Ore body group mining method and system, electronic equipment and storage medium

Abstract

The invention discloses an ore body group mining method and system, electronic equipment and a storage medium, and the method comprises the steps: calculating a comprehensive score of each mining method according to the comprehensive weight of each evaluation index; sorting the various mining methods according to the comprehensive score of each mining method; calculating a fuzzy evaluation value of each mining method according to the comprehensive weight of each evaluation index; sorting the various mining methods according to the fuzzy evaluation value of each mining method; if the first sorting result is consistent with the second sorting result, performing dimensionality reduction on the plurality of evaluation indexes by adopting a principal component analysis method to obtain a first principal component and a second principal component; constructing a two-dimensional plane based on the first principal component and the second principal component, and determining two-dimensional coordinates of each mining method on the two-dimensional plane; and determining a target combination mining method based on the two-dimensional coordinates. Objective, reliable and visual ore body group mining decision making is realized, and the optimality of a mining method combination can be demonstrated from a data essence level.

Description

Technical Field

[0001] This application relates to the field of mining engineering technology, and in particular to a method, system, electronic device and storage medium for mining ore body groups. Background Technology

[0002] In mine design, the selection of mining methods directly affects the mine's production safety, economic benefits, and resource recovery rate. For ore body groups with multiple characteristics (such as ore body thickness, dip angle, stability, and grade distribution), how to scientifically and systematically select the optimal combination of mining methods from numerous alternatives has long been a complex decision-making challenge in the field of mining engineering.

[0003] Existing mining method selection often relies on expert experience, analogy, or a single evaluation model, resulting in strong subjectivity, limited model diversity, complex dimensions, and a lack of systematic combination argumentation. Therefore, developing a method that overcomes these shortcomings, addresses the issue of how to achieve objective, reliable, and visualized mining decisions, and demonstrates the optimality of mining method combinations from a data-driven perspective, is of significant practical importance. Summary of the Invention

[0004] This application aims to propose a mining method, system, electronic equipment, and storage medium for orebody groups, which realizes the objectivity, reliability, and visualization of mining decisions for orebody groups, and can demonstrate the optimality of the combination of mining methods from the perspective of data essence.

[0005] In a first aspect, embodiments of this application provide a method for mining orebody clusters, the method comprising: Multiple evaluation indicators and various mining methods are obtained, and the comprehensive weight of each evaluation indicator is calculated. Calculate the overall score for each of the mining methods based on the comprehensive weight of each of the evaluation indicators; The mining methods are ranked by their combined scores to obtain a first ranking result. Based on the comprehensive weight of each of the evaluation indicators, calculate the fuzzy evaluation value for each of the mining methods; The multiple mining methods are ranked using the fuzzy evaluation value of each mining method to obtain a second ranking result; Compare the consistency between the first ranking result and the second ranking result. If the first ranking result and the second ranking result are consistent, then use principal component analysis to reduce the dimensionality of the multiple evaluation indicators to obtain the first principal component and the second principal component. Based on the first principal component and the second principal component, a two-dimensional plane is constructed, and the two-dimensional coordinates of each of the mining methods on the two-dimensional plane are determined; Based on the two-dimensional coordinates, a target combination mining method for mining ore body groups is determined.

[0006] Compared with the prior art, the first aspect of this application has the following beneficial effects: This method acquires multiple evaluation indicators and various mining methods, calculates the comprehensive weight of each evaluation indicator, calculates the comprehensive score of each mining method based on the comprehensive weight of each evaluation indicator, ranks the various mining methods according to the comprehensive scores of each mining method to obtain a first ranking result, calculates the fuzzy evaluation value of each mining method according to the comprehensive weight of each evaluation indicator, ranks the various mining methods according to the fuzzy evaluation value of each mining method to obtain a second ranking result, compares the consistency between the first ranking result and the second ranking result, if the first ranking result and the second ranking result are consistent, then the principal component analysis method is used to reduce the dimensionality of multiple evaluation indicators to obtain the first principal component and the second principal component, constructs a two-dimensional plane based on the first principal component and the second principal component, and determines the two-dimensional coordinates of each mining method on the two-dimensional plane, and determines the target combination mining method for ore body group mining based on the two-dimensional coordinates. Thus, by comparing the consistency between the first and second ranking results, the potential biases and subjective assumptions arising from a single model are effectively avoided, significantly improving the scientific rigor and reliability of the decision-making process. By employing principal component analysis to reduce the dimensionality of multiple evaluation indicators, the information of the original data can be preserved to the maximum extent in the low-dimensional space. By determining the two-dimensional coordinates of each mining method on a two-dimensional plane, and based on these coordinates, the target combination of mining methods for orebody group mining is determined. This allows decision-makers to intuitively and vividly understand the core competitiveness and applicable scenarios of each mining method, achieving a shift from intuitive to rational judgment. Therefore, this embodiment achieves objectivity, reliability, and visualization in orebody group mining decision-making, and can demonstrate the optimality of the combination of mining methods from the perspective of data essence.

[0007] In some implementations, calculating the comprehensive weight of each of the evaluation indicators includes: Obtain the expert scores for each evaluation indicator and the historical dataset of the ore body group containing each evaluation indicator; Calculate the standard deviation and mean of each evaluation index in the historical dataset of the ore body group; Based on the standard deviation and the mean, the coefficient of variation for each evaluation index is determined; The comprehensive weight of each evaluation indicator is calculated based on the expert score and the coefficient of variation of each evaluation indicator.

[0008] In some implementations, calculating the overall score for each mining method based on the comprehensive weight of each evaluation index includes: Each of the mining methods is scored by experts to obtain a first score on each of the evaluation metrics; A comprehensive score is calculated for each of the mining methods based on the first score and the combined weight of each of the evaluation indicators.

