A green mine evaluation method and system

Abstract

This invention provides a method and system for evaluating green mines, belonging to the field of mining development technology. The method includes: constructing a green mine evaluation index system and determining grading standards; constructing an index relationship network graph based on the index system, generating an adjacency matrix, calculating index degree centrality using graph theory, calculating index proximity centrality using a breadth-first search algorithm, and determining index weights based on a game theory combination of the two centralities; acquiring measured index data, calculating the cloud correlation degree of the indicators relative to each level using a cloud model, and constructing a cloud correlation degree matrix; calculating the membership degree of the mine relative to each level based on the weights and the cloud correlation degree matrix, and determining the green construction level of the mine based on the principle of maximum membership degree. This invention effectively solves the problems of excessive subjectivity, insufficient quantification, and low accuracy of evaluation results in existing technologies, and can be widely applied to the evaluation of the green construction level of various types of mines.

Description

Technical Field

[0001] This invention relates to the field of mining development technology, specifically to a green mine evaluation method and system. Background Technology

[0002] Traditional mining development models have long faced problems such as low efficiency in comprehensive resource utilization, large-scale ecological disturbance, and lax pollution emission control, leading to severe ecological challenges such as land degradation, water pollution, and biodiversity loss. Against this backdrop, comprehensively promoting green mine construction has become an inevitable choice for high-quality development of the mining industry. Constructing a scientific, systematic, and operable green mine evaluation system is the core support for guiding mining enterprises to conduct green construction in a standardized manner and ensuring the effectiveness of construction. Existing evaluation methods have gradually revealed several limitations in practical application, making it difficult to fully meet the practical needs of refined, intelligent, and dynamic management of green mines. Specifically, existing evaluation methods mainly have the following prominent problems: First, the weighting of indicators is too subjective. Most existing evaluation methods rely excessively on expert experience and subjective scoring in determining indicator weights, lacking objective quantitative means based on data driving, making the evaluation results susceptible to individual cognitive biases, and the scientific nature and stability of the weights difficult to guarantee. Second, the quantitative level of indicators is insufficient. Existing evaluation indicators mostly remain at the qualitative description level, lacking standardized and quantifiable data collection and processing methods, resulting in poor standardization of the evaluation process and weak comparability of evaluation results from different evaluation subjects and at different times. Third, dynamic evaluation capabilities are lacking. Existing evaluations are mostly static and phased assessments, making it difficult to achieve real-time monitoring and dynamic evaluation of the entire life cycle of mine development and utilization, and failing to reflect the evolution trend of the mine's ecological environment quality in a timely manner. Fourth, the level of intelligence is low. Existing evaluation methods have not fully integrated modern information technologies such as the Internet of Things, remote sensing monitoring, and 3D modeling, resulting in low data collection efficiency and long processing cycles, making it difficult to meet the needs of large-scale, high-frequency green mine evaluation.

[0003] To address the aforementioned technical deficiencies, some patent documents have proposed improvement schemes, but significant shortcomings remain. Chinese patent document CN119168445A proposes a green mine evaluation method based on multi-source data fusion. This method integrates on-site investigations, questionnaire surveys, and historical data, combining weight allocation and pre-set weight analysis to calculate a comprehensive score for evaluation. However, this method still relies heavily on expert subjective weighting and manual adjustment in determining indicator weights and calculating the comprehensive score, failing to fundamentally overcome the shortcomings of strong subjectivity and insufficient quantitative support in the evaluation process. The credibility and stability of the evaluation results need improvement. Another Chinese patent document, CN110210790A, proposes a dynamic monitoring method and system for green mines based on a three-dimensional visualization model. This method constructs a true three-dimensional model of the mine and collects surface parameters to achieve continuous monitoring and evaluation of mine environmental governance and restoration. Although this technology introduces spatial information technology into regulatory methods, its evaluation system is still based on the traditional qualitative indicator framework. The indicator weights and evaluation rules lack objective quantitative basis, and the scientificity and accuracy of the evaluation results are still limited by human subjective factors, making it difficult to systematically support accurate decision-making and long-term management of green mine construction.

[0004] In view of this, the current green mine evaluation system still has obvious shortcomings in terms of the objectivity of evaluation methods, the scientific nature of the indicator system, and the intelligence of data processing. There is an urgent need to build a new evaluation method and system that combines theoretical rigor, technological advancement, and practical operability in order to promote the development of green mine construction towards a higher quality and more sustainable direction and to solve the above-mentioned technical problems. Summary of the Invention

[0005] In view of the technical problems existing in the background art, the present invention provides a green mine evaluation method and system. The method involves constructing a green mine evaluation index system and classifying the indicators by referring to national standards and the actual conditions of the mine; constructing an index relationship network diagram and assigning weights to the indicators using graph theory and BFS algorithms; collecting engineering measurement data of various mine indicators; using the obtained measurement data, calculating the cloud correlation matrix of the indicators through a cloud model; and finally calculating the membership degree of the mine with different levels based on the indicator weights and the cloud correlation matrix to determine the degree of green construction of the mine.

