Deep underground engineering surrounding rock loose circle evolution process visualization method and related equipment

Abstract

The invention relates to the technical field of deep underground engineering, in particular to a deep underground engineering surrounding rock loose circle evolution process visualization method and related equipment. According to the technical scheme, the method comprises the steps that rock samples collected on site are scanned at different loading stages through a CT scanner, CT scanning images are obtained, meanwhile, monitoring sections and monitoring points are arranged on the deep underground engineering site, displacement, stress and strain sensors are installed, and surrounding rock information is obtained through the drilling camera shooting technology; finite element software is used for establishing a numerical model of deep underground engineering, a viscoelastic-plastic constitutive model considering rock damage and rheological characteristics is selected, model parameters are assigned according to CT scanning images and rock mechanical test results, and corrected numerical simulation data are obtained through parameter inversion correction. According to the method, reliable surrounding rock information is obtained by comprehensively applying multiple data acquisition means, and the accuracy of a numerical simulation result is improved by adopting the viscoelastic-plastic constitutive model considering rock damage and rheological characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of deep underground engineering, and particularly to a method for visualizing the evolution process of the loosening zone of surrounding rock in deep underground engineering and related equipment. Background Art

[0002] In deep underground engineering, the evolution of the loosening zone of surrounding rock is crucial for the stability and safety of the project. At present, the research on the loosening zone of surrounding rock mainly has the following deficiencies:

[0003] Existing technologies are difficult to comprehensively and accurately obtain information on the surrounding rock at different loading stages and cannot fully reflect the evolution process of the loosening zone of surrounding rock.

[0004] Traditional numerical simulation methods are not accurate enough when considering the complex mechanical properties of rocks, resulting in deviations between the simulation results and the actual situation.

[0005] The methods for processing and analyzing CT scan images are not perfect enough to effectively extract the structural characteristics of rocks and accurately identify the loosening zone.

[0006] There is a lack of intuitive and dynamic visualization means, which is not conducive to the understanding and analysis of the evolution of the loosening zone of surrounding rock by engineering personnel, and thus affects engineering decisions.

[0007] In view of the above deficiencies, the present application proposes a method for visualizing the evolution process of the loosening zone of surrounding rock in deep underground engineering to solve the above problems existing in the prior art, realize the accurate and intuitive display of the evolution process of the loosening zone of surrounding rock in deep underground engineering, and provide a reliable basis for engineering design and construction. Summary of the Invention

[0008] The object of the present invention is to propose a method for visualizing the evolution process of the loosening zone of surrounding rock in deep underground engineering that can accurately and intuitively display the evolution process of the loosening zone of surrounding rock and provide a reliable basis for engineering design and construction in view of the problems in the background art.

[0009] In a first aspect, the present application provides a method for visualizing the evolution process of the loosening zone of surrounding rock in deep underground engineering, including the following steps:

[0010] Data acquisition: Scanning rock samples collected on site at different loading stages through a CT scanner to obtain CT scan images. At the same time, arranging monitoring sections and monitoring points at the deep underground engineering site, installing displacement, stress, and strain sensors and using borehole camera technology to obtain surrounding rock information;

[0011] Numerical simulation: Use finite element software to establish a numerical model of deep underground engineering. Select a viscoelastic-plastic constitutive model considering rock damage and rheological properties. Assign model parameters according to CT scan images and rock mechanics test results, and correct them through parameter inversion to obtain corrected numerical simulation data;

[0012] Image processing and analysis: Preprocess the CT scan images, extract structural features, and use a specific algorithm to identify the loosening zone;

[0013] Visualization display: Use 3D modeling software to construct a 3D model of the surrounding rock. Map the corrected numerical simulation data onto the 3D model, and design an interactive function to view the evolution of the loosening zone of the surrounding rock at different times and the data at specific positions.

[0014] Optionally, the parameters of the laboratory CT scan are set as follows: voltage 160 kV - 220 kV, current 10 mA - 20 mA, slice thickness 0.1 mm - 0.5 mm, and the loading rate is controlled at 0.001 mm / s - 0.01 mm / s.