[0009] In some implementations, calculating the fuzzy evaluation value for each mining method based on the comprehensive weight of each evaluation index includes: Calculate the skewness coefficient for each of the evaluation indicators; The corresponding membership function is selected using the skewness coefficient; Based on the comprehensive weight of each evaluation index and the selected membership function, the fuzzy evaluation value of each mining method is calculated.

[0010] In some implementations, comparing the consistency between the first sorting result and the second sorting result includes: ; in, Indicating consistent results, Indicates the first The ranking value of each mining method in the first ranking result. This represents the mean of the first sorted results. Indicates the second sorting result. The ranking value of each mining method in the second ranking result. This represents the mean of the second sorting results.

[0011] In some embodiments, determining the two-dimensional coordinates of each of the mining methods on the two-dimensional plane includes: ; ; in, Indicates the first The horizontal coordinate of each mining method on the two-dimensional plane Indicates the first The first principal component score of each mining method This represents the minimum value among the first principal component scores of all mining methods. This represents the maximum value among the first principal component scores of all mining methods. Indicates the first The vertical coordinate of each mining method on the two-dimensional plane Indicates the first The second principal component score of each mining method This represents the minimum value among the second principal component scores of all mining methods. This represents the maximum value among the second principal component scores of all mining methods.

[0012] In some embodiments, the method for determining the target combination mining method for ore body group mining based on the two-dimensional coordinates includes: Obtain the overall decision-making objectives; Clustering methods are used to cluster the two-dimensional coordinates corresponding to each pair of mining methods. The two mining methods that achieve the overall decision objective in the clustering results are used as the target combination mining methods for ore body group mining.

[0013] Secondly, embodiments of this application also provide a mining system for ore body clusters, the system comprising: The first data calculation unit is used to acquire multiple evaluation indicators and multiple mining methods, and to calculate the comprehensive weight of each evaluation indicator; The second data calculation unit is used to calculate the comprehensive score of each mining method based on the comprehensive weight of each evaluation index. The first data sorting unit is used to sort the multiple mining methods by the comprehensive score of each mining method to obtain a first sorting result; The third data calculation unit is used to calculate the fuzzy evaluation value of each of the mining methods based on the comprehensive weight of each of the evaluation indicators. The second data sorting unit is used to sort the multiple mining methods by the fuzzy evaluation value of each mining method to obtain a second sorting result; The principal component analysis unit is used to compare the consistency between the first ranking result and the second ranking result. If the first ranking result and the second ranking result are consistent, the principal component analysis method is used to reduce the dimensionality of the multiple evaluation indicators to obtain the first principal component and the second principal component. A two-dimensional coordinate determination unit is used to construct a two-dimensional plane based on the first principal component and the second principal component, and to determine the two-dimensional coordinates of each of the mining methods on the two-dimensional plane; The target combination mining method determination unit is used to determine the target combination mining method for ore body group mining based on the two-dimensional coordinates.

[0014] Thirdly, embodiments of this application also provide an electronic device, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform a mineral group mining method as described above.

[0015] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform a mining method for a group of ore bodies as described above.

[0016] It is understood that the beneficial effects of the second to fourth aspects compared with the related technologies are the same as the beneficial effects of the first aspect compared with the related technologies. Please refer to the relevant description in the first aspect above, which will not be repeated here. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a schematic flowchart of an embodiment of the ore body group mining method provided in this application; Figure 2 This is a schematic diagram of the overall process in the preferred embodiment of the ore body group mining method provided in this application; Figure 3 This is a two-dimensional scatter plot of principal component scores in the best embodiment of the ore body group mining method provided in this application; Figure 4 This is a schematic diagram of the structure of an embodiment of the ore body group mining system provided in this application; Figure 5 This is a schematic diagram of the structure of an embodiment of the electronic device provided in this application. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0019] In the description of this application, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0020] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0021] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0022] Because existing mining method selection often relies on expert experience, analogy, or a single evaluation model, it suffers from strong subjectivity, limited model diversity, complex dimensions, and a lack of systematic combination argumentation. Therefore, developing a method that can overcome these shortcomings, address the issue of how to achieve objective, reliable, and visualized mining decisions, and demonstrate the optimality of mining method combinations from a data-driven perspective, is of significant practical importance.

[0023] To address the problems existing in the prior art, this application proposes a mining method, system, electronic equipment, and storage medium for ore body clusters.

[0024] Reference Figure 1 This application provides a schematic flowchart of a method for mining orebody clusters. This method is applied to an electronic device, which may be a server or a mobile terminal, etc. Figure 1 As shown, the mining method for this ore body group may include the following steps: Step S101: Obtain multiple evaluation indicators and various mining methods, and calculate the comprehensive weight of each evaluation indicator; Step S102: Calculate the comprehensive score for each mining method based on the comprehensive weight of each evaluation index; Step S103: Rank the various mining methods according to the comprehensive score of each mining method to obtain the first ranking result; Step S104: Calculate the fuzzy evaluation value for each mining method based on the comprehensive weight of each evaluation index; Step S105: Sort multiple mining methods by the fuzzy evaluation value of each mining method to obtain a second ranking result; Step S106: Compare the consistency between the first ranking result and the second ranking result. If the first ranking result and the second ranking result are consistent, then use principal component analysis to reduce the dimensionality of multiple evaluation indicators to obtain the first principal component and the second principal component. Step S107: Based on the first principal component and the second principal component, construct a two-dimensional plane and determine the two-dimensional coordinates of each mining method on the two-dimensional plane; Step S108: Based on two-dimensional coordinates, determine the target combination mining method for ore body group mining.