[0006] In a first aspect, embodiments of the present invention provide a method for evaluating green mines, which includes the following steps: S1. Based on national standards and actual conditions of mine site engineering, construct a green mine evaluation index system; S2, determine the grading standards for evaluation indicators, divide the degree of green construction of mines into multiple levels, and simultaneously classify each indicator in the evaluation indicator system into corresponding levels. S3, construct the indicator relationship network graph and indicator relationship adjacency matrix, and use graph theory and breadth-first search algorithm to assign weights to the indicators; S4, collect engineering measurement data of various indicators in the mine, and quantify the qualitative indicator data; S5. Based on the measured data from step S4, calculate the cloud correlation matrix of the indicators using the cloud model. S6. Combining the indicator weights from step S3 and the cloud correlation matrix from step S5, calculate the membership degree of the mine at different levels, and determine the green construction level of the mine based on the principle of maximum membership degree.

[0007] As a further improvement of the present invention, in step S1, a green mine evaluation index system is constructed, specifically by selecting indicators based on six aspects: mining area environment, resource development and utilization, personnel safety, green and low-carbon, ecological restoration and environmental governance, and technology research and development and enterprise management; and when selecting indicators, the proportion of each dimension of indicators is adjusted according to the regional characteristics of the mine.

[0008] As a further improvement of the present invention, in step S2, determining the evaluation index grading standard specifically includes: S21. Based on national or industry standards, preliminary grading standards for each evaluation indicator at multiple levels are initially determined. S22. Based on the actual conditions of the mine, the preliminary grading standards are modified to make the standards conform to the actual conditions of the mine, clarify the value range of the indicators under each level, and use this value range to specify the grading standards of the indicators. S23 classifies the level of green construction in mines into three levels: Level 1, Level 2, and Level 3. Level 1 represents excellent quality of green construction; Level 2 represents average quality of green construction, which has a certain degree of disturbance to the surrounding ecological environment; and Level 3 represents poor quality of green construction, in which the mining and production have a great impact on the surrounding environment and residents, and rectification is required as soon as possible.

[0009] As a further improvement of the present invention, in step S3, constructing an index relationship network graph and an index relationship adjacency matrix and assigning weights specifically includes: S31, construct an indicator relationship network structure diagram using mind mapping software to visualize the relationships between indicators; S32, based on the indicator relationship network structure diagram, construct the indicator relationship adjacency matrix. In the matrix, related indicators are represented by 1, and unrelated indicators are represented by 0. S33 uses graph theory to calculate the degree centrality of the index and breadth-first search algorithm combined with MATLAB software to calculate the proximity centrality of the index. S34 uses game theory to combine the degree centrality and proximity centrality of the indicators to assign weights to the indicators, and calculates the final weights of the indicators.

[0010] As a further improvement of the present invention, in step S3, an indicator relationship adjacency matrix is ​​constructed based on the indicator relationship network structure diagram. The network contains all indicators and the relationships between each indicator, and each indicator is a node. Key nodes in the network are found. The degree centrality (DC) of a node is defined as the number of directly connected adjacent edges between the node and other nodes in its network. The formula for calculating the degree centrality is: ; In the formula: Represents existing nodes The number of connected lines N -1 indicates that except for nodes The number of points that are connected to it from all other points; Proximity centrality (CC) is quantified and reconstructed through a mathematical inverse transformation. When its value approaches 1, the node's topological influence and network coreness in the graph are greater. The formula for calculating proximity centrality is as follows: ; ; In the formula: d i This represents the sum of the shortest path lengths from node i to all other nodes; d ij This represents the shortest path from node i to all other nodes; The proximity centrality of the indicators is calculated using MATLAB. Based on the adjacency matrix of the relationship between the indicators, the BFS algorithm is used to process and calculate the adjacency matrix, and finally the proximity centrality of each indicator is obtained. Finally, degree centrality (DC) and proximity centrality (CC) are calculated. Both have equal weighting in assigning weights to the indicators, therefore each has a weight of 0.5. The final weight value is the sum of DC and CC multiplied by their respective weights, calculated using the following formula: ; In the formula: W is the final weight, which can be used for subsequent calculations and analysis of the importance of the indicators.

[0011] As a further improvement of the present invention, step S5, which calculates the cloud correlation matrix of the indicators using the cloud model, specifically includes: The cloud model uses a three-dimensional feature parameter system for conceptual representation: among which, the central value parameter The typical location of the domain is determined using probabilistic and statistical methods; entropy value As a fuzzy measure, its numerical value directly determines the coverage of the domain space, forming the extensional boundary of the concept; hyperentropy As a second-order entropy parameter, it describes the fluctuation characteristics of entropy values, and its value corresponds to the dispersion of cloud droplet distribution, indirectly mapping the accuracy of conceptual expression; the calculation formulas for the aforementioned three cloud digital features are as follows: ; ; ; In the formula: and These represent the maximum and minimum values ​​of the evaluation index under each grading standard. k It is a constant, related to the degree of dispersion of the indicator, and is determined according to the fuzziness and randomness of the evaluation object; Finally, the cloud digital characteristics of each indicator are formed. Ex, En, He The cloud digital characteristics of each indicator are substituted into the forward cloud generator in MATLAB to calculate the random implementation value of the entropy of the indicator. ; Calculated using a forward cloud generator Then, the measured value x of the indicator, the previously calculated expected value Ex of the indicator, and the stochastic realization value of the indicator's entropy are used. Calculate the cloud relevance of each indicator relative to the three levels. The formula for calculating cloud relevance is: ; 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, and En' is the random realization value of the entropy of the indicator. The random realization value of the entropy is generated by the positive cloud generator code MATLAB. The cloud correlation degree of all indicators at the three levels is calculated, and the cloud correlation degree matrix is ​​constructed.

