Coal rock failure characteristic quantification method, device, equipment, medium and crack visualization tool
By grayscale and binarizing the coal rock images, pixel-level pores are calculated to obtain the opening and average feature sizes of each fracture, the problems of low efficiency and poor accuracy of coal rock damage characteristics in the existing technology are solved, and accurate quantification of coal rock damage and improved system reliability are achieved.
Patent Information
- Application Number
- CN202510161772.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-14
- Publication Date
- 2025-06-06
AI Technical Summary
The existing quantitative analysis methods for coal rock damage characteristics have problems such as low efficiency, high cost and poor observational effects. Especially during the identification and analysis process, the identification of coal rock cracks will be affected by factors such as material background patterns, light and stains, resulting in large errors and challenges in system reliability and accuracy.
By greying and binarizing the coal rock image, pixel-level pores are calculated to obtain the opening degree of each fracture and the average characteristic size of the fracture, and accurately quantifying coal rock damage is achieved.
The precise quantification of coal rock damage is achieved, the efficiency and accuracy of detection and analysis are improved, errors are reduced, and the reliability of the system is enhanced.
Smart Images
Figure CN120107614A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of coal-rock detection technology, and in particular to a method, device, equipment, medium and crack visualization tool for quantifying coal-rock damage characteristics. Background Art
[0002] Coal has been one of the main energy sources since the 18th century. With the increasing demand for coal, shallow coal mining can no longer meet the demand. The depth of coal mining continues to increase. At the same time, the geological conditions of deep coal mining are poor, and coal mine accidents occur frequently. As a common disaster in coal mine disasters, dynamic disasters have caused a lot of human and property losses in coal mining. Therefore, dynamic disaster prevention and control will remain the top priority of coal mine safety production work for a long time in the future. Quantitative analysis of coal and rock damage characteristics is of great significance to the study of dynamic disasters and other types of disasters.
[0003] Early quantitative analysis of coal and rock damage characteristics mainly relied on observation and analysis by the naked eye of experimenters, which was inefficient, costly, and had poor observation effects, and there were large errors in the analysis of specific information. With the rapid development of computer technology, analysis methods that rely on computer vision technology have been proposed one after another, but related technologies have prominent problems in the identification and analysis process: coal and rock crack identification will be affected by the background pattern of the material, and the error will be greater when the crack edge and the background pattern are similar in color, and will be greatly disturbed by factors such as light and stains. The above problems have posed great challenges to the reliability and accuracy of the detection and analysis system. In addition, the width of the cracks themselves varies, and the forms are complex and varied, which poses great challenges to computer identification and calculation. Summary of the invention
[0004] The purpose of this application is to provide a method, device, equipment, medium and crack visualization tool for quantifying coal and rock damage characteristics, which can achieve accurate quantification of coal and rock damage.
[0005] To achieve the above objectives, this application provides the following solutions:
[0006] In a first aspect, the present application provides a method for quantifying coal-rock damage characteristics, comprising:
[0007] Grayscale the coal-rock image to obtain a grayscale image;
[0008] Binarizing the grayscale image to obtain a binary image;
[0009] The aperture of each fracture and the average characteristic size of the fractures in the coal-rock image are calculated based on the pixel-level pores in the binary image.
[0010] Optionally, the calculation formula for each crack opening is:
[0011] Among them, d j,i is the crack opening of the jth crack, a i is the side length of the i-th pore, and each pore includes a square pixel block composed of a plurality of pixels.
[0012] Optionally, the calculation formula for the average characteristic size of the cracks in the coal-rock image is:
[0013] Among them, D c is the average characteristic size of the crack, N is the number of cracks, k is the number of pores in each crack, i∈I, j∈J, I is the set of pores, and J is the set of cracks.
[0014] Optionally, after binarization processing is performed on the grayscale image to obtain the binarized image, the coal-rock damage feature quantification method further includes:
[0015] The planar porosity is calculated based on the pixel-level pores in the binary image.
[0016] Optionally, the calculation formula of the planar porosity is:
[0017]
[0018] in, is the plane porosity, S is the area occupied by the cracks on the coal rock image, and S 0 is the area of the coal rock image, S i is the area of the i-th pixel, a i is the side length of the i-th pore, m is the number of pores on the coal rock image, M is the number of pixels on the coal rock image, n is the number of pixels of each pore, and δ is the side length of the pixel.