[0025] In this embodiment, multiple evaluation indicators and various mining methods are acquired, and the comprehensive weight of each evaluation indicator is calculated. Based on the comprehensive weight of each evaluation indicator, the comprehensive score of each mining method is calculated. The various mining methods are then ranked according to their comprehensive scores to obtain a first ranking result. Based on the comprehensive weight of each evaluation indicator, a fuzzy evaluation value for each mining method is calculated. The various mining methods are then ranked according to their fuzzy evaluation values ​​to obtain a second ranking result. The consistency between the first and second ranking results is compared. If the first and second ranking results are consistent, principal component analysis is used to reduce the dimensionality of the multiple evaluation indicators to obtain a first principal component and a second principal component. Based on the first and second principal components, a two-dimensional plane is constructed, and the two-dimensional coordinates of each mining method on the two-dimensional plane are determined. Based on the two-dimensional coordinates, a target combination mining method for orebody group mining is determined. Thus, by comparing the consistency between the first and second ranking results, the potential biases and subjective assumptions arising from a single model are effectively avoided, significantly improving the scientific rigor and reliability of the decision-making process. By employing principal component analysis to reduce the dimensionality of multiple evaluation indicators, the information of the original data can be preserved to the maximum extent in the low-dimensional space. By determining the two-dimensional coordinates of each mining method on a two-dimensional plane, and based on these coordinates, the target combination of mining methods for orebody group mining is determined. This allows decision-makers to intuitively and vividly understand the core competitiveness and applicable scenarios of each mining method, achieving a shift from intuitive to rational judgment. Therefore, the method in this embodiment achieves objectivity, reliability, and visualization in orebody group mining decision-making, and can demonstrate the optimality of the combination of mining methods from the perspective of data essence.

[0026] The aforementioned evaluation indicators can include multiple indicators such as loss rate, dilution rate, safety, process complexity, and direct ore cost.

[0027] The aforementioned mining methods can include upward entry filling, downward entry filling, shallow hole ore retention, layered filling, and sublevel caving, among others.

[0028] In some implementations, the overall weight of each evaluation metric is calculated, including: Obtain expert scores for each evaluation indicator and historical datasets of ore body clusters containing each evaluation indicator; Calculate the standard deviation and mean of each evaluation index in the historical dataset of the ore body group; The coefficient of variation for each evaluation indicator is determined based on the standard deviation and mean. The overall weight of each evaluation indicator is calculated based on the expert scores and the coefficient of variation of each evaluation indicator.

[0029] In this embodiment, the standard deviation and mean of each evaluation index in the historical dataset of the ore body group are calculated. Based on the standard deviation and mean, the coefficient of variation of each evaluation index is determined. The coefficient of variation reflects the dispersion of the evaluation index in the measured dataset (i.e., the historical dataset of the ore body group) and characterizes the amount of objective information. By calculating the comprehensive weight of each evaluation index based on the expert score and the coefficient of variation of each evaluation index, the differentiated information of the historical data can be fully utilized, expert knowledge can be preserved, and bias caused by completely data-driven approaches can be prevented. Combining expert scores and the coefficient of variation eliminates the need for multiple rounds of consistency checks on the judgment matrix.

[0030] The expert scores mentioned above can be based on the experts' experience in assigning scores to each evaluation indicator.

[0031] The aforementioned historical dataset of ore body groups can contain historical data for various evaluation indicators over different time periods.

[0032] In some implementations, a comprehensive score is calculated for each mining method based on the combined weight of each evaluation metric, including: Each mining method receives its top score on each evaluation metric through expert scoring. The overall score for each mining method is calculated based on the first score and the combined weight of each evaluation indicator.

[0033] In this embodiment, the first score of each mining method on each evaluation index is obtained through expert scoring. Then, based on the first score and the comprehensive weight of each evaluation index, the comprehensive score of each mining method is calculated, which can improve the distinguishability of high-value evaluation indicators.

[0034] In some implementations, a fuzzy evaluation value for each mining method is calculated based on the comprehensive weight of each evaluation index, including: Calculate the skewness coefficient for each evaluation indicator; The corresponding membership function is selected by using the skewness coefficient; Based on the comprehensive weight of each evaluation index and the selected membership function, the fuzzy evaluation value of each mining method is calculated.

[0035] In this embodiment, the corresponding membership function is selected by using the skewness coefficient. Based on the comprehensive weight of each evaluation index and the selected membership function, the fuzzy evaluation value of each mining method is calculated, which can reduce the scale distortion of the evaluation value and obtain a more accurate fuzzy evaluation value.

[0036] The aforementioned skewness coefficient can be used to measure the degree of skewness by comparing the mode or median with the mean, based on the properties of the mode, median, and mean. In other words, the skewness coefficient characterizes the direction and degree of distribution skewness.

[0037] The membership functions mentioned above can include asymmetric trapezoidal membership functions and standard triangular membership functions, etc.

[0038] In some implementations, comparing the consistency between the first sorting result and the second sorting result includes: ; in, Indicates consistent results. Indicates the first The ranking value of each mining method in the first ranking result. This represents the mean of the first sorted results. Indicates the second sorting result. The ranking value of each mining method in the second ranking result. This represents the mean of the second sorting results.

[0039] In this embodiment, by comparing the consistency between the first ranking result and the second ranking result, the bias and subjective assumptions that may be generated by a single model are effectively avoided, and the scientificity and reliability of the decision-making are significantly improved.

[0040] In some implementations, determining the two-dimensional coordinates of each mining method on a two-dimensional plane includes: ; ; in, Indicates the first The horizontal coordinate of a mining method on a two-dimensional plane Indicates the first The first principal component score of each mining method This represents the minimum value among the first principal component scores of all mining methods. This represents the maximum value among the first principal component scores of all mining methods. Indicates the first The ordinate of a mining method on a two-dimensional plane Indicates the first The second principal component score of each mining method This represents the minimum value among the second principal component scores of all mining methods. This represents the maximum value among the second principal component scores of all mining methods.

