A coal rock index parameter prediction method and device based on CT scanning

Abstract

The application provides a coal rock index parameter prediction method and device based on CT scanning, relates to the technical field of coal, and determines the fracture length-volume distribution characteristics of each modeling coal sample by respectively performing CT scanning on N modeling coal samples to construct the corresponding digital core of the modeling coal sample; respectively performs uniaxial or triaxial loading test on each modeling coal sample in the N modeling coal samples to obtain the corresponding uniaxial or triaxial mechanical property parameters of each modeling coal sample; sets the action between the fracture structure and the matrix structure in each digital core as a smooth joint model action, sets the fracture micro parameter corresponding to the fracture structure in each digital core as 0, and calibrates the matrix micro parameter of the matrix structure of the digital core based on the uniaxial mechanical property parameters and the triaxial mechanical property parameters; sets the stress boundary condition corresponding to the prediction index, performs simulation experiment on the calibrated digital core, and obtains the prediction parameter of the coal rock corresponding to the prediction index.

Description

Technical Field

[0001] This application relates to the field of coal technology, and in particular to a method and apparatus for predicting coal and rock index parameters based on CT scans. Background Technology

[0002] With increasing mining depth and intensity, the geostress environment experienced by coal and rock masses is becoming increasingly harsh, posing unprecedented challenges to their stability and safety. Coal and rock naturally contain complex and abundant fracture structures, with varying sizes and morphologies that significantly impact their physical and mechanical properties. The presence of fractures complicates the mechanical behavior of coal and rock, particularly under high-stress environments, making deformation, failure, and support design issues more prominent. Therefore, a comprehensive understanding of the physical and mechanical properties of coal and rock, and an in-depth analysis of its fracture characteristics and mechanical behavior, are crucial for ensuring safe and efficient coal mining. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, one objective of this application is to propose a method for predicting coal and petrographic parameters based on CT scanning. This method involves performing CT scans on N modeled coal samples and determining the fracture length-volume distribution characteristics of the fracture structure within each sample based on the scan results. For any given modeled coal sample, a digital core is constructed based on the fracture length-volume distribution characteristics, where the digital core includes fracture structure and matrix structure. Uniaxial or triaxial loading tests are performed on each of the N modeled coal samples to obtain the corresponding uniaxial or triaxial mechanical property parameters. The interaction between the fracture structure and matrix structure in each digital core is set as a smooth joint model, and the fracture micro-parameters corresponding to the fracture structure in each digital core are all set to 0. The matrix micro-parameters of the matrix structure in the digital core are calibrated based on the uniaxial and triaxial mechanical property parameters to obtain the final calibrated digital core. Stress boundary conditions corresponding to the predicted indicators are set, and simulation experiments are conducted on the calibrated digital core to obtain the predicted parameters of the coal and petrographic parameters corresponding to the predicted indicators.

[0005] This application's embodiments take into account the relatively weak influence of short fractures and reduce the computational load by modeling short fractures through random distribution, making the construction of digital cores for large-scale samples more efficient. By obtaining the fracture structure and matrix characteristics of coal and rock through CT scanning and simulating them using the digital core model, the physical and mechanical properties of coal and rock (such as compressive strength, elastic modulus, Poisson's ratio, etc.) can be predicted non-destructively. This can provide more reliable and accurate physical and mechanical property data for mining engineering, support design and decision optimization, and improve the resource utilization and safety of coal mines.

[0006] The second objective of this application is to propose a device for predicting coal and rock parameters based on CT scans.

[0007] The third objective of this application is to propose an electronic device.

[0008] The fourth objective of this application is to provide a non-transitory computer-readable storage medium.

[0009] The fifth objective of this application is to provide a computer program product.

[0010] To achieve the above objectives, the first aspect of this application proposes a method for predicting coal and petrographic parameters based on CT scanning. This method involves performing CT scans on N modeled coal samples and determining the fracture length-volume distribution characteristics of the fracture structure within each sample based on the scan results. For any given modeled coal sample, a digital core is constructed based on the fracture length-volume distribution characteristics, where the digital core includes fracture structure and matrix structure. A uniaxial loading test or a triaxial loading test is performed on each of the N modeled coal samples to obtain the corresponding uniaxial or triaxial mechanical property parameters. The interaction between the fracture structure and matrix structure in each digital core is set as a smooth joint model, and the fracture micro-parameters corresponding to the fracture structure in each digital core are all set to 0. The matrix micro-parameters of the matrix structure in the digital core are calibrated based on the uniaxial and triaxial mechanical property parameters to obtain the final calibrated digital core. Stress boundary conditions corresponding to the predicted indicators are set, and simulation experiments are conducted on the calibrated digital core to obtain the predicted parameters of the coal and petrographic structure corresponding to the predicted indicators.

[0011] According to one embodiment of this application, CT scans are performed on N modeled coal samples respectively, and the fracture length-volume distribution characteristics of the fracture structure within each modeled coal sample are determined based on the scan results. This includes: performing CT scans on the N modeled coal samples respectively to obtain scan slices corresponding to each modeled coal sample at multiple angles; for any modeled coal sample, constructing a three-dimensional model corresponding to the modeled coal sample based on the scan slices, and obtaining model slices corresponding to the three-dimensional model; for any modeled coal sample, analyzing the grayscale distribution characteristics corresponding to the three-dimensional model and model slices to determine a grayscale threshold, and dividing the three-dimensional model into fracture structure and matrix structure based on the grayscale threshold, and obtaining spatial distribution characteristic information of the fracture structure; for any modeled coal sample, dividing the fracture structure into multiple non-connected fractures based on the spatial distribution characteristic information of the fracture structure within the modeled coal sample, obtaining the length and volume corresponding to each fracture, and determining the fracture length-volume distribution characteristics of the modeled coal sample based on the length and volume corresponding to each fracture.

[0012] According to one embodiment of this application, constructing a digital core corresponding to a modeled coal sample based on the fracture length-volume distribution characteristics includes: determining a length boundary value based on the fracture length-volume distribution characteristics; classifying fractures with lengths greater than or equal to the length boundary value as long fractures, determining long fracture-related parameters corresponding to each long fracture, wherein the long fracture-related parameters include at least the dip angle, dip direction, length, width, and location of the long fracture; constructing an intermediate-state digital core corresponding to the modeled coal sample based on the long fracture-related parameters; and performing random distribution modeling of microfractures in the intermediate-state digital core to obtain a digital core corresponding to the modeled coal sample, wherein the length of each microfracture is less than the length boundary value and greater than a preset minimum microfracture length value.

