Method, device and equipment for determining connectivity index of rock mass structure network

Abstract

The present application relates to a method, device and equipment for determining the network connectivity index of a rock mass structure, and relates to the technical field of rock mass engineering. The method includes: identifying cracks in a target rock to obtain a crack rock image of the target rock; determining the surface crack density, surface crack intensity and surface crack intersection density of the target rock according to the crack rock image; and determining the network connectivity index of the target rock based on the surface crack density, surface crack intensity and surface crack intersection density. Using this method can reduce the measurement risk and improve the measurement efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of rock mass engineering, and particularly to a method, device and equipment for determining the network connectivity index of a rock mass structure. Background Art

[0002] Under the action of natural and human factors, cracks will form on the rock surface, causing the rock mass structure to lose stability, and further leading to geological disasters and incalculable dangers. In this case, it is particularly important to evaluate the rock stability based on the cracks on the rock.

[0003] In the related art, when evaluating the rock stability, precise exploration instruments are relied on to sample and measure the rocks with cracks, and the influence degree of the cracks on the rock stability is quantified as the Network Connectivity Index (NCI), and the rock stability is evaluated based on the network connectivity index.

[0004] However, the method for obtaining the network connectivity index in the related art has problems of high measurement risk and low measurement efficiency. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a method, device and equipment for determining the network connectivity index of a rock mass structure, which can reduce the measurement risk and improve the measurement efficiency.

[0006] In a first aspect, the present application provides a method for determining the network connectivity index of a rock mass structure, the method comprising:

[0007] Identifying the cracks in the target rock to obtain the cracked rock image of the target rock;

[0008] Determining the surface crack density, surface crack intensity and surface crack intersection density of the target rock according to the cracked rock image;

[0009] Determining the network connectivity index of the target rock based on the surface crack density, surface crack intensity and surface crack intersection density.

[0010] In one embodiment, determining the surface crack density, surface crack intensity and surface crack intersection density of the target rock according to the cracked rock image includes:

[0011] Obtaining the crack trace skeleton image of the target rock according to the cracked rock image;

[0012] Obtaining the proportional parameter between the image size and the actual size in the crack trace skeleton image;

[0013] Determining the surface crack density, surface crack intensity and surface crack intersection density based on the crack trace skeleton image and the proportional parameter.

[0014] In one embodiment, according to the fractured rock image, obtaining the fracture trace skeleton image of the target rock includes:

[0015] Inputting the fractured rock image into an image recognition model to obtain a binary fractured rock image;

[0016] Performing grayscale processing on the binary fractured rock image to obtain a grayscale image;

[0017] Performing erosion processing on the grayscale image to obtain the fracture trace skeleton image.

[0018] In one embodiment, based on the fracture trace skeleton image and a scale parameter, determining the areal fracture density, areal fracture intensity, and areal fracture intersection density includes:

[0019] Determining the physical area and fracture length of the fracture trace skeleton image according to the scale parameter;

[0020] Determining the areal fracture intensity according to the fracture length and physical area, determining the areal fracture density according to the physical area and the number of fractures in the fracture trace skeleton image; and determining the areal fracture intersection density according to the physical area and the number of intersecting fractures in the trace skeleton image.

[0021] In one embodiment, determining the areal fracture density according to the physical area and the number of fractures in the fracture trace skeleton image includes:

[0022] Obtaining the connected component map of the fractured rock image;

[0023] Correcting the number of fractures according to the number of fracture groups in the connected component map;

[0024] Determining the ratio of the corrected number of fractures to the physical area as the areal fracture density.

[0025] In one embodiment, based on the areal fracture density, areal fracture intensity, and areal fracture intersection density, determining the network connectivity index of the target rock includes:

[0026] Obtaining the boundary fracture criterion parameter of the fractured rock image;

[0027] Correcting the areal fracture intersection density according to the boundary fracture criterion parameter;

[0028] Determining the network connectivity index of the target rock according to the areal fracture density, areal fracture intensity, and the corrected areal fracture intersection density.

[0029] In one embodiment, the boundary crack criterion parameters include the number of upper boundary crack endpoints, the number of lower boundary crack endpoints, the number of left boundary crack endpoints, and the number of right boundary crack endpoints; according to the boundary crack criterion parameters, the surface crack intersection density is corrected, including:

[0030] Determine the number of upper and lower boundary cracks according to the number of upper boundary crack endpoints, the number of lower boundary crack endpoints, and the height-length ratio of the crack rock image; and determine the number of left and right boundary cracks according to the number of left boundary crack endpoints, the number of right boundary crack endpoints, and the length-height ratio of the crack rock image;

[0031] Determine the compensation density of the surface crack intersection density as the ratio of the sum of the number of upper and lower boundary cracks and the number of left and right boundary cracks to the physical area of the crack rock image;

[0032] Correct the surface crack intersection density by superimposing the compensation density and the surface crack intersection density.

[0033] In one embodiment, according to the surface crack density, the surface crack strength, and the corrected surface crack intersection density, determine the network connectivity index of the target rock, including:

[0034] Determine the connectivity coefficient according to the corrected surface crack intersection density and the surface crack density;

[0035] Determine the product result of the connectivity coefficient and the surface crack strength as the network connectivity index of the target rock.

[0036] In a second aspect, the present application also provides a device for determining the network connectivity index of a rock mass structure, and the device includes:

[0037] A crack identification module for identifying cracks in the target rock to obtain a crack rock image of the target rock;

[0038] A parameter determination module for determining the surface crack density, the surface crack strength, and the surface crack intersection density of the target rock according to the crack rock image;

[0039] An index calculation module for determining the network connectivity index of the target rock based on the surface crack density, the surface crack strength, and the surface crack intersection density.

[0040] In a third aspect, the present application also provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method in any one of the embodiments in the first aspect above.

[0041] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the method in any one of the embodiments in the first aspect above.

[0042] In a fifth aspect, the present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the method in any one of the embodiments in the first aspect above.