[0019] Optionally, the coal-rock damage characteristics quantification method further includes:
[0020] Obtaining the aperture of each fracture and the average characteristic size of the fracture on the coal-rock images collected at different times;
[0021] The openings of the cracks on the coal-rock images collected at different times and the average characteristic size of the cracks are compared to obtain the damage variables of the coal-rock corresponding to the coal-rock images.
[0022] In a second aspect, the present application provides a coal rock fracture visualization tool, which applies the coal rock damage characteristic quantification method, and the coal rock fracture visualization tool visualizes the aperture of each fracture and the average characteristic size of the fracture.
[0023] In a third aspect, the present application provides a coal-rock damage feature quantification device, the coal-rock damage feature quantification device applies the coal-rock damage feature quantification method, and the coal-rock damage feature quantification device includes:
[0024] A grayscale processing module is used to grayscale the coal rock image to obtain a grayscale image;
[0025] A binarization processing module, used for performing binarization processing on the grayscale image to obtain a binarized image;
[0026] The fracture aperture and fracture average characteristic size calculation module is used to calculate the aperture of each fracture and the fracture average characteristic size of the coal-rock image according to the pixel-level pores in the binary image.
[0027] In a fourth aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for quantifying coal and rock damage characteristics.
[0028] In a fifth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for quantifying coal and rock damage characteristics.
[0029] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0030] The present application provides a method, device, equipment, medium and crack visualization tool for quantifying coal and rock damage characteristics, which can achieve accurate quantification of coal and rock damage by calculating the aperture of each crack and the average characteristic size of the cracks in the coal and rock image based on the pixel-level pores in the binary image. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0032] Figure 1 A schematic diagram of a process for quantifying coal-rock damage characteristics provided in one embodiment of the present application;
[0033] Figure 2 A schematic diagram of the principle of a method for quantifying coal-rock damage characteristics provided in one embodiment of the present application;
[0034] Figure 3A schematic diagram of characteristic dimension calculation provided in one embodiment of the present application;
[0035] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0036] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. 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 creative work are within the scope of protection of this application.
[0037] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0038] This application provides a method for quantifying coal-rock damage characteristics, such as Figure 1 and Figure 2 As shown, the method for quantifying coal-rock damage characteristics includes steps 101 to 103.
[0039] Step 101: grayscale the coal rock image to obtain a grayscale image.
[0040] Step 102: binarize the grayscale image to obtain a binary image.
[0041] Step 103: Calculate the aperture of each fracture and the average characteristic size of the fractures in the coal-rock image based on the pixel-level pores in the binary image.
[0042] This application grayscales and binarizes the coal rock image. After processing, the specimen has only two contrasting colors, which can clearly distinguish the target pixels from the non-target pixels. The processed image is actually a set of pixels in a two-dimensional space, and each pixel is a square with a side length of δ. It is assumed that the pores extracted in the image are square pixel blocks composed of n pixels, with a side length of a. i ,like Figure 3 As shown in the figure, the cracks on the surface of the coal sample are formed by k interconnected pore pixel blocks, that is, a large area of connected pixel sets extracted from the image. The plane porosity of the specimen is The total area S occupied by the cracks on the specimen surface extracted after binarization and the cross-sectional area S of the specimen can be defined 0 Ratio calculation, S, S 0 and the area S of each pore pixel block i It can be calculated by the built-in program of the coal rock fracture visualization tool, based on which the characteristic size of the fracture d can be calculatedj,i , and the average characteristic size of the cracks on the specimen surface D c . j,i and D c Customization is embedded in the coal-rock crack visualization tool to calculate the characteristic size and distribution of cracks on the specimen surface; the crack length L and the fractal dimension D of the cracks on the specimen surface can be measured by the built-in software tools. This application method can accurately analyze the key information in the image, and after processing the coal-rock photos, the damage characteristics of the coal-rock are calculated based on the processing results, and the changes of the coal-rock before and after can be quantitatively analyzed based on this.
[0043] The image displayed by the computer is actually a collection of pixels in two-dimensional space. Each pixel in the image corresponds to a specific position (x, y) in two-dimensional space, and its color is given by three corresponding RGB color components (r, g, b). This kind of image is called an RGB color image, and the mathematical expression is:
[0044] f(x,y)=(r(x,y),g(x,y),b(x,y))r,g,b∈[0,255];
[0045] Among them, f(x,y) represents the color value at the position (x,y), r=r(x,y) represents the red color value at the position (x,y), g=g(x,y) represents the green color value at the position (x,y), and b=b(x,y) represents the blue color value at the position (x,y).