[0013] According to one embodiment of this application, a random distribution model of microfractures is performed on an intermediate-state digital core to obtain a digital core corresponding to the modeled coal sample. This includes: determining the fracture length-quantity distribution characteristics of the modeled coal sample based on the length of each fracture; and constructing a microfracture quantity-length distribution formula for the modeled coal sample based on the fracture length-quantity distribution characteristics.

[0014] N(l) = c × l -λ

[0015] In the above formula, N(l) represents the number of microcracks with length l, c and λ are parameters determined based on the crack length-number distribution characteristics, l represents the length of the microcrack, where the value of l is less than the length boundary value and greater than the preset minimum length value of the microcrack; based on the microcrack number-length distribution formula, the random distribution model of microcracks is performed in the intermediate state digital core to obtain the digital core corresponding to the modeled coal sample.

[0016] According to one embodiment of this application, obtaining the length corresponding to each crack includes: simplifying the crack into a smooth plane based on the crack's orientation, and determining the crack length using the Ferrette diameter calculation method; wherein, the method for obtaining the crack width includes: determining the vertical direction of the Ferrette diameter corresponding to the crack length, and selecting the longest value of the smooth plane corresponding to the crack in the vertical direction as the crack width.

[0017] According to one embodiment of this application, before performing CT scans on N modeling coal samples, the method further includes: obtaining an initial coal sample and dividing the initial coal sample into M standard cylindrical coal samples; dividing the M standard coal samples into N modeling coal samples and MN calibration coal samples; after obtaining the final calibration digital core, the method further includes: simultaneously conducting loading tests on the calibration digital core and the calibration coal samples under different stress environments to verify the correctness of the parameters of the calibration digital core.

[0018] According to one embodiment of this application, uniaxial loading tests or triaxial loading tests are performed on each of the N modeling coal samples to obtain the uniaxial mechanical property parameters or triaxial mechanical property parameters corresponding to each modeling coal sample. This includes: performing uniaxial loading tests on a first portion of the N modeling coal samples and calculating the uniaxial mechanical property parameters corresponding to each modeling coal sample in the first portion based on the test results. The uniaxial mechanical property parameters include at least uniaxial compressive strength, elastic modulus, and Poisson's ratio. Triaxial loading tests are performed on the remaining modeling coal samples (excluding the first portion) of the N modeling coal samples and the triaxial mechanical property parameters corresponding to each modeling coal sample in the remaining modeling coal samples are calculated based on the test results. The triaxial mechanical property parameters include at least triaxial compressive strength, cohesion, and internal friction angle.

[0019] According to one embodiment of this application, the predicted index is at least one of the following: uniaxial compressive strength, triaxial compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, and tensile modulus.

[0020] To achieve the above objectives, a second aspect of this application proposes a coal and petrology parameter prediction device based on CT scanning, comprising: a determination module, used to perform CT scans on N modeling coal samples respectively, and determine the fracture length-volume distribution characteristics of the fracture structure within each modeling coal sample based on the scan results; a construction module, used to construct a digital core corresponding to any modeling coal sample based on the fracture length-volume distribution characteristics of the modeling coal sample, wherein the digital core includes fracture structure and matrix structure; and an experimental module, used to perform uniaxial loading tests or triaxial loading tests on each of the N modeling coal samples respectively. The system obtains uniaxial or triaxial mechanical property parameters for each modeled coal sample. A calibration module is used to set the interaction between the fracture structure and matrix structure in each digital core as a smooth joint model, sets the fracture micro-parameters corresponding to the fracture structure in each digital core to 0, and calibrates the matrix micro-parameters of the matrix structure based on the uniaxial and triaxial mechanical property parameters to obtain the final calibrated digital core. A prediction module is used to set the stress boundary conditions corresponding to the prediction indicators, conduct simulation experiments on the calibrated digital core, and obtain the predicted parameters of the coal and rock at the predicted indicators.

[0021] According to one embodiment of this application, the determining module is further configured to: perform CT scans on N modeling coal samples respectively to obtain scan slices corresponding to each modeling coal sample at multiple angles; for any modeling coal sample, construct a three-dimensional model corresponding to the modeling coal sample based on the scan slices corresponding to the modeling coal sample, and obtain model slices corresponding to the three-dimensional model; for any modeling coal sample, analyze the grayscale distribution characteristics corresponding to the three-dimensional model and model slices corresponding to the modeling coal sample to determine the grayscale threshold, and divide the three-dimensional model into fracture structure and matrix structure based on the grayscale threshold, and obtain spatial distribution characteristic information of fracture structure; for any modeling coal sample, divide the fracture structure into multiple non-connected fractures based on the spatial distribution characteristic information of fracture structure within the modeling coal sample, obtain the length and volume corresponding to each fracture, and determine the fracture length-volume distribution characteristics corresponding to the modeling coal sample based on the length and volume corresponding to each fracture.

[0022] According to one embodiment of this application, the construction module is further configured to: determine a length boundary value based on the fracture length-volume distribution characteristics; designate fractures with a length greater than or equal to the length boundary value as long fractures, determine long fracture-related parameters corresponding to each long fracture, the long fracture-related parameters including at least the dip angle, dip direction, length, width, and location of the long fracture; construct an intermediate state digital core corresponding to the modeled coal sample based on the long fracture-related parameters; perform random distribution modeling of microfractures in the intermediate state digital core to obtain a digital core corresponding to the modeled coal sample, wherein the length of each microfracture is less than the length boundary value and greater than a preset minimum microfracture length value.

[0023] According to one embodiment of this application, the construction module is further configured to: determine the fracture length-quantity distribution characteristics of the modeling coal sample based on the length corresponding to each fracture; and construct the micro-fracture quantity-length distribution formula of the modeling coal sample based on the fracture length-quantity distribution characteristics as follows:

[0024] N(l) = c × l -λ

[0025] In the above formula, N(l) represents the number of microcracks with length l, c and λ are parameters determined based on the crack length-number distribution characteristics, l represents the length of the microcrack, where the value of l is less than the length boundary value and greater than the preset minimum length value of the microcrack; based on the microcrack number-length distribution formula, the random distribution model of microcracks is performed in the intermediate state digital core to obtain the digital core corresponding to the modeled coal sample.

[0026] According to one embodiment of this application, the construction module is further configured to: simplify the crack into a smooth plane based on the crack's orientation, and determine the crack's length using the Ferrette diameter calculation method; determine the vertical direction of the Ferrette diameter corresponding to the crack's length, and select the longest value of the smooth plane corresponding to the crack in the vertical direction as the crack's width.