[0015] Optionally, in the on-site monitoring, the accuracy of the displacement sensor reaches 0.01 mm, the accuracy of the stress sensor reaches 0.1 MPa, and the accuracy of the strain gauge reaches 10 -6 .

[0016] Optionally, the viscoelastic-plastic constitutive model considering rock damage and rheological properties is a model coupled with the generalized Kelvin model and the Mohr-Coulomb model, and the stress-strain relationship is:

[0017]

[0018] where σ is the stress, ε is the strain, E 1 , E 2 are elastic moduli, used to measure the ability of the rock to resist elastic deformation, η is the viscosity coefficient, reflecting the viscous property of the rock, c is the cohesion, which is the bonding force between rock particles, φ is the internal friction angle, reflecting the friction characteristics between rock particles, t is the time, and τ is the integration variable; determine the values of each parameter according to rock mechanics tests, substitute them into the formula to calculate the stress-strain state of the rock under different loading conditions, and provide an accurate viscoelastic-plastic constitutive model for obtaining corrected numerical simulation data.

[0019] Optionally, in the image preprocessing, the median filtering algorithm is used to remove salt-and-pepper noise in the image. The size of the filtering window is 3×3 - 5×5, and the formula of the median filtering algorithm is:

[0020] g(x,y) = median{f(x - i,y - i),(x,y) ∈ W}

[0021] Among them, f(x, y) is the pixel value of the original image at (x, y), g(x, y) is the pixel value of the filtered image at (x, y), W is the filtering window, and the formula means sorting the pixel values within the window and taking the median value as the pixel value after filtering to remove noise; the histogram equalization algorithm is used to improve the image contrast.

[0022] Optionally, in the structural feature extraction, the Canny algorithm is used to identify the edges of rock fractures. The Gaussian filtering formula in the steps of the Canny algorithm is:

[0023]

[0024] Among them, σ is the standard deviation of the Gaussian kernel, which is used to control the smoothness of the Gaussian filter, (x, y) is the image coordinate, and the formula filters the image by calculating the two-dimensional Gaussian function value, smooths the image and reduces the influence of noise; calculates the magnitude and direction of the image gradient; performs non-maximum suppression on the gradient magnitude; detects and connects edges through the double-threshold algorithm; refines and fills the fractures through morphological dilation and erosion operations, and extracts the geometric parameters such as the length, width, and azimuth angle of the fractures; uses the threshold segmentation algorithm to segment the pores in the rock and calculates the area and perimeter parameters of the pores.

[0025] Optionally, the loose circle is identified using a threshold-based region growing algorithm. According to the physical properties and mechanical parameters of the rock, combined with the gray value distribution of the CT image, an initial threshold T is determined 0 ; starting from the edge of the image, taking one of the pixel points (x 0 , y 0 ) as the seed point, if its gray value Ι(x 0 , y 0 ) is less than T 0 , and is adjacent to the pixel points already marked as the loose area, then mark this pixel point as the loose area, and then continuously expand the loose area. The expansion condition is that for the pixel point (x, y) of the currently marked loose area, its adjacent pixel point (x ′ , y ′ ) satisfies |Ι(x, y) - Ι(x ′ , y ′ )| < T 1 and Ι(x ′ , y ′ ) < T 0 , where T 1 is the set gray difference threshold, which is used to control the difference degree of adjacent pixel gray values; Ι(x, y), Ι(x ′ , y ′ ) are the pixel points (x, y), (x ′ , y ′)'s grayscale value. The formula indicates that when the difference between the grayscale values of adjacent pixel points is less than the grayscale difference threshold T 1 , and the grayscale value of the adjacent pixel point is less than the initial threshold T 0 , then the adjacent pixel point is marked as a loose area until there are no more pixel points that meet the conditions.

[0026] Optionally, using 3D modeling software such as Maya and 3ds Max, according to the CT scan images and the results of the corrected numerical simulation data, a 3D model of the surrounding rock of the deep underground project is constructed. The internal rock structure information in the CT scan images is mapped to the surface of the 3D model. Through texture mapping technology, the true texture of the rock is presented on the model surface.

