A tunnel primary support flatness calculation method and device based on a point cloud and a storage medium

Abstract

The application provides a tunnel primary support flatness calculation method and device based on point cloud, and a storage medium. The steps of the method comprise: embedding the point cloud data of a pretreated scanning section into a design contour map; obtaining a cross section of the design contour map based on the axial position of the point cloud, drawing a normal line of the design contour on the cross section, the normal line passing through each point of the point cloud, and recording the distance from the point on the normal line to the design contour line; keeping the distance from each point on the normal line to the design contour of the cross section unchanged, expanding the curved surface of the design contour map into a rectangular surface, adjusting the point cloud data into calculation point cloud data, and obtaining an expanded map; moving a detection window along a preset detection path, calculating the depth-length ratio parameter of each point after the detection window is moved each time; comparing the maximum depth-length ratio parameter after the detection window is moved each time with a preset threshold value, determining whether the detection result of each detection window is qualified, and determining whether the tunnel primary support flatness of the scanning section is qualified.

Description

Technical Field

[0001] This invention relates to the field of tunnel engineering technology, and in particular to a method, apparatus and storage medium for calculating the initial support flatness of a tunnel based on point cloud. Background Technology

[0002] After excavation and blasting in railway tunnel construction, initial support is required for the tunnel arch and the excavated contour surfaces on both sides of the arch. This is mainly achieved by installing steel arch frames and shotcreting. After the tunnel cross-section deformation converges and before the waterproof layer is applied, the flatness of the initial support needs to be tested. The purpose is to eliminate potential safety hazards such as water leakage caused by punctures in the waterproof membrane due to unevenness.

[0003] The common method for detecting the flatness of the tunnel initial support is to use a straightedge to measure the ratio of the depth of the indentation between two adjacent protrusions on the base surface to the distance between the two protrusions, i.e., the depth-to-length ratio, as an indicator of the flatness of the initial support. However, when using a straightedge manually, it is difficult to measure the arch top and other locations, and the range of manual detection is small. It is necessary to use a straightedge in conjunction with a feeler gauge to measure the depth of the indentation between the protrusions and then calculate the depth-to-length ratio. The detection steps are complicated, the detection range is wide, the detection is time-consuming and labor-intensive, and the results are greatly affected by the subjective consciousness of the surveyor. Summary of the Invention

[0004] In view of this, the present invention provides a method for calculating the initial support flatness of a tunnel based on point cloud, so as to eliminate or improve one or more defects existing in the prior art.

[0005] One aspect of the present invention provides a method for calculating the initial support smoothness of a tunnel based on point clouds, the method comprising the following steps:

[0006] The preprocessed point cloud data obtained by the LiDAR scanning tunnel is received, and the preprocessed point cloud data is embedded into the design outline diagram. The design outline diagram includes an arc surface with an arc cross-section and a rectangular surface set at the opening of the arc surface.

[0007] In the design outline diagram, for each point, the cross-section of the design outline diagram at that axial position is obtained based on the axial position of the point cloud, and the normal of the arc is drawn on the cross-section. The normal passes through each point, and the distance of the point on the normal to the design outline is recorded.

[0008] Keeping the distance between each point on the normal line on the cross section corresponding to the point cloud and the design contour unchanged, the arc surface of the design contour is unfolded into a rectangular surface, and the preprocessed point cloud data is adjusted into calculated point cloud data to obtain the unfolded diagram.

[0009] The unfolded diagram includes a rectangular surface and computed point cloud data. The rectangular surface of the unfolded diagram has a preset detection path. A preset detection window moves along the detection path. After each movement of the detection window, the depth value corresponding to the two point clouds is calculated based on the position of any two point clouds within the range of each detection line. The depth-to-length ratio parameter corresponding to the two point clouds is calculated based on the depth value. The largest depth-to-length ratio parameter within the range of the detection line is selected as the depth-to-length ratio parameter corresponding to the detection line. The depth-to-length ratio parameter of each detection line is compared, and the largest depth-to-length ratio parameter is selected as the depth-to-length ratio parameter corresponding to the detection window.

[0010] The depth-to-length ratio parameter corresponding to the detection window is compared with a preset threshold to determine whether the detection result of each detection window is qualified. The qualification status of each detection window in the detection path is statistically analyzed to determine whether the initial support flatness of the tunnel is qualified.

