Refined prediction method and device for goaf stability and storage medium

Abstract

The invention relates to a refined prediction method and device for goaf stability and a storage medium, and the method comprises the steps: constructing a block model and a three-dimensional geological model, and enabling the three-dimensional geological model to coincide with the block model; performing rock mass mechanical characterization on the exploration mine to obtain rock mass mechanical parameters of the block body model; based on the block model, the three-dimensional geologic model and the rock mass mechanical parameters, constructing a rock mass refined numerical calculation model; carrying out numerical simulation on the surrounding rock stability of the goaf by adopting a rock quality refined numerical calculation model to obtain a rock quality numerical simulation result; determining a plurality of stability evaluation indexes of the goaf according to the exploration information of the exploration mine, the rock mass mechanical parameters and the rock mass numerical simulation result; and determining the stability membership degree and the stability prediction grade of the goaf based on the plurality of stability evaluation indexes of the goaf. The stability of the goaf is evaluated comprehensively and objectively, and the accuracy of stability prediction of the goaf is improved.

Description

Technical Field

[0001] The present application relates to the technical field of underground mining of metal mines, and in particular to a refined prediction method, device and storage medium for the stability of goaf areas. Background Art

[0002] Goafs are prone to various disasters such as water seepage, collapse, roof fall and slab collapse, which often cause a large number of casualties and property losses. The stability of goafs is an extremely complex issue. It is not only related to the depth, inclination, thickness of the ore body, the lithology, occurrence state, thickness, physical and mechanical properties of the overlying rock layer, geological structure, site topography, hydrogeological conditions, mining area, roof management methods, mining times, mining methods and other geological mining conditions, but also closely related to the type, size and location of the upper load.

[0003] There are usually four treatment methods for the goaf left over from underground mining: closure, collapse, reinforcement and filling. The specific conditions of each mine are different, and the distribution and morphological characteristics of the goaf are different. The treatment methods for each goaf are often different; mines usually have complex geological structures and uneven rock mass quality.

[0004] In the existing technology, the analysis method of goaf stability usually only considers single rock mechanics parameters, mining conditions and other factors to conduct mining engineering stability evaluation and numerical simulation analysis. This method cannot accurately reflect the interaction of goaf groups and the spatial variability of engineering geology, resulting in low accuracy. Summary of the invention

[0005] To solve the above problems, the embodiments of the present application provide a refined prediction method, device and storage medium for goaf stability, which overcome or at least partially overcome the deficiencies of the prior art.

[0006] The present application embodiment adopts the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a refined prediction method for goaf stability, comprising:

[0008] Building a block model based on drilling information and core information of multiple drill holes in the exploration mine;

[0009] Constructing a three-dimensional geological model based on the image information, exploration information and production information of the exploration mine, and making the three-dimensional geological model overlap with the block model;

[0010] Performing rock mechanics characterization on the exploration mine to obtain rock mechanics parameters of the block model;

[0011] Constructing a rock quality refined numerical calculation model based on the block model, the three-dimensional geological model, and the rock mass mechanical parameters;

[0012] The rock quality refined numerical calculation model is used to perform numerical simulation on the surrounding rock stability of the goaf to obtain the rock quality numerical simulation result;

[0013] Determine several stability evaluation indicators of the goaf according to the exploration information of the exploration mine, the rock mass mechanical parameters, and the rock numerical simulation results;

[0014] Based on several stability evaluation indicators of the goaf, the stability membership and stability prediction level of the goaf are determined.

[0015] In a second aspect, the embodiment of the present application further provides a refined prediction device for goaf stability, the device comprising:

[0016] A first construction unit is used to construct a block model based on drilling information of multiple boreholes and core information of the exploration mine;

[0017] A second construction unit is used to construct a three-dimensional geological model based on the image information, exploration information and production information of the exploration mine, and to make the three-dimensional geological model overlap with the block model;

[0018] A characterization unit, used for performing rock mechanics characterization on the exploration mine to obtain rock mechanics parameters of the block model;

[0019] A third construction unit is used to construct a rock quality refined numerical calculation model based on the block model, the three-dimensional geological model, and the rock mass mechanical parameters;

[0020] A simulation unit, used to use the rock quality refined numerical calculation model to perform numerical simulation on the surrounding rock stability of the goaf to obtain a rock quality numerical simulation result;

[0021] An index calculation unit, used to determine several stability evaluation indexes of the goaf according to the exploration information of the exploration mine, the rock mass mechanical parameters, and the rock numerical simulation results;

[0022] The prediction unit is used to determine the stability membership and stability prediction level of the goaf based on several stability evaluation indicators of the goaf.