[0027] Optionally, in the data fusion and mapping, the stress, strain, and loose circle data obtained from the numerical simulation are mapped to the corresponding positions of the 3D model through an interpolation algorithm. The inverse distance weighted interpolation algorithm is used, and its formula is:

[0028]

[0029] where Z(x 0 ,y 0 ) is the interpolation result at the interpolation point (x 0 ,y 0 ), Z i is the value of the known data point i, d i is the distance from the interpolation point to the known data point i, p is the weight coefficient with a value of 2. The formula determines the value of the interpolation point by calculating the weighted average of the distances between the interpolation point and the known data points, realizing the mapping of data from the numerical simulation to the 3D model; different colors and transparencies are used to represent different physical quantities and states. Red represents the high stress area, blue represents the low stress area, yellow represents the loose area, and the transparent part represents pores and cavities; in the interactive function design, a time axis and interactive buttons are set in the visualization interface. Users can view the evolution of the loose circle of the surrounding rock at different times by dragging the time axis, and can also view the detailed stress, strain, and displacement data at specific positions by clicking the interactive buttons. At the same time, it supports users to perform operations such as rotating, scaling, and translating the 3D model.

[0030] On the other hand, the present application provides a computer device, including a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and are configured to be executed by the processor. The programs include instructions for performing the steps in the method described in the first aspect.

[0031] Compared with the prior art, the present application includes at least one of the following beneficial technical effects:

[0032] The present invention combines various data acquisition means, can comprehensively and accurately obtain surrounding rock information, and provides reliable data for subsequent analysis. By adopting a viscoelastic-plastic constitutive model considering rock damage and rheological properties, the accuracy of numerical simulation is improved, and the mechanical behavior of rocks is more realistically reflected.

[0033] Furthermore, the use of image processing and analysis methods can effectively extract rock structure features and identify the loosened zone, providing accurate data support for visual display. The visual display method enables engineering personnel to intuitively and dynamically view the evolution of the surrounding rock loosened zone, facilitating understanding and analysis, and significantly improving the accuracy of engineering decisions. Brief Description of the Drawings

[0034] Figure 1 It is a flow chart of a visualization method for the evolution process of the surrounding rock loosened zone in deep underground engineering. Detailed Embodiment

[0035] The technical solution of the present invention will be further described below in conjunction with the drawings and specific embodiments.

[0036] As Figure 1 shown, the visualization method for the evolution process of the surrounding rock loosened zone in deep underground engineering proposed by the present invention includes data acquisition: scanning the rock samples collected on site at different loading stages by a CT scanner to obtain CT scan images. At the same time, monitoring sections and monitoring points are arranged at the deep underground engineering site, displacement, stress, and strain sensors are installed, and borehole camera technology is used to obtain surrounding rock information.

[0037] In this embodiment, when the CT scanner scans the rock samples, the CT scanner is preset. Its voltage is set to 160 kV - 220 kV, the current is set to 10 mA - 20 mA. When slicing the rock samples, the slice thickness is controlled within the range of 0.1 mm - 0.5 mm, and the loading rate of the CT scanner is controlled so that its loading rate is within the range of 0.001 mm / s - 0.01 mm / s. Finally, the rock samples are scanned at different loading stages; during the on-site monitoring process, first, monitoring sections and monitoring points are arranged at the deep underground engineering site, and then displacement sensors (with an accuracy of 0.01 mm), stress sensors (with an accuracy of 0.1 MPa), and strain gauges (with an accuracy of 10 -6 ) are installed; finally, a suitable position is selected on the monitoring section for drilling. The diameter and depth of the borehole should meet the requirements of the borehole camera equipment. The borehole diameter is 50 - 100 mm, and the depth is determined according to needs. During the drilling process, attention should be paid to maintaining the verticality and stability of the borehole to avoid borehole bending or collapse; after the drilling operation is completed, the image information of the surrounding rock can be obtained by using borehole camera technology.