[0041] In this embodiment, by determining the two-dimensional coordinates of each mining method on a two-dimensional plane, the mining decision of the ore body group can be visualized.

[0042] In some implementations, a target combination mining method for mining orebody groups is determined based on two-dimensional coordinates, including: Obtain the overall decision-making objectives; Clustering methods are used to cluster the two-dimensional coordinates corresponding to each pair of mining methods. The two mining methods that achieve the overall decision-making objective in the clustering results are used as the target combination mining methods for ore body group mining.

[0043] In this embodiment, a clustering method is used to cluster the two-dimensional coordinates corresponding to each of the two mining methods. The two mining methods that achieve the overall decision-making objective in the clustering results are used as the target combination mining methods for ore group mining. This realizes the objectivity, reliability and visualization of ore group mining decision-making, and can demonstrate the optimality of the combination of mining methods from the perspective of data essence.

[0044] The overall decision-making objective mentioned above can be set according to user needs. For example, in the mining of ore groups, it is to select the mining combination method that has the best overall economy, safety, efficiency and resource recovery rate.

[0045] The clustering method described above can be a clustering method known to those skilled in the art, such as the K-Means clustering method.

[0046] To facilitate understanding by those skilled in the art, a set of preferred embodiments is provided below: Because existing mining methods rely heavily on expert experience, analogy, or single evaluation models, they suffer from the following inherent drawbacks: 1. High subjectivity: It relies too much on the personal experience of experts, lacks systematic quantitative analysis, and different experts may reach different conclusions, making it difficult to guarantee the reliability of decisions.

[0047] 2. Single model: Using a single decision model (such as only using the analytic hierarchy process or fuzzy evaluation) for evaluation may lead to biased evaluation results due to the limitations of the model itself, and there is a lack of effective cross-validation mechanisms.

[0048] 3. Complexity of dimensions: The evaluation criteria (dimensions) are usually as many as dozens, making it difficult for decision-makers to intuitively understand and grasp the core differences and advantages of each alternative in these high-dimensional spaces, resulting in opaque decision-making basis.

[0049] 4. Lack of systematic combination argumentation: For ore body groups with different characteristics, existing methods are unable to clearly reveal from the essence of the data why different combinations of mining methods are needed, as well as the core applicable scenarios of each method.

[0050] Therefore, solving the problem of how to achieve objective, reliable, and visualized mining decisions, and how to demonstrate the optimality of mining method combinations from the perspective of data essence, has important practical significance.

[0051] This embodiment discloses a mining method for orebody clusters. This method introduces an objective-subjective fusion weighting formula, an improved correlation matrix method, a dynamic fuzzy comprehensive evaluation method, and principal component analysis (PCA) normalized two-dimensional coordinate calibration and clustering to achieve parallel computation and reliability verification of different models. It also visually presents the core advantage areas of mining schemes in a two-dimensional visualized scatter plot, thus providing a basis for the optimal combination of mining methods for orebody clusters. From the perspective of data essence, it systematically demonstrates the optimal combination of mining methods for the internal differences within orebody clusters. This embodiment effectively overcomes the shortcomings of traditional methods, such as strong subjectivity, single model, and complex dimensions, achieving a scientific, systematic, and visualized approach to mining method selection. (Refer to...) Figure 2 The technical solution of this embodiment includes the following: Step S1: Construct a hierarchical decision-making system. Based on the geological characteristics, technical conditions, and economic factors of the ore body group, establish a hierarchical structure model that includes an objective layer, a criterion layer, and a scheme layer.

[0052] Target layer: This refers to the overall decision-making objective of this embodiment, namely, selecting the optimal combination of mining methods in terms of comprehensive economy, safety, efficiency, and resource recovery rate during the mining of ore groups. The overall decision-making objective can be adjusted according to actual circumstances, and this embodiment does not impose specific limitations on it.

[0053] Criteria layer: This includes multiple evaluation criteria, such as safety, economy, production efficiency, and resource recovery rate.

[0054] Scheme layer: refers to a set of practically feasible mining methods that have been pre-screened or recommended by experts, such as: upward entry filling method, downward entry filling method, shallow hole retention method, layered filling method, and sublevel caving method, etc.

[0055] The target layer is located at the top of the hierarchical model, the criteria layer is located in the middle layer of the hierarchical model, and the solution layer is located at the bottom layer of the hierarchical model. The target layer is associated with the evaluation indicators through an association matrix, which is used for subsequent ranking and verification.

[0056] Step S2: Determine objective weights based on the Analytic Hierarchy Process (AHP). Construct a judgment matrix using expert scoring, and calculate and obtain the objective weight vector for each evaluation criterion (i.e., evaluation indicator). .

[0057] This embodiment further introduces an improved objective-subjective fusion weighting method based on the Analytic Hierarchy Process (AHP). The advantages of this method include: fully utilizing the differentiated information in historical data; preserving expert knowledge to prevent bias caused by purely data-driven approaches; and unifying the degree of dispersion and expert scores into a single formula for normalization fusion, eliminating the need for multiple rounds of consistency checks on the judgment matrix. The specific calculation formula is as follows: ; in, Indicates the first The overall weight of each evaluation indicator Indicates the first The coefficient of variation for each evaluation indicator reflects the degree of dispersion of the evaluation indicator in the measured dataset and characterizes the amount of objective information. Indicates the first The standard deviation of each evaluation indicator in the measured dataset; Indicates the first The mean of each evaluation indicator in the actual test dataset. Indicates the first The expert-assigned values ​​(range 0 to 1) for each evaluation indicator are derived from the score given by domain experts on the importance of the indicator; The balance coefficient (0.4 to 0.6) representing the balance between objective information and expert experience was determined through sensitivity analysis.