[0023] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned refined prediction method for goaf stability when executing the computer program.

[0024] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is instructed by a processor, it implements the steps of the above-mentioned refined prediction method for goaf stability.

[0025] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0026] The present application first constructs a block model based on the drilling information and core information of multiple boreholes of the exploration mine, and then constructs a three-dimensional geological model based on the image information, exploration information and production information of the exploration mine, and makes the three-dimensional geological model overlap with the block model; further, the exploration mine is characterized by rock mechanics to obtain the rock mechanics parameters of the block model, and a rock refinement numerical calculation model is constructed based on the block model, the three-dimensional geological model, and the rock mechanics parameters; further, the rock refinement numerical calculation model is used to numerically simulate the stability of the surrounding rock of the goaf to obtain the rock numerical simulation results; when predicting the stability of the goaf, a number of stability evaluation indicators of the goaf are determined according to the exploration information of the exploration mine, the rock mechanics parameters, and the rock numerical simulation results; using the several stability evaluation indicators of the goaf as input, the stability membership of the goaf is determined, and the stability prediction level of the goaf is determined. Through the above method, this application has established a multi-level fuzzy comprehensive evaluation system for surrounding rock stability that comprehensively considers the occurrence conditions of mining rock masses, refined mechanical parameters of rock masses, geometric parameters and treatment methods of voids, interactions between void groups and mechanical responses. It has achieved a comprehensive and objective evaluation of the stability of voids, improved the accuracy of stability prediction of voids, and provided a theoretical basis for mine safety and ground pressure management. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0028] Figure 1 A schematic flow chart of a refined prediction method for goaf stability according to an embodiment of the present application is shown;

[0029] Figure 2 shows a drill core image according to one embodiment of the present application;

[0030] Figure 3 A schematic diagram of a block model according to an embodiment of the present application is shown;

[0031] Figure 4A schematic diagram of a three-dimensional geological model according to an embodiment of the present application is shown;

[0032] Figure 5 A schematic diagram of a rock quality refinement numerical calculation model (elastic modulus example) according to an embodiment of the present application is shown;

[0033] Figure 6 A schematic structural diagram of a refined prediction device for goaf stability according to an embodiment of the present application is shown;

[0034] Figure 7 A schematic structural diagram of a computer device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0036] At present, domestic and foreign scholars have conducted a series of studies on the stability of goafs and have achieved many results, but there are still many problems. For example, the existing technology cannot achieve refined numerical modeling and calculation, and does not reflect the spatial variability of mine rock mass quality, and cannot reflect the geological correlation of the spatial distribution of rock mass parameters.

[0037] In this regard, this application provides a refined prediction method for goaf stability. Figure 1 A schematic diagram of a process flow of a refined prediction method for goaf stability according to an embodiment of the present application is shown. Figure 1 It can be seen that this embodiment includes steps S110 to S170:

[0038] Step S110: constructing a block model based on the drilling information of multiple boreholes and core information of the exploration mine.

[0039] The relevant information required in the process of obtaining refined prediction of goaf stability includes, but is not limited to: drilling information and core information of multiple boreholes in the exploration mine, as well as image information, exploration information and production information of the exploration mine that will be needed later. More specifically, this information includes, but is not limited to: rock structure, geological structure, goaf collapse and distribution, goaf treatment methods, etc. of the on-site exploration mine, which are not listed here one by one.

[0040] A block model is constructed based on the drilling information of multiple boreholes and the drilling core information of the exploration mine, specifically comprising: obtaining images of multiple cores of the multiple boreholes, and determining the rock quality index value of each core according to the images; adopting an inverse distance weighted method to construct the block model based on the drilling information of each borehole and the rock quality index value of each core, wherein the drilling information comprises: borehole coordinates, borehole length, and borehole number; taking any borehole on the block model as a reference point, dividing the block model into multiple unit block models; and outputting the coordinate information and rock quality index value of each unit block model.

[0041] Take a picture of the core box to obtain the geological drill core image, record the drill hole number, read the image data and calculate the rock quality index value (RQD value) of each drill core. Please refer to Figure 1 , Figure 2 A drill core image according to one embodiment of the present application is shown.

[0042] Based on the RQD results and geological drilling information obtained above, the drilling information includes but is not limited to: drilling coordinates, drilling length, and drilling number, an inverse distance weighted method is used to establish a block model containing rock mass RQD information and drilling coordinate information.

[0043] Furthermore, any drill hole in the block model is selected as a reference point, and the xyz coordinates of the block model and the RQD value of the rock mass are disassembled and output in units of cubes.