[0043] The above method, device and equipment for determining the network connectivity index of the rock mass structure identify the cracks in the target rock to obtain the crack rock image of the target rock, and then determine the surface crack density, surface crack intensity and surface crack intersection density of the target rock according to the crack rock image. Finally, based on the surface crack density, surface crack intensity and surface crack intersection density, the network connectivity index of the target rock is determined. In this method, considering the potential risks brought by the cracks in the target rock, the target rock is analyzed based on the crack rock image identified from the target rock, and then the network connectivity index is determined. This is a non-contact measurement method, which has a lower risk coefficient compared with the manual measurement method in the traditional scheme. Moreover, on the basis of obtaining the crack rock image, the cracks are quantitatively evaluated from multiple dimensions to determine the surface crack density, surface crack intensity and surface crack intersection density of the target rock, restoring the interaction of the cracks in the target rock in the real scene, objectively and quantitatively determining the influencing factors of the cracks on the target rock, and then accurately determining the network connectivity index of the target rock. In addition, the method for determining the network connectivity index in this method is clear in steps and is suitable for being deployed on various mobile computer devices to analyze the crack rock image at any time and place, and can scientifically, objectively, accurately and efficiently evaluate the network connectivity degree of the geological discontinuity surface of the fractured rock mass. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is the internal structure diagram of a computer device in an embodiment;

[0046] Figure 2 It is the schematic structural diagram of a rock bridge and a crack in an embodiment

[0047] Figure 3 It is the schematic flow diagram of the method for determining the network connectivity index of the rock mass structure in an embodiment;

[0048] Figure 4 It is the schematic flow diagram of the step for obtaining the rock physical parameters in an embodiment;

[0049] Figure 5 It is a schematic flow chart of the steps for obtaining the fracture trace skeleton image in one embodiment;

[0050] Figure 6 It is a schematic flow chart of the steps for obtaining the rock physical parameters in another embodiment;

[0051] Figure 7 It is a schematic flow chart of the steps for obtaining the rock physical parameters in another embodiment;

[0052] Figure 8 It is a schematic flow chart of the trace recognition steps in one embodiment;

[0053] Figure 9 It is a schematic flow chart of the steps for obtaining the rock physical parameters in another embodiment;

[0054] Figure 10 It is a schematic flow chart of the steps for correcting the rock physical parameters in one embodiment;

[0055] Figure 11 It is a schematic flow chart of the method for determining the connectivity index of the rock mass structure network in another embodiment;

[0056] Figure 12 It is a schematic flow chart of the method for determining the connectivity index of the rock mass structure network in another embodiment;

[0057] Figure 13 It is a visualization schematic diagram of the fracture rock image in one embodiment;

[0058] Figure 14 It is a visualization schematic diagram of the binary fracture rock image in one embodiment;

[0059] Figure 15 It is a visualization schematic diagram of the fracture trace skeleton image in one embodiment;

[0060] Figure 16 It is a visualization schematic diagram of the connected component graph in one embodiment;

[0061] Figure 17 It is a visualization schematic diagram of the endpoint search graph in one embodiment;

[0062] Figure 18 It is a visualization schematic diagram of the fracture rock image processing steps in one embodiment;

[0063] Figure 19 It is a visualization schematic diagram of the fracture rock image processing steps in another embodiment;

[0064] Figure 20 It is a visualization schematic diagram of the fracture rock image processing steps in another embodiment;

[0065] Figure 21 It is a visualization diagram of the image processing steps of fractured rock in another embodiment;

[0066] Figure 22 It is a visualization diagram of the image processing steps of fractured rock in another embodiment;

[0067] Figure 23 It is a structural block diagram of a device for determining the network connectivity index of a rock mass structure in one embodiment. Detailed implementation manners

[0068] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] The network connectivity index determination method provided by the embodiments of the present application can be applied to image processing software, and the image processing software can be deployed on a computer device. The computer device can be a server, and its internal structure diagram can be as Figure 1 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, 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. Among them, 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 data for calculating the network connectivity index of the rock mass structure. The input / output interface of the computer device is used for the processor to exchange information with external devices. 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, it realizes a method for determining the network connectivity index of a rock mass structure.

[0070] Those skilled in the art can understand that Figure 1 the structure shown in

[0071] In the field of rock mass engineering technology, a rock bridge is an undamaged part of the rock that separates geological discontinuities. Its formation depends on the connectivity of the fracture network within the rock mass and the physical properties of the rock itself. The concept of the rock bridge has always been the core of rock mass stability research.

[0072] Please refer to Figure 2 , Figure 2 which is a schematic diagram of the structure of the rock bridge and the crack. According to the potential connection paths between geological cracks, it can be divided into a positive rock bridge and a negative rock bridge. The existence of the positive rock bridge will promote the slope sliding of the rock slope, while the existence of the negative rock bridge will effectively maintain the stability of the rock slope. On this basis, related technologies use the Network Connectivity Index (NCI) to quantify the influence degree of the positive rock bridge on the rock mass strength and evaluate the rock mass stability. The larger the network connectivity index, the greater the influence degree of the positive rock bridge on the rock mass strength, and the more unstable the rock mass.

[0073] In actual scenarios, to comprehensively understand the influence of geological discontinuities on the mechanical strength of rock masses, a large number of in-situ tests and laboratory tests are usually required to collect and calibrate geological parameters, which poses a huge challenge to the timely early warning of the long-term stability of fractured rock masses and the potential failure behavior of rock bridges on rock masses.

[0074] Given the uncertainty and high-risk characteristics of the stability of rock mass dangerous rock bodies, there is an urgent need for a valuable reference solution for the stability assessment and support design of dangerous rock bodies, which can not only quickly estimate the risk of potential rock bridges and the long-term stability of rock masses, but also provide an effective solution in terms of reducing human, physical and time costs.

[0075] With the rapid development of computer vision models and image processing technologies, recognition, measurement and parameterization based on image data have become an important non-contact monitoring method. The machine vision system, with its characteristics of fast response, large amount of information, high precision and non-destructive detection, significantly reduces the time, human and financial costs and becomes a key technology to improve the efficiency of rock mass safety assessment. Using computer vision models to intelligently identify the discrete fracture network of natural rock slopes and obtaining the morphological parameters of geological discontinuities through image processing technology can provide fast and effective reference data for rock mass quality assessment.

[0076] Based on this, an integrated rock mass modeling method combining the discrete fracture network model and the geomechanical model is proposed. In the analysis of rock mass stability, by revealing potential mechanical failure paths, it provides a quantitative assessment of rock mass strength directly related to the characteristics of the rock mass structure. By comprehensively considering geological parameters such as the strength, density, length, and aperture of fractures, discrete fracture network analysis can be carried out within the framework of a rock mass classification system, thus providing a method for quantitatively describing the discontinuity of the rock mass, that is, through the connectivity index of the geological discontinuity surface network. This method not only considers pre-existing geological fractures but also the intact part of the rock, describes the natural fracture and interlocking degree of the rock mass from both structural and mechanical aspects, and further explains the controlling role of rock bridges in the stability of fractured rock masses.

