Stability analysis method of metal mine goaf considering goaf group effect

Abstract

This invention provides a stability analysis method for goaf areas in metal mines that considers the goaf clustering effect, belonging to the field of mine geotechnical engineering and safety evaluation technology. The method includes: acquiring multi-source heterogeneous data of the goaf area and constructing a three-dimensional spatial model; constructing a multi-dimensional stability evaluation index system covering hydrogeology, rock strength, goaf parameters, and other factors; calculating the proximity of individual goaf areas; constructing a spatial relationship network of goaf groups based on graph theory and generating a relational adjacency matrix, calculating the goaf clustering effect influence coefficient; and coupling the proximity and clustering effect influence coefficient to obtain a comprehensive stability score for each goaf area. This invention can comprehensively consider multiple influencing factors, quantitatively reflect the structural characteristics of goaf areas, and achieve stability classification and risk identification, overcoming the technical problems of insufficient accuracy and poor engineering applicability in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of mining geotechnical engineering and safety evaluation technology, specifically to a method for stability analysis of goaf areas in metal mines that considers the clustering effect of goaf areas. Background Technology

[0002] Stability analysis and risk assessment of goaf areas in metal mines are core components for ensuring safe mine production and achieving sustainable resource extraction. Goaf stability directly affects the reliability of the mine support system, worker safety, and the overall sustainability of mine operations. However, existing stability analysis methods generally rely heavily on subjectivity, primarily depending on human experience and qualitative judgment. Furthermore, they typically assess individual goaf areas in isolation, failing to adequately consider the group effects and mutual influences between goaf areas. In complex mining geological environments, this singular, experience-based evaluation model cannot comprehensively and accurately reflect the true stability state of the goaf system, leading to significant discrepancies between assessment results and actual engineering conditions. This hinders effective support for goaf management decisions and has become a key bottleneck restricting the improvement of mine safety management.

[0003] In recent years, although some studies have attempted to improve the accuracy of stability analysis by expanding the evaluation index system or improving the calculation model, existing technologies still face multi-dimensional challenges. First, existing methods lack the quantitative characterization ability of the spatial structural characteristics of goaf areas, and lack systematic extraction and quantitative analysis methods for key structural parameters such as geometric morphology, spatial distribution, and connectivity, making it difficult for evaluation results to objectively reflect the structural stability essence of goaf areas. Second, the fusion mechanism of multi-source heterogeneous information is lacking. Multi-dimensional data sources such as geological exploration data, monitoring and sensor data, mining history data, and numerical simulation results have not been effectively integrated and synergistically utilized. The evaluation model has limited ability to characterize the coupling effects of multiple factors, resulting in low information utilization. Third, the evaluation system has poor adaptability. Weight allocation often adopts a static design, lacking a dynamic adjustment mechanism for different mine geological conditions, mining methods, and goaf evolution stages. It is highly subjective and difficult to meet the application needs of complex and variable engineering environments. Fourth, the evaluation results are not sufficiently linked to engineering governance practices, the stability grading standards are vague, the risk identification thresholds are unclear, and a closed-loop decision support system of evaluation-grading-early warning-governance has not been formed, making it difficult to transform technological achievements into practical applications.

[0004] Regarding existing patent literature, Chinese invention patent CN121089536A proposes a method and system for optimizing blasting parameters in goaf areas. This method analyzes the geological complexity based on goaf distribution and fracture density, calculates rock blast resistance strength by combining rock mechanics parameters and formation stress data, and optimizes blasting parameters through a genetic algorithm. However, this scheme focuses on the adaptability to blasting engineering, and its dynamic stability evaluation lacks clear practical guidance. It does not specify the threshold values ​​of core indicators for real-time monitoring, data acquisition frequency, or dynamic judgment criteria for stability levels. In field applications, evaluation lags or judgment biases are likely to occur, making it difficult to meet the needs of routine monitoring and dynamic control of goaf stability. Chinese invention patent CN120632285A discloses a comprehensive evaluation method for goaf stability combining numerical simulation methods. It constructs a numerical model through orthogonal experimental schemes, screens stability influencing factors, and constructs a fuzzy comprehensive evaluation matrix. While this method improves the accuracy of factor sensitivity analysis through numerical simulation, its process of classifying and screening influencing factors lacks clear quantitative standards, fails to fully consider the coupling effects between factors, and uses a static weight allocation without a dynamic adjustment mechanism, thus failing to effectively overcome the subjectivity of the evaluation results. Chinese invention patent CN119129264A proposes a refined prediction method for goaf stability, which achieves numerical simulation and refined evaluation of surrounding rock stability by constructing block models and three-dimensional geological models, combined with rock mechanics parameters. This method is highly dependent on high-precision geological exploration data and rock mechanics test data, but it lacks error compensation mechanisms and alternative solutions for missing or insufficiently accurate data. For small and medium-sized mines with low geological exploration levels and high data acquisition costs, the technology is difficult to implement and its universality is limited.