[0044] More specifically, in some embodiments, the block model is constructed based on the drilling information of each of the boreholes and the rock quality index value of each of the cores using an inverse distance weighted method, including: resampling each borehole to obtain a borehole resampling point set; constructing a block model within the specified spatial range using an inverse distance weighted method; wherein, for any target point in the specified spatial range, multiple adjacent points closest to the target point are found in the borehole resampling point set to form an adjacent point set, wherein any element of the borehole resampling point set and the adjacent point set respectively represents the stratigraphic attribute to which each point belongs; based on the distance between the target point and each of the adjacent points, the stratigraphic attribute to which the target point belongs is determined, wherein the stratigraphic attribute to which the target point belongs is consistent with one of all its adjacent points; and the block model is constructed according to the stratigraphic attributes of each point in the specified spatial range.

[0045] Specifically, step S121: resample the boreholes within the specified spatial range. Resample the boreholes according to a certain step length (step) to obtain a borehole resample point set P containing stratum attributes = {p ij ,i=1...np,j=1...n s}. Where np Represents the total number of resampling points, n s represents the total number of strata, j represents the stratum attribute, then p ij Represents the i-th point in the resampled point set and has attribute j.

[0046] Step S122: Use the inverse distance weighting method to construct a block model within the specified spatial range.

[0047] For any point x within the specified spatial range, find the n(0 <n<n p ) points, forming the adjacent point set P' = {p ij ,i=1...n n ,j=1...n j}. Among them, n n is the total number of points in the point set P', j represents the stratum attribute, n j is the number of points with attribute j in the point set P'.

[0048] The probability Z of x belonging to each stratum attribute j is calculated as follows x,j :

[0049]

[0050] Among them, D i,j Indicates the distance from x to the i-th point (with attribute j) in the point set P'.

[0051] The calculated stratigraphic attribute of the target point x is the probability Z x,j The stratigraphic attribute represented by the maximum value is used to construct a block model based on the stratigraphic attributes of all points within the specified spatial range.

[0052] Figure 3 A schematic diagram of a block model according to an embodiment of the present application is shown. Figure 3 As shown in Figure 1, the block model records the coordinates of a geological borehole as reference point A, and an example of the output rock mass RQD value result is shown in Table 1.

[0053] Table 1

[0054]

[0055] Step S120, constructing a three-dimensional geological model based on the image information, exploration information and production information of the exploration mine, and making the three-dimensional geological model overlap with the block model.

[0056] The three-dimensional geological model can integrate the surface and underground conditions of real topography, ore bodies and faults. Specifically, the three-dimensional geological model is constructed based on the image information, exploration information and production information of the exploration mine, including: obtaining the image information through drone oblique photography technology, extracting the exploration information from the mine geological exploration data, and extracting the production information from the production data, wherein the exploration information includes: rock mass structure and geological structure, and the production information includes: goaf collapse and distribution, and goaf processing methods; constructing a three-dimensional geological model based on the image information, the exploration information and the production information; locating the reference point on the block model in the three-dimensional geological model, and moving the three-dimensional geological model so that the three-dimensional geological model and the block model overlap in spatial position.

[0057] For example, a 3D geological model is established based on UAV oblique photography technology, mining geological exploration data, production data, etc. The 3D geological model integrates the surface and underground conditions of real topography, ore bodies and faults, and locates the reference point position in the 3D geological model ( Figure 3 A), move the 3D geological model so that the 3D geological model and the block model coincide with each other in spatial position. Figure 4 A schematic diagram of a three-dimensional geological model according to an embodiment of the present application is shown, which integrates surface and underground information such as real topography, ore bodies and faults.

[0058] Step S130, performing rock mechanics characterization on the exploration mine to obtain rock mechanics parameters of the block model.

[0059] Rock mass mechanics parameters include but are not limited to: geological strength index GSI, elastic modulus, cohesion, internal friction angle, and tensile strength, etc. These rock mass mechanics parameters can accurately characterize the quality of the rock.

[0060] Specifically, in some embodiments, the rock mechanics characterization of the exploration mine to obtain the rock mechanics parameters of the block model includes: extracting the structural surface data of the exploration mine from the geological exploration data of the mine; based on the Hoek-Brown criterion, according to the structural surface data and the rock quality index value of each unit block model output by the block model, calculating the rock mechanics parameters of the block model, the rock mechanics parameters including: geological strength index GSI, elastic modulus, cohesion, internal friction angle, and tensile strength.

[0061] That is, the structural surface state parameters JCond89 and Hoek-Brown constant mi are obtained based on the RQD data of the mine rock mass, the on-site exploration structural surface and other data, and the mine geological strength index GSI, elastic modulus, cohesion, internal friction angle, compressive strength (including HK compressive strength, rock mass compressive strength, etc.) are calculated based on the Hoek-Brown criterion to achieve a refined characterization of the rock mass quality. Based on the block model generated above, the rock mass mechanical parameters with spatial variability are output in units of cubes.