[0077] The following uses specific embodiments to elaborate in detail on the technical solutions of this application and how the technical solutions of this application solve the above technical problems. These several specific embodiments below can be combined with each other, and for the same or similar concepts or processes, they may not be repeated in some embodiments. The following will describe the embodiments of this application in conjunction with the accompanying drawings.

[0078] In an exemplary embodiment, as Figure 3 shown, a method for determining the connectivity index of a rock mass structure network is provided. Taking the application of this method to Figure 1 the computer device as an example for illustration, it can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. In the embodiments of this application, this method includes the following steps:

[0079] S301, Identify the fractures in the target rock to obtain the fracture rock image of the target rock.

[0080] The computer device is remotely communicatively connected to the image acquisition device. The computer device receives the image of the target rock sent by the image acquisition device and inputs the image of the target rock into the fracture recognition model. The fracture recognition model identifies the fracture area in the target rock image to obtain at least one fracture rock image of the target rock.

[0081] Among them, the image acquisition device can be a camera, an unmanned aerial vehicle camera system, a terrestrial photogrammetry device, a monitoring system, or other devices with image acquisition functions.

[0082] In another scenario, in addition to remotely receiving the image of the target rock captured by the image acquisition device, the computer device can also remotely control the movement of the image acquisition device and control the image acquisition device to acquire the fracture rock image of the target rock.

[0083] For example, the computer device shares the shooting perspective with the image acquisition device. When the computer device recognizes the crack of the target rock, it controls the image acquisition device to capture the cracked rock image and send it to the computer device. Correspondingly, the computer device directly obtains the cracked rock image sent by the image acquisition device by controlling the image acquisition device to take pictures.

[0084] S302. Determine the surface crack density, surface crack intensity, and surface crack intersection density of the target rock according to the cracked rock image.

[0085] Among them, the surface crack density is the average number of cracks per unit area of the target rock; the surface crack intensity is the average crack length per unit area of the target rock; the surface crack intersection density is the number of intersecting cracks per unit area of the target rock. It should be noted that the surface crack density, surface crack intensity, and surface crack intersection density are all numerical values in the actual scenario and have physical dimensions with practical meanings.

[0086] Input the cracked rock image into the surface crack density recognition model, surface crack intensity recognition model, and surface crack intersection density recognition model in parallel to obtain the surface crack density output by the surface crack density recognition model, the surface crack intensity output by the surface crack intensity recognition model, and the surface crack intersection density output by the surface crack intersection density recognition model. Of course, it is also possible to use a hybrid model to recognize the cracked rock image and directly output the surface crack density, surface crack intensity, and surface crack intersection density of the target rock. In this regard, the embodiments of the present application do not make any restrictions.

[0087] S303. Determine the network connectivity index of the target rock based on the surface crack density, surface crack intensity, and surface crack intersection density.

[0088] Take the surface crack density, surface crack intensity, and surface crack intersection density as independent variables and the network connectivity index as the dependent variable, and obtain the network connectivity index of the target rock according to the logical calculation formula of the surface crack density, surface crack intensity, surface crack intersection density and the network connectivity index.

[0089] In the embodiments of the present application, the calculation formula of the network connectivity index NCI is as follows:

[0090] NCI = I 20 ×P 21 / P 20 (1)

[0091] In the above formula, I 20 is the surface crack intersection density, P 21 is the surface crack intensity, and P 20 is the surface crack density.

[0092] In the embodiments of the present application, by identifying the cracks in the target rock, a cracked rock image of the target rock is obtained. Then, based on the cracked rock image, the surface crack density, surface crack intensity, and surface crack intersection density of the target rock are determined. Finally, based on the surface crack density, surface crack intensity, and surface crack intersection density, the network connectivity index of the target rock is determined. In this method, considering the potential risks brought by the cracks in the target rock, based on the cracked rock image obtained by identifying the target rock, the target rock is analyzed, and then the network connectivity index is determined. This is a non-contact measurement method, and compared with the manual measurement method in the traditional solution, the risk coefficient is lower. Moreover, based on the obtained cracked rock image, the cracks are quantitatively evaluated from multiple dimensions, the surface crack density, surface crack intensity, and surface crack intersection density of the target rock are determined, the interaction of the cracks in the target rock in the real scene is restored, the influencing factors of the cracks on the target rock are objectively and quantitatively determined, and then the network connectivity index of the target rock is accurately determined. In addition, the method for determining the network connectivity index has clear steps, is suitable for being deployed on various mobile computer devices, and can analyze the cracked rock image at any time and place, and can scientifically, objectively, accurately, and efficiently evaluate the network connectivity degree of the geological discontinuity surface of the fractured rock mass.

[0093] Based on the obtained cracked rock image, there are various ways to obtain the crack parameters of the target rock, such as those obtained by analyzing the cracked rock image based on one or more models in the foregoing embodiments. However, considering factors such as color difference or unclear cracks in the cracked rock image, it is necessary to correct the cracked rock image to improve the accuracy of the surface crack density, surface crack intensity, and surface crack intersection density.

[0094] Then in an exemplary embodiment, as Figure 4 shown, based on the cracked rock image, determining the surface crack density, surface crack intensity, and surface crack intersection density of the target rock includes:

[0095] S401, based on the cracked rock image, obtain the crack trace skeleton image of the target rock.

[0096] According to a preset step size, slide a preset filter kernel on the cracked rock image to perform noise reduction on the cracked rock image, and obtain the crack trace skeleton image of the target rock. In this case, since the crack trace skeleton image is the cracked rock image after noise elimination processing, the cracks in the crack trace skeleton image are clearer, which is convenient for subsequent counting of the number of cracks in the crack trace skeleton diagram.

[0097] S402, obtain the proportional parameter between the image size and the actual size in the crack trace skeleton image.

[0098] Among them, the proportional parameter refers to the proportional value between the image length and the actual length in the fracture trace skeleton image, with the unit of pixels per meter. Since the fracture trace skeleton image is obtained by processing the fracture rock image, the proportional parameter is also the proportional value between the image length and the actual length in the fracture rock image.

[0099] When shooting the target rock image or the fracture rock image, due to differences such as the length complexity of the fractures, it is necessary to adaptively adjust the lens focal length of the shooting device to obtain a clearer target rock image or fracture rock image.

[0100] S403. Based on the fracture trace skeleton image and the proportional parameter, determine the areal fracture density, areal fracture intensity, and areal fracture intersection density.