[0027] According to one embodiment of this application, the determining module is further configured to: obtain an initial coal sample, divide the initial coal sample into M standard cylindrical coal samples; divide the M standard coal samples into N modeling coal samples and MN verification coal samples; the calibration module is further configured to: simultaneously conduct loading tests on the calibration digital core and the verification coal samples under different stress environments to verify the correctness of the parameters of the calibration digital core.

[0028] According to one embodiment of this application, the test module is further configured to: conduct uniaxial loading tests on a first portion of the N modeled coal samples, and calculate the uniaxial mechanical property parameters corresponding to each modeled coal sample in the first portion based on the test results, wherein the uniaxial mechanical property parameters include at least uniaxial compressive strength, elastic modulus, and Poisson's ratio; conduct triaxial loading tests on the remaining modeled coal samples (excluding the first portion) in the N modeled coal samples, and calculate the triaxial mechanical property parameters corresponding to each modeled coal sample in the remaining modeled coal samples based on the test results, wherein the triaxial mechanical property parameters include at least triaxial compressive strength, cohesion, and internal friction angle.

[0029] According to one embodiment of this application, the predicted index is at least one of the following: uniaxial compressive strength, triaxial compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, and tensile modulus.

[0030] To achieve the above objectives, a third aspect of this application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to implement the CT scan-based coal and rock index parameter prediction method as described in the first aspect of this application.

[0031] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the coal and rock index parameter prediction method based on CT scan as described in the first aspect of this application.

[0032] To achieve the above objectives, a fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the method for predicting coal and rock index parameters based on CT scans as described in the first aspect of this application. Attached Figure Description

[0033] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0034] Figure 1 This is a schematic diagram illustrating an exemplary implementation of a method for predicting coal and rock index parameters based on CT scans, as shown in one embodiment of this application.

[0035] Figure 2 This is a schematic diagram illustrating an exemplary implementation of a method for predicting coal and rock index parameters based on CT scans, as shown in one embodiment of this application.

[0036] Figure 3 This is a schematic diagram illustrating the crack length-number-volume distribution characteristics of one embodiment of this application.

[0037] Figure 4 This is a schematic diagram illustrating the length and width of a crack according to one embodiment of this application.

[0038] Figure 5 This is a schematic diagram illustrating a fracture azimuth angle and polar angle in one embodiment of this application.

[0039] Figure 6 This is a schematic diagram of a coal and rock index parameter prediction device based on CT scanning, as shown in one embodiment of this application.

[0040] Figure 7 This is a schematic diagram of an electronic device according to one embodiment of this application. Detailed Implementation

[0041] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0042] Figure 1 This is a schematic diagram of an exemplary embodiment of a method for predicting coal and rock parameters based on CT scans, as shown in this application. Figure 1 As shown, this method for predicting coal and rock parameters based on CT scans includes the following steps:

[0043] S101, perform CT scans on N modeled coal samples respectively, and determine the fracture length-volume distribution characteristics of the fracture structure in each modeled coal sample based on the scan results.

[0044] In this application, standardized sampling of coal and rock is performed in advance to obtain N model coal samples.

[0045] For each modeled coal sample, a CT scan is performed to obtain a corresponding 3D model. The 3D model is then identified to determine the fracture structure within the modeled coal sample. The fracture structure within the modeled coal sample is then divided into numerous non-connected fractures, and the length and volume values ​​of each fracture are obtained, thus yielding the fracture length-volume distribution characteristics of the fracture structure within the modeled coal sample.

[0046] S102, For any modeled coal sample, construct a digital core corresponding to the modeled coal sample based on the fracture length-volume distribution characteristics of the modeled coal sample. The digital core includes fracture structure and matrix structure.

[0047] In this application, considering that long fractures can cause strong anisotropy in the mechanical properties of coal samples, and that the distribution of long fractures is relatively certain and the number is small for a single coal sample, their impact on coal rock mechanical behavior is significant; short fractures are randomly distributed angularly and spatially in the coal sample, and although they are more numerous, their impact on the mechanical behavior and anisotropy of the coal sample is weak. Therefore, this application considers modeling the fracture structure of the modeled coal sample separately for long fractures and short fractures.

[0048] In this application, the critical values ​​of fracture length and short fracture are determined based on the fracture length-volume distribution characteristics corresponding to the modeled coal sample. Fractures with a fracture length greater than or equal to the critical value are considered long fractures. For each long fracture, modeling is performed based on the actual fracture-related parameters of the long fracture. Fractures with a fracture length less than the critical value are considered short fractures. Considering that short fractures have a weaker influence on the mechanical behavior and anisotropy of the coal sample, short fractures are modeled with random distribution in order to reduce the amount of computation.

[0049] Based on the above approach, for each modeled coal sample, a digital core containing only the matrix structure is established based on the morphological and dimensional characteristics of the corresponding fracture. Then, the long and short fractures of the modeled coal sample are modeled separately on the digital core to obtain the fracture structure of the digital core of the fracture, thereby obtaining the complete digital core corresponding to the modeled coal sample.

[0050] In this application, a total of N digital cores were obtained, and each digital core corresponds one-to-one with the modeled coal sample.

[0051] Digital rock cores are digital models of coal and rock masses obtained through 3D imaging technology. The core feature of digital rock cores is the transformation of the core's microstructure into computer-processable data, enabling the analysis and prediction of the rock's physical and mechanical properties through virtual experiments or numerical simulations.

[0052] S103, conduct uniaxial loading tests or triaxial loading tests on each of the N modeling coal samples to obtain the uniaxial mechanical property parameters or triaxial mechanical property parameters corresponding to each modeling coal sample.

[0053] The coal samples used for uniaxial or triaxial loading tests are the same as the N modeled coal samples used for digital core construction mentioned above.

[0054] In this application, after obtaining the digital cores corresponding to the N modeling coal samples, uniaxial loading tests or triaxial loading tests are performed on the N modeling coal samples to obtain the uniaxial mechanical property parameters (the uniaxial mechanical property parameters include at least uniaxial compressive strength, elastic modulus and Poisson's ratio) or triaxial mechanical property parameters (the triaxial mechanical property parameters include at least triaxial compressive strength, cohesion and internal friction angle) corresponding to each modeling coal sample.

[0055] It is not difficult to understand that for a certain modeled coal sample, the modeled coal sample can only be subjected to uniaxial loading test or only triaxial loading test.

[0056] For example, the uniaxial or triaxial mechanical property parameters corresponding to each modeled coal sample can be represented in the following form:

[0057] Uniaxial mechanical property parameters of modeled coal sample 1 to modeled coal sample 1 corresponding to digital core 1 to modeled coal sample 1;

[0058] Modeled coal sample 2 ~ Digital core 2 corresponding to modeled coal sample 2 ~ Uniaxial mechanical property parameters corresponding to modeled coal sample 2; ......