[0012] As a further improvement of the present invention, in step S6, a cloud correlation matrix is ​​constructed. Z The cloud correlation matrix is ​​composed of the cloud correlation degree t of each indicator calculated in step S5, as shown in the following formula: ; Then, using the index weights obtained through network analysis, the cloud association matrix is ​​weighted and calculated to obtain the final membership matrix. G Finally, based on the principle of maximum membership, the final level of green mining construction is determined. The formula for calculating the membership matrix is ​​as follows: ; The membership matrix includes the membership degree of the mine at three levels. The level with the highest membership degree is the level of green construction of the mine.

[0013] As a further improvement of the present invention, the step of adjusting the proportion of each dimension indicator according to the differences in the regional characteristics of the mine includes: For mines in ecologically fragile areas, increase the proportion of environmentally related indicators; For mines in ordinary areas, increase the proportion of resource development and utilization-related indicators; For mines in plateau regions, increase the proportion of environmental and personnel safety-related indicators.

[0014] Secondly, embodiments of the present invention provide a green mine evaluation system for performing the above-described green mine evaluation method, comprising: The indicator screening module is used to screen evaluation indicators and construct a green mine evaluation indicator system by referring to national standards and actual engineering conditions at the mine site. The indicator grading module is used to determine the indicator grading standards, divide the degree of green construction in mines into multiple levels, and classify each indicator in the evaluation indicator system into corresponding levels. The indicator weighting module is used to construct the indicator relationship network graph and the indicator relationship adjacency matrix. It uses graph theory and BFS algorithm to calculate the final weight of the indicators and assign weights to the evaluation indicators to provide calculation conditions for subsequent evaluation. The indicator database module is used to collect and store engineering measured data of various indicators in the mine, and to quantify qualitative indicator data. The evaluation module is used to calculate the cloud correlation matrix based on measured data using a cloud model, calculate the membership matrix by combining the index weights, and output the level of green construction of the mine according to the principle of maximum membership.

[0015] As a further improvement of the present invention, the index weighting module includes: The adjacency matrix generation unit is used to convert the index relationship network graph into an index relationship adjacency matrix, wherein related indicators are represented by 1 and unrelated indicators are represented by 0. A centrality calculation unit is used to calculate the degree centrality and proximity centrality of each index based on the adjacency matrix. The game combination unit is used to determine the combination coefficients by solving the Nash equilibrium, taking the degree centrality and the proximity centrality as the two sides of the game, and then calculating the final weights by weighting. And / or, the evaluation module includes: The cloud feature generation unit is used to determine the cloud digital features corresponding to each level according to the grading standard. A random implementation unit is used to generate random implementation values ​​of entropy based on a forward cloud generator; The correlation calculation unit is used to calculate the cloud correlation degree of each indicator relative to each level based on the measured data, the expected value, and the random realization value of the entropy.

[0016] Beneficial effects: The green mine evaluation method provided by this invention has the following technical advantages: 1. Objective and Scientific Weighting Method: Based on the inherent correlation of evaluation indicators, a relational network graph is constructed. Graph theory is used to calculate degree centrality, and a breadth-first search algorithm is employed to determine proximity centrality. Finally, game theory is used to integrate the two types of centrality to achieve comprehensive weighting. This method transforms the traditional weighting process, which relies on subjective expert evaluation, into an objective quantitative analysis based on network topology. It effectively overcomes the limitations of strong subjectivity and insufficient stability in questionnaire surveys and expert scoring, significantly improving the scientific nature of indicator weights and the reliability of results.

[0017] 2. In-depth Quantification of Indicators: A cloud model is introduced to process measured data, comprehensively characterizing the randomness and ambiguity of the data through numerical features such as expected value, entropy, and hyperentropy. A positive cloud generator enables bidirectional conversion between qualitative concepts and quantitative values. This mechanism not only unifies the quantification standards for qualitative indicators but also fully considers the uncertainty of quantitative indicator data, making the evaluation results more closely reflect the fluctuation characteristics of actual engineering data and enhancing the rigor and adaptability of the analysis.

[0018] 3. Standardized and standardized evaluation process: A standardized full-process evaluation system has been constructed, consisting of "indicator system establishment - indicator classification - network weighting - cloud model calculation - membership determination". Each step is implemented based on mathematical models and algorithms to minimize human intervention. At the same time, the indicator classification standards and the mine green construction level adopt a unified three-level classification architecture, forming a complete mapping chain from micro-indicator performance to macro-system evaluation, ensuring the comparability and consistency of evaluation results from different evaluation subjects and at different times.

[0019] 4. Accurate and Intuitive Evaluation Results: By calculating the weight matrix and the cloud correlation matrix, the membership vector of the mine at each level is obtained, and the final level is determined based on the principle of maximum membership. The evaluation results are presented in a probabilistic form, which not only reflects the overall level of the mine's green construction but also reveals its proximity to each level. Compared with traditional single comprehensive scoring methods, this method provides richer information, more accurate judgments, and has stronger decision-making guidance value.

