A method and system for evaluating internal crack characteristics based on surface crack distribution

Abstract

This invention relates to the field of bio-erosion detection technology and discloses a method for evaluating internal fracture characteristics based on surface fracture distribution. The method includes: generating a three-dimensional initial roughness model; generating a fracture erosion surface feature distribution model; calculating the fractal dimension of bio-erosion using fractal geometry methods; and combining the permeability calculation results to identify the bio-erosion pattern of the fracture. Compared to existing technologies that rely on relatively coarse methods for identifying bio-erosion patterns in rock fractures, especially in complex environments such as high humidity or areas with high biological activity, this invention addresses the problem of inaccurate identification of erosion patterns by introducing a multi-parameter model combining fractal geometry methods and permeability. This model quantifies the erosion depth, roughness, and fractal dimension characteristics of the fracture surface, achieving accurate classification of fracture erosion patterns. This avoids the inaccurate identification problems of existing technologies and improves the accuracy and reliability of identifying bio-erosion in fractures.

Description

Technical Field

[0001] This invention belongs to the field of biological erosion detection technology, and in particular relates to a method and system for evaluating the characteristics of internal cracks based on the distribution of surface cracks. Background Technology

[0002] Currently, bioerosion of rock fractures has a significant impact on the stability and durability of rock structures, especially in environments with high humidity and strong biological activity, where bioerosion accelerates rock mass deterioration. However, existing technologies for identifying bioerosion patterns in rock fractures suffer from insufficient accuracy. For example, traditional identification methods typically rely on manual observation and simple physical parameter measurements, making it difficult to accurately quantify the microscopic features and erosion patterns of the fracture surface. This results in inaccurate identification of the degree of bioerosion and fails to fully meet the needs of monitoring and early warning of bioerosion in complex environments. Therefore, there is an urgent need for a method that can achieve high-precision identification of bioerosion patterns in complex environments. This method needs to accurately capture the microscopic features of the fracture surface, quantify the degree of bioerosion, and distinguish between different types of erosion patterns to improve the accuracy and reliability of bioerosion identification, providing effective technical support for geological disaster early warning and rock mass engineering safety assessment. Summary of the Invention

[0003] To address the aforementioned technical shortcomings, the purpose of this invention is to propose an assessment method for internal fracture characteristics based on surface fracture distribution. This method aims to solve the technical problem that existing technologies for identifying biological erosion patterns in rock fractures are relatively crude, especially in complex environments such as areas with high humidity or strong biological activity, where accurate identification of erosion patterns is impossible.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for evaluating internal crack characteristics based on surface crack distribution.

[0005] The method for evaluating internal crack characteristics based on surface crack distribution includes:

[0006] Step S10: Scan the fractured rock sample under biological erosion conditions using a three-dimensional laser scanning device to collect point cloud data of the fracture surface. Then, perform noise reduction and smoothing on the collected point cloud data to obtain preprocessed 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 micro-topographic features of the fracture surface under biological erosion environment, including roughness height feature information Z, surface waviness feature information X, tilt angle distribution information C, and surface area change rate information V.

[0008] Step S20: In the three-dimensional initial roughness model, the fracture surface is divided into multiple sub-regions, and the local erosion depth value h is calculated in each sub-region i. i and local roughness value r i And based on the local erosion depth value h i and local roughness value r i A surface feature distribution model for fracture erosion is generated, which is used to quantify the erosion characteristics of the fracture surface at different locations.

[0009] Step S30: Based on the surface feature distribution model of fracture erosion, a fractal geometry method is used in conjunction with the local erosion depth value h. i and local roughness value r i The formula for calculating the bio-erosion fractal dimension D of the fracture is as follows:

[0010]

[0011] Where ∈ is the side length, N(∈) is the number of grids required to cover the fracture surface with a grid of side length ∈, α is the influence coefficient used to balance the weight of local erosion features in the fractal dimension calculation, D is the bio-erosion fractal dimension used to quantitatively describe the complexity of the fracture surface structure, and n is the number of sub-regions.

[0012] Step S40: Calculate the permeability K of the fracture. The calculation formula is as follows:

[0013]

[0014] Where Q is the flow rate inside 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: Combine the numerical ranges of permeability K and fractal dimension D to identify the biological erosion patterns of the fractures, including three patterns: uniform erosion, channel erosion, and surface erosion.