[0060] Modeled coal sample 7 ~ Digital core 7 corresponding to modeled coal sample 7 ~ Triaxial mechanical property parameters corresponding to modeled coal sample 7;

[0061] Modeled coal sample 8 ~ Digital core 8 corresponding to modeled coal sample 8 ~ Triaxial mechanical property parameters corresponding to modeled coal sample 8; ......

[0063] Modeled coal sample N ~ Digital core N corresponding to modeled coal sample N ~ Triaxial mechanical property parameters corresponding to modeled coal sample N.

[0064] S104, the interaction between the fracture structure and the matrix structure in each digital core is set as a smooth joint model interaction, the fracture micro-parameters corresponding to the fracture structure in each digital core are all set to 0, and the matrix micro-parameters of the matrix structure in the digital core are calibrated based on uniaxial and triaxial mechanical property parameters to obtain the final calibrated digital core.

[0065] Among them, the fracture micro-parameters corresponding to the fracture structure in the digital core may include parameters such as fracture deformation (stiffness), strength, and friction, and these parameters are all set to 0.

[0066] Among them, the matrix microstructure parameters may include deformation, bonding, contact parameters, strength parameters, etc. of the particle bonding model.

[0067] In this application, it is assumed that the matrix structure of the N modeled coal samples is the same. The interaction between the fracture structure and the matrix structure in each digital core is set as the smooth joint model interaction. The fracture micro-parameters corresponding to the fracture structure in each digital core are all set to 0. Based on the above N sets of uniaxial or triaxial mechanical property parameters, the matrix micro-parameters of the matrix structure of the above N digital cores are repeatedly adjusted to determine the optimal matrix micro-parameters that conform to the fractured coal rock mechanics curve. These optimal matrix micro-parameters are then used as the matrix micro-parameters of the matrix structure to calibrate each digital core, resulting in the final N calibrated digital cores.

[0068] S105, set the stress boundary conditions corresponding to the prediction index, conduct a simulation experiment on the calibrated digital core, and obtain the prediction parameters of coal and rock corresponding to the prediction index.

[0069] The prediction indicators include at least one of the following: uniaxial compressive strength, triaxial compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, and tensile modulus.

[0070] To achieve multi-parameter non-destructive testing based on CT scan digital cores, a refined digital core calculation model considering the true distribution of fractures has been established. The model's parameters include the microscopic parameters of the matrix as determined by comparison and the characteristics of non-uniformly distributed fractures.

[0071] Furthermore, by setting complex stress boundary conditions, discrete element simulations were conducted on the uniaxial compression and triaxial compression of digital cores under different confining pressures (1, 2, 5, 8, 10, 15 MPa). Through these calculations, mechanical parameters such as uniaxial compressive strength, triaxial compressive strength, elastic modulus, and Poisson's ratio of the digital cores were obtained.

[0072] Furthermore, based on the Coulomb-Moore criterion, the strength envelope was plotted using simulation results under different confining pressures, and then the mechanical parameters such as the cohesion and internal friction angle of the coal sample were calculated.

[0073] Finally, key physical and mechanical parameters such as tensile strength and tensile modulus of the rock core were obtained through direct tensile simulation tests. These data provide reliable physical and mechanical property parameters to support mine engineering design.

[0074] This application's embodiments take into account the relatively weak influence of short fractures and reduce the computational load by modeling short fractures through random distribution, making the construction of digital cores for large-scale samples more efficient. By obtaining the fracture structure and matrix characteristics of coal and rock through CT scanning and simulating them using the digital core model, the physical and mechanical properties of coal and rock (such as compressive strength, elastic modulus, Poisson's ratio, etc.) can be predicted non-destructively. This can provide more reliable and accurate physical and mechanical property data for mining engineering, support design and decision optimization, and improve the resource utilization and safety of coal mines.

[0075] Figure 2 This is a schematic diagram of an exemplary embodiment of a method for predicting coal and rock parameters based on CT scans, as shown in this application. Figure 2 As shown, this method for predicting coal and rock parameters based on CT scans includes the following steps:

[0076] S201, Obtain the initial coal sample and divide the initial coal sample into M standard cylindrical coal samples.

[0077] The initial coal sample requires a relatively large size, with the shortest side being greater than 300 mm, to ensure that the sample can fully represent the physical and mechanical properties of the coal. Multiple sampling points are selected, and a cylindrical sample with a diameter of 50 mm is drilled using a standard core drill. Subsequently, the sample is sent to a sample cutter for processing, and the coal sample is cut into M standard cylindrical coal samples with a height of 100 mm.

[0078] The unevenness of the standard coal sample should be less than 0.05 mm, and the axial deviation should be less than 0.25°.

[0079] The standard coal sample should be positioned perpendicular to the coal bedding plane, and the initial coal sample should not have obvious cracks.

[0080] S202 divides M standard coal samples into N modeling coal samples and MN verification coal samples.

[0081] S203, perform CT scans on N modeled coal samples respectively to obtain scan slices of each modeled coal sample at multiple angles.

[0082] In this application, the CT scan acquisition software is first launched to ensure the equipment is ready. Then, the scanning parameters are repeatedly tested and adjusted to finally determine key parameters such as voltage, current, exposure time, number of image merges, and resolution. Next, following the experimental procedure, a blank test is performed first to calibrate the blank backplate and calibrate the detector.

[0083] After calibration, for each modeled coal sample, an X-ray source with fixed parameters is emitted through the sample. The detector records the residual intensity after the sample is projected. Data is collected at fixed angle intervals. After rotating 360°, a single scan is completed. All scan slices are stored in the same designated folder after the scan is finished. For example, if data is collected at 1° intervals, one modeled coal sample corresponds to 360 scan slices.

[0084] S204: For any modeled coal sample, construct a three-dimensional model of the modeled coal sample based on the scan slices corresponding to the modeled coal sample, and obtain the model slices corresponding to the three-dimensional model.

[0085] In this application, for any modeled coal sample, image processing techniques are used to convert the two-dimensional slice images corresponding to the modeled coal sample into three-dimensional spatial data to obtain a three-dimensional model corresponding to the modeled coal sample. Then, model slices of different levels are extracted from the generated three-dimensional model, and these slices can be used for further analysis and visualization.

[0086] S205. For any modeled coal sample, analyze the grayscale distribution characteristics of the corresponding three-dimensional model and model slices to determine the grayscale threshold, and divide the three-dimensional model into fracture structure and matrix structure based on the grayscale threshold, and obtain the spatial distribution characteristic information of fracture structure.