[0020] 5. Flexible and Adaptable Indicator Selection: The evaluation indicator system is designed around six dimensions: mining area environment, resource development and utilization, personnel safety, green and low-carbon development, ecological restoration and environmental governance, and technological research and development and enterprise management. The indicator weights and structure can be adjusted according to the geographical characteristics of the mine (e.g., ecologically fragile areas, ordinary areas, plateau areas). While adhering to the framework of relevant national standards, this method fully integrates the actual needs of the mine, significantly enhancing the applicability and relevance of the evaluation system.

[0021] 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

[0022] 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.

[0023] Figure 1 This is a flowchart illustrating a green mine evaluation method provided in Embodiment 1 of the present invention; Figure 2 This is the index relationship network diagram of The Brain brain map structure provided in Embodiment 1 of the present invention; Figure 3 These are cloud droplet diagrams of various indicators provided in Embodiment 1 of the present invention; Figure 4 This is a framework diagram of a green mine evaluation system provided in Embodiment 2 of the present invention.

[0024] Figure label: 501. Indicator Screening Module; 502. Indicator Grading Module; 503. Indicator Weighting Module; 504. Indicator Database Module; 505. Evaluation Module. Detailed Implementation

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] To address the technical problems of excessive subjectivity, insufficient quantification, and low accuracy in existing mine evaluation methods, this invention provides a green mine evaluation method and system. Through multi-level and multi-dimensional technological innovation, it fundamentally solves key issues such as strong subjectivity, insufficient quantification, and poor dynamic adaptability in traditional evaluation systems. Specifically, this invention first establishes a scalable evaluation index system based on six dimensions: mining environment, resource development, and ecological restoration. It innovatively introduces index relationship network modeling technology, utilizing degree centrality analysis from graph theory and breadth-first search (BFS) algorithm to calculate the proximity centrality of indicators. Game theory is then used to integrate these two methods to achieve objective and automated weighting of indicators, completely avoiding subjective biases caused by manual weighting. Furthermore, this invention incorporates cloud model theory, transforming the measured data of each indicator into cloud digital features with randomness and fuzziness. A forward cloud generator generates random values ​​of entropy, and then the cloud correlation degree of each indicator under different evaluation levels is calculated, forming a cloud correlation degree matrix. Finally, by comprehensively calculating the indicator weight matrix and the cloud correlation matrix, the membership degree of the mine under different green levels is obtained, and the green construction level of the mine is scientifically determined based on the principle of maximum membership degree. This method not only achieves objectivity, quantification, and intelligence throughout the entire process from indicator selection and weight allocation to result determination, but also adapts to the differentiated evaluation needs of mines in different regions and of different types, supports dynamic data updates and real-time assessments, and provides reliable technical support and decision-making basis for the precise construction, long-term supervision, and continuous optimization of green mines.

[0034] In this invention, the indicator relationship network graph transforms the indicator system from a static set into a dynamic relational network, providing topological support for graph-based centrality calculations. Degree centrality characterizes the local correlation strength of indicators, while approximation centrality characterizes the global propagation efficiency. These two are combined through game theory weighting to achieve objective quantification of indicator importance in both "local" and "global" dimensions, avoiding information loss from single-method approaches. The cloud model transforms the randomness and fuzziness of measured indicator data into cloud digital features (Ex, En, He). A forward cloud generator generates random values ​​of entropy, giving cloud correlation calculations both probability distribution and fuzzy membership characteristics. This characteristic is multiplied by the deterministic weight matrix obtained through network weighting, achieving compatible computation between "deterministic weights and uncertain data," ensuring that the evaluation process is both objectively quantified and conforms to the actual data fluctuation characteristics. The indicator grading standards and the mine green construction level adopt the same three-level architecture, so that the correlation degree of the indicator cloud and the membership degree of the mine level form a mapping channel. The weighted operation of the weight matrix on the cloud correlation degree matrix is ​​essentially to aggregate the correlation degree of the indicator level into the mine level level. Finally, through the principle of maximum membership degree, the hierarchical leap from "indicator performance" to "system evaluation" is realized, forming a complete evaluation closed loop of "indicator-matrix-vector-level".

[0035] Example 1 Please see Figure 1 As shown, Embodiment 1 of the present invention provides a green mine evaluation method, including the following steps: S1, a green mine evaluation index system is constructed with reference to national standards and actual mine site engineering conditions.

[0036] A green mine evaluation index system is constructed based on indicators selected from six aspects: mining area environment, resource development and utilization, personnel safety, green and low-carbon development, ecological restoration and environmental governance, and technology research and development and enterprise management.

[0037] Based on the "Evaluation Indicators for the Construction of National Green Mines" issued by relevant government departments in 2024 and the actual situation of mining projects, indicators were selected from the above six aspects. When selecting indicators, it is necessary to adapt to local conditions. For example, mines in ecologically fragile areas need to increase the proportion of environmental indicators, mines in ordinary areas need to pay more attention to the proportion of resource development and utilization indicators, and mines in plateau areas need to increase the proportion of environmental indicators and personnel safety indicators.