[0016] Set the penetration threshold K th1 and K th2 K th2 >K th1 Set the fractal dimension threshold D th1 and D th2 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 was 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 within each sub-region i. i The formula used is:

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

[0022] Among them, z i Represents the height value of each point within the sub-region, max(z) i ) and min(z i ) represent the maximum and minimum heights in the sub-region, respectively.

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

[0024]

[0025] Where n is the number of sub-regions, j is the j-th point, and z j Let j be the height value of the j-th point within subregion i. This represents the average height of the sub-region.

[0026] Preferably, in step S40, the cross-sectional area of ​​the crack A = b × L, where b is the equivalent aperture, reflecting the average aperture of the crack under erosion conditions, and its value is obtained by the local erosion depth value h. i and local roughness value r i The combined formula is used to estimate:

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

[0028] Preferably, in step S20, the sub-regions are divided into square regions 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] The data acquisition module is used to scan fractured rock samples under biological erosion conditions using a three-dimensional laser scanning device to acquire point cloud data of the fracture surface. The acquired point cloud data is then denoised and smoothed to obtain pre-processed point cloud data.

[0032] The feature distribution model generation module is used to divide the fractured 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 local roughness value r i And based on the local erosion depth value h i and local roughness value r i A surface feature distribution model for fracture erosion is generated, 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 surface feature distribution model of fracture erosion by combining fractal geometry methods with the local erosion depth value h. i and local roughness value r i The formula for calculating the bio-erosion fractal dimension D of the fracture is as follows:

[0034]

[0035] Where ∈ is the side length, N(∈) is the number of grids required to cover the fracture surface with a grid of side length ∈, α is the influence coefficient used to balance the weight of local erosion features in the fractal dimension calculation, D is the bio-erosion fractal dimension used to quantitatively describe the complexity of the fracture surface structure, and n is the number of sub-regions.

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

[0037]

[0038] Where Q is the flow rate inside 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 combines the numerical ranges of permeability K and fractal dimension D to identify biological erosion patterns in fractures, including three modes: uniform erosion, channel erosion, and surface erosion.

[0040] Set the penetration threshold K th1 and K th2 K th2 >K th1 Set the fractal dimension threshold D th1 and D th2 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 was 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] Furthermore, the present invention also provides an internal crack feature evaluation device based on surface crack distribution, including a memory, a processor, and an internal crack feature evaluation program based on surface crack distribution stored in the memory and executable on the processor. When the internal crack feature evaluation program based on surface crack distribution is executed by the processor, the internal crack feature evaluation method based on surface crack distribution is implemented.

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

[0046] The beneficial effects of this invention are as follows: Compared with the relatively crude methods for identifying biological erosion patterns in rock fractures in the prior art, especially in complex environments such as areas with high humidity or strong biological activity, which cannot achieve accurate identification of erosion patterns, this application, by introducing a multi-parameter model combining fractal geometry and permeability, quantifies the erosion depth, roughness, and fractal dimension characteristics of the fracture surface, and achieves accurate classification of fracture erosion patterns. This avoids the problem of inaccurate identification in the prior art and improves the accuracy and reliability of identifying biological erosion in fractures. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic flowchart of the first embodiment of the internal crack feature evaluation method based on surface crack distribution of the present invention.

[0049] Figure 2 This is a schematic diagram of an apparatus for evaluating the internal crack characteristics based on surface crack distribution, according to the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of the internal crack feature evaluation method based on surface crack distribution of the present invention, which presents the first embodiment of the internal crack feature evaluation method based on surface crack distribution of the present invention.

[0052] In the first embodiment, the method for evaluating internal crack characteristics based on surface crack distribution includes:

[0053] Step S10: Scan the fractured rock sample under biological erosion conditions using a three-dimensional laser scanning device to collect point cloud data of the fracture surface. Then, perform noise reduction and smoothing on the collected point cloud data to obtain preprocessed 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 micro-topographic features of the fracture surface under biological erosion environment, including roughness height feature information Z, surface waviness feature information X, tilt angle distribution information C, and surface area change rate information V.