[0087] Considering that different structures of coal samples (such as fractures and matrix) will exhibit different grayscale characteristics, fracture regions usually show lower grayscale values, while matrix regions show higher grayscale values. In this application, for any modeled coal sample, the grayscale distribution characteristics corresponding to the three-dimensional model and model slices of the modeled coal sample are analyzed to determine the grayscale threshold. The grayscale threshold is used to divide the three-dimensional model: the part with grayscale value below the threshold is determined to be a fracture structure, and the part with grayscale value above the threshold is classified as a matrix structure.

[0088] Among them, the spatial distribution characteristics of the fracture structure can be used to describe the connectivity characteristics of the fracture structure.

[0089] S206. For any modeled coal sample, the fracture structure is divided into multiple non-connected fractures based on the spatial distribution characteristics of the fracture structure within the modeled coal sample, and the length and volume of each fracture are obtained. Based on the length and volume of each fracture, the fracture length-volume distribution characteristics of the modeled coal sample are determined.

[0090] Based on the spatial distribution characteristics of the fracture structure within the modeled coal sample, the fracture structure is divided into multiple non-connected fractures and numbered sequentially. Then, the length and volume of each fracture are obtained sequentially, and the fracture length-volume distribution characteristics of the modeled coal sample are determined based on the length and volume of each fracture.

[0091] In obtaining the volume of the crack, the volume of each crack can be obtained by performing area integral and voxel summation on the crack boundary area based on the Crofton formula and Cauchy formula using the pixel method.

[0092] The process of obtaining the length of each crack includes: simplifying the crack into a smooth plane based on its orientation, and determining the crack length using the Ferrette diameter calculation method. In other words, it can be understood that a diameter passing through the center of this smooth plane in any direction is called a Ferrette diameter, and a diameter at every 10° angle is also recorded as a Ferrette diameter. The sum of these 36 Ferrette diameters describes a crack, and the largest Ferrette diameter is determined as the crack length.

[0093] S207. For any modeled coal sample, construct a digital core corresponding to the modeled coal sample based on the fracture length-volume distribution characteristics of the modeled coal sample. The digital core includes fracture structure and matrix structure.

[0094] Considering that long fractures can cause strong anisotropy in the mechanical properties of coal samples, and that the distribution of long fractures is relatively certain and their number is small for a single coal sample, their impact on coal rock mechanics is significant; short fractures, on the other hand, are randomly distributed angularly and spatially in coal samples, and although they are more numerous, their impact on the mechanical behavior and anisotropy of the coal sample is weaker. Therefore, in this application, we consider modeling long and short fractures separately.

[0095] Figure 3 This is a schematic diagram illustrating the distribution characteristics of crack length, number, and volume, which can be combined with... Figure 3 Analyze the crack length-volume distribution characteristics, and determine the length boundary value based on these characteristics (based on the...). Figure 3The analysis can be performed by setting the length threshold value to 3 mm; fractures with a length greater than or equal to the length threshold value are considered long fractures, and the relevant parameters of each long fracture are determined. The relevant parameters of the long fracture include at least the dip angle, dip direction, length, width and location of the long fracture (the length of each fracture has been obtained in step S206 above and can be matched one-to-one with the fracture number, so there is no need to obtain the length again here); based on the relevant parameters of the long fracture, an intermediate digital core corresponding to the modeled coal sample is constructed (wherein, the intermediate digital core includes matrix structure and long fracture structure); the random distribution model of micro fractures is performed in the intermediate digital core to obtain the digital core corresponding to the modeled coal sample, wherein the length of each micro fracture is less than the length threshold value and greater than the preset minimum length value of micro fracture.

[0096] Specifically, a random distribution model of microfractures is performed on the intermediate-state digital core to obtain the digital core corresponding to the modeled coal sample. This includes: determining the fracture length-quantity distribution characteristics of the modeled coal sample based on the length of each fracture. Figure 3 The model also shows the distribution characteristics of fracture length and number corresponding to the coal samples, such as... Figure 3 After fitting, the formula for the crack length-number distribution is: N = 4.09 × 10⁻⁶ 15 ×l -4.09 (l > 600 μm); Based on the crack length-number distribution characteristics, the formula for the microcrack number-length distribution of the modeled coal sample is as follows:

[0097] N(l) = c × l -λ

[0098] In the above formula, N(l) represents the number of microcracks with length l, c and λ are parameters determined based on the crack length-number distribution characteristics, and l represents the length of the microcrack. The value of l is less than the length boundary value and greater than the preset minimum length value of the microcrack.

[0099] Optionally, c can be set to 4.09 × 10 15 λ can be set to 4.09, and the minimum length of the microcrack can be set to 600 μm. This is only a numerical example and does not constitute a limitation of this application. Relevant personnel can make adaptive adjustments to the parameters according to the actual situation.

[0100] After determining the microfracture quantity-length distribution formula, a random distribution model of microfractures is performed in the intermediate-state digital core based on this formula, resulting in the digital core corresponding to the modeled coal sample. During the construction process, the dip direction, dip angle, and location are randomly distributed to generate a large number of microfractures within the digital core.

[0101] The method for obtaining the length of the crack has already been described above. Further, the method for obtaining the width of the crack includes: determining the vertical direction of the Feret diameter corresponding to the length of the crack, and selecting the longest value of the smooth plane corresponding to the crack in the vertical direction as the width of the crack. Figure 4 This is a schematic diagram illustrating the length and width of a crack, as shown in this application. Figure 4 As shown, based on the direction of the crack, the crack is simplified to a smooth plane. The maximum Ferete diameter is determined as the length of the crack. The vertical direction of the Ferete diameter corresponding to the length of the crack is determined. The longest value of the smooth plane corresponding to the crack in the vertical direction is selected as the width of the crack.

[0102] Furthermore, the method for obtaining the fracture dip angle and dip direction is as follows: simplify the complex fracture into a plane according to the length and width directions. Figure 5 This application illustrates a schematic diagram of a fracture azimuth and polar angle, as shown below. Figure 5 As shown, the angle of rotation counterclockwise from the X-axis is defined as the azimuth angle, and the angle between the azimuth and Z-axis is defined as the polar angle. The conversion formulas for dip and tilt angles are as follows:

[0103] θ i =θ+180°

[0104]

[0105] In the above formula, θ i θ represents the inclination; θ represents the azimuth. Represents the angle of inclination; Represents the polar angle.