[0011] Using the above scheme, point cloud data is first acquired through LiDAR, then coordinate transformation is performed in conjunction with the design outline map, and the design outline map including point cloud data is then represented as an unfolded map. Detection is performed in the unfolded map through a preset detection path and detection window, and the depth-to-length ratio within each detection range is calculated. The depth-to-length ratio is used to determine whether the detection range is qualified, and further determines whether the initial support flatness of the tunnel is qualified. This scheme does not require manual intervention from staff, has simple steps, low measurement difficulty, a large detection range, and high inspection efficiency.

[0012] In some embodiments of the present invention, the point cloud data is preprocessed point cloud data obtained by mounting a lidar on a mobile tool and scanning along the centerline of a tunnel. In the step of embedding the preprocessed point cloud data into a design contour map, a simulated centerline is set on the rectangular face of the design contour map corresponding to the centerline of the tunnel. The centerline of the tunnel coincides with the simulated centerline, and the preprocessed point cloud data is embedded into the design contour map based on the correspondence between the centerline of the tunnel and the simulated centerline.

[0013] In some embodiments of the present invention, in the step of comparing the depth-to-length ratio parameter corresponding to the detection window with a preset threshold to determine whether the detection result of each detection window is qualified, if the depth-to-length ratio parameter is less than the preset threshold, it is qualified; if the depth-to-length ratio parameter is greater than or equal to the preset threshold, it is unqualified.

[0014] In some embodiments of the present invention, in the step of determining whether the tunnel initial support smoothness is qualified by statistically analyzing the pass / fail status of each detection window in the detection path, the total number of detections and the number of times the detection window fails are obtained, and the ratio of the number of times the window fails to the total number of detections is calculated. If the ratio is greater than a preset allowable threshold, the tunnel initial support smoothness is unqualified; if the ratio is less than or equal to the preset allowable threshold, the tunnel initial support smoothness is qualified.

[0015] In some embodiments of the present invention, in the step of moving a preset detection window along the detection path and calculating the depth value corresponding to the two point clouds based on the positions of any two point clouds within each detection line after each movement of the detection window, the detection window includes multiple detection lines intersecting at a point. The detection window is set on the unfolded diagram, and the detection lines are preset with a width threshold on the unfolded diagram. Point clouds within the width threshold range of any detection line of the detection window are included in the calculation.

[0016] In some embodiments of the present invention, the step of moving a preset detection window along the detection path, and calculating the depth value corresponding to the two point clouds based on the positions of any two point clouds within each detection line after each movement of the detection window, and calculating the depth-to-length ratio parameter corresponding to the two point clouds based on the depth value, includes:

[0017] Connect any two point clouds within the detection line range, project the connecting line and all point clouds within the detection line range onto the plane where the detection window is located, extract the point cloud covered by the projection of the connecting line, calculate the distance between the point cloud covered by the projection of the connecting line and the connecting line in three-dimensional space, and extract the maximum value of the calculated distance value as the depth value corresponding to the two point clouds.

[0018] Calculate the length of the line connecting two point clouds. If the length is greater than a preset detection length, calculate the depth-to-length ratio parameter based on the depth value and the length of the line.

[0019] If the length value is less than or equal to the preset detection length, the depth-to-length ratio parameter is calculated based on the depth value and the detection length.

[0020] In some embodiments of the present invention, if the length value is greater than a preset limited detection length, the depth-to-length ratio parameter of that point is calculated based on the following formula:

[0021]

[0022] Where D represents the depth value, L' represents the length of the connecting line, and P represents the depth-to-length ratio parameter;

[0023] If the threshold distance is less than or equal to the limited detection length, the depth-to-length ratio parameter of that point is calculated based on the following formula:

[0024]

[0025] Where Lmin represents the limited detection length.

[0026] In some embodiments of the present invention, in the step of calculating the limited detection length based on the length of the detection line, the limited detection length is calculated based on the following formula:

[0027] Lmin = A * L;

[0028] Where A is a preset percentage parameter, L represents the length parameter of the detection line, and Lmin represents the limited detection length.

[0029] The present invention also provides a point cloud-based tunnel initial support smoothness calculation device, which includes a computer device, the computer device including a processor and a memory, the memory storing computer instructions, the processor executing the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the device implements the steps of the method described above.

[0030] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned point cloud-based tunnel initial support smoothness calculation method.