[0055] It should be noted that the point cloud data acquisition and preprocessing process mentioned in step S10 aims to accurately capture the microscopic features of the fracture surface using a 3D laser scanning device. To improve the accuracy and reliability of the model, the acquired data was denoised and smoothed to eliminate measurement errors and environmental interference, thereby obtaining more accurate geometric information of the fracture surface. These processed point cloud data provide basic data support for the subsequent construction of the 3D initial roughness model. The microscopic terrain feature information contained in the 3D initial roughness model mainly includes: roughness height feature information Z: used to describe the height difference of the fracture surface at different locations, reflecting the longitudinal erosion depth of the fracture; surface waviness feature information X: used to represent the wavy undulation feature of the fracture surface, which helps to identify surface irregularities caused by erosion; tilt angle distribution information C: used to quantify the tilt angle of each region of the fracture surface, describing the local slope of the fracture structure; and surface area change rate information V: used to reflect the rate of change of the fracture surface area, quantifying the impact of biological erosion on the fracture surface structure.

[0056] The extraction and analysis of these microscopic features enable more accurate identification of the biological erosion patterns of the fractures in subsequent steps, and provide a reliable data foundation for the calculation of permeability and fractal dimension.

[0057] Step S20: In the three-dimensional initial roughness model, the fracture surface is divided into multiple sub-regions, and the local erosion depth value h is calculated in each sub-region i. i and local roughness value r i And based on the local erosion depth value h i and local roughness value r i A surface feature distribution model for fracture erosion is generated, 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-regions, and the local erosion depth value h is calculated in each sub-region. i and local roughness value r i The purpose of this process is to more precisely characterize the local erosion characteristics of the fracture surface. By dividing the fracture surface into multiple small regions, the erosion differences at different locations within the fracture can be effectively captured. The local erosion depth value h... i The calculation is used to quantify the longitudinal erosion depth of the fracture surface within a certain sub-region, reflecting the erosion depth characteristics of the fracture within that region. Local roughness value r i The calculation is used to describe the roughness of the crack surface within a sub-region, capturing surface irregularities under biological erosion.

[0059] By calculating and integrating these local features, a surface feature distribution model for fracture erosion is generated. This distribution model can quantify the erosion characteristics of the fracture surface at different locations, providing richer data support for subsequent calculations of the fractal dimension of bio-erosion and facilitating the accurate identification of fracture bio-erosion patterns.

[0060] Step S30: Based on the surface feature distribution model of fracture erosion, a fractal geometry method is used in conjunction with the local erosion depth value h. i and local roughness value r i The formula for calculating the bio-erosion fractal dimension D of the fracture is as follows:

[0061]

[0062] Where ∈ is the side length, N(∈) is the number of grids required to cover the fracture surface with a grid of side length ∈, α is the influence coefficient used to balance the weight of local erosion features in the fractal dimension calculation, D is the bio-erosion fractal dimension used to quantitatively describe the complexity of the fracture surface structure, and n is the number of sub-regions.

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

[0064] It should be understood that in the formula This represents the fractal characteristics of the fracture surface, where N(∈) is the number of meshes required to cover the fracture surface with a mesh of side length ∈. As the mesh size decreases, the calculated fractal dimension reflects the self-similarity and complexity of the fracture surface structure. The formula includes... This section is used to quantify the erosion characteristics of fractures at different locations, so that the fractal dimension D is not only a measure of the overall surface morphology, but also reflects the local changes of the fracture surface under biological erosion.

[0065] Step S40: Calculate the permeability K of the fracture. The calculation formula is as follows:

[0066]

[0067] Where Q is the flow rate inside 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 features inside the fracture are combined with the physical properties of the fluid to calculate the flow conductivity of the fracture. Here, Q is the flow rate inside 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 inside 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 flow space inside the fracture, and AP is the fluid pressure difference, which is used to drive the fluid to flow inside the fracture.

[0069] The permeability calculated using this formula reflects the conductivity of the fracture; a higher value indicates better conductivity. Combining permeability and fractal dimension data allows for more accurate identification of the biological erosion patterns of the fracture, thereby further quantifying the changing characteristics of the fracture structure.

[0070] Step S50: Combine the numerical ranges of permeability K and fractal dimension D to identify the biological erosion patterns of the fractures, including three patterns: uniform erosion, channel erosion, and surface erosion.

[0071] Set the penetration threshold K th1 and K th2 K th2 >K th1 Set the fractal dimension threshold D th1 and D th2 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 was 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 permeability K and fractal dimension D are combined to identify the biological erosion mode of the fracture, and different thresholds are set to distinguish between uniform erosion, channel erosion, and surface erosion. The purpose of this step is to determine the performance of the fracture under different erosion modes by analyzing the distribution of specific values, thereby more accurately quantifying the changes in the fracture surface structure.