[0005] In view of this, existing methods for analyzing the stability of goaf areas in metal mines still have significant shortcomings in terms of quantitative characterization capabilities, multi-source information fusion, dynamic adaptability, and engineering applicability. There is an urgent need for a new analytical method that can comprehensively consider multi-dimensional data sources, quantitatively reflect the structural characteristics of goaf areas, objectively quantify the coupled effects of multiple factors, and achieve dynamic stability classification and accurate risk identification. This would improve the scientific rigor, accuracy, and engineering applicability of goaf stability assessments and provide reliable technical support for goaf area governance and safety management. Summary of the Invention

[0006] In view of the technical problems existing in the background art, this invention provides a method for stability analysis of goaf areas in metal mines considering the goaf clustering effect. First, multi-source heterogeneous data information, including geometric, mechanical, mining, and environmental parameters of the goaf group, is acquired to establish a spatial model of the goaf. Second, a three-dimensional spatial model of the goaf group is constructed based on the multi-source heterogeneous information. Then, a goaf stability evaluation index system is established with reference to national standards and industry experience. The TOPSIS method is used to evaluate the goaf, obtaining the closeness of the evaluated goaf to the optimal stability solution. Next, the goaf group containing the evaluated goaf is visualized in the form of nodes and networks, and the BFS algorithm is used to calculate the influence coefficient of the goaf group on the clustering effect of the evaluated goaf. Finally, the stability score is calculated using the calculated TOPSIS fit and the clustering effect influence coefficient, and a stability grading standard is established to clarify the stability grading of the target goaf. This invention transforms the traditional isolated evaluation method for goaf stability into a systematic evaluation method that takes into account the goaf's own geological and mechanical structural characteristics and the spatial topological correlation of the goaf. It can comprehensively consider multiple influencing factors, quantitatively reflect the structural characteristics of the goaf, and achieve stability classification and risk identification, thereby overcoming the technical problems of insufficient accuracy and poor engineering applicability of stability evaluation results in existing technologies.

[0007] In a first aspect, embodiments of the present invention provide a method for stability analysis of goaf areas in metal mines considering the goaf clustering effect, comprising the following steps: S1, acquire multi-source heterogeneous data of the goaf group, wherein the multi-source heterogeneous data includes at least the geometric parameters, mechanical parameters, mining parameters and environmental parameters of each goaf; S2, Based on the multi-source heterogeneous data, construct a three-dimensional spatial model of the goaf group to determine the spatial relationship between each goaf and the structural characteristic parameters of each goaf; S3. Construct a stability evaluation index system for goaf areas, which includes multi-dimensional factors affecting the stability of goaf areas. S4. Based on the goaf stability evaluation index system, calculate the proximity of each goaf area. The proximity represents the degree of closeness of a single goaf area to the optimal stable state. S5. Based on the three-dimensional spatial model, construct a spatial relationship network of the goaf group and generate a corresponding relationship adjacency matrix. S6. Based on the relational adjacency matrix, calculate the clustering effect influence coefficient of each goaf area; based on the coupled calculation of the proximity degree and the clustering effect influence coefficient, calculate the stability evaluation score of the target goaf area under the influence of the entire goaf area group.

[0008] As a further improvement of the present invention, in step S6, the formula for calculating the stability evaluation score of the target goaf is as follows: ; In the formula, Let be the stability evaluation score of the i-th goaf. Let i be the proximity of the i-th goaf. is the clustering effect coefficient of the i-th goaf.

[0009] As a further improvement of the present invention, in step S6, the constructed relational adjacency matrix is ​​used as a parameter, and the breadth-first search algorithm is used to calculate the clustering effect influence coefficient of each goaf area. The clustering effect influence coefficient is the proximity centrality, which is obtained by calculating the reciprocal of the sum of the shortest path distances from the target goaf to all other goafs in the goaf group.

[0010] As a further improvement of the present invention, in step S6, the calculation formula for the influence coefficient of the goaf clustering effect is as follows: ; In the formula, Let be the clustering effect coefficient of the i-th goaf. Let N be the shortest path from the i-th mining area to the j-th goaf, and N be the number of goafs in the goaf group.

[0011] As a further improvement of the present invention, in step S4, the TOPSIS method is used to calculate the proximity degree. The proximity degree ranges from 0 to 1. The closer the value is to 1, the better the stability of the single area of ​​the goaf.