[0031] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the text, or may be learned by practice of the invention. The objects and other advantages of the invention will become apparent from the specific details provided in the description and the accompanying drawings.

[0032] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0033] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to limit the scope of the invention.

[0034] Figure 1 This is a schematic diagram of one embodiment of the point cloud-based method for calculating the initial support smoothness of tunnels according to the present invention.

[0035] Figure 2 A cross-sectional schematic diagram for designing the outline;

[0036] Figure 3 This is a schematic diagram illustrating the detection process along the detection path.

[0037] Figure 4 This is a schematic diagram illustrating the operation of moving the detection window within a detection scenario. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0039] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0040] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0041] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0042] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0043] Introduction to existing technologies:

[0044] There is currently a method for detecting the flatness of tunnel lining trolley pouring. The steps are as follows: (1) Measure the initial position of the scanning module; (2) When the trolley completes the pouring of the lining surface and moves forward after retracting the template, the scanning module starts to run and collects the three-dimensional point cloud data of the lining surface; (3) Perform noise reduction and simplification processing on the three-dimensional point cloud data and store the processed three-dimensional point cloud data; (4) Unify the three-dimensional point cloud data of different scanning segments into the same coordinate system and splice them into the tunnel lining surface data that has been scanned. At the same time, determine whether the scanning module has completed the scan; (5) Fit the tunnel lining surface that has completed the point cloud splicing as the reference surface for flatness calculation; (6) Draw the flatness distribution map of the lining surface and perform flatness analysis.

[0045] The flatness algorithm is as follows:

[0046] (1) The trolley moves forward after completing the pouring, and the scanning module runs and obtains the distance and angle from each point on the plane to the scanning module in the two-dimensional plane;

[0047] (2) During the forward movement of the trolley, the displacement of the scanning module is recorded to obtain three-dimensional data of each point on the lining surface.

[0048] (3) Unify the relative coordinate systems under different scanning segments, and use the spatial coordinate system established with the initial position of the scanning module as the origin as the unified coordinate system for coordinate transformation.

[0049] (4) The converted data is stitched into the three-dimensional point cloud data of the tunnel lining surface that has been scanned.

[0050] (5) Based on the tunnel lining surface data obtained by splicing, the moving least squares method is used to obtain the fitted surface as the reference surface for flatness calculation. The flatness calculation is to draw a straight line perpendicular to the fitted surface through the three-dimensional point cloud position obtained by scanning, which is the normal of this scanning point. The length of the normal represents the distance between the actual point cloud data and the lining fitting reference surface, which is the flatness value of this point.

[0051] Disadvantages of existing technology

[0052] The algorithm requires coordinate transformation and data stitching, making the operation and calculation complex; the algorithm has errors in the process of coordinate transformation and data stitching; the algorithm is based on the scanning of the lining trolley, which has poor practicality and is not easy to promote; the method of fitting the curved surface is used to "shave the peaks and fill the valleys" on the actual tunnel initial support lining surface, and some representative feature points are removed, which causes errors in the flatness detection calculation.

[0053] In this solution, only the preprocessed point cloud data needs to be imported. The calculation is simple, and the distance from the point to the surface is calculated directly, which improves the calculation accuracy. Furthermore, it does not require surface fitting, which avoids the removal of feature points and improves the detection accuracy.

[0054] To solve the above problems, such as Figure 1 , 2 As shown, this invention proposes a method for calculating the initial support smoothness of a tunnel based on point clouds. The steps of the method include:

[0055] Step S100: Receive the preprocessed point cloud data obtained by the LiDAR scanning tunnel, and embed the preprocessed point cloud data into the design outline diagram. The design outline diagram includes an arc surface with an arc cross-section and a rectangular surface set at the opening of the arc surface.

[0056] In some embodiments of the present invention, the design outline specifications are the same as the designed tunnel parameters, which include horizontal curves, vertical curves, cross sections, etc.

[0057] In some embodiments of the present invention, the arch of the tunnel corresponds to the arc surface of the design outline, and the passage of the tunnel corresponds to the rectangular surface of the design outline.

[0058] Step S200: In the design outline drawing, for each point, obtain the cross-section of the design outline drawing at the axial position based on the axial position of the point cloud, draw the normal of the arc on the cross-section, the normal passes through the point, and record the distance of the point on the normal from the design outline.