[0106] S208, conduct uniaxial loading tests or triaxial loading tests on each of the N modeling coal samples to obtain the uniaxial mechanical property parameters or triaxial mechanical property parameters corresponding to each modeling coal sample.

[0107] Uniaxial loading tests were conducted on the first batch of modeled coal samples from N modeled coal samples. Based on the test results, the uniaxial mechanical property parameters corresponding to each modeled coal sample in the first batch were calculated. The uniaxial mechanical property parameters include at least uniaxial compressive strength, elastic modulus, and Poisson's ratio. During the uniaxial loading test, the first batch of modeled coal samples were placed between the two loading plates of a press and loaded at a stress-controlled loading rate of 0.5 MPa / s to a fixed confining pressure (0 MPa or other confining pressure value). The samples were then loaded at a strain-controlled loading rate until complete failure. During this process, stress-strain characteristics and stress-time relationships were recorded, and the uniaxial mechanical property parameters were calculated based on these characteristics. The slope of the stress-strain curve when the axial stress is 0.5 times the peak stress was used as the elastic modulus, and the ratio of lateral deformation to axial deformation in the elastic segment was calculated as Poisson's ratio.

[0108] Triaxial loading tests were conducted on the remaining modeled coal samples (excluding the first batch) from the N modeled coal samples. Based on the test results, the triaxial mechanical property parameters for each of the remaining modeled coal samples were calculated. These parameters included at least triaxial compressive strength, cohesion, and internal friction angle. During the triaxial loading test, the remaining modeled coal samples were placed between the two loading plates of a press and loaded at a stress-controlled rate of 0.5 MPa / s to a fixed confining pressure (0 MPa or other confining pressure value). The samples were then loaded at a strain-controlled rate until complete failure. During this process, stress-strain characteristics and stress-time relationships were recorded. The triaxial mechanical property parameters were calculated based on these characteristics. The intercept and slope of the strength curves were calculated according to the Coulomb-Mohr criterion and the peak strength of the coal samples under different confining pressures, representing cohesion and internal friction angle, respectively. Additionally, the peak strength under different confining pressures could also be obtained.

[0109] S209, the interaction between the fracture structure and the matrix structure in each digital core is set as the smooth joint model interaction, the fracture micro-parameters corresponding to the fracture structure in each digital core are all set to 0, and the matrix micro-parameters of the matrix structure of the digital core are calibrated based on uniaxial mechanical property parameters and triaxial mechanical property parameters to obtain the final calibrated digital core.

[0110] Among them, the fracture micro-parameters corresponding to the fracture structure in the digital core may include parameters such as fracture deformation (stiffness), strength, and friction, and these parameters are all set to 0.

[0111] Among them, the matrix microstructure parameters may include deformation, bonding, contact parameters, strength parameters, etc. of the particle bonding model.

[0112] In this application, it is assumed that the matrix structure of the N modeled coal samples is the same. The interaction between the fracture structure and the matrix structure in each digital core is set as the smooth joint model interaction. The fracture micro-parameters corresponding to the fracture structure in each digital core are all set to 0. Based on the above N sets of uniaxial or triaxial mechanical property parameters, the matrix micro-parameters of the matrix structure of the above N digital cores are repeatedly adjusted to determine the optimal matrix micro-parameters that conform to the fractured coal rock mechanics curve. These optimal matrix micro-parameters are then used as the matrix micro-parameters of the matrix structure to calibrate each digital core, resulting in the final N calibrated digital cores.

[0113] S210 involves simultaneously conducting loading tests under different stress environments on the calibration digital core and the verification coal sample to verify the correctness of the parameters of the calibration digital core.

[0114] Based on discrete element simulation, loading tests under different stress environments were carried out simultaneously through digital core simulation experiments and laboratory tests. The stress-strain curves, peak strength, elastic modulus, Poisson's ratio and other strength and deformation parameters obtained by the two methods were compared to verify the correctness of the matrix and fracture model parameters.

[0115] S211, set the stress boundary conditions corresponding to the prediction index, conduct a simulation experiment on the calibrated digital core, and obtain the prediction parameters of coal and rock corresponding to the prediction index.

[0116] The prediction indicators include at least one of the following: uniaxial compressive strength, triaxial compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, and tensile modulus.

[0117] To achieve multi-parameter non-destructive testing based on CT scan digital cores, a refined digital core calculation model considering the true distribution of fractures has been established. The model's parameters include the microscopic parameters of the matrix as determined by comparison and the characteristics of non-uniformly distributed fractures.

[0118] Furthermore, by setting complex stress boundary conditions, discrete element simulations were conducted on the uniaxial compression and triaxial compression of digital cores under different confining pressures (1, 2, 5, 8, 10, 15 MPa). Through these calculations, mechanical parameters such as uniaxial compressive strength, triaxial compressive strength, elastic modulus, and Poisson's ratio of the digital cores were obtained.

[0119] Furthermore, based on the Coulomb-Moore criterion, the strength envelope was plotted using simulation results under different confining pressures, and then the mechanical parameters such as the cohesion and internal friction angle of the coal sample were calculated.

[0120] Finally, key physical and mechanical parameters such as tensile strength and tensile modulus of the rock core were obtained through direct tensile simulation tests. These data provide reliable physical and mechanical property parameters to support mine engineering design.

[0121] This application's embodiments take into account the relatively weak influence of short fractures and reduce the computational load by modeling short fractures through random distribution, making the construction of digital cores for large-scale samples more efficient. By obtaining the fracture structure and matrix characteristics of coal and rock through CT scanning and simulating them using the digital core model, the physical and mechanical properties of coal and rock (such as compressive strength, elastic modulus, Poisson's ratio, etc.) can be predicted non-destructively. This can provide more reliable and accurate physical and mechanical property data for mining engineering, support design and decision optimization, and improve the resource utilization and safety of coal mines.

[0122] The above describes a method of modeling long and short fractures separately. In practice, however, it's also possible to avoid modeling long and short fractures separately and instead obtain the actual fracture-related parameters corresponding to each fracture. Based on these parameters, a digital core corresponding to the modeled coal sample can be constructed. These fracture-related parameters include at least the fracture's dip angle, dip direction, length, width, and location.

[0123] Figure 6 This application illustrates a schematic diagram of a coal and rock index parameter prediction device based on CT scanning, as shown below. Figure 6 As shown, the CT scan-based coal and rock index parameter prediction device 600 includes a determination module 601, a construction module 602, an experimental module 603, a calibration module 604, and a prediction module 605, wherein:

[0124] The determination module 601 is used to perform CT scans on N modeled coal samples respectively, and determine the fracture length-volume distribution characteristics of the fracture structure in each modeled coal sample based on the scan results.