[0038] As Figure 1As shown in the figure, numerical simulation is carried out on the collected data. A numerical model of deep underground engineering is established using finite element software. A viscoelastic-plastic constitutive model considering rock damage and rheological properties is selected. The model parameters are assigned according to the CT scan images and the results of rock mechanics tests, and are corrected through parameter inversion to obtain the corrected numerical simulation data.

[0039] In this embodiment, when selecting a viscoelastic-plastic constitutive model considering rock damage and rheological properties, a model coupling the generalized Kelvin model and the Mohr-Coulomb model is adopted, and the stress-strain relationship is:

[0040]

[0041] where σ is the stress, ε is the strain, E 1 、E 2 are the elastic moduli and are used to measure the ability of the rock to resist elastic deformation. η is the viscosity coefficient that can reflect the viscous properties of the rock. c is the cohesion force, which is the bonding force between rock particles. φ is the internal friction angle that can reflect the friction characteristics between rock particles. t is the time, and τ is the integration variable. The values of each parameter are determined according to rock mechanics tests and substituted into the formula to calculate the stress-strain state of the rock under different stress conditions, providing accurate viscoelastic-plastic constitutive model data for the corrected numerical simulation data;

[0042] Elastic modulus determination stage: Design and conduct uniaxial compression tests on the rock. Prepare multiple regular rock specimens, place them on a pressure testing machine, apply axial pressure at a constant loading rate, and at the same time use a strain gauge device to accurately measure the axial strain of the specimen during the loading process. According to Hooke's law, the elastic moduli E 1 and E 2 are calculated through the slope of the initial linear segment of the stress-strain curve. For rock specimens with different lithologies and structures, multiple repeated tests are required to obtain representative elastic modulus values;

[0043] Viscosity coefficient measurement stage: Conduct creep tests on the rock. Place the rock specimen under a constant axial stress, use a high-precision displacement measurement device to monitor the deformation of the specimen over time for a long time. Through the analysis of the creep curve and in combination with the constitutive relationship of viscous materials, the viscosity coefficient η is inversely deduced. During the test, attention should be paid to maintaining stable environmental conditions and reducing the interference of external factors on the test results to ensure the accuracy of the measurement data;

[0044] When the cohesion and internal friction angle are determined, a direct shear test of the rock is carried out. Multiple groups of rock specimens are prepared, and shear forces are applied to the specimens under different normal stresses, and the shear stresses when the specimens undergo shear failure are recorded. According to the Mohr-Coulomb strength theory, by drawing Mohr circles and determining their envelope lines, the cohesion c and internal friction angle φ of the rock are calculated. The normal stress values for each group of tests should have a certain gradient to ensure the accuracy of the results.

[0045] In this embodiment, for the processing and analysis of CT scan images, first, the CT scan images are preprocessed, structural features are extracted, and a specific algorithm is used to identify the loosening zone.

[0046] In this embodiment, in the image preprocessing stage, the median filtering algorithm is used to remove salt-and-pepper noise in the image. The size of the filtering window is 3×3 - 5×5. The formula for the median filtering algorithm is: g(x,y) = median{f(x - i,y - i),(x,y) ∈ W}. In the above formula, f(x,y) is the pixel value of the original image at (x,y), g(x,y) is the pixel value of the filtered image at (x,y), and W is the filtering window. The formula means that the pixel values within the window are sorted, and the middle value is taken as the pixel value after filtering, thereby removing the noise; the histogram equalization algorithm is used to improve the image contrast;

[0047] The above median filtering algorithm is divided into the following steps:

[0048] The first step is to determine the size of the filtering window: According to the noise situation and detail requirements of the image, select an appropriate size of the filtering window. For an image with dense noise, a 3×3 window is selected, which can not only reduce the calculation amount but also effectively remove the noise; if the image noise is scattered, a 5×5 window is selected to better retain the edge and texture information of the image.

[0049] The second step is to traverse the image pixels: Starting from the top-left pixel of the image, with the selected size of the filtering window as the step size, each pixel in the image is processed row by row and column by column. For the pixels at the image boundary, since the window may extend beyond the image range, the method of boundary expansion is used to copy the boundary pixel values to fill the exceeded part.

