the construction of the cloud model includes: S61 uses three-dimensional feature parameters to describe the index grading standard, where... The expected value is the central value at the center of the universe of discourse; Entropy is a measure of the ambiguity of qualitative concepts; It is hyperentropy, is The entropy represents the degree of dispersion of cloud droplets in the cloud model, and indirectly reflects the cloud thickness. S62, cloud droplets are generated using a forward cloud generator, and the membership degree of each mining method at each quality level is calculated; the implementation process is as follows: Input: 3 numerical features ( , , and the number of cloud droplets N; Output: N cloud droplets conforming to the concept distribution and a normally distributed random number. ; ; ; In the formula, t represents the cloud correlation degree; x These are the actual measured values ​​of the indicators; This represents the expected value of the indicator. ' is the random realization value of the entropy of the indicator; T is the matrix composed of cloud correlation degree t; G is the membership degree matrix; W is the weight vector; ij is the proximity degree of the i-th indicator under the j-th level.

[0012] As a further improvement of the present invention, in step S4, the evaluation index is weighted using graph theory analysis and a breadth-first search algorithm, including: S41, construct the evaluation indicators into a relational network structure graph and transform it into an adjacency matrix; where the matrix elements corresponding to indicators with correlation relationships are 1, and the matrix elements corresponding to indicators without correlation relationships are 0. S42, use graph theory analysis to calculate the degree centrality of the index, and use the breadth-first search algorithm to calculate the proximity centrality of the index; the formula for degree centrality is: ; In the formula, For the first Evaluation indicators Degree centrality; a ij Let j be the index directly associated with the i-th evaluation index; n is the number of indicators associated with the i-th evaluation index. Based on the adjacency matrix, the degree centrality of each evaluation index is calculated to characterize the degree of direct correlation between the evaluation indexes in the index relationship network. To eliminate the influence of the number of evaluation indicators on the results, the degree centrality is normalized to obtain the normalized degree centrality. The expression is: ; Meanwhile, a breadth-first search algorithm is used to calculate the proximity centrality of each evaluation index, and the proximity centrality is normalized to characterize the overall proximity of each index to other indices in the index relationship network.

[0013] As a further improvement of the present invention, step S4 also includes: S43, combine the degree centrality and proximity centrality to obtain the comprehensive weight of each evaluation index, thereby achieving objective weighting of the evaluation index; specifically: combine the normalized degree centrality... Approaching centrality with normalization By performing weighted combinations, the evaluation indicators are obtained. Overall weight The expression is: ; In the formula, normalization approximates centrality. It is a core metric in graph theory that measures the average shortest path length between a node and other nodes in the network. In a social network, if the shortest path from a given node to most members of the network is not long, it indicates a high degree of proximity centrality.

[0014] This allows for the objective weighting of each evaluation index in the evaluation index system for the optimal selection of mining methods.

[0015] As a further improvement of the present invention, in step S4, a network analysis method is used to construct each evaluation index in the preferred evaluation index system of the mining method as a node, and an index relationship network structure diagram is established according to the mutual influence relationship between the indicators to characterize the correlation between each evaluation index; the index relationship network structure diagram is then transformed into an adjacency matrix.

[0016] As a further improvement of the present invention, the mining method candidate library in step S1 includes the design loss rate, design depletion rate and mining-cutting ratio data for each mining method; In step S2, when constructing the evaluation index system for the optimal mining method, the weight of safety assurance indicators should be increased.

[0017] Secondly, embodiments of the present invention provide a preferred system for a metal mine mining method, which is used to perform the preferred method of the aforementioned metal mine mining method, comprising: The mining method preliminary selection database module is used to store mining methods selected based on the mine site conditions and to build a candidate database; The evaluation index system module is used to construct an evaluation index system selected from three dimensions: technical feasibility, safety assurance, and economic rationality. The evaluation result grading module is used to grade the excellence of mining methods, grade the evaluation indicators accordingly, and determine the grading evaluation criteria. The evaluation index weighting module is used to assign weights to the indicators in the evaluation index system. The weighting is implemented based on graph theory analysis and breadth-first search algorithm. The optimization and evaluation module is used to evaluate and compare the mining methods in the candidate database and select the optimal mining method.

