Rock sliding risk assessment method and system for goaf areas based on detection data analysis

Abstract

The present invention discloses a method and system for assessing the risk of rock sliding in goaf areas based on detection data analysis, which relates to the technical field of rock risk analysis. The block characteristics of the rock in the goaf area are sampled to determine the rock strength and crack distribution, and obtain the rock material parameters; according to the rock appearance and structural characteristics, a rock sidewall performance model is constructed to delineate and associate the protruding blocks; the discrete element method is used to combine the rock material parameters and the sidewall performance model to determine the risk blocks; the protruding block combination in the focus block is associated with the rock material parameters to form a reference rock data group, and the reference rock data group is classified and integrated; finally, the protruding block group and the rock material parameters are used as comparison elements to find the appropriate reference data group in the data set, and the corresponding risk block is used as the risk assessment result. The above technical scheme of the present invention realizes the assessment of the sliding risk of the rock in the goaf area and improves the accuracy of the risk assessment.

Description

Technical Field

[0001] The invention relates to the technical field of rock mass risk analysis, and in particular to a method and system for assessing rock mass sliding risk in a goaf area based on detection data analysis. Background Art

[0002] The risk of rock mass sliding is one of the main hidden dangers threatening safe production, which may cause serious casualties and property losses. However, there are many problems in the practical application of existing technologies: First, traditional methods mainly rely on on-site observations and empirical judgments, and data acquisition is inefficient and subjective, making it difficult to achieve a comprehensive and objective risk assessment. For example, rock mass material parameters (such as strength, crack distribution, etc.) are usually estimated through limited samples, lacking systematicity and representativeness, resulting in limited accuracy of the assessment results. Secondly, the existing technology is insufficient in quantitative analysis of rock mass structural characteristics, often relying only on simple geometric descriptions, and cannot fully reflect the complex structure of the rock mass and its impact on the risk of sliding. For example, key features such as protruding areas and crack networks on the side walls of the rock mass are often ignored, resulting in potential risks not being effectively identified. Thirdly, traditional methods lack efficient numerical simulation technology support, and cannot accurately simulate the stress distribution, displacement changes and failure process of the rock mass, making it difficult to scientifically predict the occurrence of sliding risks. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system that can assist manual evaluation of the sliding risk of mined rock masses.

[0004] The present invention discloses a method for assessing the risk of rock mass sliding in a goaf area based on detection data analysis, comprising:

[0005] Carry out block characteristic sampling of the rock mass in the goaf to determine the rock mass strength and crack distribution, and obtain the rock mass material parameters;

[0006] Determine the appearance and structural features of the rock mass in the goaf, build a rock mass side wall performance model based on the appearance and structural features, delineate the protruding blocks of the rock mass side wall performance model, and associate the protruding blocks with each other;

[0007] Using the discrete element method, the rock mass material parameters and the rock mass side wall performance model are analyzed for rock mass sliding risk, and the risk blocks with sliding risk are determined. Based on the location and area of ​​the risk blocks, the focus blocks that need to be analyzed are determined;

[0008] The combination of the protruding blocks contained in the focus block is recorded as a protruding block group, the protruding block group is associated with the rock mass material parameters to obtain a reference rock mass data group, and the reference rock mass data group is classified and integrated to obtain a reference rock mass data set;

[0009] Taking the protruding block group and rock material parameters as comparison elements, the appropriate reference rock data group is found in the reference rock data set, and the risk block corresponding to the reference rock data group is used as the risk assessment result.

[0010] In an embodiment disclosed in the present invention, a method for constructing a rock mass sidewall performance model includes:

[0011] A three-dimensional space coordinate system is established. Based on the relative positions of nodes at different positions of the rock side wall, several position mapping points are set in the three-dimensional space coordinate system, and adjacent position mapping points are connected by mapping lines, which are recorded as mapping lines. The independent surfaces formed between adjacent mapping lines are determined, and the adjacent independent surfaces are correlated with each other to obtain the rock side wall performance model.

[0012] In an embodiment disclosed in the present invention, a method for delineating a protruding block of a rock mass sidewall representation model includes:

[0013] A virtual comparison vertical surface is constructed, and a number of protrusion detection points are randomly set on the rock mass side wall performance model, and the protrusion detection points are used as reference points to construct a protrusion detection line perpendicular to the vertical surface, and the intersection of the protrusion detection line and the virtual comparison vertical surface is marked;

[0014] The density change rate of the intersection distribution density on the virtual comparison vertical plane as the position changes is analyzed. If the density change rate is greater than or equal to a preset value, the corresponding area on the virtual comparison vertical plane is marked. If the minimum distance between the marked areas is less than or equal to a preset value, the marked areas are associated to obtain a regional cluster.

[0015] The blocks mapped by the regional clusters on the virtual comparison vertical plane in the rock sidewall performance model are identified as protruding blocks.