[0050] The third step is to extract the pixel values within the window: With the currently processed pixel as the center, according to the selected window size, the gray values of all pixels within the window are extracted. For a 3×3 window, the gray values of the current pixel and its 8 adjacent pixels around it will be included; for a 5×5 window, the gray values of 25 pixels will be included.

[0051] Step 4, Sort pixel values: Sort the grayscale values of all pixels within the extracted window in ascending (or descending) order. Use common sorting algorithms, including quicksort and bubble sort. The purpose of sorting is to find the pixel value at the middle position.

[0052] Step 5, Determine the filtered pixel value: According to the sorting result, take the pixel value at the middle position as the grayscale value of the currently processed pixel after filtering. If the number of pixels within the window is odd, directly take the middle value; if it is even, take the average of the two middle values as the filtered pixel value. For a 3×3 window, the number of pixels is 9, and take the 5th sorted pixel value; for a 4×4 window, the number of pixels is 16, and take the average of the 8th and 9th sorted pixel values.

[0053] Step 6, Update the image: Assign the calculated filtered pixel value to the corresponding pixel position in the original image to complete the filtering process of this pixel. Continue to traverse the next pixel of the image and repeat Steps 3 to 6 until all pixels in the image are processed, obtaining an image with salt-and-pepper noise removed.

[0054] The specific usage method of the above histogram equalization algorithm is as follows:

[0055] Step 1, Calculate the image histogram: Count the number of pixels for each gray level in the image. The gray levels of the image are usually between 0 - 255 (8-bit gray image). Create an array with a length of 256 to record the number of pixels for each gray level. Traverse all pixels of the image, and for each pixel encountered, increment the count corresponding to its gray level in the array by 1.

[0056] Step 2, Calculate the cumulative distribution function (CDF): Calculate the cumulative distribution function for each gray level, that is, the proportion of the total number of pixels from gray level 0 to the current gray level in the total number of pixels in the image. Let the total number of pixels in the image be N, and the number of pixels at gray level i be ni. Then the cumulative distribution function for gray level i is:

[0057]

[0058] Step 3, Map gray values: According to the cumulative distribution function, map each gray level in the original image to a new gray level to achieve histogram equalization. The new gray level g(i) = round{(L - 1) × CDF(i)}, where L is the total number of gray levels in the image (for an 8-bit gray image, L = 256), and the round() function is a rounding function. Through this formula, the gray values of the original image are redistributed according to the cumulative distribution function, making the gray distribution of the image more uniform, thereby enhancing the image contrast.

[0059] Step 4: Generate the equalized image: Traverse all the pixels of the original image, and replace the gray value of each pixel with a new gray value according to the mapping relationship calculated in Step 3, so as to generate the histogram equalized image.

[0060] In the structural feature extraction stage, the Canny algorithm is used to identify the edges of rock fractures in the image. The Gaussian filtering formula in the steps of the Canny algorithm is:

[0061]

[0062] In the above formula, σ is the standard deviation of the Gaussian kernel function, which is used to control the smoothness of the Gaussian filter. (x, y) are the image coordinates. The formula filters the image by calculating the two-dimensional Gaussian function value, smooths the image and reduces the influence of noise; calculates the magnitude and direction of the image gradient; suppresses non-maximum values of the gradient magnitude; detects and connects edges through the double-threshold algorithm; refines and fills the fractures through morphological dilation and erosion operations, and extracts the geometric parameters such as the length, width, and azimuth angle of the fractures; uses the threshold segmentation algorithm to segment the pores in the rock and calculates the area and perimeter parameters of the pores.