[0012] As a further improvement of the present invention, in step S5, a spatial relationship network diagram of the goaf is constructed using graph theory. The spatial relationship network diagram is constructed with each goaf as a node and the spatial relationship between adjacent goafs as edges. The relational adjacency matrix uses binary values ​​to indicate whether there is an edge connection between nodes, where 1 represents a node that is connected to another node and 0 represents a node that is not connected to another node.

[0013] As a further improvement of the present invention, in step S3, the multi-dimensional factors include at least hydrogeological factors, rock strength factors, void area parameters, and other factors; The hydrogeological factors include at least rock mass structure, geological structure, and hydrology around the goaf; the rock strength factors include at least rock compressive / tensile / shear strength and rock water resistance; the goaf parameters include at least goaf volume, goaf height-to-span ratio, roof exposure area, and pillar / roof stability; and the other factors include at least the influence of surrounding mining, goaf exposure time, and illegal goaf.

[0014] As a further improvement of the present invention, in step S1, the multi-source heterogeneous data is obtained through at least one geophysical exploration method among field measurement, three-dimensional laser scanning, UAV detection and geological exploration; The geometric parameters include at least the height, span, burial depth, and volume of the goaf; the mechanical parameters include at least the compressive strength, elastic modulus, and integrity coefficient of the surrounding rock; the mining parameters include at least the mining method, mining sequence, backfilling method, and support type; and the environmental parameters include at least the ground stress, geological structure, and hydrogeological conditions.

[0015] Secondly, embodiments of the present invention provide a stability analysis system for goaf areas in metal mines that considers the goaf clustering effect, which is used to perform the aforementioned stability analysis method for goaf areas in metal mines that considers the goaf clustering effect, including: The multi-source heterogeneous data module for goaf areas is electrically connected to the on-site geophysical exploration equipment. It is used to store multi-source heterogeneous data of geometric parameters, mechanical parameters, mining parameters, and environmental parameters of goaf areas obtained through geophysical exploration technology, and to perform multi-source heterogeneous data preprocessing. The goaf spatial modeling module is connected to the goaf multi-source heterogeneous data module. It is used to perform spatial visualization modeling on the measured multi-source heterogeneous data, construct a three-dimensional spatial model of the goaf group, and determine the spatial positional relationship between each goaf and the structural characteristic parameters of each goaf. The goaf stability evaluation index system module is used to construct the goaf stability evaluation index system; The proximity calculation module is connected to the goaf stability evaluation index system module. It is used to construct the TOPSIS evaluation model based on the goaf stability evaluation index system and calculate the proximity between the target goaf and the optimal stability ideal solution. The goaf group relationship construction module is connected to the goaf spatial modeling module. It is used to visualize the relationship between the goaf groups based on the three-dimensional spatial model using graph theory, construct the spatial relationship network of the goaf groups, and establish a relationship adjacency matrix as a subsequent evaluation parameter. The coupled evaluation module is connected to the goaf group relationship construction module and the proximity calculation module, respectively. It is used to calculate the goaf cluster effect influence coefficient and combine the proximity and cluster effect influence coefficient to coupled calculate the stability evaluation score of the target goaf.

[0016] As a further improvement of the present invention, it is applied to the stability analysis of goaf areas in mines with complex structures or multiple overlapping mining areas.

[0017] Beneficial effects: This invention overcomes the shortcomings of traditional methods that rely solely on a single indicator or a few parameters for evaluation by constructing a multi-source heterogeneous data information fusion method for goaf stability analysis. By constructing a three-dimensional spatial model of goaf clusters, the spatial distribution characteristics and interaction mechanisms of the internal structure of the goaf clusters are visualized, making it particularly suitable for mining environments with complex structures or multiple overlapping mining areas. By introducing a graph theory-based goaf cluster association network, the goaf clustering effect is transformed into the influence coefficient of a single goaf, and combined with TOPSIS proximity, the objectivity and adaptive determination of the evaluation results are achieved. The influence of the goaf clustering effect is taken into account in the evaluation results, improving the scientificity and stability of the evaluation process.

[0018] 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

[0019] 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.