[0059] In the specific implementation process, all points are three-dimensional point clouds. The x-axis parameter of the point cloud corresponds to the horizontal direction of the tunnel, the y-axis parameter of the point cloud corresponds to the axial direction of the tunnel, and the z-axis parameter of the point cloud corresponds to the height direction of the tunnel.

[0060] In the specific implementation process, the axial position of the point cloud is the y-axis parameter of the point cloud, and the cross-section of the axial position is the plane formed by the x-axis and z-axis at the position of the y-axis parameter of the point cloud in the design outline drawing.

[0061] In the specific implementation process, the center position of the arch is the origin of the coordinate system, with negative on the left and positive on the right, and the axial direction is the y-axis.

[0062] In the actual implementation process, all sections in the length direction of the design outline are the same, and each section includes an arc and a line segment connecting the two ends of the arc.

[0063] Step S300: Keep the distance between each point and the point on the normal line on the cross section corresponding to the point cloud unchanged, unfold the arc surface of the design contour map into a rectangular surface, and adjust the preprocessed point cloud data into calculated point cloud data to obtain the unfolded map;

[0064] In some embodiments of the present invention, in the step of unfolding the arc surface of the design outline into a rectangular surface, the normal is always kept perpendicular to the arc surface, and the point cloud is on the normal. After the arc surface is unfolded into a rectangular surface, the normal is perpendicular to the rectangular surface.

[0065] like Figure 3 , 4 As shown, in step S400, the unfolded diagram includes a rectangular surface and calculated point cloud data. The rectangular surface of the unfolded diagram has a preset detection path. A preset detection window moves along the detection path. After each detection window moves, the depth value corresponding to the two point clouds is calculated based on the position of any two point clouds within the range of each detection line. The depth-to-length ratio parameter corresponding to the two point clouds is calculated based on the depth value. The largest depth-to-length ratio parameter within the range of the detection line is selected as the depth-to-length ratio parameter corresponding to the detection line. The depth-to-length ratio parameter of each detection line is compared, and the largest depth-to-length ratio parameter is selected as the depth-to-length ratio parameter corresponding to the detection window.

[0066] In some embodiments of the present invention, the detection window includes multiple detection lines intersecting at a point, each detection line being located on the rectangular surface. The detection window is moved along the detection path on the rectangular surface to complete the detection calculation of the point cloud data.

[0067] In the specific implementation process, the intersection of multiple detection lines is moved along the detection path, so that the detection window moves along the detection path.

[0068] like Figure 3 , 4 As shown, in some embodiments of the present invention, the detection window may have four detection lines, which intersect to form a cross shape.

[0069] Figure 3 The grid in the calculation can be a pre-planned 5cm*5cm grid, which facilitates calculation.

[0070] In the specific implementation process, the detection window moves 5cm along the detection path each time.

[0071] Step S500: Compare the maximum depth-to-length ratio parameter obtained in each detection window, compare the maximum depth-to-length ratio parameter with a preset threshold, determine whether the detection result of each detection window is qualified, statistically analyze the qualification status of each detection window in the detection path, and determine whether the tunnel initial support flatness is qualified.

[0072] In some embodiments of the present invention, in the step of comparing and obtaining the maximum depth-to-length ratio parameter in each detection window, after each detection window moves, the depth-to-length ratio parameter corresponding to the point cloud within the range of each detection line is calculated, and the maximum depth-to-length ratio parameter within the range of each detection line is obtained by comparing the maximum depth-to-length ratio parameters of each detection line, and the maximum depth-to-length ratio parameter of the detection window in this detection is obtained by comparing the maximum depth-to-length ratio parameters of each detection line.

[0073] Using the above scheme, point cloud data is first acquired through LiDAR, then coordinate transformation is performed in conjunction with the design outline map, and the design outline map including point cloud data is then represented as an unfolded map. Detection is performed in the unfolded map through a preset detection path and detection window, and the depth-to-length ratio within each detection range is calculated. The depth-to-length ratio is used to determine whether the scanning range is qualified, and further determines whether the initial support flatness of the tunnel is qualified. This scheme does not require manual intervention from staff, has simple steps, low measurement difficulty, a large detection range, and high inspection efficiency.

[0074] In some embodiments of the present invention, the arc can be expanded using the formula δ=n*π*r / 180, where n is the central angle, r is the radius, and δ represents the length of the expanded line segment.