[0125] Module 602 is used to construct a digital core corresponding to any modeled coal sample based on the fracture length-volume distribution characteristics of the modeled coal sample. The digital core includes fracture structure and matrix structure.

[0126] The test module 603 is used to perform uniaxial loading tests or triaxial loading tests on each of the N modeled coal samples to obtain the uniaxial mechanical property parameters or triaxial mechanical property parameters corresponding to each modeled coal sample.

[0127] The calibration module 604 is used to set the interaction between the fracture structure and the matrix structure in each digital core as a smooth joint model, set the fracture micro-parameters corresponding to the fracture structure in each digital core to 0, and calibrate the matrix micro-parameters of the matrix structure of the digital core based on uniaxial and triaxial mechanical property parameters to obtain the final calibrated digital core.

[0128] The prediction module 605 is used to set the stress boundary conditions corresponding to the prediction index, conduct simulation experiments on the calibrated digital core, and obtain the prediction parameters of coal and rock corresponding to the prediction index.

[0129] This device takes into account the relatively weak influence of short fractures and reduces the computational load by modeling short fractures through random distribution, making the construction of digital cores for large-scale samples more efficient. By acquiring the fracture structure and matrix characteristics of coal and rock through CT scans and simulating them using the digital core model, it can predict the physical and mechanical properties of coal and rock (such as compressive strength, elastic modulus, Poisson's ratio, etc.) without damage. This provides more reliable and accurate physical and mechanical property data for mining engineering, supports design and decision optimization, and improves the resource utilization and safety of coal mines.

[0130] Furthermore, the determining module 601 is also used for: performing CT scans on N modeling coal samples respectively to obtain scan slices corresponding to each modeling coal sample at multiple angles; for any modeling coal sample, constructing a three-dimensional model corresponding to the modeling coal sample based on the scan slices corresponding to the modeling coal sample, and obtaining model slices corresponding to the three-dimensional model; for any modeling coal sample, analyzing the grayscale distribution characteristics corresponding to the three-dimensional model and model slices corresponding to the modeling coal sample to determine the grayscale threshold, and dividing the three-dimensional model into fracture structure and matrix structure based on the grayscale threshold, and obtaining spatial distribution characteristic information of fracture structure; for any modeling coal sample, dividing the fracture structure into multiple non-connected fractures based on the spatial distribution characteristic information of fracture structure within the modeling coal sample, obtaining the length and volume corresponding to each fracture, and determining the fracture length-volume distribution characteristics corresponding to the modeling coal sample based on the length and volume corresponding to each fracture.

[0131] Furthermore, the construction module 602 is also used for: determining the length boundary value based on the fracture length-volume distribution characteristics; taking fractures with a length greater than or equal to the length boundary value as long fractures, determining the long fracture-related parameters corresponding to each long fracture, the long fracture-related parameters including at least the dip angle, dip direction, length, width and location of the long fracture; constructing an intermediate state digital core corresponding to the modeled coal sample based on the long fracture-related parameters; performing random distribution modeling of microfractures in the intermediate state digital core to obtain the digital core corresponding to the modeled coal sample, wherein the length of each microfracture is less than the length boundary value and greater than the preset minimum length value of the microfracture.

[0132] Furthermore, module 602 is also used to: determine the fracture length-quantity distribution characteristics of the modeling coal sample based on the length corresponding to each fracture; and construct the micro-fracture quantity-length distribution formula of the modeling coal sample based on the fracture length-quantity distribution characteristics as follows:

[0133] N(l) = c × l -λ

[0134] In the above formula, N(l) represents the number of microcracks with length l, c and λ are parameters determined based on the crack length-number distribution characteristics, l represents the length of the microcrack, where the value of l is less than the length boundary value and greater than the preset minimum length value of the microcrack; based on the microcrack number-length distribution formula, the random distribution model of microcracks is performed in the intermediate state digital core to obtain the digital core corresponding to the modeled coal sample.

[0135] Furthermore, the construction module 602 is also used to: simplify the crack into a smooth plane based on the crack's orientation, and determine the crack's length using the Ferrette diameter calculation method; determine the vertical direction of the Ferrette diameter corresponding to the crack's length, and select the longest value of the smooth plane corresponding to the crack in the vertical direction as the crack's width.

[0136] Furthermore, the determination module 601 is also used for: acquiring an initial coal sample, dividing the initial coal sample into M standard cylindrical coal samples; dividing the M standard coal samples into N modeling coal samples and MN verification coal samples; the calibration module is also used for: simultaneously conducting loading tests on the calibration digital core and the verification coal samples under different stress environments to verify the correctness of the parameters of the calibration digital core.

[0137] Furthermore, the test module 603 is also used to: conduct uniaxial loading tests on the first part of the N modeled coal samples, and calculate the uniaxial mechanical property parameters corresponding to each modeled coal sample in the first part based on the test results. The uniaxial mechanical property parameters include at least uniaxial compressive strength, elastic modulus, and Poisson's ratio. It also conducts triaxial loading tests on the remaining modeled coal samples (excluding the first part of the N modeled coal samples) and calculates the triaxial mechanical property parameters corresponding to each modeled coal sample in the remaining modeled coal samples based on the test results. The triaxial mechanical property parameters include at least triaxial compressive strength, cohesion, and internal friction angle.

[0138] Furthermore, the prediction indicators are at least one of the following: uniaxial compressive strength, triaxial compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, and tensile modulus.

[0139] To implement the above embodiments, this application also proposes an electronic device 700, such as... Figure 7 As shown, the electronic device 700 includes a processor 701 and a memory 702 communicatively connected to the processor. The memory 702 stores instructions that can be executed by at least one processor. The instructions are executed by at least one processor 701 to implement the coal and rock index parameter prediction method based on CT scan as shown in the above embodiment.

[0140] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to implement the coal and rock index parameter prediction method based on CT scan as shown in the above embodiments.

[0141] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the coal and rock index parameter prediction method based on CT scans as shown in the above embodiments.