[0046] Step 101 specifically includes: in order to facilitate the extraction of key information in the image, the color image is generally grayed before binarization, that is, the process of converting the RGB color image into a gray image. A gray image refers to the case where r=g=b in the RGB color model, and the color of the image is expressed by only one color component (G). The size of the G value represents the intensity of the color, which is called the gray value. The graying of an RGB color image can usually be performed according to the following formula:
[0047]
[0048] Among them, G=G(x,y) represents the grayscale value at the position (x,y), and l, m′ and n′ are coefficients.
[0049] Step 102 specifically includes: the binarization process of the grayscale image is to segment the grayscale values of the target pixel and the non-target pixel in the grayscale image with a threshold T, and convert them into two specific grayscale values respectively to obtain a binary image. The binarization formula is expressed as:
[0050]
[0051] Among them, t 0It is a specific grayscale value, which is selected according to the actual situation of the coal and rock specimen image.
[0052] The binarized image has only two contrasting colors, which can clearly distinguish target pixels from non-target pixels, making it easier to analyze key information in the image. The coal and rock sample photos are binarized using the coal and rock fracture visualization tool, and the fracture characteristic parameters of the coal and rock samples are calculated based on the processing results. The changes before and after the test are compared, and the damage of the coal samples under different test conditions is quantitatively analyzed.
[0053] Image binarization can convert a color or grayscale image into an image with only two colors (usually black and white). Binarization is widely used in edge detection and other fields. The binarized image can show the edge of the image more clearly, and then be used for subsequent edge detection or contour extraction operations. Therefore, image binarization can be used to identify coal and rock cracks.
[0054] After step 102, the coal-rock damage feature quantification method further includes: calculating the plane porosity according to the pixel-level pores in the binary image.
[0055] The calculation formula of the plane porosity is:
[0056] in, is the plane porosity, S is the area occupied by the cracks on the coal rock image, and S 0 is the area of the coal-rock image, i.e. the cross-sectional area of the specimen, S i is the area of the i-th pixel, a i is the side length of the i-th pore, m is the number of pores on the coal rock image, M is the number of pixels on the coal rock image, n is the number of pixels of each pore, and δ is the side length of the pixel.
[0057] In an exemplary embodiment, S, S 0 and the area S of each pore pixel block i It can be calculated by the built-in program of the coal rock fracture visualization tool, based on which the characteristic size of the fracture can be calculated, denoted by d j,i is the characteristic size of the jth crack, whose length is the diagonal length of the square pore pixel block, and the characteristic size d j,i It can be approximated as the crack opening. That is, the calculation formula for each crack opening is:
[0058] Among them, d j,i is the crack opening of the jth crack, a i is the side length of the i-th pore, and each pore includes a square pixel block composed of a plurality of pixels.
[0059] The calculation formula for the average characteristic size of the cracks in the coal rock image is:
[0060] Among them, D c is the average characteristic size of the crack, N is the number of cracks, k is the number of pores in each crack, i∈I, j∈J, I is the set of pores, and J is the set of cracks.
[0061] This application can obtain the image features of coal and rock specimens before and after destruction by taking photos with a high-definition camera or scanning with a CT scanner, and then analyze the destruction features through this method, where information such as area can be obtained through mature software (such as Avizo, CAD, etc.), and then the specific data obtained will be used for analysis by the method of this application.
[0062] The coal-rock damage feature quantification method further includes: obtaining the aperture of each crack on the coal-rock image collected at different times and the average characteristic size of the crack. Comparing the aperture of each crack on the coal-rock image collected at different times and the average characteristic size of the crack, obtaining the damage variable of the coal-rock corresponding to the coal-rock image.
[0063] In an exemplary embodiment, the present application also provides a coal rock fracture visualization tool, which applies the coal rock damage characteristic quantification method, and the coal rock fracture visualization tool visualizes the aperture of each fracture and the average characteristic size of the fracture.
[0064] The calculation formulas for the aperture of each crack and the average characteristic size of the cracks are embedded in the coal and rock fracture visualization tool to calculate the characteristic size and distribution of cracks on the specimen surface. The crack length and the fractal dimension of the cracks on the specimen surface can be measured using the built-in tools in the software.