[0075] In some embodiments of the present invention, the point cloud data is preprocessed point cloud data obtained by mounting a lidar on a mobile tool and scanning along the centerline of a tunnel. In the step of embedding the preprocessed point cloud data into a design contour map, a simulated centerline is set on the rectangular face of the design contour map corresponding to the centerline of the tunnel. The centerline of the tunnel coincides with the simulated centerline, and the preprocessed point cloud data is embedded into the design contour map based on the correspondence between the centerline of the tunnel and the simulated centerline.

[0076] In the specific implementation process, the mobile tool can be a lining trolley. The staff controls the movement of the lining trolley and scans it with a lidar installed on the lining trolley to obtain pre-processed point cloud data.

[0077] In the specific implementation process, before embedding the preprocessed point cloud data into the design outline map, a step is also included to perform noise reduction processing on the scanned point cloud data.

[0078] In the specific implementation process, in the step of embedding the preprocessed point cloud data into the design outline based on the correspondence between the tunnel centerline and the simulated centerline, the positional relationship between the preprocessed point cloud data and the simulated centerline is established based on the positional relationship between the preprocessed point cloud data and the tunnel centerline, and the preprocessed point cloud data is embedded into the preset design outline based on the positional relationship between the preprocessed point cloud data and the simulated centerline.

[0079] In some embodiments of the present invention, in the step of comparing the depth-to-length ratio parameter corresponding to the detection window with a preset threshold to determine whether the detection result of each detection window is qualified, if the depth-to-length ratio parameter is less than the preset threshold, it is qualified; if the depth-to-length ratio parameter is greater than or equal to the preset threshold, it is unqualified.

[0080] In specific implementation, the threshold can be 0.01, 0.05 or 0.1, etc., preferably 0.05.

[0081] By adopting the above scheme, the coverage area of ​​the detection window is accurately calculated after each movement of the detection window, thereby improving the detection accuracy and enabling automated calculation, thus improving calculation efficiency.

[0082] In some embodiments of the present invention, in the step of determining whether the tunnel initial support smoothness is qualified by statistically analyzing the pass / fail status of each detection window in the detection path, the total number of detections and the number of times the detection window fails are obtained, and the ratio of the number of times the window fails to the total number of detections is calculated. If the ratio is greater than a preset allowable threshold, the tunnel initial support smoothness is unqualified; if the ratio is less than or equal to the preset allowable threshold, the tunnel initial support smoothness is qualified.

[0083] In some embodiments of the present invention, the allowable threshold can be 0.1, 0.2 or 0.3, etc., preferably 0.2.

[0084] In some embodiments of the present invention, in the step of moving a preset detection window along the detection path and calculating the depth value corresponding to the two point clouds based on the positions of any two point clouds within each detection line after each movement of the detection window, the detection window includes multiple detection lines intersecting at a point. The detection window is set on the unfolded diagram, and the detection lines are preset with a width threshold on the unfolded diagram. Point clouds within the width threshold range of any detection line of the detection window are included in the calculation.

[0085] In some embodiments of the present invention, in the unfolded diagram, the axial direction of the tunnel corresponds to the y-axis direction, the transverse direction of the tunnel corresponds to the x-axis direction, and the height direction of the tunnel corresponds to the z-axis direction. The rectangular surface of the unfolded diagram is a plane formed by the x-axis and y-axis with a fixed z-axis parameter. The width threshold of the detection line is the distance between the detection line and two line segments parallel to the detection line on the rectangular surface. The two ends of the two parallel lines are connected to form a rectangle on the rectangular surface. The range of the cuboid formed by stretching the rectangle in the z-axis direction is the width threshold range of the detection line.

[0086] In the specific implementation process, the two parallel line segments are both 5cm away from the detection line.

[0087] In practice, the detection window can rotate around the intersection of the detection lines as it moves along the detection path.

[0088] In some embodiments of the present invention, the detection range after the detection window is moved overlaps with the previous detection range.

[0089] The above method improves detection accuracy.

[0090] In some embodiments of the present invention, the step of moving a preset detection window along the detection path, and calculating the depth value corresponding to the two point clouds based on the positions of any two point clouds within each detection line after each movement of the detection window, and calculating the depth-to-length ratio parameter corresponding to the two point clouds based on the depth value, includes:

[0091] Connect any two point clouds within the detection line range, project the connecting line and all point clouds within the detection line range onto the plane where the detection window is located, extract the point cloud covered by the projection of the connecting line, calculate the distance between the point cloud covered by the projection of the connecting line and the connecting line in three-dimensional space, and extract the maximum value of the calculated distance value as the depth value corresponding to the two point clouds.