[0142] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0143] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0144] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0145] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting coal and rock parameters based on CT scans, characterized in that, include: CT scans were performed on N modeled coal samples, and the fracture length-volume distribution characteristics of the fracture structure in each modeled coal sample were determined based on the scan results. For any of the modeled coal samples, the length boundary value is determined based on the fracture length-volume distribution characteristics; Cracks with a length greater than or equal to the length boundary value are defined as long cracks. The long crack-related parameters corresponding to each long crack are determined. The long crack-related parameters include at least the dip angle, dip direction, length, width and location of the long crack. Based on the long fracture-related parameters, an intermediate state digital core corresponding to the modeled coal sample is constructed. Random distribution modeling of microfractures is performed on the intermediate state digital core to obtain the digital core corresponding to the modeled coal sample. The length of each microfracture is less than the length boundary value and greater than the preset minimum length value of microfracture. The digital core includes fracture structure and matrix structure. Each of the N modeling coal samples is subjected to a uniaxial loading test or a triaxial loading test to obtain the uniaxial mechanical property parameters or triaxial mechanical property parameters corresponding to each modeling coal sample. The interaction between the fracture structure and the matrix structure in each digital core is set as a smooth joint model. The fracture micro-parameters corresponding to the fracture structure in each digital core are all set to 0. The matrix micro-parameters of the matrix structure of the digital core are calibrated based on the uniaxial mechanical property parameters and the triaxial mechanical property parameters to obtain the final calibrated digital core. By setting the stress boundary conditions corresponding to the prediction index, a simulation experiment is conducted on the calibrated digital core to obtain the prediction parameters of the coal and rock corresponding to the prediction index.

2. The method according to claim 1, characterized in that, The step of performing CT scans on N modeled coal samples and determining the fracture length-volume distribution characteristics of the fracture structure within each modeled coal sample based on the scan results includes: CT scans were performed on N modeled coal samples to obtain scan slices of each modeled coal sample at multiple angles. For any of the modeled coal samples, a three-dimensional model corresponding to the modeled coal sample is constructed based on the scan slices corresponding to the modeled coal sample, and model slices corresponding to the three-dimensional model are obtained; For any of the modeled coal samples, the grayscale distribution characteristics of the three-dimensional model and the model slices corresponding to the modeled coal sample are analyzed to determine the grayscale threshold. Based on the grayscale threshold, the three-dimensional model is divided into fracture structure and matrix structure, and the spatial distribution characteristic information of the fracture structure is obtained. For any of the modeled coal samples, the fracture structure is divided into multiple non-connected fractures based on the spatial distribution characteristics of the fracture structure within the modeled coal sample, and the length and volume of each fracture are obtained. Based on the length and volume of each fracture, the fracture length-volume distribution characteristics of the modeled coal sample are determined.

3. The method according to claim 1, characterized in that, The process of modeling the random distribution of microfractures in the intermediate-state digital core to obtain the digital core corresponding to the modeled coal sample includes: The crack length-quantity distribution characteristics of the modeled coal sample are determined based on the length of each crack. Based on the fracture length-number distribution characteristics, the formula for the microfracture number-length distribution of the modeled coal sample is as follows: In the above formula, Indicates length is The number of microcracks, and All parameters are determined based on the crack length-number distribution characteristics. Indicates the length of the microcrack, where, The value is less than the length boundary value and greater than the preset minimum length value of the microcrack; Based on the microfracture quantity-length distribution formula, a random distribution model of microfractures is performed on the intermediate state digital core to obtain the digital core corresponding to the modeled coal sample.

4. The method according to claim 2, characterized in that, The step of obtaining the length corresponding to each of the cracks includes: Based on the direction of the crack, the crack is simplified to a smooth plane, and the length of the crack is determined by combining the Ferrett diameter calculation method. The method for obtaining the width of the crack includes: Determine the vertical direction of the Ferrette diameter corresponding to the length of the crack, and select the longest value of the smooth plane corresponding to the crack in the vertical direction as the width of the crack.

5. The method according to any one of claims 1-4, characterized in that, Before performing CT scans on the N modeling coal samples, the process also includes: Obtain an initial coal sample and divide the initial coal sample into M standard cylindrical standard coal samples; The M standard coal samples are divided into N modeling coal samples and MN verification coal samples; After obtaining the final calibrated digital core, the process also includes: Loading tests under different stress environments were simultaneously conducted on the calibration digital core and the verification coal sample to verify the correctness of the parameters of the calibration digital core.

6. The method according to claim 5, characterized in that, The step of conducting uniaxial loading tests or triaxial loading tests on each of the N modeling coal samples to obtain the uniaxial mechanical property parameters or triaxial mechanical property parameters corresponding to each modeling coal sample includes: Uniaxial loading tests were conducted on the first part of the N modeled coal samples, and the uniaxial mechanical property parameters corresponding to each modeled coal sample in the first part of the modeled coal samples were calculated based on the test results. The uniaxial mechanical property parameters include at least uniaxial compressive strength, elastic modulus and Poisson's ratio. Triaxial loading tests were conducted on the remaining modeling coal samples (excluding the first part of the modeling coal samples) among the N modeling coal samples. Based on the test results, the triaxial mechanical property parameters corresponding to each modeling coal sample among the remaining modeling coal samples were calculated. The triaxial mechanical property parameters include at least triaxial compressive strength, cohesion and internal friction angle.

7. The method according to claim 6, characterized in that, The prediction indicator is at least one of the following: Uniaxial compressive strength, triaxial compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, tensile modulus.

8. A device for predicting coal and rock index parameters based on CT scanning, characterized in that, include: The determination module is used to perform CT scans on N modeled coal samples respectively, and determine the fracture length-volume distribution characteristics of the fracture structure in each modeled coal sample based on the scan results; The module is used to determine the length boundary value based on the fracture length-volume distribution characteristics for any of the modeled coal samples. Fractures with a length greater than or equal to the length threshold are defined as long fractures. For each long fracture, relevant parameters are determined, including at least the dip angle, dip direction, length, width, and location. An intermediate-state digital core corresponding to the modeled coal sample is constructed based on these parameters. A random distribution model of microfractures is then performed on the intermediate-state digital core to obtain the digital core corresponding to the modeled coal sample. The length of each microfracture is less than the length threshold and greater than a preset minimum microfracture length. The digital core includes fracture structure and matrix structure. The test module is used to perform uniaxial loading tests or triaxial loading tests on each of the N modeled coal samples to obtain the uniaxial mechanical property parameters or triaxial mechanical property parameters corresponding to each modeled coal sample. The calibration module is used to set the interaction between the fracture structure and the matrix structure in each digital core as a smooth joint model, set the fracture micro-parameters corresponding to the fracture structure in each digital core to 0, and calibrate the matrix micro-parameters of the matrix structure of the digital core based on the uniaxial mechanical property parameters and the triaxial mechanical property parameters to obtain the final calibrated digital core. The prediction module is used to set the stress boundary conditions corresponding to the prediction index, conduct simulation experiments on the calibrated digital core, and obtain the prediction parameters of the coal and rock corresponding to the prediction index.

9. An electronic device, comprising: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

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