[0062] The geological strength index GSI can be calculated according to the following formulas (1) to (4):

[0063] GSI=1.5*Jcond89+RQD / 2 (1);

[0064]

[0065] m b =m i e (GSI-100) / (28-14D)(3);

[0066] s=e (GSI-100) / (9-3D) (4);

[0067] Where, GSI is the geological strength index, with the unit of 1; a, m b , s is the Hoek-Brown criterion parameter, the unit is 1, and e represents a natural constant.

[0068] HK compressive strength can be calculated according to the following formulas (5) to (7):

[0069] σ cm ={σ ci *[m b +4s-a*(m b -8s)]*(s+m b / 4) (a-1)} / [2(1+a)(2+a)] (5);

[0070] σ 3max =0.72*σ cm / (ρgH) -0.91 (6);

[0071] σ 3n =σ 3max / σ ci (7);

[0072] In the formula, σ ci is the uniaxial compressive strength of rock in non-stress state, in MPa; σ cmHK is the compressive strength, in MPa; H is the depth of the goaf, in m; ρ is the rock mass density, in kg / m 3 ; σ 3max , σ 3n is an undetermined parameter; g represents the acceleration due to gravity.

[0073] The compressive strength of rock mass can be calculated according to the following formulas (8) to (9):

[0074] σ c =σ ci *s a (8);

[0075] σ t =-s*σ ci / m b (9);

[0076] Where: c is the compressive strength of rock mass, in MPa; σ t It is the tensile strength of rock mass, in MPa.

[0077] The elastic modulus can be calculated according to the following formula (10):

[0078]

[0079] Where: Em is the elastic modulus, the unit is GPa; D is the disturbance coefficient, the unit is 1.

[0080] The internal friction angle and cohesion can be calculated according to the following formulas (11) to (12):

[0081]

[0082] Where: φ is the internal friction angle, unit is rad; c is the cohesion, unit is MPa.

[0083] Based on the generated block model, the rock mass mechanical parameters with spatial variability are output in units of cubes. The results are shown in Table 2.

[0084] Table 2

[0085]

[0086] Step S140, constructing a rock quality refined numerical calculation model based on the block model, the three-dimensional geological model, and the rock mass mechanical parameters.

[0087] By finely characterizing the rock mass quality, the above-mentioned rock mass mechanical parameters are obtained, which can characterize the spatial variability of mining rocks. On this basis, numerical simulation software is used for secondary development, and a fine numerical calculation model of rock quality is obtained by transforming the block model and the three-dimensional geological model.

[0088] Specifically, in some embodiments, constructing a refined numerical calculation model of rock quality based on the block model, the three-dimensional geological model, and the rock mechanical parameters includes: converting the three-dimensional geological model into a numerical calculation model; implanting the exploration information of the exploration mine and the rock mechanical parameters into the numerical calculation model to obtain the refined numerical calculation model of rock quality.

[0089] The three-dimensional geological model is converted into a numerical calculation model, and the numerical calculation model is matched with the unit body in the block model by means of coordinate query. The rock mass mechanical parameters with spatial variability are given to the unit body of the numerical calculation model, and the external import of rock mass mechanical parameters is realized, thereby constructing a refined numerical calculation model of rock quality and realizing refined numerical modeling of rock mass quality. Please refer to Figure 5 , Figure 5 A schematic diagram of a rock refinement numerical calculation model (elastic modulus example) according to an embodiment of the present application is shown.

[0090] Step S150, using the rock quality refined numerical calculation model, numerically simulate the stability of the surrounding rock of the goaf to obtain the rock quality numerical simulation result.

[0091] According to the refined numerical calculation model obtained above, the stability of the surrounding rock in the goaf is refined numerically simulated to obtain the rock numerical simulation results, which include but are not limited to the volume of the plastic zone and the stress field of the surrounding rock.

[0092] Step S160, determining several stability evaluation indicators of the goaf according to the exploration information of the exploration mine, the rock mass mechanical parameters, and the rock numerical simulation results.

[0093] Based on the on-site exploration information, the refined characterization results of rock mass quality (the above-mentioned rock mass mechanical parameters) and the results of rock numerical simulation, a multi-level fuzzy comprehensive evaluation system for surrounding rock stability was established. This multi-level fuzzy comprehensive evaluation system for surrounding rock stability integrates multiple factors such as the occurrence conditions of mining rock masses, refined mechanical parameters of rock masses, geometric parameters and treatment methods of voids, interaction between goaf groups and mechanical responses, and has high accuracy.