[0101] Render the fracture trace skeleton image through the proportional parameter to obtain the true length of the fractures, the total number of fractures, the number of intersecting fractures, and the true area of the fracture rock area in the actual scene. Based on the above parameters, calculate the areal fracture density, areal fracture intensity, and areal fracture intersection density.

[0102] In the embodiments of the present application, based on the fracture trace skeleton image, render it through the proportional parameter to restore the interaction between the fractures of the target rock in the real scene, and objectively and accurately obtain the areal fracture density, areal fracture intensity, and areal fracture intersection density parameters related to the fractures in the target rock.

[0103] Next, through an embodiment, another implementation manner of obtaining the fracture trace skeleton image in S401 in the foregoing embodiments will be described. Then, in an exemplary embodiment, as Figure 5 shown, according to the fracture rock image, obtaining the fracture trace skeleton image of the target rock includes:

[0104] S501. Input the fracture rock image into the image recognition model to obtain a binary fracture rock image.

[0105] The image recognition model can be a deep learning computer vision model framework, such as the Unet model. In addition, the image recognition model in the embodiments of the present application adopts the Inception ResnetV2 encoder, combined with the scale hybridization module, to enhance the accuracy and robustness of the image recognition model for identifying multi-scale linear fracture structures.

[0106] Taking the Unet model as an example of the image recognition model, identify the fracture area in the fracture rock image through the Unet model and output a binary fracture rock image.

[0107] In the binary image of fractured rock, the pixel value of 255 represents the fracture structure, which appears white in the binary image of fractured rock, and the pixel value of 0 represents the background of the target rock, which appears black in the binary image of fractured rock.

[0108] S502. Perform gray-scale processing on the binary image of fractured rock to obtain a grayscale image.

[0109] Among them, the binary image of fractured rock is a three-channel image of Red Green Blue (RGB). In order to further improve the quality of the binary image of fractured rock, enable the binary image of fractured rock to display more details of the fractures, and improve the contrast of the binary image of fractured rock, perform gray-scale processing on the binary image of fractured rock to obtain a grayscale image.

[0110] In the grayscale image, the pixel value of 1 represents the fracture structure, which appears white in the grayscale image, and the pixel value of 0 represents the background of the target rock, which appears black in the grayscale image.

[0111] S503. Perform erosion processing on the grayscale image to obtain a fracture trace skeleton image.

[0112] Adopt an image morphological processing algorithm, set the number of image erosion times and the erosion structure element, and perform erosion processing on the grayscale image to obtain a fracture trace skeleton image with a fracture width of one pixel.

[0113] In the embodiments of the present application, by sequentially performing binaryzation, gray-scale processing, and morphological erosion processing on the fractured rock image, the contrast between the foreground (fractures) and the background (noise) in the fractured rock image is enhanced. The fracture trace skeleton image obtained by such processing can more clearly and definitely represent the details of the fracture structure in the fractured rock image, so as to facilitate obtaining more accurate fracture parameters.

[0114] The following further illustrates the acquisition methods of the surface fracture density, surface fracture intensity, and surface fracture intersection density in S403 in the foregoing embodiments through an embodiment. Then, in an exemplary embodiment, as Figure 6 shown, based on the fracture trace skeleton image and the scale parameter, determine the surface fracture density, surface fracture intensity, and surface fracture intersection density, including:

[0115] S601. According to the scale parameter, determine the physical area and fracture length of the fracture trace skeleton image.

[0116] Taking the scale parameter of the fracture trace skeleton image as PPM as an example, the unit of PPM is pixels per meter, that is, a value less than 1; if the image height is H and the image width is W, then the physical height H s of the fracture trace skeleton image is expressed as follows:

[0117]

[0118] The physical height W of the crack trace skeleton image s The expression is as follows:

[0119]

[0120] The expression of the physical area Area of the crack trace skeleton image is as follows:

[0121] Area = H s ×W s (4)

[0122] The crack length L of the crack trace skeleton image f The expression is as follows:

[0123]

[0124] In the above expressions, M f is the number of pixels occupied by the crack in the crack trace skeleton image.

[0125] S602. Determine the surface crack intensity according to the crack length and the physical area, determine the surface crack density according to the physical area and the number of cracks in the crack trace skeleton image; and determine the surface crack intersection density according to the physical area and the number of intersecting cracks in the trace skeleton image.

[0126] In the case of obtaining the number of cracks and the number of intersecting cracks in the crack trace skeleton image, the ratio of the crack length to the physical area can be determined as the surface crack intensity; the ratio of the number of cracks to the physical area can be determined as the surface crack density; the ratio of the number of intersecting cracks to the physical area can be determined as the surface crack intersection density.

[0127] In the embodiments of the present application, according to the proportional parameter, obtain the actual size of the crack corresponding to the crack trace skeleton image and the actual area of the crack rock area, and further determine the surface crack intensity, the surface crack density and the surface crack intersection density. The dimensions of the determined crack parameters are unified with the dimensions in the actual engineering field, which is convenient for testers to read each parameter during the test process.

[0128] The crack trace skeleton image is obtained through a series of image processing methods such as binarization, grayscale conversion, and erosion on the cracked rock image. Although such a processing process can enhance the contrast between cracks and noise in the crack trace skeleton image, it also loses the actual width of the cracks to a certain extent. Especially in multiple erosion processes, a single crack may be eroded into two or more cracks, and the number of cracks obtained in this way is obviously inaccurate. Then, the areal crack density obtained based on the number of cracks is also inaccurate. The following will illustrate another feasible method for determining the crack density through an embodiment.

[0129] In an exemplary embodiment, as Figure 7 shown, determining the areal crack density based on the physical area and the number of cracks in the crack trace skeleton image includes:

[0130] S701, obtaining the connected component graph of the cracked rock image.

[0131] Based on the crack trace skeleton image of the cracked rock image, using the 8-neighborhood search algorithm, taking any point on the non-image boundary as the center and the surrounding 8 neighborhood pixel squares as the search targets, looking for the area with a pixel value of 1 to obtain a connected complete trace, and obtaining the connected component graph.

[0132] Please refer to Figure 8 , Figure 8 which is a schematic diagram of obtaining the crack trace by the 8-neighborhood search algorithm. Figure 8 Among the nine pixel points shown, connect the pixel points with a pixel value of 1 to construct a trace.

