Internal fracture characteristic evaluation method and system based on surface fracture distribution

Abstract

The invention relates to the technical field of biological erosion detection, and discloses an internal fracture characteristic evaluation method based on surface fracture distribution, and the method comprises the steps: generating a three-dimensional initial roughness model; generating a fracture erosion surface feature distribution model; and calculating the biological erosion fractal dimension by adopting a fractal geometry method, and identifying the biological erosion mode of the fracture in combination with a permeability calculation result. Compared with the prior art that the identification means for the rock mass fracture biological erosion mode is rough, and especially in a complex environment such as a high-humidity or high-biological-activity area, the technical problem that accurate identification for the erosion mode cannot be achieved is solved, due to the fact that the multi-parameter model combining the fractal geometric method and the permeability is introduced, the identification accuracy of the rock mass fracture biological erosion mode is improved, and the identification accuracy of the rock mass fracture biological erosion mode is improved. According to the method, the erosion depth, roughness and fractal dimension features of the fracture surface are quantified, and accurate classification of fracture erosion modes is realized, so that the problem of inaccurate identification in the prior art is avoided, and the identification precision and reliability of fracture biological erosion are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of biological erosion detection, and in particular relates to a method and system for evaluating internal crack characteristics based on surface crack distribution. Background Art

[0002] At present, the biological erosion of rock fissures has a significant impact on the stability and durability of rock structures, especially in environments with high humidity and strong biological activity, where the biological erosion of fissures accelerates the degradation of rock masses. However, the existing rock fissure biological erosion pattern recognition technology has the problem of insufficient accuracy. For example, traditional recognition methods usually rely on manual observation and simple physical parameter measurement, which makes it difficult to accurately quantify the microscopic characteristics and erosion patterns of the fissure surface, resulting in inaccurate identification of the degree of fissure biological erosion, and cannot fully meet the needs of fissure biological erosion monitoring and early warning in complex environments. Therefore, there is an urgent need for a method that can still achieve high-precision fissure biological erosion pattern recognition in complex environments. This method needs to be able to accurately capture the microscopic characteristics of the fissure surface, quantify the degree of biological erosion, and distinguish different types of erosion patterns, so as to improve the recognition accuracy and reliability of fissure biological erosion, and provide effective technical support for geological disaster early warning, rock mass engineering safety evaluation, etc. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a method for evaluating internal crack characteristics based on surface crack distribution, aiming to solve the technical problem that the existing methods for identifying biological erosion patterns of rock cracks are relatively rough, especially in complex environments, such as high humidity or areas with strong biological activity, and it is impossible to accurately identify erosion patterns.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides an internal crack feature evaluation method based on surface crack distribution.

[0005] The internal crack characteristic evaluation method based on surface crack distribution includes:

[0006] Step S10: Scanning the fractured rock sample under biological erosion conditions by a three-dimensional laser scanning device to collect point cloud data of the fracture surface, and performing denoising and smoothing on the collected point cloud data to obtain pre-processed point cloud data;

[0007] A three-dimensional initial roughness model of the fracture surface is generated based on the preprocessed point cloud data to represent the microscopic topographic characteristics of the fracture surface in a biological erosion environment, including roughness height characteristic information Z, surface waviness characteristic information X, tilt angle distribution information C, and surface area change rate information V;

[0008] Step S20: Divide the crack surface into multiple sub-regions in the three-dimensional initial roughness model, and calculate the local erosion depth value h in each sub-region i. i and the local roughness value r i , and according to the local erosion depth value h i and the local roughness value r i Generate a fracture erosion surface characteristic distribution model, which is used to quantify the erosion characteristics of the fracture surface at different locations;

[0009] Step S30: Based on the crack erosion surface characteristic distribution model, a fractal geometry method is used in combination with the local erosion depth value h i and the local roughness value r i Calculate the bioerosion fractal dimension D of the cracks using the following formula:

[0010]

[0011] Where ∈ is the edge length, N(∈) is the number of grids required to cover the crack surface with grids of edge length ∈, α is the influence coefficient, which is used to balance the weight of local erosion characteristics in the fractal dimension calculation, D is the bioerosion fractal dimension used to quantitatively describe the complexity of the crack surface structure, and n is the number of sub-regions;

[0012] Step S40: Calculate the permeability K of the crack, the calculation formula is:

[0013]