[0094] The multi-level fuzzy comprehensive evaluation system for surrounding rock stability includes three analysis layers: mining technology influencing factor analysis layer, goaf group interaction influencing factor analysis layer, and geological condition influencing factor analysis layer. There are 11 evaluation indicators belonging to the three analysis layers, including rock structure, geological structure, rock compressive strength, RQD, height-span ratio, goaf treatment method, goaf volume, goaf burial depth, goaf dispersion, goaf penetration, and plastic zone volume.

[0095] Among them, rock mass structure, geological structure, rock compressive strength, and RQD belong to the geological conditions influencing factor analysis layer; height-span ratio, void area treatment method, void area volume, and void area burial depth belong to the mining technology influencing factor analysis layer; and void area dispersion, void area penetration, and plastic zone volume belong to the goaf group interaction influencing factor analysis layer.

[0096] Taking a certain goaf as an example, the quantified stability evaluation indicators shown in Table 3 are used for quantification, and the results are shown in Table 4.

[0097] Table 3

[0098]

[0099] Table 4

[0100]

[0101] Step S170, based on a number of stability evaluation indicators of the goaf, determining the stability membership and stability prediction level of the goaf.

[0102] After obtaining the quantitative values ​​of the above-mentioned several stability evaluation indicators of the goaf, the stability membership and stability prediction level of the goaf can be determined according to these indicators.

[0103] Specifically, in some embodiments, the stability membership and stability prediction level of the goaf are determined based on several stability evaluation indicators of the goaf, including: dividing the several stability evaluation indicators into several analysis layers; using a descending semi-gradient distribution function or an ascending semi-gradient distribution function to calculate the membership of each of the stability evaluation indicators, and constructing an evaluation indicator membership matrix; assigning weights to each of the stability evaluation indicators, and constructing a first weight matrix; determining the product of the evaluation indicator membership matrix and the first weight matrix to obtain the stability membership matrix of each analysis layer; constructing an analysis layer membership matrix according to the stability membership of each of the analysis layers; assigning weights to each of the analysis layers, and constructing a second weight matrix; determining the product of the analysis layer membership matrix and the second weight matrix to obtain the stability membership matrix of the goaf and the stability prediction level of the goaf.

[0104] The membership of the participating index can be calculated by using the descending half gradient distribution function or the ascending half gradient distribution function. For the participating index with the larger value, the better, the ascending half gradient distribution function is used, such as formula (13) to (16):

[0105]

[0106]

[0107] For the evaluation index with smaller values, the better, the descending half gradient distribution function is used, such as formula (17)~(20):

[0108]

[0109] In the formula: x is the value of the participating indicator, a, b, c are constants, and their values ​​depend on the quantitative basis of the participating indicators; μ1, μ2, μ3, μ4 are the four-level warning membership matrices of each participating indicator (the matrix here is generalized, and when the matrix is ​​one-dimensional, it is a vector).

[0110] It should be noted that the level setting can be set according to the needs. The above-mentioned four-level warning affiliation of the analysis layer is only an exemplary description and can be set to fewer levels or more levels. This application does not limit this.

[0111] The calculation formula of the four-level warning membership matrix of each analysis layer is as follows (21):

[0112] V = v*w(21);

[0113] Where: V is the four-level warning membership of the analysis layer; v is the membership matrix of the participating indicators (μ1, μ2, μ3, μ4) T ; w is the weight matrix of the evaluation indicators (w1, w2, w3, w4).

[0114] The calculation formula of the four-level early warning membership matrix of the stability of the goaf is as follows (22):

[0115] X = V * W (22);

[0116] Where: X is the stability four-level early warning membership matrix of the goaf; V is the four-level early warning membership matrix of the analysis layer (V1, V2, V3, V4) T ; W is the analysis layer weight matrix (W1, W2, W3, W4).

[0117] The stability prediction level of the goaf can be determined by comparing the four-level early warning membership matrix X of the goaf stability and the preset threshold range.

[0118] Based on the results given in Table 4, equations (13) to (20) are used to calculate the goaf stability membership matrix and warning level. The calculation process example is shown below:

[0119] Substitute the above data into the calculation formula for the membership of the participating indicators, where a = 0.1, b = 0.3, c = 0.6. The membership of the void treatment method indicator μ1 = [0, 1, 0, 0]. Similarly, the height-to-span ratio, void volume, and void depth indicator membership μ2, μ3, and μ4 are calculated. The indicator membership matrix of each parameter is organized as follows:

[0120]

[0121] The first weight matrix w = [0.5644, 0.2136, 0.1602, 0.0617] of the evaluation parameters is obtained by expert scoring, and the membership matrix V1 = [0.2621, 0.8485, 0.0203, 0] of the mining technology influencing factor analysis layer is calculated.