[0133] To more clearly compare the cracks shown in the connected component graph with those shown in the crack trace skeleton image, the connected component graph can be subjected to an inversion process. After the inversion process, the cracks in the connected component graph appear black and the background appears white. Further, different cracks in the connected component graph can be randomly filled with colors to obtain a connected component graph with a white background and different cracks in different colors.

[0134] S702, correcting the number of cracks according to the number of crack groups in the connected component graph.

[0135] Among them, the number of cracks is statistically obtained by the computer device based on the crack trace skeleton image, denoted as N f ; the number of crack groups is statistically obtained by the computer device based on the connected regions in the connected component graph, denoted as N c .

[0136] In the embodiment of the present application, by comparing the number of cracks (N f ) in the crack trace skeleton image and the number of connected cracks (N c), correct the number of cracks (N f ) in the crack trace skeleton image. It can be divided into the following three cases:

[0137] (1) N f is equal to N c , which means that there is no adhesion of cracks in the crack trace skeleton image, so there is no need to correct the number of cracks. Then, N f or N c is determined as the corrected number of cracks.

[0138] (2) N f is less than N c , which means that some cracks that are too small in the crack trace skeleton image are corroded during the image processing process, resulting in some cracks being missed. Then, the number of connected cracks N c is determined as the corrected number of cracks.

[0139] (3) N f is greater than N c , which means that there are multiple cracks in the crack trace skeleton image that are decomposed from a single real crack by multiple corrosions, resulting in an overestimated number of cracks N f in the crack trace skeleton image. Then, the number of connected cracks N c is determined as the corrected number of cracks. In another scenario, it is also possible to determine the number of adhered cracks that meet the preset conditions in the connected component graph, and subtract the number of adhered cracks from the number of cracks and then add 1 to obtain the corrected number of cracks. Among them, the preset condition is: there are two cracks in the connected component graph that are relatively close to each other and are in one connected component. For example, if the number of adhered cracks is x, then the corrected number of cracks is: N f -x + 1.

[0140] S703, determine the ratio of the corrected number of cracks to the physical area as the surface crack density.

[0141] In the embodiments of the present application, according to the number of crack groups in the connected component graph, the number of cracks obtained from the crack trace skeleton image is corrected, avoiding the situation of missed statistics and misstatistics of the number of cracks in the crack trace skeleton image, improving the accuracy of the number of cracks, and further improving the accuracy of calculating the surface crack density based on the number of cracks.

[0142] When counting the cracks in the cracked rock image, factors such as the cracked rock image not being a regular rectangle and the cracks in the cracked rock image may not be complete may occur, resulting in missed detection of the number of intersecting cracks, and thus the accuracy of the surface crack intersection density is relatively low, and the accuracy of the network connectivity index obtained therefrom is relatively low. Based on this, the following is an example to illustrate the method for obtaining the network connectivity index.

[0143] In an exemplary embodiment, as Figure 9 shown, based on the surface crack density, surface crack intensity, and surface crack intersection density, determining the network connectivity index of the target rock includes:

[0144] S901, obtaining the boundary crack criterion parameters of the cracked rock image.

[0145] Among them, the boundary crack criterion parameters include the number of upper boundary crack endpoints X t of the cracked rock image, the number of lower boundary crack endpoints X b of the cracked rock image, the number of left boundary crack endpoints X l of the cracked rock image, and the number of right boundary crack endpoints X r of the cracked rock image.

[0146] S902, correcting the surface crack intersection density according to the boundary crack criterion parameters.

[0147] In an exemplary embodiment, as Figure 10 shown, correcting the surface crack intersection density according to the boundary crack criterion parameters includes:

[0148] S1001, determining the number of upper and lower boundary cracks according to the number of upper boundary crack endpoints, the number of lower boundary crack endpoints, and the height-length ratio of the cracked rock image; and determining the number of left and right boundary cracks according to the number of left boundary crack endpoints, the number of right boundary crack endpoints, and the length-height ratio of the cracked rock image.

[0149] The expression for the number of upper and lower boundary cracks is: H s (X t + X b ) / W s , where H s is the physical height of the cracked rock, W s is the physical width of the cracked rock, X t is the number of upper boundary crack endpoints of the cracked rock image, and X b is the number of lower boundary crack endpoints of the cracked rock image.

[0150] The expression for the number of left and right boundary cracks is: W s (X l + X r ) / H s , where H s is the physical height of the cracked rock, W s is the physical width of the cracked rock, X l is the number of left boundary crack endpoints of the cracked rock image, and X r is the number of right boundary crack endpoints of the cracked rock image.

[0151] S1002: Determine the ratio of the sum of the number of upper and lower boundary cracks and the number of left and right boundary cracks to the physical area of the cracked rock image as the compensation density of the surface crack intersection density.

[0152] The expression for the compensation density of the surface crack intersection density is:

[0153]

[0154] In the above expression, H s is the physical height of the cracked rock image, and W s is the physical width of the cracked rock image. H s W s is the physical area of the cracked rock image.

[0155] S1003: Correct the surface crack intersection density by superimposing the compensation density and the surface crack intersection density.

[0156] The expression for the corrected surface crack intersection density is:

[0157]

[0158] In the above expression, is the corrected surface crack intersection density, X int is the number of intersecting cracks in the non-boundary area of the cracked rock image; X int / (H s W s ) is the surface crack intersection density before correction.

[0159] In the embodiments of the present application, considering the possible shape effect and truncation effect in the statistical process, the crack intersection density on the boundary is obtained according to the boundary crack criterion parameters and superimposed on the surface crack intersection density to achieve the compensation of the surface crack intersection density and improve the accuracy of the surface crack intersection density.

[0160] S903: Determine the network connectivity index of the target rock according to the surface crack density, surface crack strength, and corrected surface crack intersection density.

[0161] Take the surface crack density, surface crack strength, and corrected surface crack intersection density as independent variables and the network connectivity index as the dependent variable, and obtain the network connectivity index of the target rock according to the logical calculation formula of the surface crack density, surface crack strength, corrected surface crack intersection density and the network connectivity index.

[0162] In the embodiments of the present application, the surface crack intersection density is corrected according to the boundary crack criterion parameters, improving the accuracy of the surface crack intersection density. On this basis, the network connectivity index determined according to the corrected surface crack intersection density is more accurate.

[0163] The following uses an embodiment to further illustrate the method for determining the network connectivity index. In an exemplary embodiment, as Figure 11 shown, according to the surface crack density, surface crack strength, and the corrected surface crack intersection density, the network connectivity index of the target rock is determined, including:

[0164] S1101. Determine the connectivity coefficient according to the corrected surface crack intersection density and the surface crack density.