[0058] It should be noted that the measured dataset in this embodiment refers to the historical dataset of the ore body group.

[0059] Step S3: Parallel computation and verification of multiple models.

[0060] S3a: Apply the improved correlation matrix method for ranking. Construct the correlation matrix between the alternative mining schemes and the evaluation criteria, and use the weight vector W obtained in step S2 to calculate the comprehensive score of each scheme, thus obtaining the first ranking result R1.

[0061] When constructing the scheme-indicator correlation matrix, instead of using a single weighted average method, a non-linear amplification function is introduced to improve the discriminative power of high-value evaluation indicators: ; in, Indicates the first Mining schemes in the first The highest score on each evaluation metric This represents the total number of evaluation indicators. Indicates the magnification factor. Highlighting the advantages of different mining schemes; when At that time, the distinction between advantageous mining schemes was significantly enhanced, while the medium and low-cost mining schemes were not subject to extreme compression.

[0062] S3b: Ranking is performed using the dynamic fuzzy comprehensive evaluation method. Membership functions for each criterion are established, a fuzzy evaluation matrix is ​​constructed, and a weight vector is generated. A synthesis operation is performed to obtain the fuzzy evaluation values ​​of each scheme, and then the second ranking result R2 is obtained.

[0063] Traditional methods use fixed triangular fuzzy numbers. This embodiment adaptively adjusts the membership function parameters based on the skewness coefficients of each evaluation index data. For example: The evaluation index for highly skewed (i.e., skewness coefficient greater than 0.5) uses the asymmetric trapezoidal membership function; The evaluation index for low skewness (i.e., skewness coefficient less than or equal to 0.5) uses the standard triangular membership function; The membership function center value moves dynamically with the actual sample quantiles (P50, P75) to reduce the scaling distortion of the evaluation values.

[0064] The fuzzy comprehensive calculation formula remains as follows: ; in, The membership matrix is ​​generated based on the aforementioned dynamic function. This indicates transpose.

[0065] It should be noted that the asymmetric trapezoidal membership function and the standard triangular membership function in this embodiment are membership functions known to those skilled in the art, and will not be described in detail in this embodiment. The skewness coefficients of each index data are also calculated using techniques known to those skilled in the art, and will not be described in detail in this embodiment.

[0066] S3c: Result Comparison and Cross-validation. Compare the first ranking result R1 with the second ranking result R2. If the two conclusions are consistent or highly similar, the reliability of the decision result is verified. If there are significant differences, it is necessary to backtrack and check the original data and model parameters until a stable and reliable final ranking conclusion is obtained. The ranking results of the schemes must be tested for Pearson correlation coefficient. ; in, Indicating consistent results, Indicates the first The ranking value of each mining method in the first ranking result. This represents the mean of the first sorted results. Indicates the second sorting result. The ranking value of each mining method in the second ranking result. This represents the mean of the second sorting results.

[0067] when If the sorting results are considered highly consistent, then backtracking and adjustment are required. and .

[0068] Step S4: Visualization and calibration based on the core advantages of principal component analysis.

[0069] Eleven evaluation criteria were treated as eleven average dimensions, and standardized data for each alternative scheme across these dimensions were collected. Principal Component Analysis (PCA) was applied to reduce the dimensionality of this high-dimensional dataset, extracting the two principal components with the highest cumulative contribution: the first principal component PC1 and the second principal component PC2. The coordinates of each alternative mining method were then mapped onto a two-dimensional plane formed by PC1 and PC2. After extracting the first two principal components PC1 and PC2, this embodiment introduces a standardized score mapping formula to ensure consistency between the two-dimensional coordinates and the overall performance of the scheme. The two-dimensional coordinates are: ; ; in, Indicates the first The x-axis of each mining scheme (i.e., mining method) Indicates the first The PC1 principal component score (i.e., the first principal component score) of each mining scheme. This represents the minimum PC1 principal component score among all mining schemes. This represents the maximum value among all mining schemes' PC1 principal component scores. Indicates the first The vertical axis of each mining scheme Indicates the first PC2 principal component scores (i.e., second principal component scores) of each mining scheme. This represents the minimum PC2 principal component score among all mining schemes. This represents the maximum value among all mining schemes' PC2 principal component scores. The PC1 and PC2 principal component scores are the scores of each mining scheme on the PC1 or PC2 principal component dimension obtained through PCA analysis, based on the expert scores of each mining scheme on various evaluation indicators.

[0070] The principal component score is mapped to the range [0,1], which facilitates color / size encoding of the two-dimensional scatter plot; the PCA coordinate values ​​are normalized to form a two-dimensional advantage map with physical meaning; and then the two-dimensional coordinates are further partitioned by a clustering algorithm (K-Means, K is 3 to 5) to realize automatic grouping of mining schemes and recommendation of the best combination (i.e., target combination mining method).

[0071] By analyzing the distribution and clustering of various mining methods in a two-dimensional graph (i.e., a two-dimensional plane), the core advantages of each mining method are vividly revealed, thereby demonstrating the optimal combination of mining methods corresponding to the differences within the ore body group from the perspective of data essence.

[0072] To better illustrate this, this embodiment takes a metal mine with a group of steeply dipping, thin veins as an example, specifically including: First, the objective weights of each evaluation criterion are determined by fusion objective weight calculation. Then, the improved correlation matrix method and the dynamic fuzzy comprehensive evaluation method are applied to rank the alternative schemes. By comparing the conclusions of the two methods, the reliability of the decision results is ensured. Finally, the 11 average dimensions are reduced to 2 key dimensions (resource security recovery and comprehensive cost dimension, and mechanization efficiency and flexibility dimension) through principal component analysis (PCA). This vividly marks the core advantages of each mining method and demonstrates the optimal combination of mining methods covering the differences within steeply dipping thin vein groups from the perspective of data essence.