[0014] Where Q is the flow rate in the fracture, μ is the fluid viscosity, L is the fracture length, A is the fracture cross-sectional area, and ΔP is the fluid pressure difference;

[0015] Step S50: combining the numerical ranges of the permeability K and the fractal dimension D to identify the biological erosion mode of the fracture, including three modes: uniform erosion, channel erosion and surface erosion;

[0016] Set the permeability threshold K th1 and K th2 , where K th2 >K th1 , set the fractal dimension threshold D th1 and D th2 , where D th2 >D th1 ;

[0017] When K<K th1 And D <D th1 When , the erosion mode is determined to be uniform erosion;

[0018] When K th1 ≤K≤K th2 And Dth1 ≤D≤D th2 , the erosion mode is determined to be channel erosion;

[0019] When K>K th1 And D>D th1 When , the erosion mode is determined to be surface erosion.

[0020] Preferably, in step S20, the local erosion depth value h is calculated in each sub-region i. i The formula used is:

[0021] h i =max(z i )-min(z i )

[0022] Among them, z i Indicates the height value of each point in the sub-area, max(z i ) and min(z i ) are the maximum height and minimum height in the sub-area respectively.

[0023] Preferably, in step S20, the local roughness value r is calculated in each sub-region i. i The formula used is:

[0024]

[0025] Where n is the number of sub-regions, j is the jth point, and z j is the height value of the jth point in sub-region i, is the average height of the sub-area.

[0026] Preferably, in step S40, the crack cross-sectional area A=b×L, wherein b is the equivalent opening, which reflects the average opening of the crack under erosion conditions, and its value is calculated by the local erosion depth value h i and the local roughness value r i The combined formula is used to estimate:

[0027] Preferably, in step S10, the frequency of collecting point cloud data is 1000 times per second.

[0028] Preferably, in step S20, the sub-areas are divided into square areas with a grid size of 1 mm.

[0029] Preferably, in step S50, the permeability threshold K th1 and K th2 The settings are adjusted according to the rock type and experimental environment.

[0030] The present invention also provides an internal crack feature evaluation system based on surface crack distribution, comprising:

[0031] A data acquisition module is used to scan the fractured rock samples under biological erosion conditions by means of a three-dimensional laser scanning device to collect point cloud data of the fracture surface, and to perform denoising and smoothing on the collected point cloud data to obtain pre-processed point cloud data;

[0032] The feature distribution model generation module is used to divide the crack surface into multiple sub-areas in the three-dimensional initial roughness model and calculate the local erosion depth value h in each sub-area i. i and the local roughness value r i , and according to the local erosion depth value h i and the local roughness value r i Generate a fracture erosion surface characteristic distribution model, which is used to quantify the erosion characteristics of the fracture surface at different locations;

[0033] The fractal dimension calculation module is used to calculate the local erosion depth value h according to the distribution model of the crack erosion surface characteristics using fractal geometry methods. i and the local roughness value r i Calculate the bioerosion fractal dimension D of the cracks using the following formula:

[0034]

[0035] Where ∈ is the edge length, N(∈) is the number of grids required to cover the crack surface with grids of edge length ∈, α is the influence coefficient, which is used to balance the weight of local erosion characteristics in the fractal dimension calculation, D is the bioerosion fractal dimension used to quantitatively describe the complexity of the crack surface structure, and n is the number of sub-regions;

[0036] The permeability calculation module is used to calculate the permeability K of the crack. The calculation formula is:

[0037]

[0038] Where Q is the flow rate in the fracture, μ is the fluid viscosity, L is the fracture length, A is the fracture cross-sectional area, and ΔP is the fluid pressure difference;

[0039] The erosion pattern recognition module is used to combine the numerical range of permeability K and fractal dimension D for the biological erosion mode of the fracture, including three modes: uniform erosion, channel erosion and surface erosion;

[0040] Set the permeability threshold K th1 and K th2 , where K th2 >K th1 , set the fractal dimension threshold D th1 and D th2 , where D th2 >Dth1 ;

[0041] When K<K th1 And D <D th1 When , the erosion mode is determined to be uniform erosion;

[0042] When K th1 ≤K≤K th2 And D th1 ≤D≤D th2 , the erosion mode is determined to be channel erosion;

[0043] When K>K th1 And D>D th1 When , the erosion mode is determined to be surface erosion.