[0122] Similarly, the membership matrix V2 and V3 of the analysis layer of the interaction influencing factors within the goaf group and the geological condition influencing factors are calculated and sorted out as follows:

[0123]

[0124] The second weight matrix of each analysis layer is W = [0.6144, 0.2684, 0.1172], and the goaf warning level membership matrix X = V*W = [0.3419, 0.6792, 0.1079, 0.0837] is calculated.

[0125] The goaf area warning level is judged to be level 2, and the evaluation result shows that the goaf area is relatively stable.

[0126] The calculation results of the membership of some dangerous airspace warning levels are shown in Table 5.

[0127] Table 5

[0128]

[0129] Depend on Figure 1It can be seen that the present application first constructs a block model based on the drilling information and core information of multiple boreholes of the exploration mine, and then constructs a three-dimensional geological model based on the image information, exploration information and production information of the exploration mine, and makes the three-dimensional geological model coincide with the block model; further, the exploration mine is subjected to rock mechanics characterization to obtain the rock mechanics parameters of the block model, and a rock refinement numerical calculation model is constructed based on the block model, the three-dimensional geological model, and the rock mechanics parameters; further, the rock refinement numerical calculation model is used to numerically simulate the stability of the surrounding rock of the goaf to obtain the rock numerical simulation results; when predicting the stability of the goaf, several stability evaluation indicators of the goaf are determined according to the exploration information of the exploration mine, the rock mechanics parameters, and the rock numerical simulation results; using the several stability evaluation indicators of the goaf as input, the stability membership of the goaf is determined, and the stability prediction level of the goaf is determined. Through the above method, this application has established a multi-level fuzzy comprehensive evaluation system for surrounding rock stability that comprehensively considers the occurrence conditions of mining rock masses, refined mechanical parameters of rock masses, geometric parameters and treatment methods of voids, interactions between void groups and mechanical responses. It has achieved a comprehensive and objective evaluation of the stability of voids, improved the accuracy of stability prediction of voids, and provided a theoretical basis for mine safety and ground pressure management.

[0130] Figure 6 A schematic diagram of the structure of a refined prediction device for goaf stability according to an embodiment of the present application is shown. Figure 6 It can be seen that the refined prediction device 600 for goaf stability includes:

[0131] A first construction unit 610 is used to construct a block model based on drilling information and core information of multiple boreholes for exploring the mine;

[0132] A second construction unit 620 is used to construct a three-dimensional geological model based on the image information, exploration information and production information of the exploration mine, and to make the three-dimensional geological model overlap with the block model;

[0133] A characterization unit 630 is used to characterize the rock mechanics of the exploration mine to obtain rock mechanics parameters of the block model;

[0134] A third construction unit 640 is used to construct a rock quality refined numerical calculation model based on the block model, the three-dimensional geological model, and the rock mass mechanical parameters;

[0135] The simulation unit 650 is used to use the rock quality refined numerical calculation model to perform numerical simulation on the surrounding rock stability of the goaf to obtain the rock quality numerical simulation result;

[0136] An index calculation unit 660 is used to determine several stability evaluation indexes of the goaf according to the exploration information of the exploration mine, the rock mass mechanical parameters, and the rock numerical simulation results;

[0137] The prediction unit 670 is used to determine the stability membership of the goaf and the stability prediction level of the goaf based on a number of stability evaluation indicators of the goaf.

[0138] In some embodiments, in the above-mentioned device, the first construction unit 610 is used to obtain images of multiple cores of the multiple boreholes, and determine the rock quality index value of each core based on the images; use the inverse distance weighted method to construct the block model based on the drilling information of each borehole and the rock quality index value of each core, wherein the drilling information includes: borehole coordinates, borehole length, and borehole number; divide the block model into multiple unit block models with any borehole on the block model as a reference point; and output the coordinate information and rock quality index value of each unit block model.

[0139] In some embodiments, in the above-mentioned device, the first construction unit 610 is used to resample each of the boreholes to obtain a borehole resampling point set; adopt an inverse distance weighted method to construct a block model within the specified spatial range; wherein, for any target point in the specified spatial range, multiple adjacent points closest to the target point are found in the borehole resampling point set to form an adjacent point set, wherein any element of the borehole resampling point set and the adjacent point set respectively represents the stratigraphic attribute of each point; based on the distance between the target point and each of the adjacent points, determine the stratigraphic attribute of the target point, wherein the stratigraphic attribute of the target point is consistent with one of all its adjacent points; and construct the block model according to the stratigraphic attributes of each point in the specified spatial range.