[0165] By calculating the ratio of the corrected surface crack intersection density to the surface crack density, the connectivity coefficient is obtained. The connectivity coefficient is positively correlated with the network connectivity index. The larger the connectivity coefficient, the larger the network connectivity index. Correspondingly, the target rock is more unstable.

[0166] Taking the corrected surface crack intersection density as the surface crack density as P 20 as an example, the expression of the connectivity coefficient is / P 20 .

[0167] S1102. Determine the product result of the connectivity coefficient and the surface crack strength as the network connectivity index of the target rock.

[0168] Taking the corrected surface crack intersection density as the surface crack density as P 20 and the surface crack strength as P 21 as an example, the expression of the network connectivity index NCI is:

[0169]

[0170] In the embodiment of the present application, according to the corrected surface crack intersection density and the surface crack density, the connectivity coefficient of the target rock is evaluated, and then according to the product result of the connectivity coefficient and the surface crack strength, the network connectivity index of the target rock is obtained to objectively and accurately evaluate the stability of the target rock.

[0171] In an exemplary embodiment, as Figure 12 shown, a method for determining the network connectivity index of a rock mass structure is provided, and the method includes:

[0172] S1201. Identify the cracks in the target rock to obtain the crack rock image of the target rock.

[0173] Please refer to Figure 13 , Figure 13 which is a visualization schematic diagram of the crack rock image. In practical applications, the crack rock image is an RGB three-channel image.

[0174] Optionally, write the code file for the network connectivity index determination method using Matlab image processing software. When running the code file, the calculation code and image data are in the same path to facilitate the regular use of defined functions. When reading data, use the default imread() function in Matlab.

[0175] S1202. Input the cracked rock image into the image recognition model to obtain a binary cracked rock image.

[0176] Please refer to Figure 14 , Figure 14 is a visualization schematic diagram of the binary cracked rock image. In actual applications, the binary cracked rock image is an RGB three-channel image. The pixel value 255 represents the crack structure and appears white, while the pixel 0 represents the rock background and appears black.

[0177] Optionally, perform binarization using Matlab image processing software. Use the imbinarize() function to convert the three-channel image data into a single-channel binary image, with the method parameter set to "global".

[0178] S1203. Grayscale the binary cracked rock image to obtain a grayscale image.

[0179] Optionally, perform grayscaling using Matlab image processing software. Use the built-in rgb2gray() function in Matlab to process and recognize the binary cracked rock image to obtain a grayscale image.

[0180] S1204. Erode the grayscale image to obtain a crack trace skeleton image.

[0181] Please refer to Figure 15 , Figure 15 is a visualization schematic diagram of the crack trace skeleton image. In actual applications, the crack trace skeleton image is a single-channel image. The pixel value 1 represents the crack structure and appears white, while the pixel 0 represents the rock background and appears black.

[0182] Optionally, perform erosion using Matlab image processing software. Use the built-in bwmorph() function in Matlab to implement it, with the method parameter set to "thin" and the erosion times set to 40.

[0183] S1205. Obtain the scale parameter between the image size and the actual size in the crack trace skeleton image.

[0184] S1206. Determine the physical area and crack length of the crack trace skeleton image according to the scale parameter.

[0185] S1207. Obtain the connected component map of the cracked rock image; correct the number of cracks according to the number of crack groups in the connected component map; determine the surface crack density as the ratio of the corrected number of cracks to the physical area.

[0186] Please refer to Figure 16 , Figure 16 is a visualization schematic diagram of the connected component map. In practical applications, different cracks in the connected component map are randomly filled with different colors, and the rock background is white.

[0187] Moreover, when counting the number of crack groups in the connected component map, all pixel points on the boundary of the connected component image are traversed for endpoint search, as Figure 17 shown, Figure 17 is a visualization schematic diagram of the endpoint search map; if the connected trace contains only one crack, then set the endpoints of the crack as circles; if there are two connected cracks and they meet, set the meeting point of the two cracks as a cross; if there are two edges and they intersect in the connected trace, set the intersection point of the two cracks as a pentagram.

[0188] Optionally, use Matlab image processing software to perform connected region recognition and processing. The connected component map of the fracture network structure is extracted using the bwlabel() function, and the function label2rgb() is used to count the number of connected components and number them.

[0189] S1208. Determine the surface crack density according to the physical area and the number of cracks in the crack trace skeleton image; and determine the surface crack intersection density according to the physical area and the number of intersecting cracks in the trace skeleton image.

[0190] S1209. Obtain the boundary crack criterion parameters of the cracked rock image.

[0191] Among them, the boundary crack criterion parameters include the number of upper boundary crack endpoints, the number of lower boundary crack endpoints, the number of left boundary crack endpoints, and the number of right boundary crack endpoints.

[0192] S1210. Determine the number of upper and lower boundary cracks according to the number of upper boundary crack endpoints, the number of lower boundary crack endpoints, and the height-length ratio of the cracked rock image; and determine the number of left and right boundary cracks according to the number of left boundary crack endpoints, the number of right boundary crack endpoints, and the length-height ratio of the cracked rock image.

[0193] S1211. Determine the compensation density of the surface crack intersection density as the ratio of the sum of the number of upper and lower boundary cracks and the number of left and right boundary cracks to the physical area of the cracked rock image.

[0194] S1212. Correct the surface crack intersection density by superimposing the compensation density and the surface crack intersection density.

[0195] S1213. Determine the connectivity coefficient according to the corrected surface crack intersection density and surface crack density.

[0196] S1214. Determine the product of the connectivity coefficient and the surface crack strength as the network connectivity index of the target rock.

[0197] In the embodiment of the present application, by identifying the cracks in the target rock, the crack rock image of the target rock is obtained. Then, according to the crack rock image, the surface crack density, surface crack strength and surface crack intersection density of the target rock are determined. Finally, based on the surface crack density, surface crack strength and surface crack intersection density, the network connectivity index of the target rock is determined. In this method, considering the potential risks brought by the cracks in the target rock, based on the crack rock image obtained by identifying the target rock, the target rock is analyzed, and then the network connectivity index is determined. This is a non-contact measurement method. Compared with the manual measurement method in the traditional scheme, the risk coefficient is lower. Moreover, on the basis of obtaining the crack rock image, the cracks are quantitatively evaluated from multiple dimensions to determine the surface crack density, surface crack strength and surface crack intersection density of the target rock, restoring the interaction of the cracks in the target rock in the real scene, objectively and quantitatively determining the influencing factors of the cracks on the target rock, and then accurately determining the network connectivity index of the target rock. In addition, the method for determining the network connectivity index in this method is clear in steps, suitable for being deployed on various mobile computer devices to analyze the crack rock image at any time and place, and can scientifically, objectively, accurately and efficiently evaluate the network connectivity degree of the geological discontinuity surface of the fractured rock mass.