[0063] When identifying the loosened zone, a threshold-based region growing algorithm is adopted. According to the physical properties and mechanical parameters of the rock, combined with the gray value distribution of the CT image, an initial threshold T is determined 0 ; Starting from the edge of the image, taking one of the pixel points (x 0 , y 0 ) as the seed point, if its gray value Ι(x 0 , y 0 ) is less than T 0 , and it is adjacent to the pixel points already marked as the loosened area, then mark this pixel point as the loosened area, and then continuously expand the loosened area. The expansion condition is that for the pixel point (x, y) of the currently marked loosened area, its adjacent pixel point (x ′ , y ′ ) satisfies |Ι(x, y) - Ι(x ′ , y ′ )| < T 1 and Ι(x ′ , y ′ ) < T 0 , where T 1 is the set gray difference threshold, which is used to control the difference degree of adjacent pixel gray values; Ι(x, y), Ι(x ′ , y ′ ) are the gray values of the pixel points (x, y), (x ′ , y ′ ) respectively. The formula means that when the difference between the gray values of adjacent pixel points is less than the gray difference threshold T 1, and the gray value of adjacent pixel points is less than the initial threshold T 0 When this is the case, the adjacent pixel points are marked as loose areas until there are no pixel points that meet the conditions; the above algorithm determines the initial threshold based on the physical properties and mechanical parameters of the rock, combined with the gray value distribution of the CT image, and is designed closely around the characteristics of the rock loose circle, which can accurately locate the areas related to rock loosening and improve the recognition accuracy; starting from the pixel points at the image edge as seed points, making full use of the image edge features, through reasonable growth and expansion conditions, gradually excavating potential loose areas, effectively integrating the rock structure information contained in the gray value in the CT image, by setting strict expansion conditions, that is, using the gray value difference threshold to control the difference degree of adjacent pixel gray values, only when the difference between the gray values of adjacent pixel points is less than the gray value difference threshold and the gray value is less than the initial threshold, it is marked as a loose area, ensuring that the expansion of the loose area is stable and in line with the actual situation of rock loosening, avoiding excessive or incorrect expansion, and improving the reliability of the recognition result; and once the algorithm determines the initial threshold and seed points, it can automatically expand the loose area continuously according to the set conditions until there are no pixel points that meet the conditions, reducing manual intervention and improving the recognition efficiency, which is suitable for the processing of large-scale CT image data; the gray value difference threshold parameter can be adjusted according to different rock characteristics and actual needs, enhancing the adaptability of the algorithm to different geological conditions and rock types, and being able to accurately identify the loose circle under various complex conditions.

[0064] Such as Figure 1 As shown, further use 3D modeling software to construct a 3D model of the surrounding rock, map the corrected numerical simulation data to the 3D model, and design an interactive function to view the evolution of the surrounding rock loose circle at different times and the data at specific positions, achieving the purpose of visual display.

[0065] In this embodiment, first, a 3D model is constructed. Using 3D modeling software such as Maya and 3ds Max, according to the CT scan image and the results of the corrected numerical simulation data, a 3D model of the surrounding rock of the deep underground project is constructed, mapping the internal rock structure information in the CT scan image to the surface of the 3D model, and through texture mapping technology, making the surface of the model present the real texture of the rock.

[0066] In data fusion and mapping, the data of stress, strain, and loose circle obtained from numerical simulation are mapped to the corresponding positions of the 3D model through an interpolation algorithm. The inverse distance weighted interpolation algorithm is used, and its formula is:

[0067]

[0068] Z(x 0 ,y 0 ) in the above formula is the point to be interpolated (x 0 ,y0 ) interpolation result at, Z i is the value of the known data point i, d i is the distance from the interpolation point to the known data point i, p is the weight coefficient with a value of 2. The formula determines the value of the interpolation point by calculating the weighted average of the distances between the interpolation point and the known data points, realizing the mapping of data from numerical simulation to the 3D model; different colors and transparencies are used to represent different physical quantities and states. For example, red represents the high stress area, blue represents the low stress area, yellow represents the loosened area, and the transparent part represents pores and cavities. Through texture mapping technology, the real texture of the rock is presented on the model surface; the stress, strain, and loosening zone data obtained from numerical simulation are mapped to the corresponding positions of the surrounding rock 3D model through the inverse distance weighted interpolation algorithm. Different colors and transparencies are used to represent different physical quantities and states, with clear color differentiation, facilitating the staff to record and observe.