[0073] 1. Weight calculation.

[0074] This embodiment selects 11 evaluation criteria (i.e., evaluation indicators), including loss rate (U1), dilution rate (U2), safety (U3), process complexity (U4), direct ore cost (U5), 1,000-ton mining-to-cut ratio (U6), stope production capacity (U7), mechanization level (U8), mining labor productivity (U9), mining flexibility (U10), and ventilation effect (U11). First, the coefficient of variation (CV) for each indicator is calculated based on historical data of the ore body group. For example, the standard deviation of safety (U3) in historical data is 0.54, and the mean is 2.00, so the CV is 0.27. Then, the importance scores (E) of each indicator are collected from five industry experts, such as safety (U3)... The score is 0.95. An objective-subjective weighting method was used to determine the weights, and the weight calculation formula is as follows: ; Summing the original values ​​of all indicators yields a total of 6.7395. Therefore: Please refer to Table 1 for details.

[0075] Table 1 shows the weight calculation results.

[0076] 2. Improved correlation matrix method.

[0077] Establish score matrices (from the original data) for each alternative mining method and 11 evaluation criteria, and use the weight vectors obtained in step 1. Substituting into the formula of the improved correlation matrix method: ; in, The value is set to 1.2. Taking the safety (U3) score of the "upward pass filling method" as an example: the score of this method on U3. maximum value Then: contribution value The contribution values ​​of all indicators are calculated sequentially and summed to obtain the comprehensive score of each mining method, forming the first ranking result R1, as shown in Table 2.

[0078] Table 2 shows the first sorting results.

[0079] 3. Dynamic fuzzy analysis method.

[0080] Based on the skewness coefficients of each evaluation indicator, indicators with skewness coefficients greater than 0.5 are assigned an asymmetric trapezoidal membership function, while those with skewness coefficients less than or equal to 0.5 are assigned a standard triangular membership function. The parameters of the membership function are set according to the P50 / P75 quantiles of the indicator. A membership matrix is ​​then constructed. , and weight vector Perform composition operation: ; Based on the evaluation set assignment (Excellent=95, Good=80, Average=65, Poor=50), the second ranking result R2 is obtained, as detailed in Table 3.

[0081] Table 3 shows the second sorting results.

[0082] Next, the consistency of the sorting results is verified by calculating the Pearson correlation coefficient between the two sorting results. ,because The two methods were found to have good consistency in sorting, and the reliability of the scheme was verified.

[0083] 4. Principal component analysis (PCA).

[0084] First, the applicability of the 11 defined evaluation indicators was tested. The results showed that the KMO (Kaiser-Meyer-Olkin) sampling suitability score was 0.705, which is greater than the empirical threshold of 0.7. Meanwhile, the significance level of the Bartlett's test of sphericity was less than 0.001 (see Table 4 for details). These two indicators together confirm that there is a significant linear correlation between the original variables, and the data is very suitable for principal component analysis.

[0085] Table 4 shows the results of the KMO and Bartlett tests.

[0086]

[0087] Principal component analysis (KMO=0.705, Bartlett's test p<0.001) was performed on the standardized evaluation data of each mining scheme. The principal components with eigenvalues ​​greater than 1 were PC1 (7.022) and PC2 (3.256), with a cumulative variance contribution rate of 93.431%. PC1 represents the "resource security and overall cost" dimension, and PC2 represents the "mechanization efficiency and flexibility" dimension. The results were calculated using the normalized formula: ; ; Taking the "upward approach filling method" as an example: , Normalized values ​​yield X=0.86 and Y=0.78. Using K-means clustering (k=3), the schemes are divided into three classes: A (recommended combination), B, and C.

[0088] Principal component analysis (PCA) was used for factor extraction, and the number of principal components was determined based on an eigenvalue greater than 1. As shown in Table 5, the analysis yielded two principal components with initial eigenvalues ​​of 7.022 and 3.256, respectively, contributing 93.431% of the cumulative variance. This indicates that these two principal components can explain 93.43% of the information contained in the original 11 variables, demonstrating a highly significant dimensionality reduction effect. This allows high-dimensional data to be projected onto a two-dimensional plane for visualization analysis with almost no loss of information.

[0089] Table 5 shows the explanation of total variance.

[0090] To enhance the practical explanatory power of the principal components, the Caesar normalized maximum variance method (Varimax) was used for factor rotation, which converged after three iterations. The rotated component matrix clearly reveals the economic and engineering implications represented by the two principal components, as detailed in Table 6.

[0091] Principal Component 1 (PC1): This component exhibits extremely high positive loadings on process complexity, direct ore cost, 1,000-tonnage mining-to-cut ratio, and stope production capacity; simultaneously, it displays extremely high loadings on loss rate, dilution rate, and operational safety. This indicates that PC1 comprehensively reflects the trade-off between technical and economic efficiency and production safety costs in mining methods. Methods with high scores typically imply high production capacity, relatively simple processes, and lower costs and mining-to-cut ratios, but correspondingly, lower resource recovery efficiency (higher loss rate and dilution rate) and lower operational safety. Therefore, PC1 can be interpreted as the dimension of "resource safety recovery and overall cost."

[0092] Principal Component 2: This component exhibits extremely high positive loadings on mechanization level and mining labor productivity, while showing a relatively high load on mining flexibility. This indicates that Principal Component 2 clearly characterizes the technological equipment level and labor input efficiency of mining methods. Methods with high scores have high levels of mechanization and automation, and outstanding labor productivity, but their operational flexibility is relatively limited. Therefore, Principal Component 2 can be defined as the "mechanization efficiency and flexible adaptability" dimension.