[0016] In the embodiment disclosed in the present invention, the method for performing rock sliding risk analysis on rock material parameters and rock side wall performance model using discrete element method includes:

[0017] The rock mass side wall performance model is imported into the discrete element system to obtain the initial discretized rock mass model, which is discretized into a number of discrete units, and boundary conditions and constraint conditions are configured for the initial discretized rock mass model;

[0018] According to the rock mass material parameters, each discrete unit of the initial discretized rock mass model is configured with corresponding mechanical properties to obtain a reference discretized rock mass model;

[0019] The stress field is calculated using a reference discretized rock model to determine stress concentration areas. The displacement and deformation of discrete units under load are calculated to determine areas with abnormal displacement or deformation. The sliding, fracture and collapse processes of the rock mass are simulated to determine unstable areas. Stress concentration areas, areas with abnormal displacement or deformation, and unstable areas are identified as risk areas.

[0020] In an embodiment disclosed in the present invention, based on the location and area of ​​the risk block, a method for determining a block of interest that needs to be analyzed includes:

[0021] According to the area of ​​the risk block, the association distance of the risk block is determined, and based on the association distance of the risk block, other risk blocks within the association distance with itself are analyzed, and the corresponding risk block is associated with itself. Through the association process of the risk block, the risk block cluster is obtained;

[0022] The area corresponding to the risk block in the risk block cluster and other areas between the risk blocks are recorded as the initial focus blocks. The block area ratio of the total risk block area of ​​the risk blocks in the risk block cluster and the initial focus block is calculated. Based on the block area ratio, the outward expansion distance of the initial focus block is determined, and the expanded initial focus block is identified as the focus block that needs to be analyzed.

[0023] In an embodiment disclosed in the present invention, a method for determining an extension distance of an initial focus block extending outward includes:

[0024] Determine the single block area of ​​each risk block in the initial focus block, calculate the average block area of ​​all risk blocks, calculate the block area difference of the single block area of ​​each risk block relative to the average block area, and calculate the total block area difference of all block area differences;

[0025] Combining the initial block ratio and the total block area difference, determine the outward expansion distance of the initial focus block;

[0026] The expression for calculating the extended distance is: ;

[0027] in, To extend the distance, To expand the distance conversion factor, is the weight adjustment coefficient of the block area ratio, is the block area ratio, is the weight adjustment coefficient of the total block area difference, is the total block area difference, The adjustment constant for the difference between the block area ratio and the total block area.

[0028] In an embodiment disclosed in the present invention, the method of using the protruding block group and the rock mass material parameters as comparison elements and finding an adapted reference rock mass data set in the reference rock mass data set includes:

[0029] Construct a rock sidewall performance model for the goaf that currently needs to be risk assessed, determine the protruding blocks corresponding to the rock sidewall performance model, and construct a protruding block group;

[0030] Carry out block feature sampling for the goaf that needs to be risk assessed and determine the corresponding rock material parameters;

[0031] Substitute the protruding block group and rock material parameters corresponding to the goaf that currently needs to be risk assessed into the reference rock data set. If the position difference between each protruding block is less than or equal to the preset value, it is recorded as position adaptation. If the block area difference between each protruding block is less than or equal to the preset value, it is recorded as area adaptation. If the parameter difference between each rock material parameter is less than or equal to the preset value, it is recorded as parameter adaptation.

[0032] If the projecting block group and rock material parameters of the goaf that currently needs to be risk assessed and the reference rock data group meet the requirements of position adaptation, area adaptation and parameter adaptation, the reference rock data group is deemed to be adapted.

[0033] In the embodiment disclosed in the present invention, a rock mass sliding risk assessment system for goaf areas based on detection data analysis is also disclosed, including:

[0034] The first module is used to sample the rock mass in the goaf, determine the rock mass strength and crack distribution, and obtain the rock mass material parameters;

[0035] The second module is used to determine the appearance structural characteristics of the rock mass in the goaf, build a rock mass side wall performance model based on the appearance structural characteristics, delineate the protruding blocks of the rock mass side wall performance model, and associate the protruding blocks with each other;

[0036] The third module is used to use the discrete element method to analyze the rock mass sliding risk on the rock mass material parameters and the rock mass side wall performance model, determine the risk blocks with sliding risks, and determine the focus blocks that need to be analyzed based on the location and area of ​​the risk blocks;

[0037] The fourth module is used to record the combination of protruding blocks contained in the focus block as a protruding block group, associate the protruding block group with the rock material parameters to obtain a reference rock data group, and classify and integrate the reference rock data group to obtain a reference rock data set;

[0038] The fifth module is used to use the protruding block group and rock material parameters as comparison elements, find the appropriate reference rock data group in the reference rock data set, and use the risk block corresponding to the reference rock data group as the risk assessment result.