[0065] Based on the same inventive concept, the embodiment of the present application also provides a coal-rock damage characteristic quantification device for implementing the above-mentioned coal-rock damage characteristic quantification method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the coal-rock damage characteristic quantification device provided below can refer to the limitations of the coal-rock damage characteristic quantification method above, and will not be repeated here.
[0066] In an exemplary embodiment, the present application provides a coal-rock damage feature quantification device, the coal-rock damage feature quantification device applies the coal-rock damage feature quantification method, and the coal-rock damage feature quantification device includes:
[0067] The grayscale processing module is used to grayscale the coal rock image to obtain a grayscale image.
[0068] The binarization processing module is used to perform binarization processing on the grayscale image to obtain a binarized image.
[0069] The fracture aperture and fracture average characteristic size calculation module is used to calculate the aperture of each fracture and the fracture average characteristic size of the coal-rock image according to the pixel-level pores in the binary image.
[0070] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-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 database of the computer device is used to store coal-rock damage characteristic quantification data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for quantifying coal-rock damage characteristics is implemented.
[0071] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.
[0072] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0073] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0074] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0075] Those of ordinary skill 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0076] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a data processing logic of a programmable logic device, etc., but is not limited thereto.
[0077] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0078] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for quantifying coal-rock damage characteristics, characterized in that: The coal-rock damage characteristics quantification method comprises: Grayscale the coal-rock image to obtain a grayscale image; Binarizing the grayscale image to obtain a binary image; The aperture of each fracture and the average characteristic size of the fractures in the coal-rock image are calculated based on the pixel-level pores in the binary image.
2. The method for quantifying coal-rock damage characteristics according to claim 1, characterized in that: The calculation formula for each crack opening is: Among them, d j,i is the crack opening of the jth crack, a i is the side length of the i-th pore, and each pore includes a square pixel block composed of a plurality of pixels.
3. The method for quantifying coal-rock damage characteristics according to claim 2, characterized in that: The calculation formula for the average characteristic size of the cracks in the coal-rock image is: Among them, D c is the average characteristic size of the crack, N is the number of cracks, k is the number of pores in each crack, i∈I, j∈J, I is the set of pores, and J is the set of cracks.
4. The method for quantifying coal-rock damage characteristics according to claim 1, characterized in that: After the grayscale image is binarized to obtain a binarized image, the coal-rock damage feature quantification method further includes: The planar porosity is calculated based on the pixel-level pores in the binary image.
5. The method for quantifying coal-rock damage characteristics according to claim 4, characterized in that: The calculation formula of the plane porosity is: in, is the plane porosity, S is the area occupied by the cracks on the coal rock image, S0 is the area of the coal rock image, S i is the area of the i-th pixel, a i is the side length of the i-th pore, m is the number of pores on the coal rock image, M is the number of pixels on the coal rock image, n is the number of pixels of each pore, and δ is the side length of the pixel.
6. The method for quantifying coal-rock damage characteristics according to claim 1, characterized in that: The coal-rock damage characteristics quantification method also includes: Obtaining the aperture of each fracture and the average characteristic size of the fracture on the coal-rock images collected at different times; The openings of the cracks on the coal-rock images collected at different times and the average characteristic size of the cracks are compared to obtain the damage variables of the coal-rock corresponding to the coal-rock images.
7. A coal rock fracture visualization tool, characterized in that: The coal-rock fracture visualization tool applies the coal-rock damage characteristic quantification method described in any one of claims 1 to 6, and the coal-rock fracture visualization tool visualizes the aperture of each fracture and the average characteristic size of the fracture.
8. A device for quantifying coal-rock damage characteristics, characterized in that: The coal-rock damage characteristic quantification device applies the coal-rock damage characteristic quantification method described in any one of claims 1 to 6, and the coal-rock damage characteristic quantification device comprises: A grayscale processing module is used to grayscale the coal rock image to obtain a grayscale image; A binarization processing module, used for performing binarization processing on the grayscale image to obtain a binarized image; The fracture aperture and fracture average characteristic size calculation module is used to calculate the aperture of each fracture and the fracture average characteristic size of the coal-rock image according to the pixel-level pores in the binary image.
9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for quantifying coal and rock damage characteristics as described in any one of claims 1 to 6.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for quantifying coal and rock damage characteristics described in any one of claims 1 to 6 is implemented.