[0038] Ultimately, a green mine evaluation index system was formed, with 6 primary indicators as guidance and 30 secondary indicators (the secondary indicators can be dynamically adjusted), such as: mining recovery rate, surface damage level, mine greening coverage rate, occupational disease incidence rate, carbon emission accounting, satisfaction of people in the mining area, and disposal rate of waste, etc.

[0039] S2, determine the grading standards for evaluation indicators, and determine the final evaluation results of the mine as three levels: Level 1, Level 2, and Level 3. Each indicator in the evaluation indicator system is also divided according to this level.

[0040] According to national and industry standards, the level of green construction in mines is divided into three levels: Level 1 represents excellent quality of green construction; Level 2 represents average quality of green construction, which has a certain degree of disturbance to the surrounding ecological environment; and Level 3 represents very poor quality of green mine construction, where mining and production have a great impact on the surrounding environment and residents, and rectification is required as soon as possible. The evaluation index system is also divided into three levels. Based on past standards and experience and the actual situation of the mine, the index grading standards are differentiated. For mines that need to pay more attention to environmental indicators, the grading standards for environmental indicators should be stricter, and the lower limit of each level should be raised. This will not only make the evaluation results more scientific, but also adapt to the different needs of mines.

[0041] S3: Construct an indicator relationship network graph and an indicator relationship adjacency matrix, and assign weights to the indicators using graph theory and BFS algorithm.

[0042] In an indicator system, no indicator exists in isolation; there are correlations between them. To determine these correlations, a network diagram of indicator relationships can be constructed using software like The Brain or other mind mapping tools, visualizing the relationships between the indicators.

[0043] An adjacency matrix is ​​constructed based on the indicator relationship network structure graph. The network contains all indicators and the relationships between each indicator, with each indicator being a node. Principles of network science are used to find key nodes in the network, and the degree centrality (DC) and proximity centrality (CC) of each node are calculated as the basis for evaluating the importance of each node. The relationships between indicators are transformed into a mathematical matrix, where related indicators are represented by 1, and unrelated indicators are represented by 0.

[0044] In graph structure analysis, the degree centrality (DC) of a node is defined as the number of adjacent edges that directly connect that node to all other nodes in its network. The formula for calculating degree centrality is: ; In the formula: Represents existing nodes The number of connected lines N -1 indicates that except for nodes The number of points that are connected to it from all other points.

[0045] Proximity centrality (CC) is quantified and reconstructed through a mathematical inverse transformation. When its value approaches 1, the node's topological influence and network coreness in the graph are greater. The formula for calculating proximity centrality is as follows: ; ; In the formula: d i This represents the sum of the shortest path lengths from node i to all other nodes; d ij This represents the shortest path from node i to all other nodes; The proximity centrality of the indicators is calculated using MATLAB. Based on the adjacency matrix of the relationships between the indicators, the BFS algorithm is used to process and calculate the adjacency matrix, and finally the proximity centrality of each indicator is obtained.

[0046] Finally, degree centrality (DC) and proximity centrality (CC) are calculated. Both have equal weight when assigning weights to the indicators, therefore each has a weight of 0.5. The final weight value is the sum of DC and CC multiplied by their respective weights, calculated using the following formula: ; W represents the final weight, which can be used for subsequent calculations and analysis of the importance of the indicators.

[0047] S4 collects engineering measurement data of various indicators in the mine and quantifies the qualitative indicator data.

[0048] Based on the mine's report and on-site investigation, the indicator data were compiled and statistically analyzed. The results of quantitative indicators were directly given by the measured data; the qualitative indicators were quantified by scoring, combining previous reports and expert consultation, thus converting the qualitative descriptions of the qualitative indicators into quantitative values.

[0049] S5 uses the obtained measured data to calculate the cloud correlation matrix of the indicators through the cloud model.

[0050] Construct an evaluation cloud model, which uses a three-dimensional feature parameter system for conceptual representation: among which, the central value parameter... The typical location of the domain is determined using probabilistic and statistical methods; entropy value As a fuzzy measure, its numerical value directly determines the coverage of the domain space, forming the extensional boundary of the concept; hyperentropy As a second-order entropy parameter, it describes the fluctuation characteristics of entropy values, and its value corresponds to the dispersion of cloud droplet distribution, indirectly mapping the accuracy of conceptual expression. The calculation formulas for these three cloud digital features are: ; ; ; In the formula and These represent the maximum and minimum values ​​of the evaluation index under each grading standard. k It is a constant related to the dispersion of the indicator, and is determined based on the fuzziness and randomness of the evaluation object.

[0051] Finally, the cloud digital features (Ex, En, He) of each indicator are formed. These cloud digital features are then substituted into the forward cloud generator in MATLAB to calculate the stochastic implementation value of the entropy of the indicator. The results were obtained using a forward cloud generator. Then, the measured value x of the indicator, the previously calculated expected value Ex of the indicator, and the stochastic realization value of the indicator's entropy are used. Calculate the cloud relevance of each indicator relative to the three levels. The formula for calculating cloud relevance is: ; In the formula, t represents the cloud correlation degree, x represents the measured value of the indicator, Ex represents the expected value of the indicator, and En' represents the random realization value of the entropy of the indicator. The random realization value of the entropy is generated in MATLAB by the positive cloud generator code. The cloud correlation degree of all indicators at three levels is calculated, and the cloud correlation degree matrix is ​​constructed.