[0018] As a further improvement of the present invention, the preferred evaluation module includes: The TOPSIS evaluation unit is used to calculate the closeness of each mining method to the optimal ideal solution; The cloud model evaluation unit is used to calculate the membership degree of each mining method at each level of excellence. A dual-model coupled evaluation unit is used to couple proximity and maximum membership based on a joint probability function, calculate the final score, and select the optimal mining method. And / or, the evaluation index weighting module includes: The relational network construction unit is used to construct the evaluation indicators into a relational network structure graph and transform it into an adjacency matrix; Degree centrality calculation unit, used to calculate the degree centrality of each evaluation index; The proximity centrality calculation unit is used to calculate the proximity centrality of each indicator using a breadth-first search algorithm. The weight combination unit is used to combine degree centrality and proximity centrality into a comprehensive weight.

[0019] Beneficial effects: This invention achieves the effect of transforming qualitative adaptation relationships into quantitative evaluation results and enhancing the comparability of evaluation results by setting multi-level mining method excellence grades and establishing corresponding hierarchical evaluation standards. By constructing an evaluation index relationship network based on graph theory analysis and using a breadth-first search algorithm to calculate index centrality, it achieves the effect of objectively reflecting the importance of evaluation indicators in the overall system and reducing the influence of subjective weighting. By combining degree centrality and proximity centrality to determine the weights of evaluation indicators, it achieves the effect of balancing the local correlation of indicators with the overall structural influence and improving the rationality of indicator weights. By constructing a coupled evaluation model using a joint probability function, it improves the accuracy of the evaluation and allows the evaluation process to present more diversified information. This provides a quantifiable, programmable, and scalable engineering solution for the scientific selection of mining methods.

[0020] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0021] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the present invention will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0022] Figure 1 This is a flowchart illustrating a preferred method of the metal mine mining method provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the evaluation index system provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of a preferred system for a metal mine mining method provided in Embodiment 2 of the present invention.

[0023] Explanation of reference numerals in the attached figures: 401. Preliminary selection database module for mining methods; 402. Evaluation index system module; 403. Evaluation result classification module; 404. Evaluation index weighting module; 405. Optimization evaluation module. Detailed Implementation

[0024] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the invention, are intended to cover non-exclusive inclusion.

[0026] In the description of the embodiments of this invention, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this invention, "multiple" means two or more, unless otherwise explicitly defined.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In the description of the embodiments of this invention, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0029] In the description of the embodiments of the present invention, the term "multiple" refers to two or more (including two), similarly, "multiple groups" refers to two or more (including two groups), and "multiple pieces" refers to two or more (including two pieces).

[0030] In the description of the embodiments of the present invention, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of the present invention.

[0031] In the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention according to the specific circumstances.

[0032] To address the technical problems of existing mining method optimization techniques, such as strong subjectivity, computational complexity, neglect of uncertainty, and assumption of independent factors while ignoring the coupling relationships between factors, this invention provides a method and system for optimizing mining methods in metal mines. By constructing a relational network of evaluation indicators, it utilizes graph theory to calculate the degree centrality (local correlation) of the indicators and the BFS algorithm to calculate the proximity centrality (overall structure), combining them to obtain a comprehensive weight, completely avoiding the subjective weighting bias of traditional methods such as AHP. Simultaneously, it employs the TOPSIS evaluation model (calculating the closeness of the scheme to the ideal solution) and a cloud model (handling the uncertainty and fuzziness of indicator classification to obtain a membership matrix) to quantitatively evaluate the mining method from two different dimensions. Finally, based on a joint probability function (weighted geometric mean model), it couples the TOPSIS closeness with the maximum membership of the cloud model to obtain a final comprehensive score, which takes into account both linear and nonlinear effects and improves the accuracy and information diversity of the evaluation results. In the construction of the evaluation indicator system, the weight of safety assurance indicators is explicitly increased to adapt to the high importance that mining enterprises place on safety.

[0033] Example 1 Please refer to Figure 1 As shown, Embodiment 1 of the present invention provides a preferred method for mining metal mines, which specifically includes the following steps: S1, conduct a preliminary selection of mining methods based on the specific site conditions.

[0034] Based on the occurrence characteristics of the ore body in the mining area, the spatial morphology, thickness variation, dip angle characteristics, surrounding rock stability and geological structural conditions of the ore body are analyzed. In combination with the existing technical conditions, equipment capabilities, safety requirements and mining needs of the mining area, the applicable mining methods are preliminarily screened to obtain several candidate mining methods. The candidate mining methods are then compiled to construct a mining method candidate library.