[0092] Calculate the length of the line connecting two point clouds. If the length is greater than a preset detection length, calculate the depth-to-length ratio parameter based on the depth value and the length of the line.

[0093] If the length value is less than or equal to the preset detection length, the depth-to-length ratio parameter is calculated based on the depth value and the detection length.

[0094] In some embodiments of the present invention, if the length value is greater than a preset limited detection length, the depth-to-length ratio parameter of that point is calculated based on the following formula:

[0095]

[0096] Where D represents the depth value, L' represents the length of the connecting line, and P represents the depth-to-length ratio parameter;

[0097] If the threshold distance is less than or equal to the limited detection length, the depth-to-length ratio parameter of that point is calculated based on the following formula:

[0098]

[0099] Where Lmin represents the limited detection length.

[0100] In some embodiments of the present invention, in the step of calculating the limited detection length based on the length of the detection line, the limited detection length is calculated based on the following formula:

[0101] Lmin = A * L;

[0102] Where A is a preset percentage parameter, L represents the length parameter of the detection line, and Lmin represents the limited detection length.

[0103] Using the above scheme, this scheme provides a method for calculating the depth-to-length ratio. In actual implementation, the preset percentage parameter A can be adjusted according to the actual situation to adjust the calculated depth-to-length ratio parameter, thereby improving the flexibility of the detection method.

[0104] In the specific implementation process, the detection lines on the unfolded diagram have a preset width threshold. Point clouds that are within the width threshold range of any detection line in the detection window are all within the detection line range. In the specific implementation process, the point cloud is projected onto the unfolded diagram. If the projection position is within the detection line range, then the point cloud is within the detection line range.

[0105] In some embodiments of the present invention, in the step of calculating the limited detection length based on the length of the detection line, the limited detection length is calculated based on the following formula:

[0106] Lmin = A * L;

[0107] Where A is a preset percentage parameter, L represents the length parameter of the detection line, and Lmin represents the limited detection length.

[0108] The beneficial effects of this plan include:

[0109] 1. This solution can simulate the process of manually using a straightedge to check flatness on-site, which is fast and efficient;

[0110] 2. This solution can scan the entire area in one scan, and all points in the point cloud are regarded as feature points and participate in the calculation.

[0111] 3. The data in this scheme is highly accurate, directly calculating the distance from a point to a surface (line), which reduces the calculation error compared to the surface fitting method;

[0112] 4. This solution covers a wide range of points, with a single measuring station containing tens of thousands of points, far more than manual measurements. It enables rapid flatness detection, effectively improving both the accuracy and efficiency of the results.

[0113] The present invention also provides a point cloud-based tunnel initial support smoothness calculation device, which includes a computer device, the computer device including a processor and a memory, the memory storing computer instructions, the processor executing the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the device implements the steps of the method described above.

[0114] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned point cloud-based tunnel initial support smoothness calculation method. The computer-readable storage medium can be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, floppy disk, hard disk, removable storage disk, CD-ROM, or any other form of storage medium known in the art.

[0115] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0116] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0117] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0118] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations of the embodiments of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calculating the initial support smoothness of a tunnel based on point clouds, characterized in that, The steps of the method include: The preprocessed point cloud data obtained by the LiDAR scanning tunnel is received, and the preprocessed point cloud data is embedded into the design outline diagram. The design outline diagram includes an arc surface with an arc cross-section and a rectangular surface set at the opening of the arc surface. In the design outline diagram, for each point, the cross section of the design outline diagram at that axial position is obtained based on the axial position of the point cloud, and the normal of the arc is drawn on the cross section. The normal passes through the point cloud, and the distance of the point on the normal line from the design outline line is recorded. Keeping the distance between each point and the point on the normal line on the cross section corresponding to the point cloud unchanged, the arc surface of the design contour is unfolded into a rectangular surface, and the preprocessed point cloud data is adjusted into calculated point cloud data to obtain the unfolded diagram. The unfolded diagram includes a rectangular surface and computed point cloud data. The rectangular surface of the unfolded diagram has a preset detection path. A preset detection window moves along the detection path. After each movement of the detection window, the depth value corresponding to the two point clouds is calculated based on the positions of any two point clouds within the range of each detection line. The detection window includes multiple detection lines that intersect at a point. The detection window is set on the unfolded diagram. The detection line has a preset width threshold on the unfolded diagram. Point clouds within the width threshold range of any detection line of the detection window are included in the calculation. The depth-to-length ratio parameter corresponding to the two point clouds is calculated based on the depth value. The maximum depth-to-length ratio parameter within the detection line range is selected as the depth-to-length ratio parameter corresponding to the detection line. The depth-to-length ratio parameter of each detection line is compared, and the maximum depth-to-length ratio parameter is selected as the depth-to-length ratio parameter corresponding to the detection window. The depth-to-length ratio parameter corresponding to the detection window is compared with a preset threshold to determine whether the detection result of each detection window is qualified. The qualification status of each detection window in the detection path is statistically analyzed to determine whether the initial support flatness of the tunnel is qualified.