[0039] The present invention discloses a method and system for assessing the risk of rock sliding in goaf areas based on detection data analysis, which relates to the technical field of rock risk analysis. The block characteristics of the rock in the goaf area are sampled to determine the rock strength and crack distribution, and obtain the rock material parameters; according to the rock appearance and structural characteristics, a rock sidewall performance model is constructed to delineate and associate the protruding blocks; the discrete element method is used to combine the rock material parameters and the sidewall performance model to determine the risk blocks; the protruding block combination in the focus block is associated with the rock material parameters to form a reference rock data group, and the reference rock data group is classified and integrated; finally, the protruding block group and the rock material parameters are used as comparison elements to find the appropriate reference data group in the data set, and the corresponding risk block is used as the risk assessment result. The above technical scheme of the present invention realizes the assessment of the sliding risk of the rock in the goaf area and improves the accuracy of the risk assessment.

[0040] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a method step diagram of a method for assessing rock sliding risk in goaf areas based on detection data analysis disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0043] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solution of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should be the common meanings understood by the technical personnel described in the present invention.

[0044] Example:

[0045] The present invention discloses a method for assessing the risk of rock mass sliding in goaf areas based on detection data analysis. Figure 1 ,include:

[0046] Step S100, performing block feature sampling on the rock mass in the goaf, determining the rock mass strength and crack distribution, and obtaining rock mass material parameters.

[0047] The core of this step is to sample the block characteristics of the rock mass in the goaf area, determine the strength and crack distribution of the rock mass, and thus obtain the material parameters of the rock mass. In the mining process, the strength and crack distribution of the rock mass are the key factors that determine its stability. Traditional methods usually rely on manual observation or limited field tests, with low data acquisition efficiency and easily affected by subjective factors, making it difficult to fully reflect the actual situation of the rock mass. In order to solve this problem, this step adopts a systematic block characteristic sampling method, using geological radar, acoustic wave detection, drilling sampling and other technologies to sample the rock mass in layers or zones. Through these detection methods, the strength parameters of the rock mass (such as compressive strength, shear strength, elastic modulus, etc.) and the morphology, density and spatial distribution characteristics of the cracks can be obtained. The acquisition of these data is not just a simple local sampling, but through scientific block division and sampling strategies to ensure the representativeness and systematicness of the data. For example, the goaf can be divided into several homogeneous blocks, multiple samplings are performed in each block, and the average value is taken as the material parameter of the block, thereby avoiding the influence of local anomalies on the overall evaluation results. In addition, the quantitative analysis of crack distribution is also the focus of this step. Through 3D scanning or digital imaging technology, the spatial position, length, width and connectivity of rock mass cracks are obtained to provide basic data support for subsequent sliding risk analysis. Accurate acquisition of rock mass material parameters is the cornerstone of sliding risk assessment, and its quality is directly related to the reliability of subsequent modeling and analysis.

[0048] Step S200, determining the appearance structural features of the rock mass in the goaf, constructing a rock mass side wall representation model based on the appearance structural features, delineating the protruding blocks of the rock mass side wall representation model, and associating the positions of the protruding blocks with each other.

[0049] The main task of this step is to construct a rock side wall performance model based on the appearance and structural characteristics of the rock mass and delineate the protruding blocks. The appearance and structural characteristics of the rock mass, especially the geometric shape of the side wall, are important factors affecting the risk of sliding. Traditional methods usually rely on two-dimensional profiles or simple manual measurements, which are difficult to fully describe the complex structure of the rock mass. This step establishes a three-dimensional spatial coordinate system, accurately measures the nodes at different positions of the rock side wall, generates position mapping points, and forms independent surfaces by connecting lines, and finally constructs a rock side wall performance model. This process utilizes three-dimensional modeling technology (such as laser scanning or photogrammetry), which can restore the actual shape of the rock side wall with high precision. After the model is built, this step further delineates the protruding blocks of the rock side wall by virtually comparing the vertical surface and the protruding detection line. Specifically, a number of protruding detection points are randomly set in the model, and a detection line perpendicular to the vertical surface is constructed based on these points, and the intersection of the detection line and the virtual vertical surface is marked. By analyzing the distribution density and change rate of the intersection points, the protruding area of ​​the rock side wall can be identified. If the density change rate of a certain area exceeds a preset value, it will be marked as a protruding block. This method can effectively quantify the geometric characteristics of the rock wall, especially the structural complexity of the protruding area, and provide intuitive model support for subsequent sliding risk analysis. Through this step, not only can the appearance structure of the rock mass be fully described, but also potential sliding risk areas can be identified.