[0035] For each mining method in the candidate pool, a corresponding technical parameter dataset is established. The dataset includes key technical indicators such as the design loss rate, design dilution rate, and mining-cutting ratio of the mining method. The process structure and applicable conditions of each mining method are summarized and organized to provide basic data support for the evaluation, comparison, and optimization selection of mining methods in subsequent steps.

[0036] S2, Construct an evaluation index system for the optimal selection of mining methods.

[0037] Based on the pool of alternative mining methods, a comprehensive analysis of mining methods is conducted from three aspects: technical feasibility, safety assurance, and economic rationality. Evaluation indicators that can reflect the technical characteristics, safety level, and economic benefits of different mining methods are selected to construct a mining method optimization evaluation index system. Among them, the technical feasibility index is used to characterize the degree of matching between the mining method and the ore body occurrence conditions and existing technical equipment; the safety assurance index is used to characterize the safety assurance capability of the mining method for operators and the mining system during the production process; and the economic rationality index is used to characterize the cost input and economic benefit level of the mining method.

[0038] Given the increasing emphasis that mining enterprises place on safe production, when constructing the evaluation index system for the preferred mining method, the weight of safety assurance indicators in the evaluation system should be appropriately increased to enhance the sensitivity and guidance of the evaluation results to safety factors.

[0039] S3 determines the quality level of the mining method, which is divided into three levels: Level 1, Level 2, and Level 3.

[0040] The applicability of mining methods is classified into three levels: Level 1, Level 2, and Level 3. Level 1 represents excellent, indicating that the mining method is highly compatible with the ore body occurrence conditions and technical conditions of the target mine; Level 2 represents good, indicating that the mining method can be applied to the target mine but the overall implementation effect is average; and Level 3 represents passable, indicating that the mining method is poorly compatible with the target mine.

[0041] Based on the mining method optimization evaluation index system, a graded evaluation standard corresponding to the first, second, and third levels of excellence is established for each evaluation index. Combining relevant industry standards and the importance of each evaluation index to the target mine, the value range of each evaluation index under different excellence levels is determined, thereby realizing the quantitative classification of the excellence level of the mining method.

[0042] S4 assigns weights to indicators using graph theory analysis and the BFS algorithm.

[0043] Using network analysis, each evaluation index in the preferred evaluation index system of the mining method is constructed as a node, and an index relationship network structure diagram is established according to the mutual influence relationship between the indicators to represent the correlation between the evaluation indicators. The index relationship network structure diagram is transformed into an adjacency matrix, wherein the matrix element corresponding to the indicators with correlation is 1, and the matrix element corresponding to the indicators without correlation is 0.

[0044] Based on the adjacency matrix, the degree centrality of each evaluation index is calculated using graph theory analysis to characterize the degree of direct association between each index in the index relationship network. The formula for degree centrality calculation is as follows: ; Based on the adjacency matrix, the degree centrality of each evaluation index is calculated to characterize the degree of direct correlation between the evaluation indexes in the index relationship network. Evaluation indicators Degree centrality .

[0045] To eliminate the influence of the number of indicators on the results, the degree centrality is normalized to obtain the normalized degree centrality: ; Simultaneously, the breadth-first search (BFS) algorithm is used to calculate the proximity centrality of each evaluation index, characterizing the overall proximity of each index to other indicators in the index relationship network. In one specific implementation, the evaluation index weighting process of the BFS algorithm is implemented using MATLAB code. The adjacency matrix and the code are run in MATLAB to obtain the proximity centrality.

[0046] Combining the degree centrality and proximity centrality yields the comprehensive weights of each evaluation indicator, thus achieving objective weighting of the evaluation indicators. Normalized degree centrality... Approaching centrality with normalization By performing weighted combinations, the evaluation indicators are obtained. Overall weight Its expression is: ; This allows for the objective weighting of each evaluation index in the evaluation index system for the optimal selection of mining methods.

[0047] S5. Construct the TOPSIS evaluation model. By setting the ideal optimal value and ideal worst value of the scheme, calculate the closeness of each mining method to the optimal ideal solution.

[0048] Collect indicator data for various mining methods, standardize the data, and obtain a data standardization matrix. The standardization method is as follows: For positive indicators (the larger the better): ; For negative indicators (the smaller the better): ; In the formula: To standardize evaluation indicators, The maximum value of the indicator. The minimum value of the indicator. This refers to the indicator value.