[0052] S6 calculates the degree of membership of the mine at different levels based on the indicator weights and cloud correlation matrix, and judges the degree of green construction of the mine.

[0053] Constructing a cloud correlation matrix Z The cloud correlation matrix is ​​composed of the cloud correlation degree t of each indicator calculated above, as shown in the following formula: ; Then, using the indicator weights obtained through network analysis, the cloud correlation matrix is ​​weighted and calculated to obtain the final membership matrix. G Finally, based on the principle of maximum membership, the final level of green mining construction is determined. The formula for calculating the membership matrix is ​​as follows: ; The membership matrix includes the membership degree of the mine at three levels. The level with the highest membership degree is the level of green construction of the mine.

[0054] Specifically, when this method is applied to a certain mine, the network diagram of its indicator relationships is as follows: Figure 2 As shown in the Brain mind map structure: according to Figure 2The network structure shown can be converted into an adjacency matrix of indices, where related indices are represented by 1 and unrelated indices by 0, as shown in the matrix below: ; The degree centrality of the indicators was calculated using graph theory, and the proximity centrality was calculated using the adjacency matrix and the breadth-first search algorithm in MATLAB. The final calculation results are shown in the table below.

[0055] The indicators are divided into three standards: Level I, Level II, and Level III, as shown in the table below.

[0056] Based on the indicator grading standards and the following formula, three cloud digital characteristics of the indicator are calculated. These cloud digital characteristics are then used to generate a randomized implementation value of the indicator's entropy through a forward cloud generator. As shown in the table below.

[0057] ; ; ; In MATLAB, the forward cloud generator produces cloud drop plots for various indicators, such as... Figure 3 As shown, the spatial distribution density of cloud droplets in the cloud map intuitively reflects the discrete characteristics of the indicator: the denser the cloud droplet distribution, the higher the stability and certainty of the indicator value within that range; while the sparse and widely distributed cloud droplets indicate that the indicator has stronger fuzziness and randomness within that range.

[0058] Based on the data obtained on-site, the on-site construction data was substituted into the model constructed above, and the calculated indicators, digital characteristics, and random numbers were used to determine the final result. The cloud correlation matrix is ​​calculated using MATLAB using the following formula, as shown in the table below.

[0059] ; The membership matrix of the index is calculated according to the formula as follows: Based on the principle of maximum membership, the green evaluation level of this mine is Level I.

[0060] The green mine evaluation method provided in this embodiment constructs a green mine evaluation index system based on national standards and actual mine conditions, and classifies the indicators; it constructs an index relationship network diagram and assigns weights to the indicators using graph theory and BFS algorithms; it collects engineering measurement data for each mine indicator; using the obtained measurement data, it calculates the cloud correlation matrix of the indicators through a cloud model; finally, based on the indicator weights and the cloud correlation matrix, it calculates the membership degree of the mine with different levels, and judges the degree of green construction of the mine. This provides a more flexible indicator selection method, a more objective indicator weighting method, and a more intuitive evaluation calculation model.

[0061] Example 2 Corresponding to Embodiment 1 above, this embodiment proposes a green mine evaluation system for executing the method steps in Embodiment 1. The system includes an indicator screening module 501, an indicator grading module 502, an indicator weighting module 503, an indicator database module 504, and an evaluation module 505.

[0062] Please see Figure 4 As shown, the indicator screening module 501 is used to screen green mine evaluation indicators and construct a complete evaluation indicator system; The indicator grading module 502 is used to determine the indicator grading standard, and to define the grading standard of the indicator based on the established mine grading situation. The indicator weighting module 503 is used to assign weights to the evaluation indicators and provide preliminary calculation conditions for subsequent evaluation. The indicator database module 504 is used to collect on-site engineering data of evaluation indicators and provide raw data for evaluation, including a data quantification unit; The evaluation module 505 is used to process, calculate, analyze, and draw conclusions from the indicator data.

[0063] The indicator weighting module 503 includes: The adjacency matrix generation unit is used to convert the index relationship network graph into an index relationship adjacency matrix, wherein related indicators are represented by 1 and unrelated indicators are represented by 0. A centrality calculation unit is used to calculate the degree centrality and proximity centrality of each index based on the adjacency matrix. The game combination unit is used to determine the combination coefficients by solving the Nash equilibrium, taking the degree centrality and the proximity centrality as the two sides of the game, and then calculating the final weights by weighting. And / or, the evaluation module 505 includes: The cloud feature generation unit is used to determine the cloud digital features corresponding to each level according to the grading standard. A random implementation unit is used to generate random implementation values ​​of entropy based on a forward cloud generator; The correlation calculation unit is used to calculate the cloud correlation degree of each indicator relative to each level based on the measured data, the expected value, and the random realization value of the entropy. The membership calculation unit uses the index weights obtained through network analysis to assign weights to the cloud correlation matrix, resulting in the final membership matrix G. Finally, based on the principle of maximum membership, the final degree of green mining construction is determined.