[0050] In an embodiment disclosed in the present invention, a method for constructing a rock mass sidewall performance model includes:

[0051] Step S201, establish a three-dimensional space coordinate system, set a number of position mapping points in the three-dimensional space coordinate system based on the relative positions of nodes at different positions of the rock side wall, and connect adjacent position mapping points, which are recorded as mapping lines, determine the independent surfaces formed between adjacent mapping lines, and associate adjacent independent surfaces with each other to obtain a rock side wall representation model.

[0052] In an embodiment disclosed in the present invention, a method for delineating a protruding block of a rock mass sidewall representation model includes:

[0053] Step S202, construct a virtual comparison vertical surface, and randomly set a number of protrusion detection points on the rock side wall performance model, and use the protrusion detection points as reference points to construct a protrusion detection line perpendicular to the vertical surface, and mark the intersection of the protrusion detection line and the virtual comparison vertical surface.

[0054] The core of this step is to use virtual comparison vertical planes and protrusion detection lines to establish a quantitative analysis framework for the geometric characteristics of the rock sidewall. First, a virtual comparison vertical plane is constructed in the three-dimensional space of the rock sidewall performance model. This plane is usually parallel or perpendicular to a reference plane (such as the design section or average plane) of the rock sidewall as a comparison reference. Then, a number of protrusion detection points are randomly set on the rock sidewall performance model. The distribution of these detection points should cover the entire sidewall area as much as possible to ensure the comprehensiveness of the analysis. With each protrusion detection point as a reference, a protrusion detection line perpendicular to the virtual comparison vertical plane is constructed. The direction of these detection lines is usually perpendicular to the comparison surface, but it can also be adjusted according to actual needs. Finally, mark the intersection of the protrusion detection line and the virtual comparison vertical plane. The position of these intersections reflects the degree of deviation of the rock sidewall relative to the virtual comparison vertical plane, which is the basic data for quantifying the protrusion characteristics.

[0055] Step S203, analyze the density change rate of the intersection distribution density on the virtual comparison vertical plane as the position changes. If the density change rate is greater than or equal to a preset value, mark the corresponding area on the virtual comparison vertical plane. If the minimum distance between the marked areas is less than or equal to the preset value, associate the marked areas to obtain a regional cluster.

[0056] The focus of this step is to identify the protruding areas of the rock sidewall by analyzing the change rate of intersection distribution density and mark them as regional clusters. First, the change of intersection distribution density on the virtual comparison vertical plane with position is quantitatively analyzed to calculate the density change rate. The density change rate refers to the degree of change in the number of intersections in a unit area. The calculation formula is Δρ / Δs, where Δρ is the density change and Δs is the position change. If the density change rate of a certain area is greater than or equal to the preset value, it indicates that the area has significant protruding features and is marked as a protruding area. Next, the spatial relationship of these marked areas is analyzed. If the minimum distance between multiple marked areas is less than or equal to the preset value, they are associated as a regional cluster.

[0057] Step S204: The block mapped by the rock mass side wall performance model on the region cluster on the virtual comparison vertical plane is identified as a protruding block.

[0058] The ultimate goal of this step is to map the regional clusters on the virtual comparison vertical plane back to the rock side wall performance model and identify the actual protruding blocks. First, according to the position of the regional clusters on the virtual comparison vertical plane, combined with the geometric relationship between the protruding detection line and the rock side wall performance model, the mapping position of these clusters in the model is determined. For example, each regional cluster on the virtual comparison vertical plane can be mapped to the corresponding block on the rock side wall through spatial coordinate transformation or projection relationship. Then, the mapped blocks are identified as protruding blocks on the rock side wall. These protruding blocks have significant deviations or uplifts in geometric features and are key areas for sliding risk analysis. Through this step, the protruding areas of the rock side wall can be clearly identified, providing refined data for subsequent risk assessment.

[0059] Step S300, using discrete element method, rock mass material parameters and rock mass side wall performance model are analyzed for rock mass sliding risk, risk blocks with sliding risk are determined, and based on the location and area of ​​the risk blocks, focus blocks that need to be analyzed are determined.

[0060] In this step, the discrete element method is used to analyze the sliding risk of the rock mass material parameters and the side wall performance model. The discrete element method is a numerical simulation method that can simulate the mechanical behavior of the rock mass under load, including stress distribution, displacement change and destruction process. Specifically, in this step, the constructed rock mass side wall performance model is first imported into the discrete element system, discretized into a number of discrete units, and corresponding mechanical properties (such as elastic modulus, Poisson's ratio, shear strength, etc.) are configured for each unit. These properties are determined according to the rock mass material parameters obtained in step S100. Subsequently, boundary conditions and constraints are configured in the model to simulate the stress state of the rock mass in the actual mining environment. By calculating the stress field in the rock mass, stress concentration areas can be identified, which are usually high-risk areas for sliding. At the same time, the displacement and deformation of the discrete unit under load are calculated to determine the area with abnormal displacement or deformation. In addition, this step also simulates the sliding, fracture and collapse process of the rock mass and identifies the unstable area. By comprehensively analyzing the stress concentration blocks, displacement or deformation abnormal blocks and unstable blocks, the risk blocks with sliding risks are determined. The application of discrete element method makes the simulation of rock sliding risk more refined, can effectively predict potential unstable areas, and provides a scientific basis for risk assessment.