2. The method for calculating the initial support smoothness of a tunnel based on point clouds according to claim 1, characterized in that, The point cloud data is preprocessed point cloud data obtained by mounting a lidar on a mobile tool and scanning along the centerline of the tunnel. In the step of embedding the preprocessed point cloud data into the design outline drawing, the rectangular face of the design outline drawing is provided with a simulated centerline corresponding to the centerline of the tunnel. The centerline of the tunnel coincides with the simulated centerline, and the preprocessed point cloud data is embedded into the design outline drawing based on the correspondence between the centerline of the tunnel and the simulated centerline.

3. The method for calculating the initial support smoothness of a tunnel based on point clouds according to claim 1, characterized in that, In the step of comparing the depth-to-length ratio parameter corresponding to the detection window with a preset threshold to determine whether the detection result of each detection window is qualified, if the depth-to-length ratio parameter is less than the preset threshold, it is qualified; if the depth-to-length ratio parameter is greater than or equal to the preset threshold, it is unqualified.

4. The method for calculating the initial support smoothness of a tunnel based on point clouds according to claim 1, characterized in that, In the step of determining whether the tunnel initial support smoothness is qualified by statistically analyzing the pass / fail status of each inspection window in the inspection path, the total number of inspections and the number of times the inspection window fails are obtained, and the ratio of the number of times the inspection window fails to the total number of inspections is calculated. If the ratio is greater than a preset allowable threshold, the tunnel initial support smoothness is unqualified; if the ratio is less than or equal to the preset allowable threshold, the tunnel initial support smoothness is qualified.

5. The method for calculating the initial support smoothness of a tunnel based on point clouds according to claim 1, characterized in that, The steps of moving the preset detection window along the detection path, calculating the depth value corresponding to two point clouds based on the positions of any two point clouds within each detection line after each movement of the detection window, and calculating the depth-to-length ratio parameter corresponding to the two point clouds based on the depth value include: Connect any two point clouds within the detection line range, project the connecting line and all point clouds within the detection line range onto the plane where the detection window is located, extract the point cloud covered by the projection of the connecting line, calculate the distance between the point cloud covered by the projection of the connecting line and the connecting line in three-dimensional space, and extract the maximum value of the calculated distance value as the depth value corresponding to the two point clouds. Calculate the length of the line connecting two point clouds. If the length is greater than a preset detection length, calculate the depth-to-length ratio parameter based on the depth value and the length of the line. If the length value is less than or equal to the preset detection length, the depth-to-length ratio parameter is calculated based on the depth value and the detection length.

6. The method for calculating the initial support smoothness of a tunnel based on point clouds according to claim 5, characterized in that, If the length value is greater than the preset detection length, the depth-to-length ratio parameter of that point is calculated based on the following formula: in, Indicates the depth value. This represents the length of the connecting line. This represents the depth-to-length ratio parameter; If the threshold distance is less than or equal to the limited detection length, the depth-to-length ratio parameter of that point is calculated based on the following formula: in, This indicates a limit on the detection length.

7. The method for calculating the initial support smoothness of a tunnel based on point clouds according to claim 5, characterized in that, In the step of calculating the limited detection length based on the length of the detection line, the limited detection length is calculated using the following formula: in, This is a preset percentage parameter. This indicates the length parameter of the detection line. This indicates a limit on the detection length.

8. A device for calculating the initial support smoothness of a tunnel based on point clouds, characterized in that, The device includes a computer device, which includes a processor and a memory, the memory storing computer instructions, and the processor executing the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps of the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-7.

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