[0093] The variable “ventilation effect” has significant loadings on both principal components, indicating that it is a cross-referenced indicator, but it is more likely to be associated with safety represented by principal component 1 and mechanized ventilation demand represented by principal component 2.

[0094] Table 6 shows the rotated component matrix.

[0095] 5. Two-dimensional scatter plot positioning method.

[0096] Based on the two principal components mentioned above, the factor scores for each mining method were calculated, and a two-dimensional scatter plot of the principal component scores was plotted (e.g., Figure 3 (As shown in the figure). This figure precisely positions each mining method in a two-dimensional coordinate system with "production efficiency and overall cost" on the horizontal axis and "mechanization and labor efficiency" on the vertical axis.

[0097] 6. Mutual verification with the previous decision-making model.

[0098] Comparing the dimensionality reduction results of PCA with the ranking conclusions of AHP-association matrix / fuzzy comprehensive evaluation reveals a high degree of consistency. The optimal solutions ranking high in the comprehensive decision-making model are typically located in regions on the principal component scatter plot that achieve the best balance between production efficiency, cost control, mechanization level, and resource recovery rate, rather than simply pursuing an extreme value in a single dimension. This finding, from the perspective of the inherent structure of the data, strongly confirms the scientific validity and reliability of the previous decision-making results. PCA not only vividly illustrates the core advantages of each method but also, starting from the inherent correlations of variables, demonstrates that the optimal mining method covering the complex technical and economic differences within steeply dipping thin vein groups is not a single method but a combination of methods occupying a specific advantageous position in the two-dimensional feature space.

[0099] 7. Output the results.

[0100] For the mines targeted in this calculation, the recommended optimal mining combination method (i.e., the target combination mining method) is a combination of segmented filling method and downward access filling mining method, which can meet the production needs of basically all types of ore bodies in the mine.

[0101] In this embodiment, the system implements the above module functions through software programming (such as Python and MATLAB) to provide users with a graphical user interface.

[0102] Compared with the prior art, the beneficial effects of this embodiment are as follows: 1. Objective and reliable decision-making: The objective weights are determined by calculating weights using an objective-subjective fusion weighting method. The conclusions of two independent models, the improved correlation matrix and the dynamic fuzzy evaluation, are used to verify each other. This effectively avoids the bias and subjective assumptions that may be generated by a single model, and significantly improves the scientificity and reliability of decision-making.

[0103] 2. Visualizing High-Dimensional Information: To deeply reveal the intrinsic characteristics of each alternative mining method from a data-driven perspective and intuitively display its core advantages, after completing the comprehensive decision-making based on the objective-subjective fusion weighting method, the improved correlation matrix method, and the dynamic fuzzy comprehensive evaluation method, Principal Component Analysis (PCA) was further introduced, along with normalization and clustering. PCA, as a classic unsupervised dimensionality reduction technique, can transform multiple correlated variables hidden in the original high-dimensional data into a few uncorrelated comprehensive variables (i.e., principal components), thereby preserving the information of the original data to the maximum extent in the low-dimensional space and revealing its internal structure. The innovative introduction of PCA dimensionality reduction technology compresses the complex 11-dimensional evaluation space into a two-dimensional visual plane, enabling decision-makers to intuitively and vividly understand the core competitiveness and applicable scenarios of each mining method, achieving a shift from intuitive judgment to rational judgment.

[0104] 3. Systematic combination argumentation: Through dimensionality reduction and visualization calibration, it is possible to clearly demonstrate from the data distribution why different characteristics of ore bodies in a group of ore bodies require different combinations of mining methods, providing a solid data-based basis for mines to formulate differentiated and refined mining strategies.

[0105] 4. High applicability: This system and method do not depend on specific mineral types or deposit types. By adjusting the evaluation index system, it can be widely applied to the selection of mining methods in various metal and non-metal mines.

[0106] Reference Figure 4 This application also provides a mining system for ore body clusters, the system comprising: The first data calculation unit 401 is used to acquire multiple evaluation indicators and multiple mining methods, and calculate the comprehensive weight of each evaluation indicator. The second data calculation unit 402 is used to calculate the comprehensive score of each mining method based on the comprehensive weight of each evaluation index. The first data sorting unit 403 is used to sort multiple mining methods by the comprehensive score of each mining method to obtain a first sorting result; The third data calculation unit 404 is used to calculate the fuzzy evaluation value of each mining method based on the comprehensive weight of each evaluation index. The second data sorting unit 405 is used to sort multiple mining methods by the fuzzy evaluation value of each mining method to obtain a second sorting result; Principal component analysis unit 406 is used to compare the consistency between the first ranking result and the second ranking result. If the first ranking result and the second ranking result are consistent, the principal component analysis method is used to reduce the dimensionality of multiple evaluation indicators to obtain the first principal component and the second principal component. The two-dimensional coordinate determination unit 407 is used to construct a two-dimensional plane based on the first principal component and the second principal component, and to determine the two-dimensional coordinates of each mining method on the two-dimensional plane; The target combination mining method determination unit 408 is used to determine the target combination mining method for ore body group mining based on two-dimensional coordinates.

[0107] It should be noted that since the ore group mining system in this embodiment is based on the same inventive concept as the ore group mining method described above, the corresponding content in the method embodiment is also applicable to this system embodiment, and will not be described in detail here.

[0108] Reference Figure 5 This application also provides an electronic device, which includes: At least one memory; At least one processor; At least one program; The program is stored in memory, and the processor executes at least one program to implement the above-described ore group mining method of this disclosure.

[0109] This electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0110] The electronic devices according to embodiments of this application will now be described in detail.

[0111] The processor 1600 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure. The memory 1700 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1700 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1700 and is called and executed by the processor 1600 to execute the ore group mining method of the embodiments of this disclosure.