[0049] The ideal optimal solution for the scheme is defined as all indicators taking the best values ​​within a Level 1 interval, and the ideal worst solution is defined as all indicators taking the worst values ​​within a Level 3 interval. The differences between each evaluation indicator and its optimal and worst values ​​are calculated to determine the closeness of the mining method to the optimal ideal solution.

[0050] ; In the formula, The difference between each evaluation indicator and the ideal optimal value. The difference between each evaluation indicator and the ideal worst value, Let j be the weight of the index. The ideal optimal value for index j; The j-index represents the ideal worst-case value.

[0051] ; In the formula, This represents the degree to which the mining method closely approximates the ideal optimal value.

[0052] S6. Construct a cloud model, calculate the three cloud digital features of each indicator through the indicator classification standard, use a positive cloud generator to calculate the cloud correlation degree, and obtain the membership matrix.

[0053] The cloud model uses a three-dimensional feature parameter system for conceptual representation: The expected value is the central value at the center of the universe of discourse; Entropy is a measure of the ambiguity of a qualitative concept. It represents the range of all possible values ​​in the domain of discourse. The larger the entropy, the larger this range, indicating the stronger the ambiguity. For hyperentropy is The entropy represents the degree of dispersion of cloud droplets in a cloud model, indirectly reflecting the cloud thickness. In MATLAB, a forward cloud generator is used to calculate cloud correlation.

[0054] The algorithm implementation of the forward cloud generator in the cloud model is as follows: Input: 3 numerical features (E x En H e and the number of cloud droplets N; Output: N cloud droplets conforming to the distribution of this concept and a normally distributed random number. .

[0055] ; ; In the formula: t is the cloud correlation degree, x is the measured value of the indicator, Ex is the expected value of the indicator, En' is the random realization value of the entropy of the indicator, T is the matrix composed of cloud correlation degrees t, G is the membership matrix, and W is the weight vector.

[0056] The membership degree of each mining method under three excellence levels is calculated using a cloud model. The level with the highest membership degree is taken as the process reference level, and its highest membership degree is used as one of the parameters in the calculation to construct the coupled model.

[0057] S7. A multidimensional random variable relationship model is constructed based on the joint probability function. The final mining method score is calculated using the calculated proximity and maximum membership degree.

[0058] Based on the previously calculated cloud model membership and TOPSIS proximity, a joint probability function is constructed using a weighted geometric mean model. This model can balance equilibrium and nonlinear effects, and the model is as follows: ; Where: P is the final score of the coupled model ( The higher the score, the more suitable the evaluation method is for the target mine. X 1 This represents the mean membership degree; This represents the mean of TOPSIS proximity. The weights for each dimension are (in this embodiment, there are two dimensions, and they are equally important, so both are set to 0.5). The sensitivity index is usually set to 1.

[0059] The preferred method of Example 1 is simulated and applied. The following is only to illustrate the calculation process of the present invention and has no practical significance. The specific application process is as follows: S1. Mine A needs to determine the mining method. Based on the preliminary work and the determination of professional and technical personnel, three mining methods have been initially selected as the mining method candidate pool: upward horizontal layered filling mining method (method a), upward approach filling method (method b), and strip medium-deep hole subsequent filling method (method c).

[0060] S2, Construct an evaluation index system as follows Figure 2 As shown.

[0061] S3, Determine the grading standards for each indicator: S4. The evaluation indicators are constructed as network nodes. An evaluation indicator relationship network is constructed based on the correlation between the evaluation indicators. The weight of each evaluation indicator is calculated using graph theory centrality analysis.

[0062] Construct an adjacency matrix based on the constructed network nodes: ; Calculate the degree centrality and proximity centrality of the indicators, and then calculate the weights of the indicators based on these two parameters. The indicator weights are: S5, based on the calculated weights, uses the TOPSIS evaluation module in SPSS software to calculate the following: The similarity of method a is 0.4971, that of method b is 0.5632, and that of method c is 0.7122.

[0063] S6, Construct the evaluation cloud model and calculate the membership vector for each mining method: The membership vector for method a is: ; The membership vector for method b is: ; The membership vector for method c is: .

[0064] S7, Calculate the final score for each mining method based on the joint probability function: Method a: P = 0.5180; Method b: P = 0.5780; Method c: P=0.5960.

[0065] Therefore, it can be concluded that method c, which is the deep hole subsequent filling method in the strip mining, has the highest score, and this mining method is the optimal mining scheme.