[0069] In the interactive function design, a timeline and interactive buttons are set in the visualization interface. Users can view the evolution of the surrounding rock loosening zone at different times by dragging the timeline. Users can intuitively see the evolution of the surrounding rock loosening zone over time, which helps users understand the development trend of the surrounding rock loosening zone during different construction stages of underground engineering or under the influence of different external factors, so as to predict potential risks in advance and provide strong evidence for engineering decision-making. During the tunnel construction process, it can be clearly observed how the range of the loosening zone expands as the excavation depth increases, so as to adjust the support plan in a timely manner; users can also click the interactive button to view the detailed data of stress, strain, and displacement at a specific location. The design of the interactive button enables users to quickly obtain the detailed data of stress, strain, and displacement at a specific location, which is crucial for engineering technicians to analyze the stress and deformation conditions of key parts. When analyzing the stability of underground caverns, it can accurately locate the parts prone to problems such as the crown and corners of the cavern, view their stress and strain states, and judge whether they exceed the safety threshold, providing data support for engineering reinforcement or adjustment of construction technology; at the same time, it supports users to perform operations such as rotating, zooming, and translating on the 3D model, greatly enhancing the user's observation experience of the model. Users can comprehensively observe the overall situation of the surrounding rock and the distribution characteristics of the loosening zone from different angles and different zoom ratios. This helps to more deeply analyze the spatial structure of the surrounding rock and the relationship between the loosening zone and the surrounding rock mass, discover potential overlooked detail problems, and thus more accurately evaluate the stability of underground engineering; this interactive function design integrates multiple information acquisition methods, enabling users to quickly understand the dynamic changes of the surrounding rock loosening zone and the detailed data of key parts, and also enabling them to observe the 3D model of the surrounding rock in all directions. These rich and intuitive information can help engineering personnel and decision-makers quickly make scientific and reasonable decisions, avoid decision-making mistakes caused by incomplete or untimely information, and improve the safety and construction efficiency of the project.

[0070] The above specific embodiments are merely several alternative embodiments of the present invention. Based on the technical solution of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A visualization method for the evolution process of the surrounding rock loosening zone of deep underground engineering, characterized in that: The following steps are involved: Data collection: The rock samples collected on site are scanned by CT scanner at different loading stages to obtain CT scan images. At the same time, monitoring sections and monitoring points are arranged at the deep underground engineering site, displacement, stress, and strain sensors are installed, and the surrounding rock information is obtained by using borehole camera technology; Numerical simulation: Use finite element software to establish a numerical model of deep underground engineering, select a viscoelastic-plastic constitutive model that takes into account rock damage and rheological properties, assign model parameters based on CT scan images and rock mechanics test results, and obtain corrected numerical simulation data through parameter inversion correction; Image processing and analysis: preprocessing of CT scan images, extraction of structural features, and identification of loose circles using specific algorithms; Visual display: Use 3D modeling software to build a 3D model of the surrounding rock, map the corrected numerical simulation data onto the 3D model, and design interactive functions to realize the viewing of the evolution of the surrounding rock loosening zone at different times and the data of specific locations.

2. The method for visualizing the evolution process of the surrounding rock loosening zone of a deep underground engineering according to claim 1 is characterized in that: The parameters of the laboratory CT scan were set as voltage 160 kV-220 kV, current 10 mA-20 mA, slice thickness 0.1 mm-0.5 mm, and loading rate controlled at 0.001 mm / s-0.01 mm / s.

3. The method for visualizing the evolution process of the surrounding rock loosening zone of a deep underground engineering according to claim 1 is characterized in that: In the on-site monitoring, the displacement sensor accuracy reaches 0.01mm, the stress sensor accuracy reaches 0.1MPa, and the strain gauge accuracy reaches 10 -6 .