[0112] The input / output interface 1800 is used to realize information input and output. The communication interface 1900 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 2000 transmits information between various components of the device (e.g., processor 1600, memory 1700, input / output interface 1800, and communication interface 1900); The processor 1600, memory 1700, input / output interface 1800 and communication interface 1900 are connected to each other within the device via bus 2000.

[0113] This disclosure also provides a storage medium, which is a computer-readable storage medium, storing computer-executable instructions for causing a computer to execute the above-described ore body mining method.

[0114] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0115] The embodiments described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided by this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by this disclosure are also applicable to similar technical problems.

[0116] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this disclosure, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0117] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0119] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0120] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0124] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. The embodiments of this application have been described in detail above with reference to the accompanying drawings, but this application is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of this application.

Claims

1. A method for mining a group of ore bodies, characterized in that, The method includes: Multiple evaluation indicators and various mining methods are obtained, and the comprehensive weight of each evaluation indicator is calculated. Calculate the overall score for each of the mining methods based on the comprehensive weight of each of the evaluation indicators; The mining methods are ranked by their combined scores to obtain a first ranking result. Based on the comprehensive weight of each of the evaluation indicators, calculate the fuzzy evaluation value for each of the mining methods; The multiple mining methods are ranked using the fuzzy evaluation value of each mining method to obtain a second ranking result; Compare the consistency between the first ranking result and the second ranking result. If the first ranking result and the second ranking result are consistent, then use principal component analysis to reduce the dimensionality of the multiple evaluation indicators to obtain the first principal component and the second principal component. Based on the first principal component and the second principal component, a two-dimensional plane is constructed, and the two-dimensional coordinates of each of the mining methods on the two-dimensional plane are determined; Based on the two-dimensional coordinates, a target combination mining method for mining ore body groups is determined.

2. The mining method for ore body groups according to claim 1, characterized in that, The calculation of the comprehensive weight of each of the evaluation indicators includes: Obtain the expert scores for each evaluation indicator and the historical dataset of the ore body group containing each evaluation indicator; Calculate the standard deviation and mean of each evaluation index in the historical dataset of the ore body group; Based on the standard deviation and the mean, the coefficient of variation for each evaluation index is determined; The comprehensive weight of each evaluation indicator is calculated based on the expert score and the coefficient of variation of each evaluation indicator.

3. The mining method for ore body groups according to claim 1, characterized in that, The step of calculating the comprehensive score for each mining method based on the comprehensive weight of each evaluation index includes: Each of the mining methods is scored by experts to obtain a first score on each of the evaluation metrics; A comprehensive score is calculated for each of the mining methods based on the first score and the combined weight of each of the evaluation indicators.

4. The mining method for ore body groups according to claim 1, characterized in that, The step of calculating the fuzzy evaluation value for each mining method based on the comprehensive weight of each evaluation index includes: Calculate the skewness coefficient for each of the evaluation indicators; The corresponding membership function is selected using the skewness coefficient; Based on the comprehensive weight of each evaluation index and the selected membership function, the fuzzy evaluation value of each mining method is calculated.

5. The mining method for ore body groups according to claim 1, characterized in that, The comparison of the consistency between the first sorting result and the second sorting result includes: ； in, Indicating consistent results, Indicates the first The ranking value of each mining method in the first ranking result. This represents the mean of the first sorted results. Indicates the second sorting result. The ranking value of each mining method in the second ranking result. This represents the mean of the second sorting results.

6. The mining method for ore body groups according to claim 1, characterized in that, Determining the two-dimensional coordinates of each of the mining methods on the two-dimensional plane includes: ； ； in, Indicates the first The horizontal coordinate of each mining method on the two-dimensional plane Indicates the first The first principal component score of each mining method This represents the minimum value among the first principal component scores of all mining methods. This represents the maximum value among the first principal component scores of all mining methods. Indicates the first The vertical coordinate of each mining method on the two-dimensional plane Indicates the first The second principal component score of each mining method This represents the minimum value among the second principal component scores of all mining methods. This represents the maximum value among the second principal component scores of all mining methods.

7. The mining method for ore body groups according to claim 1, characterized in that, The method for determining the target combination mining method for ore body group mining based on the two-dimensional coordinates includes: Obtain the overall decision-making objectives; Clustering methods are used to cluster the two-dimensional coordinates corresponding to each pair of mining methods. The two mining methods that achieve the overall decision objective in the clustering results are used as the target combination mining methods for ore body group mining.

8. A mining system for a group of ore bodies, characterized in that, The system includes: The first data calculation unit is used to acquire multiple evaluation indicators and multiple mining methods, and to calculate the comprehensive weight of each evaluation indicator; The second data calculation unit is used to calculate the comprehensive score of each mining method based on the comprehensive weight of each evaluation index. The first data sorting unit is used to sort the multiple mining methods by the comprehensive score of each mining method to obtain a first sorting result; The third data calculation unit is used to calculate the fuzzy evaluation value of each of the mining methods based on the comprehensive weight of each of the evaluation indicators. The second data sorting unit is used to sort the multiple mining methods by the fuzzy evaluation value of each mining method to obtain a second sorting result; The principal component analysis unit is used to compare the consistency between the first ranking result and the second ranking result. If the first ranking result and the second ranking result are consistent, the principal component analysis method is used to reduce the dimensionality of the multiple evaluation indicators to obtain the first principal component and the second principal component. A two-dimensional coordinate determination unit is used to construct a two-dimensional plane based on the first principal component and the second principal component, and to determine the two-dimensional coordinates of each of the mining methods on the two-dimensional plane; The target combination mining method determination unit is used to determine the target combination mining method for ore body group mining based on the two-dimensional coordinates.

9. An electronic device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the ore body group mining method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the ore body mining method as described in any one of claims 1 to 7.

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