[0198] To further verify the method for determining the network connectivity index provided in the embodiment of the present application, rock samples were collected in a study area at 36.26516804°N, 116.94287360°E and an altitude of 210m, and were processed using the method for determining the network connectivity index. The lithology of this study area is mainly late Mesozoic diorite and Archean granite gneiss, mostly high-steep rock slopes, with strong weathering and obvious erosion of geological outcrops. A large number of secondary weathering fissures are distributed on the rock mass surface. The rock mass as a whole is in a massive structure, and the tectonic action is extremely complex. A large number of joints, fissures and faults are distributed on the rock mass surface, and the hydrogeological conditions are good. Many fissure water outcrops and algae can be seen.

[0199] In the embodiment of the present application, a total of five crack rock images were compared, as Figures 18 to 22 shown, for Figures 18 to 22Any of the groups of images shown: (a) is an image of fractured rock; (b) is a binary image of fractured rock; (c) is a connected component map based on the binary recognition results of the statistical window; (d) is an overlay map of the fracture trace skeleton image and the endpoint search map after being processed by the image morphology algorithm. The fracture statistical parameters and network connectivity index obtained after image processing are shown in Table 1. Table 1 is the statistical result of the physical parameters of five rocks.

[0200] Table 1

[0201]

[0202] In the embodiments of the present application, the trace network identified based on the computer vision model can maximize the restoration of the interaction form of the real fracture network in the actual site. Different from the fracture evaluation system that statistically counts the number and length of fractures from the perspectives of lines, planes, and volumes based on a simplified trace network model, it reduces the workload and subjectivity brought by manual data processing to a certain extent, and provides a scientific, objective, accurate, and efficient method for evaluating the network connectivity degree of the geological discontinuity surface of fractured rock masses.

[0203] In summary, the method for determining the network connectivity index of the rock mass structure proposed in the embodiments of the present application can quickly and accurately evaluate the network connectivity index and other key fracture statistical parameters by using the image data of fractured rock. This non-contact and fast way of obtaining the fracture surface density (P 20 ), surface strength (P 21 ), and surface intersection density (I 20 ) significantly reduces the workload of geological fracture statistical measurement at the rock mass engineering site. In addition, by calculating the corrected surface fracture intersection density, the network connectivity index (NCI) of the geological discontinuity surface is obtained, which improves the timeliness of geological discontinuity statistical measurement, avoids human errors caused by long-term fatigue operation, and measurement errors caused by truncation effect and shape effect during the statistical process. While improving work efficiency, it ensures the real-time nature of data and the accuracy of fracture recognition, and reduces the on-site work risks.

[0204] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0205] Based on the same inventive concept, an embodiment of the present application further provides a network connectivity index determination device for implementing the above-mentioned network connectivity index determination method. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the network connectivity index determination device provided below can refer to the limitations on the network connectivity index determination method in the above text, and will not be repeated here.

[0206] In an exemplary embodiment, as Figure 23 shown, a device for determining the network connectivity index of a rock mass structure is provided, including: a crack identification module 2301, a parameter determination module 2302, and an index calculation module 2303, where:

[0207] The crack identification module 2301 is configured to identify cracks in a target rock to obtain a crack rock image of the target rock;

[0208] The parameter determination module 2302 is configured to determine the surface crack density, surface crack intensity, and surface crack intersection density of the target rock according to the crack rock image;

[0209] The index calculation module 2303 is configured to determine the network connectivity index of the target rock based on the surface crack density, surface crack intensity, and surface crack intersection density.

[0210] In an exemplary embodiment, the parameter determination module 2302 includes a trace acquisition unit, a ratio acquisition unit, and a parameter determination unit, where:

[0211] The trace acquisition unit is configured to obtain a crack trace skeleton image of the target rock according to the crack rock image;

[0212] The ratio acquisition unit is configured to obtain a ratio parameter between the image size and the actual size in the crack trace skeleton image;

[0213] A parameter determination unit, configured to determine the areal fracture density, areal fracture intensity, and areal fracture intersection density based on the fracture trace skeleton image and the scale parameter.

[0214] In an exemplary embodiment, the trace acquisition unit includes a binarization processing subunit, a grayscale processing subunit, and an erosion subunit, where:

[0215] The binarization processing subunit is configured to input the fractured rock image into an image recognition model to obtain a binarized fractured rock image;

[0216] The grayscale processing subunit is configured to perform grayscale processing on the binarized fractured rock image to obtain a grayscale image;

[0217] The erosion subunit is configured to perform erosion processing on the grayscale image to obtain a fracture trace skeleton image.

[0218] In an exemplary embodiment, the parameter determination unit includes a first acquisition subunit and a second acquisition subunit, where:

[0219] The first acquisition subunit is configured to determine the physical area and the fracture length of the fracture trace skeleton image according to the scale parameter;

[0220] The second acquisition subunit is configured to determine the areal fracture intensity according to the fracture length and the physical area, determine the areal fracture density according to the physical area and the number of fractures in the fracture trace skeleton image; and determine the areal fracture intersection density according to the physical area and the number of intersecting fractures in the trace skeleton image.

[0221] In an exemplary embodiment, the second acquisition subunit is further configured to obtain a connected component map of the fractured rock image; correct the number of fractures according to the number of fracture groups in the connected component map; and determine the ratio of the corrected number of fractures to the physical area as the areal fracture density.

[0222] In an exemplary embodiment, the index calculation module 2303 includes a criterion acquisition unit, a density correction unit, and an index acquisition unit, where:

[0223] The criterion acquisition unit is configured to obtain the boundary fracture criterion parameter of the fractured rock image;

[0224] The density correction unit is configured to correct the areal fracture intersection density according to the boundary fracture criterion parameter;

[0225] The index acquisition unit is configured to determine the network connectivity index of the target rock according to the areal fracture density, areal fracture intensity, and the corrected areal fracture intersection density.