[0064] Please see Figure 4 As shown, the five modules of the green mine evaluation system form a complete evaluation closed loop through a data flow-driven collaborative mechanism: the indicator screening module 501 constructs an evaluation indicator system based on national standards and actual mine conditions, and outputs it in parallel to the indicator grading module 502, the indicator weighting module 503, and the indicator database module 504; the indicator grading module 502 formulates indicator grading standards and inputs them into the evaluation module 505; the indicator weighting module 503 generates a weight matrix through a three-level processing of "adjacency matrix generation - centrality calculation - game combination"; the indicator database module 504 collects and quantifies measured data, and the results of both are synchronously incorporated into the evaluation module 505; the evaluation module 505 integrates cloud feature generation, randomization, and correlation calculation units, merges grading standards, weight matrices, and measured data, and finally outputs the mine green construction level through membership degree calculation and the maximum membership degree principle.

[0065] In summary, this invention provides a green mine evaluation method and system, belonging to the field of mining development technology. The method includes: constructing a green mine evaluation index system and determining grading standards; constructing an index relationship network graph based on the index system, generating an adjacency matrix, calculating index degree centrality using graph theory, calculating index proximity centrality using a breadth-first search algorithm, and determining index weights based on a game theory combination of the two centralities; acquiring measured index data, calculating the cloud correlation degree of the indicators relative to each level using a cloud model, and constructing a cloud correlation degree matrix; calculating the membership degree of the mine relative to each level based on the weights and the cloud correlation degree matrix, and determining the green construction level of the mine based on the principle of maximum membership degree. This invention achieves objective weighting through the index network topology structure and handles data uncertainty through a cloud model, establishing a standardized and quantitative green mine evaluation system. It effectively solves the problems of excessive subjectivity, insufficient quantification, and low accuracy of evaluation results in existing technologies, and can be widely applied to the evaluation of the green construction degree of various types of mines; it effectively improves the scientific nature of the weighting method, the degree of index quantification, and the objectivity of the evaluation process.

[0066] 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 method for evaluating green mines, characterized in that, Includes the following steps: S1. Based on national standards and actual conditions of mine site engineering, construct a green mine evaluation index system; S2, determine the grading standards for evaluation indicators, divide the degree of green construction of mines into multiple levels, and simultaneously classify each indicator in the evaluation indicator system into corresponding levels. S3, construct the indicator relationship network graph and indicator relationship adjacency matrix, and use graph theory and breadth-first search algorithm to assign weights to the indicators; S4, collect engineering measurement data of various indicators in the mine, and quantify the qualitative indicator data; S5. Based on the measured data from step S4, calculate the cloud correlation matrix of the indicators using the cloud model. S6. Combining the indicator weights from step S3 and the cloud correlation matrix from step S5, calculate the membership degree of the mine at different levels, and determine the green construction level of the mine based on the principle of maximum membership degree.

2. The green mine evaluation method according to claim 1, characterized in that, In step S1, a green mine evaluation index system is constructed, specifically by selecting indicators based on six aspects: mining area environment, resource development and utilization, personnel safety, green and low-carbon development, ecological restoration and environmental governance, and technology research and development and enterprise management. When selecting indicators, the proportion of each dimension of indicators is adjusted according to the regional characteristics of the mine.

3. The green mine evaluation method according to claim 2, characterized in that, In step S2, the grading criteria for the evaluation indicators are determined, specifically including: S21. Based on national or industry standards, preliminary grading standards for each evaluation indicator at multiple levels are initially determined. S22. Based on the actual conditions of the mine, the preliminary grading standards are modified to make the standards conform to the actual conditions of the mine, clarify the value range of the indicators under each level, and use this value range to specify the grading standards of the indicators. S23 classifies the level of green construction in mines into three levels: Level 1, Level 2, and Level 3. Level 1 represents excellent quality of green construction; Level 2 represents average quality of green construction, which has a certain degree of disturbance to the surrounding ecological environment; and Level 3 represents poor quality of green construction, in which the mining and production have a great impact on the surrounding environment and residents, and rectification is required as soon as possible.

4. The green mine evaluation method according to claim 3, characterized in that, In step S3, the indicator relationship network graph and indicator relationship adjacency matrix are constructed and weighted, specifically including: S31, construct an indicator relationship network structure diagram using mind mapping software to visualize the relationships between indicators; S32, based on the indicator relationship network structure diagram, construct the indicator relationship adjacency matrix. In the matrix, related indicators are represented by 1, and unrelated indicators are represented by 0. S33 uses graph theory to calculate the degree centrality of the index and breadth-first search algorithm combined with MATLAB software to calculate the proximity centrality of the index. S34 uses game theory to combine the degree centrality and proximity centrality of the indicators to assign weights to the indicators, and calculates the final weights of the indicators.

5. The green mine evaluation method according to claim 4, characterized in that, In step S3, an adjacency matrix of indicator relationships is constructed based on the indicator relationship network structure graph. The network contains all indicators and the relationships between each indicator, with each indicator being a node. Key nodes in the network are then identified. The degree centrality (DC) of a node is defined as the number of directly connected adjacent edges between that node and other nodes in its network. The formula for calculating degree centrality is: ； In the formula: Represents existing nodes The number of connected lines N -1 indicates that except for nodes The number of points that are connected to it from all other points; Proximity centrality (CC) is quantified and reconstructed through a mathematical inverse transformation. When its value approaches 1, the node's topological influence and network coreness in the graph are greater. The formula for calculating proximity centrality is as follows: ； ； In the formula: d i This represents the sum of the shortest path lengths from node i to all other nodes; d ij This represents the shortest path from node i to all other nodes; The proximity centrality of the indicators is calculated using MATLAB. Based on the adjacency matrix of the relationship between the indicators, the BFS algorithm is used to process and calculate the adjacency matrix, and finally the proximity centrality of each indicator is obtained. Finally, degree centrality (DC) and proximity centrality (CC) are calculated. Both have equal weighting in assigning weights to the indicators, therefore each has a weight of 0.