[0061] In the embodiment disclosed in the present invention, the method for performing rock sliding risk analysis on rock material parameters and rock side wall performance model using discrete element method includes:

[0062] Step S301, importing the rock mass side wall performance model into a discrete element system to obtain an initial discretized rock mass model, discretizing it into a plurality of discrete units, and configuring boundary conditions and constraint conditions for the initial discretized rock mass model.

[0063] The core of this step is to convert the rock side wall performance model into a discretized model recognizable by the discrete element system, and configure reasonable boundary conditions and constraints for it. First, the rock side wall performance model constructed in step S200 is imported into the discrete element system. The discrete element system is a numerical simulation tool based on unit decomposition, which can divide the continuous rock mass into several discrete units, each of which can independently simulate its mechanical behavior. After importing, the system discretizes the rock side wall performance model into an initial discretized rock model. The size and shape of the discrete unit need to be selected according to the geometric characteristics of the rock mass and the requirements of calculation accuracy. Tetrahedrons, hexahedrons or irregular polygonal units are usually used.

[0064] Then, boundary conditions and constraints are configured for the initial discretized rock model. Boundary conditions include load conditions (such as gravity, external pressure, etc.) and displacement constraints (such as fixed end constraints, sliding constraints, etc.). These conditions need to be set according to the actual environment and working conditions of the mining project. For example, if the slope stability of an open-pit mine is simulated, the bottom and sides of the model are usually set as fixed constraints, and a gravity load is applied to the top. By configuring reasonable boundary conditions and constraints, it is ensured that the discrete element model can accurately reflect the stress state of the rock mass under actual working conditions.

[0065] Step S302: According to the rock mass material parameters, each discrete unit of the initial discretized rock mass model is configured with corresponding mechanical properties to obtain a reference discretized rock mass model.

[0066] The task of this step is to configure the corresponding mechanical properties for each discrete unit of the initial discretized rock model according to the rock material parameters obtained in step S100, and generate a reference discretized rock model. Rock material parameters include compressive strength, shear strength, elastic modulus, Poisson's ratio, etc. These parameters determine the mechanical response of the rock under load. The discrete element system allows independent configuration of properties for each discrete unit, thereby accurately simulating the heterogeneity of the rock.

[0067] Specifically, the material parameters of the rock mass are first mapped to each discrete unit. For example, if the compressive strength of the rock mass in a certain area is high, a higher strength value is configured for the corresponding discrete unit; if there are a large number of cracks in a certain area, a lower elastic modulus and a high Poisson's ratio are configured for it. Through this process, the initial discretized rock mass model is transformed into a reference discretized rock mass model, which not only contains the geometric information of the rock mass, but also fully describes its mechanical properties. This step lays the foundation for the subsequent stress field calculation and sliding risk analysis.

[0068] Step S303, using the reference discretized rock model to calculate the stress field, determine the stress concentration blocks, calculate the displacement and deformation of the discrete unit under the load, determine the displacement or deformation abnormal blocks, simulate the sliding, fracture and collapse process of the rock mass, determine the unstable blocks, and identify the stress concentration blocks, displacement or deformation abnormal blocks and unstable blocks as risk blocks.

[0069] The core of this step is to use the reference discretized rock model to perform stress field calculation and instability analysis, and finally identify the sliding risk blocks. First, the stress field calculation of the reference discretized rock model is performed in the discrete element system. Stress field calculation is to solve the stress distribution of the rock mass under load by numerical methods, usually using the finite element method or discrete element method. Through stress field analysis, stress concentration blocks can be identified. These areas are usually high-risk areas for sliding because the stress they bear may exceed the strength limit of the rock mass.

[0070] Next, the displacement and deformation of the discrete unit under the load are calculated to determine the abnormal displacement or deformation blocks. The abnormal displacement area usually shows a significantly higher displacement than the surrounding area, which may be a precursor to rock mass instability. The abnormal deformation area is manifested as a significant change in the unit shape, which may indicate local damage to the rock mass. In addition, this step also simulates the sliding, fracture and collapse process of the rock mass. By loading gradually increasing loads or disturbances, observing the dynamic response of the rock mass, and identifying unstable blocks. Unstable blocks are usually manifested as separation, fracture or overall collapse between units, and are areas with the most significant sliding risks.

[0071] Finally, stress concentration blocks, abnormal displacement or deformation blocks, and unstable blocks are identified as risk blocks. These areas are the focus of sliding risk analysis and require further engineering measures for reinforcement or monitoring. Through numerical simulation of the discrete element method, the sliding risk of the rock mass can be comprehensively and accurately evaluated, providing a scientific basis for mine safety management.