[0066] Example 2 Please see Figure 3 As shown, Embodiment 2 of the present invention provides a preferred system for a metal mine mining method, the system comprising: Mining method preliminary selection database module 401 is used to store mining methods selected based on the mine site conditions and to build a candidate database; Evaluation index system module 402 is used to construct an evaluation index system selected from three dimensions: technical feasibility, safety assurance, and economic rationality. The evaluation result grading module 403 is used to grade the excellence of mining methods, grade the evaluation indicators accordingly, and determine the grading evaluation criteria. The evaluation index weighting module 404 is used to assign weights to the indicators in the evaluation index system. The weighting is implemented based on graph theory analysis and breadth-first search algorithm. The optimal evaluation module 405 is used to evaluate and compare the mining methods in the candidate database and select the optimal mining method.

[0067] The optimal evaluation module 405 reads candidate mining methods from the mining method preliminary selection database module 401, and performs a comprehensive evaluation and comparison by combining the indicator system of the evaluation indicator system module 402, the grading standard of the evaluation result grading module 403, and the weight of the evaluation indicator weighting module 404, and finally outputs the optimal mining method.

[0068] The preferred evaluation module 405 includes: The TOPSIS evaluation unit is used to calculate the closeness of each mining method to the optimal ideal solution; The cloud model evaluation unit is used to calculate the membership degree of each mining method at each level of excellence. A dual-model coupled evaluation unit is used to couple proximity and maximum membership based on a joint probability function, calculate the final score, and select the optimal mining method. The evaluation index weighting module 404 includes: The relational network construction unit is used to construct the evaluation indicators into a relational network structure graph and transform it into an adjacency matrix; Degree centrality calculation unit, used to calculate the degree centrality of each evaluation index; The proximity centrality calculation unit is used to calculate the proximity centrality of each indicator using a breadth-first search algorithm. The weight combination unit is used to combine degree centrality and proximity centrality into a comprehensive weight.

[0069] The optimization system provided by this invention first constructs a candidate library of mining methods and a multi-dimensional evaluation index system. Then, it constructs an index relationship network using graph theory analysis and a breadth-first search algorithm, objectively calculating the degree centrality and proximity centrality of each index, and automatically assigning index weights, significantly reducing subjectivity and improving evaluation fairness. Next, it uses the TOPSIS model to calculate the closeness of each mining method to the ideal solution, and the cloud model to calculate the membership matrix of each method at different excellence levels. Finally, it couples the closeness and maximum membership based on a joint probability function to obtain the final score, thereby optimizing the mining method. Coupling the TOPSIS model and the cloud model takes into account both ideal solution comparison and fuzzy handling, and fusing the two types of results through a joint probability function significantly improves the evaluation accuracy and robustness. The system strengthens the weight of safety assurance indicators, aligning with the actual safety needs of mining engineering. The coupled model can output multi-dimensional information such as closeness and membership, enhancing decision support capabilities and process interpretability. Simultaneously, the overall method has a clear process flow and well-defined modules, making it easy to program and implement in the field, effectively improving the efficiency and operability of mining method optimization.

[0070] In summary, this invention provides a method and system for optimizing mining methods in metal mines, relating to the field of mining method optimization technology. The method first constructs a candidate mining method library based on ore body occurrence characteristics, technical conditions, and mining requirements, and then establishes an evaluation index system for optimizing mining methods from three aspects: technical feasibility, safety assurance, and economic rationality. Secondly, it sets up multi-level mining method excellence grades and establishes a grading standard for the evaluation indicators. Further, it constructs an evaluation indicator relationship network, uses graph theory analysis to calculate the degree centrality of the indicators, and employs a breadth-first search algorithm to calculate the proximity centrality of the indicators, thereby achieving objective weighting of the evaluation indicators. It constructs a TOPSIS evaluation model to calculate the proximity of each mining scheme to its ideal optimal value. It constructs a cloud model to calculate the membership matrix of each mining method. Finally, it couples the proximity and maximum membership of each mining method using a joint probability function to obtain the final score, and selects the mining method with the highest score as the final mining method. This invention can reduce the influence of subjective human weighting on evaluation results, strengthen the role of safety factors in the selection of mining methods, and improve the accuracy of evaluation results by constructing a coupled evaluation method through a joint probability function. This allows the evaluation process to present more diverse information and provides a quantitative basis for scientific decision-making on mining methods.