[0044] In addition, the present invention also provides an internal crack feature assessment device based on surface crack distribution, comprising a memory, a processor, and an internal crack feature assessment program based on surface crack distribution stored in the memory and executable on the processor, wherein the internal crack feature assessment program based on surface crack distribution implements the internal crack feature assessment method based on surface crack distribution when executed by the processor.

[0045] The present invention also provides a computer program product, comprising an internal crack feature evaluation program based on surface crack distribution, wherein the internal crack feature evaluation program based on surface crack distribution, when executed by a processor, implements the internal crack feature evaluation method based on surface crack distribution.

[0046] The beneficial effect of the present invention is that compared with the prior art, which has a relatively rough means of identifying biological erosion patterns in rock fissures, especially in complex environments, such as areas with high humidity or strong biological activity, the technical problem of being unable to achieve accurate identification of erosion patterns is solved. This application introduces a multi-parameter model combining fractal geometry methods and permeability to quantify the erosion depth, roughness and fractal dimension characteristics of the fracture surface, thereby achieving accurate classification of fracture erosion patterns, thereby avoiding the problem of inaccurate identification in the prior art and improving the accuracy and reliability of identifying biological erosion in fractures. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 This is a schematic flow chart of a first embodiment of a method for evaluating internal crack characteristics based on surface crack distribution according to the present invention.

[0049] Figure 2 This is a schematic diagram of equipment for a method for evaluating internal crack characteristics based on surface crack distribution according to the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Embodiment 1: Figure 1 2 is a flow chart of the first embodiment of the internal crack characteristic evaluation method based on surface crack distribution of the present invention, which proposes the first embodiment of the internal crack characteristic evaluation method based on surface crack distribution of the present invention.

[0052] In a first embodiment, the internal crack feature evaluation method based on surface crack distribution includes:

[0053] Step S10: Scanning the fractured rock sample under biological erosion conditions by a three-dimensional laser scanning device to collect point cloud data of the fracture surface, and performing denoising and smoothing on the collected point cloud data to obtain pre-processed point cloud data;

[0054] A three-dimensional initial roughness model of the fracture surface is generated based on the preprocessed point cloud data to represent the microscopic topographic characteristics of the fracture surface in a biological erosion environment, including roughness height characteristic information Z, surface waviness characteristic information X, tilt angle distribution information C, and surface area change rate information V;

[0055] It should be noted that the point cloud data collection and preprocessing process mentioned in step S10 is intended to accurately capture the microscopic features of the crack surface through a three-dimensional laser scanning device. In order to improve the accuracy and reliability of the model, the collected data was denoised and smoothed to eliminate measurement errors and environmental interference, so as to obtain more accurate geometric information of the crack surface. These processed point cloud data provide basic data support for the subsequent construction of a three-dimensional initial roughness model. In the three-dimensional initial roughness model, the micro-topographic feature information contained mainly includes: roughness height feature information Z: used to describe the height difference of the crack surface at different positions, reflecting the longitudinal erosion depth of the crack; surface waviness feature information X: used to represent the wavy undulating features of the crack surface, which helps to identify surface irregularities caused by erosion; inclination angle distribution information C: used to quantify the inclination angle of each area of ​​the crack surface and describe the local slope of the crack structure; surface area change rate information V: used to reflect the rate of change of the crack surface area and quantify the impact of biological erosion on the crack surface structure;

[0056] The extraction and analysis of these microscopic characteristic information enables more accurate identification of the bioerosion patterns of fractures in subsequent steps and provides a reliable data basis for the calculation of permeability and fractal dimension.

[0057] Step S20: Divide the crack surface into multiple sub-regions in the three-dimensional initial roughness model, and calculate the local erosion depth value h in each sub-region i. i and the local roughness value r i , and according to the local erosion depth value h i and the local roughness value r i Generate a fracture erosion surface characteristic distribution model, which is used to quantify the erosion characteristics of the fracture surface at different locations;

[0058] It should be noted that in step S20, the crack surface is divided into multiple sub-areas, and the local erosion depth value h is calculated in each sub-area. i and the local roughness value r i The purpose of this process is to more finely characterize the local erosion characteristics of the crack surface. By dividing the crack surface into multiple small areas, the erosion differences at different locations of the crack can be effectively captured. The local erosion depth value h i The calculation of the local roughness value r is used to quantify the longitudinal erosion depth of the crack surface in a certain sub-region, reflecting the erosion depth characteristics of the cracks in this region. i The calculation of is used to describe the roughness of the fracture surface within the sub-region and capture the surface irregularities under the action of bioerosion.