[0072] In an embodiment disclosed in the present invention, based on the location and area of ​​the risk block, a method for determining a block of interest that needs to be analyzed includes:

[0073] Step S304, according to the area of ​​the risk block, determine the association distance of the risk block, and based on the association distance of the risk block, analyze other risk blocks within the association distance with itself, and associate the corresponding risk block with itself, and obtain the risk block cluster through the association process of the risk block.

[0074] Step S305, record the area corresponding to the risk block in the risk block cluster and other areas between the risk blocks as initial focus blocks, calculate the block area ratio of the total risk block area of ​​the risk blocks in the risk block cluster to the initial focus block, and based on the block area ratio, determine the outward expansion distance of the initial focus block, and identify the expanded initial focus block as the focus block that needs to be analyzed.

[0075] In an embodiment disclosed in the present invention, a method for determining an extension distance of an initial focus block extending outward includes:

[0076] Step S3051, determining the single block area of ​​each risk block in the initial focus block, calculating the average block area of ​​all risk blocks, calculating the block area difference between the single block area of ​​each risk block and the average block area, and calculating the total block area difference of all block area differences;

[0077] Step S3052, combining the initial block ratio and the total block area difference, determining the extension distance of the initial focus block outward;

[0078] The expression for calculating the extended distance is: .

[0079] in, To extend the distance, To expand the distance conversion factor, is the weight adjustment coefficient of the block area ratio, is the block area ratio, is the weight adjustment coefficient of the total block area difference, is the total block area difference, The adjustment constant for the difference between the block area ratio and the total block area.

[0080] Step S400, record the combination of protruding blocks included in the focus block as a protruding block group, associate the protruding block group with rock material parameters to obtain a reference rock data group, classify and integrate the reference rock data group to obtain a reference rock data set.

[0081] The task of this step is to associate the combination of protruding blocks in the block of interest with the rock material parameters to form a reference rock data group, and classify and integrate them into a reference rock data set. Among the risk blocks identified in step S300, some blocks need special attention due to the particularity of their position, area or geometric characteristics. This step first combines the protruding blocks contained in the block of interest to form a protruding block group. Subsequently, these protruding block groups are associated with the rock material parameters obtained in step S100 to generate a reference rock data group. Each reference rock data group contains the geometric characteristics of the protruding blocks (such as position, area) and the corresponding rock material parameters (such as strength, crack distribution, etc.). By classifying and integrating multiple reference rock data groups, a reference rock data set is finally formed. The construction of this data set provides a standardized comparison benchmark for subsequent risk assessment, so that the sliding risks of different mining areas can be evaluated under a unified framework. Through this step, not only can the rock data be managed systematically, but also a scientific basis can be provided for subsequent risk assessment.

[0082] Step S500: Taking the protruding block group and the rock mass material parameters as comparison elements, finding an appropriate reference rock mass data group in the reference rock mass data set, and taking the risk block corresponding to the reference rock mass data group as the risk assessment result.

[0083] The goal of this step is to use the protruding block group and rock material parameters as comparison elements, find an adapted reference data group in the reference rock data set, and use its corresponding risk block as the risk assessment result. Specifically, first, for the goaf that currently needs to be risk assessed, a rock sidewall performance model is constructed, and its corresponding protruding block group and rock material parameters are determined. Subsequently, these data are substituted into the reference rock data set for comparison. The elements of comparison include the position difference, area difference and rock material parameter difference of the protruding blocks. If the data of the current mining area meets the adaptation conditions with a certain reference rock data group in terms of position, area and parameters (that is, the difference is less than the preset value), the reference rock data group is considered to have reference value. Finally, the risk block corresponding to the adapted reference rock data group is used as the risk assessment result of the current mining area. This step realizes the precision and efficiency of risk assessment through automated data comparison and matching, and provides a scientific basis for mine safety management.

[0084] In an embodiment disclosed in the present invention, the method of using the protruding block group and the rock mass material parameters as comparison elements and finding an adapted reference rock mass data set in the reference rock mass data set includes:

[0085] Step S501, constructing a rock sidewall performance model for the goaf that currently needs to be risk assessed, determining the protruding blocks corresponding to the rock sidewall performance model, and constructing a protruding block group.

[0086] Step S502, performing block feature sampling on the goaf that currently needs to be risk assessed, and determining the corresponding rock material parameters.

[0087] Step S503, substitute the protruding block group and rock material parameters corresponding to the goaf that currently needs to be risk assessed into the reference rock data set; if the position difference between each protruding block is less than or equal to the preset value, it is recorded as position adaptation; if the block area difference between each protruding block is less than or equal to the preset value, it is recorded as area adaptation; if the parameter difference between each rock material parameter is less than or equal to the preset value, it is recorded as parameter adaptation.