4. The method for visualizing the evolution process of the loosening zone of surrounding rock in deep underground engineering according to claim 1 is characterized in that: The viscoelastic-plastic constitutive model considering rock damage and rheological properties is a coupling model of the generalized Kelvin model and the Mohr-Coulomb model, and the stress-strain relationship is: Among them, σ is stress, ε is strain, E1 and E2 are elastic moduli, which are used to measure the ability of rock to resist elastic deformation, η is the viscosity coefficient, which reflects the viscosity of rock, c is cohesion, which is the bonding force between rock particles, φ is the internal friction angle, which reflects the friction characteristics between rock particles, t is time, and τ is the integral variable.

5. The method for visualizing the evolution process of the surrounding rock loosening zone of a deep underground engineering according to claim 1 is characterized in that: In the image preprocessing, the median filter algorithm is used to remove the salt and pepper noise in the image. The filter window size is 3×3-5×5. The median filter algorithm formula is: g(x,y)=median{f(xi,yi),(x,y)∈W} Among them, f(x,y) is the pixel value of the original image at (x,y), g(x,y) is the pixel value of the filtered image at (x,y), and W is the filter window.

6. The method for visualizing the evolution process of the surrounding rock loosening zone of a deep underground engineering according to claim 1 is characterized in that: In the structural feature extraction, the Canny algorithm is used to identify the rock crack edges in the image. The Gaussian filter formula in the Canny algorithm step is: Among them, σ is the standard deviation of the Gaussian kernel, which is used to control the smoothness of the Gaussian filter, and (x, y) is the image coordinate.

7. The method for visualizing the evolution process of the surrounding rock loosening zone of a deep underground engineering according to claim 1 is characterized in that: The identification of the loose circle adopts a region growing algorithm based on thresholds. According to the physical properties and mechanical parameters of the rock, combined with the gray value distribution of the CT image, an initial threshold T0 is determined. Starting from the edge of the image, taking a pixel point (x0, y0) as the seed point, if its gray value Ι(x0, y0) is less than T0 and it is adjacent to the pixel points already marked as the loose area, then this pixel point is marked as the loose area, and then the loose area is continuously expanded. The expansion condition is that for the pixel point (x, y) in the currently marked loose area, its adjacent pixel point (x ′ , y ′ ) satisfies |Ι(x, y) - Ι(x ′ , y ′ )| < T1 and Ι(x ′ , y ′ ) < T0, where T1 is the set gray difference threshold used to control the difference degree of the gray values of adjacent pixels; Ι(x, y) and Ι(x ′ , y ′ ) are the gray values of the pixel points (x, y) and (x ′ , y ′ ) respectively. The formula indicates that when the difference between the gray values of adjacent pixel points is less than the gray difference threshold T1 and the gray value of the adjacent pixel point is less than the initial threshold T0, this adjacent pixel point is marked as the loose area until there are no pixel points that meet the conditions.

8. The method for visualizing the evolution process of the surrounding rock loosening zone of a deep underground engineering according to claim 1 is characterized in that: In constructing the three-dimensional model of the surrounding rock using the three-dimensional modeling software, three-dimensional modeling software Maya and 3ds Max are used to construct a three-dimensional model of the surrounding rock of the deep underground engineering according to the CT scan image and the corrected numerical simulation data results, and the internal structure information of the rock in the CT scan image is mapped to the surface of the three-dimensional model.

9. The method for visualizing the evolution process of the loosening zone of surrounding rock in deep underground engineering according to claim 1 is characterized in that: In the data fusion and mapping, the stress, strain and loose circle data obtained by numerical simulation are mapped to the corresponding positions of the three-dimensional model through an interpolation algorithm, and an inverse distance weighted interpolation algorithm is used, and its formula is: Among them, Z(x0,y0) is the interpolation result at the interpolation point (x0,y0), Z i is the value of the known data point i, d i is the distance from the interpolation point to the known data point i, p is the weight coefficient, which takes the value of 2. The formula calculates the weighted average of the distance between the interpolation point and the known data point to determine the value of the interpolation point, thereby realizing the mapping of data from numerical simulation to three-dimensional model.

10. A computer device, characterized in that: The user equipment includes a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1 to 9.

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