[0226] In an exemplary embodiment, a density correction unit includes a first determination subunit, a second determination subunit, and a density superposition subunit, where:

[0227] The first determination subunit is configured to determine the number of upper and lower boundary cracks according to the number of upper boundary crack endpoints, the number of lower boundary crack endpoints, and the height-length ratio of the crack rock image; and determine the number of left and right boundary cracks according to the number of left boundary crack endpoints, the number of right boundary crack endpoints, and the length-height ratio of the crack rock image;

[0228] The second determination subunit is configured to determine the compensated density of the surface crack intersection density by taking the ratio of the sum of the number of upper and lower boundary cracks and the number of left and right boundary cracks to the physical area of the crack rock image;

[0229] The density superposition subunit is configured to correct the surface crack intersection density by superimposing the compensated density and the surface crack intersection density.

[0230] In an exemplary embodiment, an exponent acquisition unit includes a coefficient determination subunit and a connectivity calculation subunit, where:

[0231] The coefficient determination subunit is configured to determine a connectivity coefficient according to the corrected surface crack intersection density and the surface crack density;

[0232] The connectivity calculation subunit is configured to determine the network connectivity exponent of the target rock by taking the product result of the connectivity coefficient and the surface crack strength.

[0233] Each module in the above-described apparatus for determining the network connectivity exponent of the rock mass structure can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in a computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0234] In an exemplary embodiment, a computer device is provided, including a memory and a processor, where a computer program is stored in the memory, and when the processor executes the computer program, the steps in the above-described method embodiments are implemented.

[0235] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above-described method embodiments are implemented.

[0236] 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-described method embodiments are implemented.

[0237] 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 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 need to comply with relevant regulations.

[0238] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. 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), magnetoresistive 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 can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., and are not limited thereto.

[0239] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope described in this specification.

[0240] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for determining the network connectivity index of a rock mass structure, characterized in that, The method includes: Identifying the fractures in the target rock to obtain the fracture rock image of the target rock; Determining the surface fracture density, surface fracture intensity, and surface fracture intersection density of the target rock according to the fracture rock image; Correcting the surface fracture intersection density according to the boundary fracture criterion parameters of the fracture rock image; the boundary fracture criterion parameters include the number of upper boundary fracture endpoints, the number of lower boundary fracture endpoints, the number of left boundary fracture endpoints, and the number of right boundary fracture endpoints; Determining the network connectivity index of the target rock according to the surface fracture density, the surface fracture intensity, and the corrected surface fracture intersection density.

2. The method according to claim 1, wherein The determining the surface fracture density, surface fracture intensity, and surface fracture intersection density of the target rock according to the fracture rock image includes: Obtaining the fracture trace skeleton image of the target rock according to the fracture rock image; Obtaining the ratio parameter between the image size and the actual size in the fracture trace skeleton image; Determining the surface fracture density, the surface fracture intensity, and the surface fracture intersection density based on the fracture trace skeleton image and the ratio parameter.

3. The method according to claim 2, characterized in that The obtaining the fracture trace skeleton image of the target rock according to the fracture rock image includes: Inputting the fracture rock image into an image recognition model to obtain a binary fracture rock image; Performing gray-scale processing on the binary fracture rock image to obtain a gray-scale image; Performing erosion processing on the gray-scale image to obtain the fracture trace skeleton image.

4. The method according to claim 2, wherein The determining the surface fracture density, the surface fracture intensity, and the surface fracture intersection density based on the fracture trace skeleton image and the ratio parameter includes: Determining the physical area and fracture length of the fracture trace skeleton image according to the ratio parameter; Determining the surface fracture intensity according to the fracture length and the physical area, determining the surface fracture density according to the physical area and the number of fractures in the fracture trace skeleton image; and determining the surface fracture intersection density according to the physical area and the number of intersecting fractures in the trace skeleton image.

5. The method according to claim 4, characterized in that, The determining the surface fracture density according to the physical area and the number of fractures in the fracture trace skeleton image includes: Obtaining the connected component map of the fracture rock image; the connected component map is determined based on the fracture trace skeleton image, using the 8-neighborhood search algorithm, taking any point on the non-image boundary as the center, and the surrounding 8 neighborhood pixel squares as the search target to find the area with a pixel value of 1 to obtain the connected complete trace; Correcting the number of fractures according to the number of fracture groups in the connected component map; Determining the ratio of the corrected number of fractures to the physical area as the surface fracture density.

6. The method according to any one of claims 1-5, characterized in that The correcting the surface fracture intersection density according to the boundary fracture criterion parameters includes: Determine the number of upper and lower boundary cracks based on the number of upper boundary crack endpoints, the number of lower boundary crack endpoints, and the height-length ratio of the cracked rock image; and determine the number of left and right boundary cracks based on the number of left boundary crack endpoints, the number of right boundary crack endpoints, and the length-height ratio of the cracked rock image; Determine the compensation density of the surface crack intersection density as the ratio of the sum of the number of upper and lower boundary cracks and the number of left and right boundary cracks to the physical area of the cracked rock image; Correct the surface crack intersection density by superimposing the compensation density and the surface crack intersection density.

7. The method according to any one of claims 1-5, characterized in that, The determining of the network connectivity index of the target rock according to the surface crack density, the surface crack intensity, and the corrected surface crack intersection density includes: Determine the connectivity coefficient according to the corrected surface crack intersection density and the surface crack density; Determine the product result of the connectivity coefficient and the surface crack intensity as the network connectivity index of the target rock.

8. An apparatus for determining the connectivity index of a rock mass structure network, characterized in that, The device includes: A crack identification module for identifying cracks in the target rock to obtain a cracked rock image of the target rock; A parameter determination module for determining the surface crack density, the surface crack intensity, and the surface crack intersection density of the target rock according to the cracked rock image; An index calculation module for correcting the surface crack intersection density according to the boundary crack criterion parameters of the cracked rock image; the boundary crack criterion parameters include the number of upper boundary crack endpoints, the number of lower boundary crack endpoints, the number of left boundary crack endpoints, and the number of right boundary crack endpoints; determine the network connectivity index of the target rock according to the surface crack density, the surface crack intensity, and the corrected surface crack intersection density.

9. The device according to claim 8, wherein, The parameter determination module includes: A trace acquisition unit for acquiring a crack trace skeleton image of the target rock according to the cracked rock image; A ratio acquisition unit for acquiring a ratio parameter between the image size and the actual size in the crack trace skeleton image; A parameter determination unit for determining the surface crack density, the surface crack intensity, and the surface crack intersection density based on the crack trace skeleton image and the ratio parameter.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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