[0088] Step S503: If the projecting block group and rock material parameters of the goaf that currently needs to be risk assessed and the reference rock data group satisfy position adaptation, area adaptation and parameter adaptation, the reference rock data group is deemed to be adapted.

[0089] In the embodiment disclosed in the present invention, a rock mass sliding risk assessment system for goaf areas based on detection data analysis is also disclosed, including:

[0090] The first module is used to sample the block characteristics of the rock mass in the goaf, determine the rock mass strength and crack distribution, and obtain the rock mass material parameters.

[0091] The second module is used to determine the appearance structural characteristics of the rock mass in the goaf, build a rock side wall performance model based on the appearance structural characteristics, delineate the protruding blocks of the rock side wall performance model, and associate the positions of the protruding blocks with each other.

[0092] The third module is used to use the discrete element method to conduct rock sliding risk analysis on rock material parameters and rock side wall performance models, determine the risk blocks with sliding risks, and determine the focus blocks that need to be analyzed based on the location and area of ​​the risk blocks.

[0093] The fourth module is used to record the combination of protruding blocks contained in the focus block as a protruding block group, associate the protruding block group with rock material parameters to obtain a reference rock data group, and classify and integrate the reference rock data group to obtain a reference rock data set.

[0094] The fifth module is used to use the protruding block group and rock material parameters as comparison elements, find the appropriate reference rock data group in the reference rock data set, and use the risk block corresponding to the reference rock data group as the risk assessment result.

[0095] The present invention discloses a method and system for assessing the risk of rock sliding in goaf areas based on detection data analysis, which relates to the technical field of rock risk analysis. The block characteristics of the rock in the goaf area are sampled to determine the rock strength and crack distribution, and obtain the rock material parameters; according to the rock appearance and structural characteristics, a rock sidewall performance model is constructed to delineate and associate the protruding blocks; the discrete element method is used to combine the rock material parameters and the sidewall performance model to determine the risk blocks; the protruding block combination in the focus block is associated with the rock material parameters to form a reference rock data group, and the reference rock data group is classified and integrated; finally, the protruding block group and the rock material parameters are used as comparison elements to find the appropriate reference data group in the data set, and the corresponding risk block is used as the risk assessment result. The above technical scheme of the present invention realizes the assessment of the sliding risk of the rock in the goaf area and improves the accuracy of the risk assessment.

[0096] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for assessing rock sliding risk in goaf areas based on detection data analysis, characterized in that: include: Carry out block characteristic sampling of the rock mass in the goaf to determine the rock mass strength and crack distribution, and obtain the rock mass material parameters; Determine the appearance and structural features of the rock mass in the goaf, build a rock mass side wall performance model based on the appearance and structural features, delineate the protruding blocks of the rock mass side wall performance model, and associate the protruding blocks with each other; Using the discrete element method, the rock mass material parameters and the rock mass side wall performance model are analyzed for rock mass sliding risk, and the risk blocks with sliding risk are determined. Based on the location and area of ​​the risk blocks, the focus blocks that need to be analyzed are determined; The combination of the protruding blocks contained in the focus block is recorded as a protruding block group, the protruding block group is associated with the rock mass material parameters to obtain a reference rock mass data group, and the reference rock mass data group is classified and integrated to obtain a reference rock mass data set; Taking the protruding block group and rock mass material parameters as comparison elements, find the appropriate reference rock mass data group in the reference rock mass data set, and take the risk block corresponding to the reference rock mass data group as the risk assessment result; Methods for constructing rock mass sidewall performance models include: A three-dimensional space coordinate system is established. Based on the relative positions of nodes at different positions of the rock mass side wall, a number of position mapping points are set in the three-dimensional space coordinate system. Adjacent position mapping points are connected by mapping lines, which are recorded as mapping lines. Independent surfaces formed between adjacent mapping lines are determined, and adjacent independent surfaces are correlated with each other to obtain a rock mass side wall representation model. Methods for delineating the protruding area of ​​the rock mass sidewall performance model include: A virtual comparison vertical surface is constructed, and a number of protrusion detection points are randomly set on the rock mass side wall performance model, and the protrusion detection points are used as reference points to construct a protrusion detection line perpendicular to the vertical surface, and the intersection of the protrusion detection line and the virtual comparison vertical surface is marked; The density change rate of the intersection distribution density on the virtual comparison vertical plane as the position changes is analyzed. If the density change rate is greater than or equal to a preset value, the corresponding area on the virtual comparison vertical plane is marked. If the minimum distance between the marked areas is less than or equal to a preset value, the marked areas are associated to obtain a regional cluster. The blocks mapped by the regional clusters on the virtual comparison vertical plane in the rock sidewall performance model are identified as protruding blocks.