[0071] It should be noted that the present invention is not limited to the above-described embodiments. The above embodiments are merely examples, and any embodiments that have the same structure and perform the same effects as the technical concept within the scope of the present invention are included within the scope of the present invention. Furthermore, various modifications that can be conceived by those skilled in the art to the embodiments, and other ways of constructing by combining some of the constituent elements of the embodiments, without departing from the spirit of the present invention, are also included within the scope of the present invention.

Claims

1. A preferred method for mining metal mines, characterized in that, Includes the following steps: S1. Based on the ore body occurrence characteristics, technical conditions and mining requirements, preliminary screening of candidate mining methods is conducted to establish a mining method candidate pool. S2. Based on the pool of alternative mining methods, evaluation indicators are selected from three dimensions: technical feasibility, safety assurance, and economic rationality, to construct an evaluation indicator system for the optimal selection of mining methods. S3, set the quality level of mining methods, and establish a graded evaluation standard corresponding to the quality level for each evaluation indicator; S4, construct the evaluation indicators as network nodes, construct the evaluation indicator relationship network based on the correlation between the evaluation indicators, and calculate the weight of each evaluation indicator using graph theory centrality analysis; S5. Based on the weights of the evaluation indicators, a TOPSIS evaluation model is constructed to calculate the closeness of each mining method to the optimal ideal solution. S6. Based on the aforementioned grading evaluation criteria, the membership degree of each mining method under the excellence level is calculated using a cloud model. S7. Construct a multidimensional random variable relationship model based on the joint probability function, couple the proximity degree and membership degree to obtain the comprehensive evaluation value of each mining method; select the mining method with the highest score as the final preferred solution.

2. A preferred method of metal mine mining according to claim 1, characterized in that, In step S7, based on the membership degree calculated by the cloud model and the closeness calculated by the TOPSIS evaluation model, a joint probability function is constructed using a weighted geometric mean model, taking into account both equilibrium and nonlinear effects. The multidimensional random variable relationship model is as follows: ； In the formula, P is the final score of the coupled model. The higher the score, the more suitable the evaluation method is for the target mine; X 1 Membership degree; For closeness; Weights for each dimension; is the sensitivity index; k is the k-th model coupled to the joint probability function; n is the number of different models coupled to the joint probability function.

3. A preferred method of metal mine mining according to claim 2, characterized in that, In step S5, the TOPSIS evaluation model is constructed. By setting the ideal optimal value and the ideal worst value, the closeness of each mining method to the optimal ideal solution is calculated. The specific process is as follows: S51. Collect and standardize the evaluation index data of each mining method to obtain the data standardization matrix. The standardization method is as follows: For positive indicators: ； For negative indicators: ； In the formula, Standardized evaluation indicators; The maximum value of the indicator; The minimum value of the indicator; The index value; S52, calculate the difference between each evaluation index and the ideal optimal value and the ideal worst value, expressed as: ； In the formula, The difference between each evaluation indicator and the ideal optimal value; The difference between each evaluation indicator and the ideal worst value; The weight of index j; The ideal optimal value for index j; The ideal worst value for index j; Step S53: Calculate the closeness between the mining method and the optimal ideal solution, expressed as: In the formula, This represents the closeness of the mining method to the optimal ideal solution.

4. A preferred method of metal mine mining according to claim 3, characterized in that, In step S3, the grading evaluation criteria corresponding to the quality level of the mining method include: Level 1 indicates excellent, signifying a high degree of compatibility between the mining method and the target mine; Level 2 indicates good, meaning the mining method is applicable but the implementation effect is average; Level 3 indicates passing grade, signifying poor compatibility between the mining method and the target mine; By referring to industry standards and the importance of each evaluation indicator to the target mine, the range of values ​​for each evaluation indicator under different quality levels is determined. The ideal optimal value refers to all indicators taking the optimal value within the Level 1 range, while the ideal worst value refers to all indicators taking the worst value within the Level 3 range.