[0020] Figure 1 This is a flowchart illustrating the stability analysis method for goaf areas in metal mines that considers the goaf clustering effect provided in this embodiment of the invention. Figure 2 This is a schematic diagram of a three-dimensional spatial model of a goaf area provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the goaf stability evaluation index system provided in Embodiment 1 of the present invention; Figure 4 This is the adjacency matrix of goaf relationships provided in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the structure of the metal mine goaf stability analysis system considering the goaf clustering effect provided in Embodiment 2 of the present invention. Detailed Implementation

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] To address the technical problems of traditional goaf stability assessment relying solely on a single indicator or a few parameters, neglecting the goaf clustering effect, and the significant shortcomings of existing metal mine goaf stability analysis methods in terms of quantitative characterization, multi-source information fusion, dynamic adaptability, and engineering practicality, this invention provides a metal mine goaf stability analysis method that considers the goaf clustering effect. By transforming the spatial distribution characteristics and interaction mechanisms of goaf groups into quantifiable clustering effect influence coefficients, and jointly calculating these coefficients with stability proximity, the objectification of the clustering effect is achieved. This method can identify goafs with key topological locations within a goaf group. Even if individual goaf parameters are acceptable, a high clustering effect influence coefficient can result in a lower overall score, thus providing early warning of cascading instability risks and significantly improving the scientific rigor and stability of the evaluation process.

[0030] This invention provides a method for stability analysis of goaf areas in metal mines considering the goaf clustering effect, comprising the following steps: S1, using geophysical exploration technology to obtain multi-source heterogeneous data of the goaf area; S2, constructs a three-dimensional spatial model of the goaf group by acquiring multi-source data; S3, Construct a stability evaluation index system for goaf areas; S4. Use the TOPSIS method to comprehensively evaluate the goaf area and calculate the proximity of the optimal solution. S5. Based on the three-dimensional spatial model of the goaf group, construct the spatial relationship network graph of the goaf using graph theory methods to obtain the relationship adjacency matrix; S6. Calculate the stability score of the goaf, calculate the influence coefficient of the goaf group on the goaf clustering effect using the BFS algorithm, and calculate the goaf stability score using the clustering effect influence coefficient and proximity.

[0031] Preferably, geophysical exploration techniques include: acquiring geometric, mechanical, mining, and environmental parameters of the goaf through on-site measurements, 3D laser scanning, and geological exploration; and performing data preprocessing to ensure the completeness and accuracy of the parameters. Geometric parameters include the height, span, depth, and volume of the goaf; mechanical parameters include the compressive strength, elastic modulus, and integrity coefficient of the surrounding rock; mining parameters include mining methods, mining sequence, backfilling methods, and support types; and environmental parameters include in-situ stress, geological structure, and hydrogeological conditions.

[0032] Preferably, the specific process of constructing a three-dimensional model of a goaf group is as follows: using the data obtained above, construct a three-dimensional structural model of the entire goaf group. This model can be used to display the geometric and mechanical characteristics of the goaf and the spatial relationship between the goafs.

[0033] Preferably, the goaf stability evaluation index system is constructed based on existing national standards and assessments of the goaf engineering conditions. This index system includes the main factors affecting goaf stability, such as geometric structure, surrounding rock mechanical properties, mining disturbance, and environmental factors, and is constructed based on these aspects.

[0034] Preferably, the TOPSIS method is used to comprehensively evaluate the goaf and calculate the proximity of the optimal solution. This includes: setting an optimal parameter for the goaf based on the constructed goaf stability evaluation index system, which is the parameter of each index that the goaf can achieve in the optimal state under the conditions of meeting the standards and actual situation; and setting a worst-case parameter for the goaf, which is the corresponding parameter of each index when the goaf is in the worst case in the project; and using SPSS software and the TOPSIS method, calculating the proximity of the goaf. The proximity value ranges from 0 to 1. The proximity value means the degree of proximity of the goaf to the optimal situation and the degree of distance from the worst situation. Therefore, the closer the proximity value is to 1, the better the stability of the goaf.

[0035] Preferably, constructing a network diagram of goaf cluster relationships includes: constructing a spatial relationship network diagram of goaf areas using graph theory based on the constructed three-dimensional goaf cluster model, treating goaf areas as nodes in the relationship network diagram, and connecting adjacent goaf areas with lines; constructing a goaf area relationship adjacency matrix based on the goaf cluster relationship network diagram, where nodes with connections are represented by 1 and nodes without connections are represented by 0, thus digitizing the relationships between various goaf areas in the goaf cluster.

[0036] Preferably, the calculation of the goaf stability score includes: using the constructed goaf relationship adjacency matrix as a parameter, and employing the BFS algorithm in MATLAB to calculate the proximity centrality of each goaf to represent the influence coefficient of the goaf clustering effect; proximity centrality is one of the core indicators for measuring the global importance of nodes in a network, which quantifies the "shortest distance" from a node to all other nodes in the network. The shorter the shortest path from a node to all other nodes, the higher its proximity centrality, indicating that the node is topologically closer to the geometric center of the network.