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

[0077] It should be understood that when the permeability K and fractal dimension D are in the middle range, relatively obvious flow channels are formed in the cracks, and the surface structure exhibits a certain degree of complexity. In this case, the crack erosion mode is identified as channel erosion.

[0078] For example, if the actual measured permeability of a certain fracture is K = 0.5 and the fractal dimension is D = 1.8, and assuming the threshold is K... th1 =0.3, K th2 =0.7, D th1 =1.5, D th1 If the permeability and fractal dimension of this fracture are both in the middle range, then its erosion mode can be determined to be channel erosion. This classification method allows for more accurate identification of the erosion mode of fractures under different conditions, providing a reliable basis for subsequent analysis and treatment.

[0079] Furthermore, the present invention provides an internal crack feature evaluation system based on surface crack distribution, employing an internal crack feature evaluation method based on surface crack distribution as described in the above embodiments, which can solve the technical problem of internal crack feature evaluation based on surface crack distribution. Compared with the prior art, the beneficial effects of the internal crack feature evaluation system based on surface crack distribution provided by the present invention are the same as those of the internal crack feature evaluation method based on surface crack distribution provided in the above embodiments, and other technical features of the internal crack feature evaluation system based on surface crack distribution are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0080] This invention provides an internal crack feature evaluation device based on surface crack distribution. Please refer to [link / reference]. Figure 2An internal crack feature evaluation device based on surface crack distribution includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the internal crack feature evaluation method based on surface crack distribution described in Embodiment 1 above. The internal crack feature evaluation device based on surface crack distribution in this embodiment of the invention may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This internal crack feature evaluation device based on surface crack distribution is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the invention. An internal fracture feature assessment device based on surface fracture distribution may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the internal fracture feature assessment device based on surface fracture distribution. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected 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 I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows an internal fracture characteristic assessment device based on surface fracture distribution to wirelessly or wiredly communicate with other devices to exchange data. Although an internal fracture characteristic assessment device based on surface fracture distribution with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0081] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for evaluating internal crack characteristics based on surface crack distribution. The computer program product provided by this 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 this invention are the same as those of the method for evaluating internal crack characteristics based on surface crack distribution provided in the above embodiments, and will not be repeated here.

[0082] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

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

[0084] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for evaluating internal fracture features based on surface fracture distribution, characterized by, The internal fracture feature evaluation method based on surface fracture distribution comprises the following steps: Step S10: scanning the fracture rock sample under the biological erosion condition by a three-dimensional laser scanning device to collect point cloud data of the fracture surface, and performing denoising and smoothing processing on the collected point cloud data to obtain preprocessed point cloud data; Based on the preprocessed point cloud data, a three-dimensional initial roughness model of the fracture surface is generated, which is used to represent the micro-topographic features of the fracture surface under the biological erosion environment, including roughness height feature information Z, surface waviness feature information X, inclination angle distribution information C, and surface area change rate information V; Step S20: dividing the fracture surface into a plurality of sub-regions in the three-dimensional initial roughness model, calculating a local erosion depth value within each sub-region i and a local roughness value , and generating a fracture erosion surface feature distribution model according to the local erosion depth value and the local roughness value , the fracture erosion surface feature distribution model being used to quantify the erosion characteristics of the fracture surface at different positions; wherein the local erosion depth value within each sub-region i is calculated using the formula: , wherein represents the height value of each point within the sub-region, and are the maximum height and the minimum height in the sub-region, respectively; Step S30: According to the crack erosion surface feature distribution model, the fractal geometry method is combined with the local erosion depth value and the local roughness value to calculate the biological erosion fractal dimension D of the crack, and the calculation formula is: wherein, is the side length, is the number of grids required to cover the fracture surface with a grid of side length is the number of grids required to cover the fracture surface with a grid of side length is the influence coefficient, used to balance the weight of local erosion features in the calculation of the fractal dimension, D is the bioerosion fractal dimension used to quantitatively describe the complexity of the fracture surface structure, n is the number of sub-regions; Step S40: calculating the permeability K of the fracture, and the calculation formula is: wherein, is the fracture internal flow rate, is the fluid viscosity, is the fracture length, is the fracture cross-sectional area, is the fluid differential pressure; 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 of uniform erosion, channel erosion, and surface erosion; Setting a permeability threshold and wherein > Setting a fractal dimension threshold and wherein > ; when K and D , the erosion pattern is determined to be uniform erosion; When ≤ K ≤ and ≤ D ≤ , the erosion pattern is determined to be channel erosion; When K and D , the erosion pattern is determined to be surface erosion.