5. The final weight value is the sum of DC and CC multiplied by their respective weights, calculated using the following formula: ； In the formula: W is the final weight, which can be used for subsequent calculations and analysis of the importance of the indicators.

6. The green mine evaluation method according to claim 5, characterized in that, In step S5, the cloud correlation matrix of the indicators is calculated using the cloud model, specifically including: The cloud model uses a three-dimensional feature parameter system for conceptual representation: among which, the central value parameter The typical location of the domain is determined using probabilistic and statistical methods; entropy value As a fuzzy measure, its numerical value directly determines the coverage of the domain space, forming the extensional boundary of the concept; hyperentropy As a second-order entropy parameter, it describes the fluctuation characteristics of entropy values, and its value corresponds to the dispersion of cloud droplet distribution, indirectly mapping the accuracy of conceptual expression; the calculation formulas for the aforementioned three cloud digital features are as follows: ； ； ； In the formula: and These represent the maximum and minimum values ​​of the evaluation index under each grading standard. k It is a constant, related to the degree of dispersion of the indicator, and is determined according to the fuzziness and randomness of the evaluation object; Finally, the cloud digital characteristics of each indicator are formed. Ex, En, He The cloud digital characteristics of each indicator are substituted into the forward cloud generator in MATLAB to calculate the random implementation value of the entropy of the indicator. ; Calculated using a forward cloud generator Then, the measured value x of the indicator, the previously calculated expected value Ex of the indicator, and the stochastic realization value of the indicator's entropy are used. Calculate the cloud relevance of each indicator relative to the three levels. The formula for calculating cloud relevance is: ； 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, and En' is the random realization value of the entropy of the indicator. The random realization value of the entropy is generated by the positive cloud generator code MATLAB. The cloud correlation degree of all indicators at the three levels is calculated, and the cloud correlation degree matrix is ​​constructed.

7. The green mine evaluation method according to claim 6, characterized in that, In step S6, a cloud correlation matrix is ​​constructed. Z The cloud correlation matrix is ​​composed of the cloud correlation degree t of each indicator calculated in step S5, as shown in the following formula: ； Then, using the index weights obtained through network analysis, the cloud association matrix is ​​weighted and calculated to obtain the final membership matrix. G Finally, based on the principle of maximum membership, the final level of green mining construction is determined. The formula for calculating the membership matrix is ​​as follows: ； The membership matrix includes the membership degree of the mine at three levels. The level with the highest membership degree is the level of green construction of the mine.

8. The green mine evaluation method according to claim 2, characterized in that, The adjustment of the proportions of each dimension indicator based on the differences in the regional characteristics of the mine includes: For mines in ecologically fragile areas, increase the proportion of environmentally related indicators; For mines in ordinary areas, increase the proportion of resource development and utilization-related indicators; For mines in plateau regions, increase the proportion of environmental and personnel safety-related indicators.

9. A green mine evaluation system, characterized in that, A green mine evaluation method for performing any one of claims 1 to 8 includes: The indicator screening module is used to screen evaluation indicators and construct a green mine evaluation indicator system by referring to national standards and actual engineering conditions at the mine site. The indicator grading module is used to determine the indicator grading standards, divide the degree of green construction in mines into multiple levels, and classify each indicator in the evaluation indicator system into corresponding levels. The indicator weighting module is used to construct the indicator relationship network graph and the indicator relationship adjacency matrix. It uses graph theory and BFS algorithm to calculate the final weight of the indicators and assign weights to the evaluation indicators to provide calculation conditions for subsequent evaluation. The indicator database module is used to collect and store engineering measured data of various indicators in the mine, and to quantify qualitative indicator data. The evaluation module is used to calculate the cloud correlation matrix based on measured data using a cloud model, calculate the membership matrix by combining the index weights, and output the level of green construction of the mine according to the principle of maximum membership.

10. A green mine evaluation system according to claim 9, characterized in that, The indicator weighting module includes: The adjacency matrix generation unit is used to convert the index relationship network graph into an index relationship adjacency matrix, wherein related indicators are represented by 1 and unrelated indicators are represented by 0. A centrality calculation unit is used to calculate the degree centrality and proximity centrality of each index based on the adjacency matrix. The game combination unit is used to determine the combination coefficients by solving the Nash equilibrium, taking the degree centrality and the proximity centrality as the two sides of the game, and then calculating the final weights by weighting. And / or, the evaluation module includes: The cloud feature generation unit is used to determine the cloud digital features corresponding to each level according to the grading standard. A random implementation unit is used to generate random implementation values ​​of entropy based on a forward cloud generator; The correlation calculation unit is used to calculate the cloud correlation degree of each indicator relative to each level based on the measured data, the expected value, and the random realization value of the entropy.

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