[0037] Example 1 Please refer to Figure 1 As shown, Embodiment 1 of the present invention provides a method for stability analysis of goaf areas in metal mines considering the goaf clustering effect, comprising the following steps: S1 utilizes geophysical exploration technology to obtain multi-source data of the goaf.

[0038] Geometric, mechanical, mining, and environmental parameters of the goaf are obtained through on-site measurement, 3D laser scanning, and geological exploration. Data preprocessing is then performed to ensure the integrity and accuracy of the parameters.

[0039] In some specific implementations, geometric, mechanical, mining, and environmental parameters of the goaf are measured using laser scanning, drone detection, and other geophysical methods. Geometric parameters include the height, span, depth, and volume of the goaf; mechanical parameters include the compressive strength, elastic modulus, and integrity coefficient of the surrounding rock; mining parameters include the mining method, mining sequence, backfilling method, and support type; and environmental parameters include in-situ stress, geological structure, and hydrogeological conditions. The data is then analyzed to ensure the accuracy and reasonableness of the detection results, providing a reliable reference for subsequent modeling.

[0040] S2 constructs a three-dimensional spatial model of the goaf group using multi-source data.

[0041] The point cloud data of the goaf obtained from the 3D laser scanning in step S1 is processed, and a 3D visualization model of the goaf is constructed using 3D software to characterize the positional relationships and distances between goaf areas in detail, such as... Figure 2 As shown.

[0042] S3, construct a stability evaluation index system for goaf areas.

[0043] This paper constructs a stability evaluation index system for goaf areas based on existing national standards and assessments of goaf engineering conditions. This system is grounded in four main factors influencing goaf stability: hydrogeological factors, rock strength factors, goaf parameters, and other factors. Figure 3 As shown.

[0044] S4. The TOPSIS method is used to comprehensively evaluate the goaf area and calculate the proximity of the optimal solution.

[0045] Based on the constructed goaf stability evaluation index system, an optimal goaf condition parameter is set, which is the parameter of each index that can reach the optimal state of the goaf under the conditions of meeting the standards and actual situation. At the same time, an unfavorable goaf condition parameter is set, which is the corresponding parameter of each index when the goaf is in the worst situation in the project. Using SPSS software and the TOPSIS method, the proximity score C of the goaf was calculated. The proximity score C ranges from 0 to 1. This proximity score represents how close the goaf is to the optimal situation and how far it is from the worst situation. Therefore, the closer the proximity score is to 1, the better the stability of the goaf. The main calculation process of the TOPSIS method is as follows: ; In the formula, The difference between each evaluation indicator and the ideal optimal value. The difference between each evaluation indicator and the ideal worst value, Let j be the weight of the index. For index j, the ideal optimal value is... The ideal worst value for index j is... This serves as an evaluation index for the mining method.

[0046] ; In the formula, This represents the degree to which the mining method closely approximates the ideal optimal value.

[0047] S5. Construct a relationship network diagram of the goaf group. Based on the three-dimensional model of the goaf group, a spatial relationship network diagram of the goaf is constructed using graph theory methods, and the relationship adjacency matrix is ​​obtained.

[0048] Based on the constructed three-dimensional goaf group model, a spatial relationship network diagram of the goaf is constructed using graph theory. The goaf is used as a node in the relationship network diagram, and adjacent goafs are connected by lines. Based on the network diagram of the goaf group, a goaf relationship adjacency matrix is ​​constructed. Nodes connected by lines are represented by 1, and those not connected by 0. This digitizes the relationships between the various goafs within the goaf group. Figure 4 As shown.

[0049] S6. Calculate the stability score of the goaf. Calculate the influence coefficient of the goaf group on the goaf using the BFS algorithm. Calculate the goaf stability score using the influence coefficient and proximity.

[0050] Using the constructed adjacency matrix of goaf relationships as parameters, the proximity centrality of each goaf is calculated in MATLAB using the BFS algorithm to represent the influence coefficient of goaf clustering effect.

[0051] Proximity centrality is one of the core metrics for measuring the global importance of nodes in a network. It quantifies the "shortest distance" from a node to all other nodes in the network. The shorter the shortest path from a node to all other nodes, the higher its proximity centrality, indicating that the node is closer to the geometric center of the network topologically. The calculation formula is as follows: ; In the formula, The proximity centrality of the i-th goaf is represented by the goaf clustering effect coefficient. Let N be the shortest path from the i-th goaf to the j-th goaf, and N be the number of goafs in the goaf group.