2. The method of claim 1, wherein, In step S20, a local roughness value is calculated within each sub-region i using the formula: where j is the jth point, is the height value of the jth point within the sub-region i, is the average height value of the sub-region.

3. The method of claim 1, wherein the method further comprises: determining a distribution of the surface fractures; and determining the internal fracture feature based on the distribution of the surface fractures. In step S40, the fracture cross-sectional area A = b x L, where b is the equivalent opening, reflecting the average opening of the fracture under erosion conditions, is estimated by a combination of the local erosion depth value and the local roughness value : .

4. The method of claim 1, wherein the method further comprises: determining a distribution of the surface fractures; and determining the internal fracture feature based on the distribution of the surface fractures. In step S10, the collection frequency of the point cloud data is 1000 times per second.

5. The method of claim 1, wherein the method further comprises: determining a distribution of the surface fractures; and determining the internal fracture feature based on the distribution of the surface fractures. In step S20, the division of the sub-regions uses a square region with a grid size of 1 millimeter.

6. The method of claim 1, wherein, In step S50, when the value combination of K and D does not belong to the judgment conditions of uniform erosion, channel erosion, or surface erosion, the erosion mode is determined as unknown erosion, and the specific values of K and D and the prompt information for manual verification are pushed to the front end.

7. A system for evaluating internal fracture features based on surface fracture distribution, characterized by, The internal fracture feature evaluation system based on surface fracture distribution comprises: A data acquisition module is configured to scan a fracture rock sample under a biological erosion condition by a three-dimensional laser scanning device to collect point cloud data of a fracture surface, and perform denoising and smoothing processing on the collected point cloud data to obtain preprocessed point cloud data; a feature distribution model generating module, configured to divide the fracture surface into a plurality of sub-regions in the three-dimensional initial roughness model, calculate a local erosion depth value in each sub-region i and a local roughness value , and generate a fracture erosion surface feature distribution model according to the local erosion depth value and the local roughness value , the fracture erosion surface feature distribution model being used to quantify the erosion characteristics of the fracture surface at different positions; wherein the local erosion depth value is calculated in each sub-region i by using the formula: , wherein represents a height value of each point in the sub-region, and are the maximum height and the minimum height in the sub-region, respectively. A fractal dimension calculation module is configured to calculate a fractal dimension D of biological erosion of the crack according to a crack erosion surface feature distribution model by using a fractal geometry method in combination with a local erosion depth value and a local roughness value The formula is: wherein, is the side length, is the number of grids required to cover the fracture surface with a grid of side length is the number of grids required to cover the fracture surface with a grid of side length is the influence coefficient, used to balance the weight of local erosion features in the calculation of the fractal dimension, D is the bioerosion fractal dimension used to quantitatively describe the complexity of the fracture surface structure, n is the number of sub-regions; A permeability calculation module is configured to calculate the permeability K of the fracture, and the calculation formula is: wherein, is the flow rate in the fracture, is the fluid viscosity, is the fracture length, is the fracture cross-sectional area, is the fluid pressure differential; An erosion mode identification module is configured to combine the numerical ranges of the permeability K and the fractal dimension D to identify the biological erosion mode of the fracture, including three modes of uniform erosion, channel erosion, and surface erosion. Setting a permeability threshold and wherein > Setting a fractal dimension threshold and wherein > ; When K < And D < When the erosion mode is determined to be uniform erosion; When ≤ K ≤ and ≤ D ≤ , the erosion pattern is determined to be channel erosion. When K and D , the erosion pattern is determined to be surface erosion.

8. An internal fracture feature evaluation apparatus based on surface fracture distribution, characterized by, The internal fracture feature evaluation device based on surface fracture distribution comprises: A memory, a processor, and an internal fracture feature evaluation program based on surface fracture distribution stored on the memory and executable on the processor, wherein the internal fracture feature evaluation program based on surface fracture distribution, when executed by the processor, implements the internal fracture feature evaluation method based on surface fracture distribution in any one of claims 1 to 7.

9. A computer program product, characterised in that, The computer program product comprises an internal fracture feature evaluation program based on surface fracture distribution, which, when executed by a processor, implements the internal fracture feature evaluation method based on surface fracture distribution in any one of claims 1 to 7.

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