2. The method for assessing rock mass sliding risk in goaf areas based on detection data analysis according to claim 1 is characterized in that: The methods for analyzing the risk of rock mass sliding using the discrete element method for rock mass material parameters and rock mass side wall performance models include: The rock mass side wall performance model is imported into the discrete element system to obtain the initial discretized rock mass model, which is discretized into a number of discrete units, and boundary conditions and constraint conditions are configured for the initial discretized rock mass model; According to the rock mass material parameters, each discrete unit of the initial discretized rock mass model is configured with corresponding mechanical properties to obtain a reference discretized rock mass model; The stress field is calculated using a reference discretized rock model to determine stress concentration areas. The displacement and deformation of discrete units under load are calculated to determine areas with abnormal displacement or deformation. The sliding, fracture and collapse processes of the rock mass are simulated to determine unstable areas. Stress concentration areas, areas with abnormal displacement or deformation, and unstable areas are identified as risk areas.

3. The method for assessing rock mass sliding risk in goaf areas based on detection data analysis according to claim 1, characterized in that: Based on the location and area of ​​the risk block, the method of determining the block of concern that needs to be analyzed includes: According to the area of ​​the risk block, the association distance of the risk block is determined, and based on the association distance of the risk block, other risk blocks within the association distance with itself are analyzed, and the corresponding risk block is associated with itself. Through the association process of the risk block, the risk block cluster is obtained; The area corresponding to the risk block in the risk block cluster and other areas between the risk blocks are recorded as the initial focus blocks. The block area ratio of the total risk block area of ​​the risk blocks in the risk block cluster and the initial focus block is calculated. Based on the block area ratio, the outward expansion distance of the initial focus block is determined, and the expanded initial focus block is identified as the focus block that needs to be analyzed.

4. The method for assessing rock mass sliding risk in goaf areas based on detection data analysis according to claim 3 is characterized in that: Methods for determining the extension distance of the initial focus block include: Determine the single block area of ​​each risk block in the initial focus block, calculate the average block area of ​​all risk blocks, calculate the block area difference of the single block area of ​​each risk block relative to the average block area, and calculate the total block area difference of all block area differences; Combining the initial block ratio and the total block area difference, determine the outward expansion distance of the initial focus block; The expression for calculating the extended distance is: ； in, To extend the distance, To expand the distance conversion factor, is the weight adjustment coefficient of the block area ratio, is the block area ratio, is the weight adjustment coefficient of the total block area difference, is the total block area difference, The adjustment constant for the difference between the block area ratio and the total block area.

5. The method for assessing rock mass sliding risk in goaf areas based on detection data analysis according to claim 1, characterized in that: The method of using the protruding block group and the rock mass material parameters as comparison elements and finding the appropriate reference rock mass data set in the reference rock mass data set includes: Construct a rock sidewall performance model for the goaf that currently needs to be risk assessed, determine the protruding blocks corresponding to the rock sidewall performance model, and construct a protruding block group; Carry out block feature sampling for the goaf that needs to be risk assessed and determine the corresponding rock material parameters; Substitute the protruding block group and rock material parameters corresponding to the goaf that currently needs to be risk assessed into the reference rock data set. If the position difference between each protruding block is less than or equal to the preset value, it is recorded as position adaptation. If the block area difference between each protruding block is less than or equal to the preset value, it is recorded as area adaptation. If the parameter difference between each rock material parameter is less than or equal to the preset value, it is recorded as parameter adaptation. If the projecting block group and rock material parameters of the goaf that currently needs to be risk assessed and the reference rock data group meet the requirements of position adaptation, area adaptation and parameter adaptation, the reference rock data group is deemed to be adapted.

6. The risk assessment system for rock mass sliding in goaf areas based on detection data analysis is characterized by: A method for assessing rock mass sliding risk in a goaf area for implementing any one of claims 1 to 5, comprising: The first module is used to sample the rock mass in the goaf, determine the rock mass strength and crack distribution, and obtain the rock mass material parameters; The second module is used to determine the appearance structural characteristics of the rock mass in the goaf, build a rock mass side wall performance model based on the appearance structural characteristics, delineate the protruding blocks of the rock mass side wall performance model, and associate the protruding blocks with each other; The third module is used to use the discrete element method to analyze the rock mass sliding risk on the rock mass material parameters and the rock mass side wall performance model, determine the risk blocks with sliding risks, and determine the focus blocks that need to be analyzed based on the location and area of ​​the risk blocks; The fourth module is used to record the combination of protruding blocks contained in the focus block as a protruding block group, associate the protruding block group with the rock material parameters to obtain a reference rock data group, and classify and integrate the reference rock data group to obtain a reference rock data set; The fifth module is used to use the protruding block group and rock material parameters as comparison elements, find the appropriate reference rock data group in the reference rock data set, and use the risk block corresponding to the reference rock data group as the risk assessment result.

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