[0140] In some embodiments, in the above-mentioned device, the second construction unit 620 is used to obtain the image information through drone oblique photography technology, extract the exploration information from mine geological exploration data, and extract the production information from production data, wherein the exploration information includes: rock structure, and geological structure, and the production information includes: goaf collapse and distribution, and goaf processing methods; construct a three-dimensional geological model based on the image information, the exploration information and the production information; locate the reference point on the block model in the three-dimensional geological model, and move the three-dimensional geological model so that the three-dimensional geological model and the block model overlap in spatial position.

[0141] In some embodiments, in the above-mentioned device, the characterization unit 630 is used to extract the structural surface data of the exploration mine from the geological exploration data of the mine; based on the Hoek-Brown criterion, according to the structural surface data and the rock quality index value of each unit block model output by the block model, the rock mechanical parameters of the block model are calculated, and the rock mechanical parameters include: geological strength index GSI, elastic modulus, cohesion, internal friction angle, and tensile strength.

[0142] In some embodiments, in the above-mentioned device, the third construction unit 640 is used to convert the three-dimensional geological model into a numerical calculation model; implant the exploration information of the exploration mine and the rock mechanical parameters into the numerical calculation model to obtain the rock refined numerical calculation model.

[0143] In some embodiments, in the above-mentioned device, the stability evaluation indicators include: rock structure, geological structure, rock compressive strength, RQD, height-span ratio, void area treatment method, void area volume, void area burial depth, void area dispersion, void area penetration condition, and at least one of the plastic zone volume.

[0144] In some embodiments, in the above-mentioned device, the prediction unit 670 is used to divide the several stability evaluation indicators into several analysis layers; use the descending semi-gradient distribution function or the ascending semi-gradient distribution function to calculate the membership of each of the stability evaluation indicators, and construct an evaluation indicator membership matrix; assign weights to each of the stability evaluation indicators, and construct a first weight matrix; determine the product of the evaluation indicator membership matrix and the first weight matrix to obtain the stability membership matrix of each analysis layer; construct an analysis layer membership matrix according to the stability membership of each of the analysis layers; assign weights to each of the analysis layers, and construct a second weight matrix; determine the product of the analysis layer membership matrix and the second weight matrix to obtain the stability membership matrix of the goaf and the stability prediction level of the goaf.

[0145] It should be noted that the refined prediction device for goaf stability of the present application can implement the aforementioned refined prediction method for goaf stability one by one, and will not be repeated here.

[0146] Figure 7 A schematic diagram showing the structure of a computer device according to an embodiment of the present application is shown. Figure 7As shown, the internal structure of the computer device may include a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, the functions or steps of the refined prediction method for goaf stability of the present application are realized.

[0147] In one embodiment, the computer device provided in the present application includes a memory and a processor, the memory stores a database and a computer program that can be run on the processor, and when the processor executes the computer program, it implements the functions or steps of the refined prediction method for goaf stability of the present application.

[0148] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the functions or steps of the refined prediction method for goaf stability of the present application are implemented.

[0149] It should be noted that the above functions or steps that can be implemented by the computer device or computer-readable storage medium can refer to the relevant description in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0151] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0152] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A refined prediction method for goaf stability, characterized in that: include: Building a block model based on drilling information and core information of multiple drill holes in the exploration mine; Constructing a three-dimensional geological model based on the image information, exploration information and production information of the exploration mine, and making the three-dimensional geological model overlap with the block model; Performing rock mechanics characterization on the exploration mine to obtain rock mechanics parameters of the block model; Constructing a rock quality refined numerical calculation model based on the block model, the three-dimensional geological model, and the rock mass mechanical parameters; The rock quality refined numerical calculation model is used to perform numerical simulation on the surrounding rock stability of the goaf to obtain the rock quality numerical simulation result; Determine several stability evaluation indicators of the goaf according to the exploration information of the exploration mine, the rock mass mechanical parameters, and the rock numerical simulation results; Based on several stability evaluation indicators of the goaf, the stability membership and stability prediction level of the goaf are determined.

2. The method according to claim 1, characterized in that The block model is constructed based on the drilling information of multiple boreholes and the drilling core information of the exploration mine, including: Acquire images of multiple rock cores of the multiple boreholes, and determine a rock quality index value of each of the rock cores based on the images; The block model is constructed based on the drilling information of each of the boreholes and the rock quality index value of each of the cores by using an inverse distance weighting method, wherein the drilling information includes: drilling coordinates, drilling length, and drilling number; Taking any drill hole on the block model as a reference point, dividing the block model into a plurality of unit block models; The coordinate information and rock quality index value of each unit block model are output.