[0052] By jointly calculating the proximity and influence coefficient of the obtained goaf area, the stability evaluation score of the target goaf area under the influence of the entire goaf area group is obtained. The formula for calculating the stability evaluation score is as follows: ; In the formula, Let be the stability evaluation score of the i-th goaf. Let i be the proximity of the i-th goaf. is the clustering effect coefficient of the i-th goaf.

[0053] Below are simulated application examples. The data and models are only simulated data, used to demonstrate the application of the method, and have no practical significance: An underground metal mine, A, employs the sublevel stope mining method. The ore body is buried at a depth of approximately 300 m to 450 m. After mining, multiple irregular goaf areas have formed. Some areas suffer from problems such as large roof spans, fractured surrounding rock, and overlapping historical mining conditions, posing certain stability risks. Therefore, the method of this invention is used to conduct a stability analysis of the goaf areas in this mine. The specific application process is as follows: In S1, laser scanning, drone exploration, and other geophysical methods were used at Mine A to identify a total of 34 goaf areas. Geometric, mechanical, mining, and environmental parameters of these goaf areas were measured. Geometric parameters included the height, span, depth, and volume of the goaf; mechanical parameters included the compressive strength, elastic modulus, and integrity coefficient of the surrounding rock; mining parameters included the mining method, mining sequence, backfilling method, and support type; and environmental parameters included in-situ stress, geological structure, and hydrogeological conditions. The data was then analyzed to ensure the accuracy and reasonableness of the findings, providing a reliable reference for subsequent modeling.

[0054] S2. The point cloud data of the goaf obtained from the previous 3D laser scanning is processed, and a 3D visualization model of the goaf is constructed using 3D software. This helps technicians understand the positional relationships and distances between goaf areas and gain a general understanding of their morphology. A schematic diagram of the constructed 3D goaf model is shown below. Figure 2 As shown.

[0055] S3 constructs a stability evaluation index system for goaf areas based on existing national standards and assessments of goaf engineering conditions. This index system is built upon four main factors influencing goaf stability: hydrogeological factors, rock strength factors, goaf area parameters, and other factors. A schematic diagram of the index system is shown below. Figure 3 As shown.

[0056] S4. Collect measured parameters of each goaf area. Based on the constructed goaf stability evaluation index system, set an optimal condition parameter for the goaf area, which is the parameter of each index that can reach the optimal state of the goaf area under the conditions of meeting the standards and actual situation. At the same time, set an worst condition parameter for the goaf area, which is the corresponding parameter of each index when the goaf area is in the worst situation in the project.

[0057] Using SPSS software and the TOPSIS method, the proximity C of each goaf was calculated by substituting the parameters of each goaf into the calculation. i Proximity C i The value range is 0 to 1. The meaning of this proximity is the degree to which the evaluated goaf is close to the optimal situation and the degree to which it is far from the worst situation. Therefore, the closer the proximity is to 1, the better the stability of the goaf, as shown in Table 1.

[0058] Table 1. Proximity C of i goaf areas i Data S4. Based on the constructed 3D goaf group model, a spatial relationship network diagram of the goaf was built using graph theory. Goafs were treated as nodes in the network diagram, and adjacent goafs were connected by lines. There were a total of 34 goafs, as shown below. Figure 4 As shown.

[0059] Based on the goaf group relationship network diagram, a goaf relationship adjacency matrix is ​​constructed. Nodes with connections are represented by 1, and those without connections are represented by 0. This digitizes the relationships between various goafs in the goaf group.

[0060] Using the constructed adjacency matrix of goaf relationships as parameters, the proximity centrality of each goaf is calculated in MATLAB using the BFS algorithm to represent the influence coefficient of goaf clustering effect.

[0061] The formula for calculating proximity centrality (the influence coefficient of goaf clustering effect) is as follows (in actual calculations, it is obtained through code in MATLAB): ; In the formula, Let i represent the proximity centrality of the goaf, i.e., the goaf clustering effect coefficient. Let N be the shortest path from the i-th goaf to the j-th goaf, and N be the number of goafs in the goaf group. The calculated influence coefficients are shown in Table 2 below.

[0062] Table 2. Proximity centrality of i goaf areas, i.e., influence coefficient of goaf clustering effect. Data By jointly calculating the proximity and influence coefficient of the target goaf, the stability evaluation score of the target goaf under the influence of the entire goaf group is obtained. The formula for calculating the stability evaluation score is as follows: ; In the formula, Let be the stability evaluation score of the i-th goaf. Let i be the proximity of the i-th goaf. is the clustering effect coefficient of the i-th goaf.

[0063] The final stability evaluation scores of the goaf are shown in Table 3 below. The scores range from 0 to 1, with higher values ​​indicating better stability of the goaf.