[0059] By calculating and integrating these local features, a distribution model of crack erosion surface characteristics is generated. This distribution model can quantify the erosion characteristics of the crack surface at different locations, providing richer data support for subsequent bioerosion fractal dimension calculations and helping to accurately identify crack bioerosion patterns.

[0060] Step S30: Based on the crack erosion surface characteristic distribution model, a fractal geometry method is used in combination with the local erosion depth value h i and the local roughness value r i Calculate the bioerosion fractal dimension D of the cracks using the following formula:

[0061]

[0062] Where ∈ is the edge length, N(∈) is the number of grids required to cover the crack surface with grids of edge length ∈, α is the influence coefficient, which is used to balance the weight of local erosion characteristics in the fractal dimension calculation, D is the bioerosion fractal dimension used to quantitatively describe the complexity of the crack surface structure, and n is the number of sub-regions;

[0063] It should be noted that the formula for calculating the bioerosion fractal dimension D in step S30 combines fractal geometry and local erosion characteristics to more accurately quantify the complexity of the crack surface structure.

[0064] It should be understood that the It represents the fractal characteristics of the crack surface. N(∈) is the number of grids required to cover the crack surface with a grid with an edge length of ∈. As the grid becomes smaller, the calculation result of the fractal dimension can reflect the self-similarity and complexity of the crack surface structure. This part is used to quantify the erosion characteristics of the crack at different locations, so that the fractal dimension D is not only an overall measure of the surface morphology, but also reflects the local changes of the crack surface under the action of biological erosion.

[0065] Step S40: Calculate the permeability K of the crack, the calculation formula is:

[0066]

[0067] Where Q is the flow rate in the fracture, μ is the fluid viscosity, L is the fracture length, A is the fracture cross-sectional area, and ΔP is the fluid pressure difference;

[0068] It should be noted that based on the fluid flow characteristics inside the fracture, the geometric characteristics inside the fracture are combined with the physical properties of the fluid to calculate the conductivity of the fracture, where Q is the flow rate in the fracture, which represents the volume of fluid passing through the fracture per unit time; μ is the fluid viscosity, which is used to characterize the flow resistance of the fluid in the fracture; L is the fracture length, which is used to define the length of the flow path; A is the cross-sectional area of ​​the fracture, which reflects the size of the circulation space inside the fracture; and AP is the fluid pressure difference, which is used to drive the fluid to flow in the fracture.

[0069] The permeability calculated by this formula can reflect the conductivity of the fracture. The larger the value, the better the conductivity of the fracture. Combining the permeability and fractal dimension data, the bioerosion pattern of the fracture can be more accurately identified, thereby further quantifying the changing characteristics of the fracture structure.

[0070] Step S50: combining the numerical ranges of the permeability K and the fractal dimension D to identify the biological erosion mode of the fracture, including three modes: uniform erosion, channel erosion and surface erosion;

[0071] Set the permeability threshold K th1 and K th2 , where K th2 >K th1 , set the fractal dimension threshold D th1 and D th2 , where D th2 >D th1 ;

[0072] When K<K th1 And D <D th1 When , the erosion mode is determined to be uniform erosion;

[0073] When K th1 ≤K≤K th2 And D th1 ≤D≤D th2 , the erosion mode is determined to be channel erosion;

[0074] When K>K th1 And D>D th1 When , the erosion mode is determined to be surface erosion.

[0075] It should be noted that in step S50, the numerical ranges of the permeability K and the fractal dimension D are combined to identify the biological erosion mode of the fissure, and different thresholds are set to distinguish uniform erosion, channel erosion and surface erosion. The purpose of this step is to determine the performance of the fissure under different erosion modes through the distribution of specific values, so as to more accurately quantify the changes in the surface structure of the fissure.

[0076] It should be understood that when the permeability K and the fractal dimension D are both low, there are fewer flow channels inside the fracture, the surface structure is relatively flat, and the degree of erosion is relatively light, so it is classified as uniform erosion.

[0077] It should be understood that when the permeability K and the fractal dimension D are in the medium range, obvious flow channels are formed in the cracks and the surface structure shows a certain complexity. In this case, the crack erosion mode is judged to be channel erosion.