5. A preferred method of metal mine mining according to claim 4, characterized in that, Step S6, the construction of the cloud model, includes: S61 uses three-dimensional feature parameters to describe the index grading standard, where... The expected value is the central value at the center of the universe of discourse; Entropy is a measure of the ambiguity of qualitative concepts; It is hyperentropy, is The entropy represents the degree of dispersion of cloud droplets in the cloud model, and indirectly reflects the cloud thickness. S62, cloud droplets are generated using a forward cloud generator, and the membership degree of each mining method at each quality level is calculated. The implementation process is as follows: Input: 3 numerical features (E x E n H e and the number of cloud droplets N; Output: N cloud droplets conforming to the concept distribution and a normally distributed random number. ; ； ； In the formula, t represents the cloud correlation degree; x These are the actual measured values ​​of the indicators; This represents the expected value of the indicator. ' is the random realization value of the entropy of the indicator; T is the matrix composed of cloud correlation degree t; G is the membership degree matrix; W is the weight vector; ij is the proximity degree of the i-th indicator under the j-th level.

6. A preferred method of metal mine mining according to claim 2, characterized in that, In step S4, the evaluation metrics are weighted using graph theory analysis and a breadth-first search algorithm, including: S41, construct the evaluation indicators into a relational network structure graph and transform it into an adjacency matrix; where the matrix elements corresponding to indicators with correlation relationships are 1, and the matrix elements corresponding to indicators without correlation relationships are 0. S42, use graph theory analysis to calculate the degree centrality of the index, and use the breadth-first search algorithm to calculate the proximity centrality of the index; the formula for degree centrality is: ； In the formula, For the first Evaluation indicators Degree centrality; a ij Let j be the index directly associated with the i-th evaluation index; n is the number of indicators associated with the i-th evaluation index. Based on the adjacency matrix, the degree centrality of each evaluation index is calculated to characterize the degree of direct correlation between the evaluation indexes in the index relationship network. To eliminate the influence of the number of evaluation indicators on the results, the degree centrality is normalized to obtain the normalized degree centrality. The expression is: ； Meanwhile, a breadth-first search algorithm is used to calculate the proximity centrality of each evaluation index, and the proximity centrality is normalized to characterize the overall proximity of each index to other indicators in the index relationship network.

7. A preferred method of metal mine mining according to claim 6, characterized in that, Step S4 also includes: S43, combine the degree centrality and proximity centrality to obtain the comprehensive weight of each evaluation index, thereby achieving objective weighting of the evaluation index; specifically: combine the normalized degree centrality... Approaching centrality with normalization By performing weighted combinations, the evaluation indicators are obtained. Overall weight The expression is: ； In the formula, normalization approximates centrality. It is a core metric in graph theory for measuring the average shortest path length between a node and other nodes in the network; This allows for the objective weighting of each evaluation index in the evaluation index system for the optimal selection of mining methods.

8. A preferred method of a metal mine mining method according to claim 1, characterized in that, The mining method candidate library mentioned in step S1 includes the design loss rate, design dilution rate, and mining-to-cut ratio data for each mining method; In step S2, when constructing the evaluation index system for the optimal mining method, the weight of safety assurance indicators should be increased.

9. A preferred system for a metal mine mining method, characterized in that, A preferred method for carrying out a metal mine mining method according to any one of claims 1 to 8 includes: The mining method preliminary selection database module is used to store mining methods selected based on the mine site conditions and to build a candidate database; The evaluation index system module is used to construct an evaluation index system selected from three dimensions: technical feasibility, safety assurance, and economic rationality. The evaluation result grading module is used to grade the excellence of mining methods, grade the evaluation indicators accordingly, and determine the grading evaluation criteria. The evaluation index weighting module is used to assign weights to the indicators in the evaluation index system. The weighting is implemented based on graph theory analysis and breadth-first search algorithm. The optimization and evaluation module is used to evaluate and compare the mining methods in the candidate database and select the optimal mining method.

10. The preferred system of the metal mine mining method according to claim 9, characterized in that, The preferred evaluation module includes: The TOPSIS evaluation unit is used to calculate the closeness of each mining method to the optimal ideal solution; The cloud model evaluation unit is used to calculate the membership degree of each mining method at each level of excellence. A dual-model coupled evaluation unit is used to couple proximity and maximum membership based on a joint probability function, calculate the final score, and select the optimal mining method. And / or, the evaluation index weighting module includes: The relational network construction unit is used to construct the evaluation indicators into a relational network structure graph and transform it into an adjacency matrix; Degree centrality calculation unit, used to calculate the degree centrality of each evaluation index; The proximity centrality calculation unit is used to calculate the proximity centrality of each indicator using a breadth-first search algorithm. The weight combination unit is used to combine degree centrality and proximity centrality into a comprehensive weight.

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