[0064] Table 3 Stability evaluation scores for i goaf areas Data The above data indicates that the stability evaluation score of a goaf can characterize the stability of a goaf under the influence of the goaf clustering effect. The higher the stability evaluation score, the better the stability of the goaf and the smaller its impact on the mining face. Goafs with a stability evaluation score below 0.5 should be dealt with immediately. Unstable goafs can be controlled by filling them or manually erecting supports to prevent danger. Goafs with a stability evaluation score above 0.5 should be sealed off and ground pressure monitoring equipment should be installed to monitor the stress in the goaf in real time.

[0065] Example 2 Please see Figure 5 As shown, Embodiment 2 of the present invention provides a stability analysis system for goaf areas in metal mines that considers the goaf clustering effect. This system is used to perform the above-mentioned analysis method and includes: The multi-source heterogeneous data module for goaf areas is electrically connected to the on-site geophysical exploration equipment. It is used to store multi-source heterogeneous data of geometric parameters, mechanical parameters, mining parameters, and environmental parameters of goaf areas obtained through geophysical exploration technology, and to perform multi-source heterogeneous data preprocessing. The goaf spatial modeling module is connected to the goaf multi-source heterogeneous data module. It is used to perform spatial visualization modeling on the measured multi-source heterogeneous data, construct a three-dimensional spatial model of the goaf group, and determine the spatial positional relationship between each goaf and the structural characteristic parameters of each goaf. The goaf stability evaluation index system module is used to construct the goaf stability evaluation index system; The proximity calculation module is connected to the goaf stability evaluation index system module. It is used to construct the TOPSIS evaluation model based on the goaf stability evaluation index system and calculate the proximity between the target goaf and the optimal stability ideal solution. The goaf group relationship construction module is connected to the goaf spatial modeling module. It is used to visualize the relationship between the goaf groups based on the three-dimensional spatial model using graph theory, construct the spatial relationship network of the goaf groups, and establish a relationship adjacency matrix as a subsequent evaluation parameter. The coupled evaluation module is connected to the goaf group relationship construction module and the proximity calculation module, respectively. It is used to calculate the goaf cluster effect influence coefficient and combine the proximity and cluster effect influence coefficient to coupled calculate the stability evaluation score of the target goaf.

[0066] In summary, this invention provides a stability analysis method for goaf areas in metal mines that considers the goaf clustering effect, belonging to the field of mine geotechnical engineering and safety evaluation technology. The method includes: acquiring multi-source heterogeneous data of the goaf area and constructing a three-dimensional spatial model; constructing a multi-dimensional stability evaluation index system covering hydrogeology, rock strength, goaf parameters, and other factors; calculating the proximity of individual goaf areas; constructing a spatial relationship network of the goaf group based on graph theory and generating a relational adjacency matrix, calculating the goaf clustering effect influence coefficient; and coupling the proximity and clustering effect influence coefficient to obtain a comprehensive stability score for each goaf area. This invention achieves the quantitative inclusion of the goaf clustering effect through a two-dimensional coupled evaluation of individual attributes and group topology, and is suitable for stability analysis and risk warning of complex goaf groups.

[0067] 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 analyzing the stability of goaf in a metal mine considering the group effect of goaf, characterized in that, Includes the following steps: S1, acquire multi-source heterogeneous data of the goaf group, wherein the multi-source heterogeneous data includes at least the geometric parameters, mechanical parameters, mining parameters and environmental parameters of each goaf; S2, Based on the multi-source heterogeneous data, construct a three-dimensional spatial model of the goaf group to determine the spatial relationship between each goaf and the structural characteristic parameters of each goaf; S3. Construct a stability evaluation index system for goaf areas, which includes multi-dimensional factors affecting the stability of goaf areas. S4. Based on the goaf stability evaluation index system, calculate the proximity of each goaf area. The proximity represents the degree of closeness of a single goaf area to the optimal stable state. S5. Based on the three-dimensional spatial model, construct a spatial relationship network of the goaf group and generate a corresponding relationship adjacency matrix. S6. Based on the aforementioned adjacency matrix, calculate the clustering effect influence coefficient of each goaf area; Based on the coupled calculation of the proximity degree and the clustering effect influence coefficient, the stability evaluation score of the target goaf area under the influence of the entire goaf area group is calculated.

2. The method for analyzing the stability of goafs in a metal mine considering the group effect of goafs according to claim 1, characterized in that, In step S6, the formula for calculating the stability evaluation score of the target goaf is as follows: ； In the formula, is the stability evaluation score of the ith goaf, is the closeness of the ith goaf, is the group clustering effect influence coefficient of the ith goaf.