[0078] For example, if the actual measured permeability of a fracture is K = 0.5 and the fractal dimension is D = 1.8, assuming the threshold is K th1 =0.3, K th2 =0.7, D th1 =1.5, D th1 =2.0, the permeability and fractal dimension of this crack are both in the middle range, so its erosion mode can be determined to be channel erosion. This classification method can more accurately identify the erosion mode of cracks under different conditions, providing a reliable basis for subsequent analysis and processing.

[0079] In addition, the internal crack feature assessment system based on surface crack distribution provided by the present invention adopts an internal crack feature assessment method based on surface crack distribution in the above embodiment, which can solve the technical problem of internal crack feature assessment based on surface crack distribution. Compared with the prior art, the beneficial effects of the internal crack feature assessment system based on surface crack distribution provided by the present invention are the same as the beneficial effects of the internal crack feature assessment method based on surface crack distribution provided by the above embodiment, and the other technical features of the internal crack feature assessment system based on surface crack distribution are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0080] The present invention provides an internal crack feature evaluation device based on surface crack distribution, please refer to Figure 2, an internal crack feature evaluation device based on surface crack distribution includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the internal crack feature evaluation method based on surface crack distribution in the above-mentioned embodiment 1. An internal crack feature evaluation device based on surface crack distribution in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. An internal crack feature evaluation device based on surface crack distribution is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. An internal crack feature evaluation device based on surface crack distribution may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. Various programs and data required for the operation of an internal crack feature evaluation device based on surface crack distribution are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow an internal crack feature evaluation device based on surface crack distribution to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an internal crack feature evaluation device based on surface crack distribution with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0081] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for evaluating internal crack characteristics based on surface crack distribution. The computer program product provided by the present invention can solve the technical problem of evaluating internal crack characteristics based on surface crack distribution. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the method for evaluating internal crack characteristics based on surface crack distribution provided by the above-mentioned embodiment, which will not be described in detail here.

[0082] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0083] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0084] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for evaluating internal crack characteristics based on surface crack distribution, characterized in that: The internal crack characteristic evaluation method based on surface crack distribution includes: Step S10: Scanning the fractured rock sample under biological erosion conditions by a three-dimensional laser scanning device to collect point cloud data of the fracture surface, and performing denoising and smoothing on the collected point cloud data to obtain pre-processed point cloud data; A three-dimensional initial roughness model of the fracture surface is generated based on the preprocessed point cloud data to represent the microscopic topographic characteristics of the fracture surface in a biological erosion environment, including roughness height characteristic information Z, surface waviness characteristic information X, tilt angle distribution information C, and surface area change rate information V; Step S20: Divide the crack surface into multiple sub-regions in the three-dimensional initial roughness model, and calculate the local erosion depth value h in each sub-region i. i and the local roughness value r i , and according to the local erosion depth value h i and the local roughness value r i Generate a fracture erosion surface characteristic distribution model, which is used to quantify the erosion characteristics of the fracture surface at different locations; Step S30: Based on the crack erosion surface characteristic distribution model, a fractal geometry method is used in combination with the local erosion depth value h i and the local roughness value r i Calculate the bioerosion fractal dimension D of the cracks using the following formula: Where ∈ is the edge length, N(∈) is the number of grids required to cover the crack surface with grids of edge length ∈, α is the influence coefficient, which is used to balance the weight of local erosion characteristics in the fractal dimension calculation, D is the bioerosion fractal dimension used to quantitatively describe the complexity of the crack surface structure, and n is the number of sub-regions; Step S40: Calculate the permeability K of the crack, the calculation formula is: Where Q is the flow rate in the fracture, μ is the fluid viscosity, L is the fracture length, A is the fracture cross-sectional area, and ΔP is the fluid pressure difference; Step S50: combining the numerical ranges of the permeability K and the fractal dimension D to identify the biological erosion mode of the fracture, including three modes: uniform erosion, channel erosion and surface erosion; Set the permeability threshold K th1 and K th2 , where K th2 >K th1 , set the fractal dimension threshold D th1 and D th2 , where D th2 >D th1 ; When K <K th1 And D <D th1 When , the erosion mode is determined to be uniform erosion; When K th1 ≤K≤K th2 And D th1 ≤D≤D th2 , the erosion mode is determined to be channel erosion; When K>K th1 And D>D th1 When , the erosion mode is determined to be surface erosion.