3. The method according to claim 2, characterized in that The block model is constructed based on the drilling information of each borehole and the rock quality index value of each core by using the inverse distance weighting method, including: Resampling each of the boreholes to obtain a borehole resampling point set; The block model within the specified space range is constructed by using the inverse distance weighting method; wherein, For any target point in the specified spatial range, multiple adjacent points with the closest distance to the target point are found in the borehole resampling point set to form an adjacent point set, wherein any element of the borehole resampling point set and the adjacent point set respectively represents the stratigraphic attribute to which each point belongs; Determine the stratum attribute to which the target point belongs based on the distance between the target point and each of the adjacent points, wherein the stratum attribute to which the target point belongs is consistent with one of all its adjacent points; The block model is constructed according to the stratigraphic attributes of each point in the specified spatial range.

4. The method according to claim 1, characterized in that The three-dimensional geological model is constructed based on the image information, exploration information and production information of the exploration mine, including: The image information is obtained by using the drone oblique photography technology, the exploration information is extracted from the mine geological exploration data, and the production information is extracted from the production data, wherein the exploration information includes: rock mass structure and geological structure, and the production information includes: goaf collapse and distribution, and goaf treatment methods; constructing a three-dimensional geological model based on the image information, the exploration information and the production information; The reference point on the block model is located on the three-dimensional geological model, and the three-dimensional geological model is moved so that the three-dimensional geological model and the block model overlap in spatial position.

5. The method according to claim 1, characterized in that The rock mechanics characterization of the exploration mine to obtain the rock mechanics parameters of the block model includes: Extracting structural surface data of the exploration mine from the mine geological exploration data; Based on the Hoek-Brown criterion, the rock mass mechanical parameters of the block model are calculated according to the structural surface data and the rock quality index value of each unit block model output by the block model. The rock mass mechanical parameters include: geological strength index GSI, elastic modulus, cohesion, internal friction angle, and tensile strength.

6. The method according to claim 1, characterized in that The method of constructing a rock quality refined numerical calculation model based on the block model, the three-dimensional geological model, and the rock mass mechanical parameters includes: Converting the three-dimensional geological model into a numerical calculation model; The exploration information of the exploration mine and the rock mass mechanical parameters are implanted into the numerical calculation model to obtain the rock quality refined numerical calculation model.

7. The method according to claim 1, characterized in that The stability evaluation indicators include: rock structure, geological structure, rock compressive strength, RQD, height-span ratio, void area treatment method, void area volume, void area burial depth, void area dispersion, void area penetration, and at least one of the following:

8. The method according to claim 1, characterized in that The step of determining the stability membership and stability prediction level of the goaf area based on several stability evaluation indicators of the goaf area includes: Dividing the plurality of stability evaluation indicators into a plurality of analysis layers; The membership of each stability evaluation index is calculated by using a descending semi-gradient distribution function or an ascending semi-gradient distribution function, and a membership matrix of the evaluation index is constructed; Assigning weights to each of the stability evaluation indicators to construct a first weight matrix; Determine the product of the membership matrix of the evaluation index and the first weight matrix to obtain the stability membership matrix of each analysis layer; According to the stability membership of each analysis layer, constructing an analysis layer membership matrix; Assigning weights to each of the analyses to construct a second weight matrix; The product of the analysis layer membership matrix and the second weight matrix is ​​determined to obtain the stability membership matrix of the goaf and the stability prediction grade of the goaf.

9. A refined prediction device for goaf stability, characterized in that: The device comprises: A first construction unit is used to construct a block model based on drilling information of multiple boreholes and core information of the exploration mine; A second construction unit is used to construct a three-dimensional geological model based on the image information, exploration information and production information of the exploration mine, and to make the three-dimensional geological model overlap with the block model; A characterization unit, used for performing rock mechanics characterization on the exploration mine to obtain rock mechanics parameters of the block model; A third construction unit is used to construct a rock quality refined numerical calculation model based on the block model, the three-dimensional geological model, and the rock mass mechanical parameters; A simulation unit, used to use the rock quality refined numerical calculation model to perform numerical simulation on the surrounding rock stability of the goaf to obtain a rock quality numerical simulation result; An index calculation unit, used to determine several stability evaluation indexes of the goaf according to the exploration information of the exploration mine, the rock mass mechanical parameters, and the rock numerical simulation results; The prediction unit is used to determine the stability membership of the goaf and the stability prediction level of the goaf based on several stability evaluation indicators of the goaf.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is instructed by a processor, it implements the step of refined prediction of goaf stability as claimed in any one of claims 1 to 8.

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