3. The method for analyzing the stability of goafs in a metal mine considering the group effect of goafs according to claim 2, characterized in that, In step S6, the constructed relational adjacency matrix is ​​used as a parameter, and the breadth-first search algorithm is used to calculate the clustering effect influence coefficient of each goaf area. The clustering effect influence coefficient is the proximity centrality, which is obtained by calculating the reciprocal of the sum of the shortest path distances from the target goaf to all other goafs in the goaf group.

4. The method for stability analysis of goaf areas in metal mines considering the goaf clustering effect according to claim 3, characterized in that, In step S6, the formula for calculating the influence coefficient of the goaf clustering effect is as follows: ； In the formula, Let be the clustering effect coefficient of the i-th goaf. Let N be the shortest path from the i-th mining area to the j-th goaf, and N be the number of goafs in the goaf group.

5. The method for stability analysis of goaf areas in metal mines considering the goaf clustering effect according to claim 1, characterized in that, In step S4, the TOPSIS method is used to calculate the proximity degree. The proximity degree ranges from 0 to 1. The closer the value is to 1, the better the stability of the single area of ​​the goaf.

6. The method for stability analysis of goaf areas in metal mines considering the goaf clustering effect according to claim 1, characterized in that, In step S5, a spatial relationship network diagram of the goaf is constructed using graph theory. The spatial relationship network diagram is constructed with each goaf as a node and the spatial relationship between adjacent goafs as edges. The relational adjacency matrix uses binary values ​​to indicate whether there is an edge connection between nodes, where 1 represents a node that is connected to another node and 0 represents a node that is not connected to another node.

7. The method for stability analysis of goaf areas in metal mines considering the goaf clustering effect according to claim 1, characterized in that, In step S3, the multi-dimensional factors include at least hydrogeological factors, rock strength factors, void area parameters, and other factors; The hydrogeological factors include at least rock mass structure, geological structure, and hydrology around the goaf; the rock strength factors include at least rock compressive / tensile / shear strength and rock water resistance; the goaf parameters include at least goaf volume, goaf height-to-span ratio, roof exposure area, and pillar / roof stability; and the other factors include at least the influence of surrounding mining, goaf exposure time, and illegal goaf.

8. The method for stability analysis of goaf areas in metal mines considering the goaf clustering effect according to claim 1, characterized in that, In step S1, the multi-source heterogeneous data is obtained through at least one geophysical exploration method among on-site measurement, three-dimensional laser scanning, UAV detection, and geological exploration; The geometric parameters include at least the height, span, burial depth, and volume of the goaf; the mechanical parameters include at least the compressive strength, elastic modulus, and integrity coefficient of the surrounding rock; the mining parameters include at least the mining method, mining sequence, backfilling method, and support type; and the environmental parameters include at least the ground stress, geological structure, and hydrogeological conditions.

9. A stability analysis system for goaf areas in metal mines considering the clustering effect of goaf areas, characterized in that, The method for performing the stability analysis of goaf areas in metal mines considering the goaf clustering effect as described in any one of claims 1 to 8 includes: The multi-source heterogeneous data module for goaf areas is electrically connected to the on-site geophysical exploration equipment. It is used to store multi-source heterogeneous data of geometric parameters, mechanical parameters, mining parameters, and environmental parameters of goaf areas obtained through geophysical exploration technology, and to perform multi-source heterogeneous data preprocessing. The goaf spatial modeling module is connected to the goaf multi-source heterogeneous data module. It is used to perform spatial visualization modeling on the measured multi-source heterogeneous data, construct a three-dimensional spatial model of the goaf group, and determine the spatial positional relationship between each goaf and the structural characteristic parameters of each goaf. The goaf stability evaluation index system module is used to construct the goaf stability evaluation index system; The proximity calculation module is connected to the goaf stability evaluation index system module. It is used to construct the TOPSIS evaluation model based on the goaf stability evaluation index system and calculate the proximity between the target goaf and the optimal stability ideal solution. The goaf group relationship construction module is connected to the goaf spatial modeling module. It is used to visualize the relationship between the goaf groups based on the three-dimensional spatial model using graph theory, construct the spatial relationship network of the goaf groups, and establish a relationship adjacency matrix as a subsequent evaluation parameter. The coupled evaluation module is connected to the goaf group relationship construction module and the proximity calculation module, respectively. It is used to calculate the goaf cluster effect influence coefficient and combine the proximity and cluster effect influence coefficient to coupled calculate the stability evaluation score of the target goaf.

10. The stability analysis system for goaf areas in metal mines considering the goaf clustering effect according to claim 9, characterized in that, It is applied to the stability analysis of goaf areas in mines with complex structures or multiple overlapping mining areas.

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