2. The method for evaluating internal crack characteristics based on surface crack distribution according to claim 1, characterized in that: In step S20, the local erosion depth value h is calculated in each sub-region i. i The formula used is: h i =max(from i )-min(z i ) Among them, z i Indicates the height value of each point in the sub-area, max(z i ) and min(z i ) are the maximum height and minimum height in the sub-area respectively.

3. The method for evaluating internal crack characteristics based on surface crack distribution according to claim 1, characterized in that: In step S20, the local roughness value r is calculated in each sub-region i. i The formula used is: Among them, j is the jth point, z j is the height value of the jth point in sub-region i, is the average height of the sub-area.

4. The method for evaluating internal crack characteristics based on surface crack distribution according to claim 1, characterized in that: In step S40, the crack cross-sectional area A = b × L, where b is the equivalent opening, reflecting the average opening of the crack under erosion conditions, and its value is calculated by the local erosion depth value h i and the local roughness value r i The combined formula is used to estimate:

5. The method for evaluating internal crack characteristics based on surface crack distribution according to claim 1, characterized in that: In step S10, the frequency of collecting point cloud data is 1000 times per second.

6. The method for evaluating internal crack characteristics based on surface crack distribution according to claim 1, characterized in that: In step S20, the sub-areas are divided into square areas with a grid size of 1 mm.

7. The method for evaluating internal crack characteristics based on surface crack distribution according to claim 1, characterized in that: In step S50, when the value combination of K and D does not meet the judgment conditions of uniform erosion, channel erosion or surface erosion, the erosion mode is determined to be unknown erosion, and the specific values ​​of K and D and prompt information to be manually checked are pushed to the front end.

8. An internal crack feature evaluation system based on surface crack distribution, characterized in that: The internal crack feature evaluation system based on surface crack distribution includes: A data acquisition module is used to scan the fractured rock samples under biological erosion conditions by means of a three-dimensional laser scanning device to collect point cloud data of the fracture surface, and to perform denoising and smoothing on the collected point cloud data to obtain pre-processed point cloud data; The feature distribution model generation module is used to divide the crack surface into multiple sub-areas in the three-dimensional initial roughness model and calculate the local erosion depth value h in each sub-area i. i and the local roughness value r i , and according to the local erosion depth value h i and the local roughness value r i Generate a fracture erosion surface characteristic distribution model, which is used to quantify the erosion characteristics of the fracture surface at different locations; The fractal dimension calculation module is used to calculate the local erosion depth value h according to the distribution model of the crack erosion surface characteristics using fractal geometry methods. i and the local roughness value r i Calculate the bioerosion fractal dimension D of the cracks using the following formula: Where ∈ is the edge length, N(∈) is the number of grids required to cover the crack surface with grids of edge length ∈, α is the influence coefficient, which is used to balance the weight of local erosion characteristics in the fractal dimension calculation, D is the bioerosion fractal dimension used to quantitatively describe the complexity of the crack surface structure, and n is the number of sub-regions; The permeability calculation module is used to calculate the permeability K of the crack. The calculation formula is: Where Q is the flow rate in the fracture, μ is the fluid viscosity, L is the fracture length, A is the fracture cross-sectional area, and ΔP is the fluid pressure difference; The erosion pattern recognition module is used to combine the numerical range of permeability K and fractal dimension D for the biological erosion mode of the fracture, including three modes: uniform erosion, channel erosion and surface erosion; Set the permeability threshold K th1 and K th2 , where K th2 >K th1 , set the fractal dimension threshold D th1 and D th2 , where D th2 >D th1 ; When K <K th1 And D <D th1 When , the erosion mode is determined to be uniform erosion; When K th1 ≤K≤K th2 And D th1 ≤D≤D th2 , the erosion mode is determined to be channel erosion; When K>K th1 And D>D th1 When , the erosion mode is determined to be surface erosion.

9. An internal crack feature evaluation device based on surface crack distribution, characterized in that: The internal crack characteristic evaluation device based on surface crack distribution includes: A memory, a processor, and an internal crack feature assessment program based on surface crack distribution stored in the memory and executable on the processor, wherein the internal crack feature assessment program based on surface crack distribution, when executed by the processor, implements the internal crack feature assessment method based on surface crack distribution described in any one of Claims 1 to 7.

10. A computer program product, characterized in that The computer program product includes an internal crack feature evaluation program based on surface crack distribution, and when the internal crack feature evaluation program based on surface crack distribution is executed by a processor, it implements the internal crack feature evaluation method based on surface crack distribution described in any one of Claims 1 to 7.

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