A method and system for detecting tunnel flatness

Abstract

The application discloses a kind of methods and systems for detecting tunnel flatness, comprising: after carrying out initial support shotcrete construction to target tunnel, obtain the three-dimensional point cloud information of tunnel to be detected;Three-dimensional point cloud information is preprocessed, and obtain the three-dimensional point cloud to be calculated;According to excavation construction information, the design tunnel contour about the tunnel to be detected is constructed, and the three-dimensional point cloud to be calculated is compared with design tunnel contour, based on this, the flatness of each concave-convex part in the three-dimensional point cloud to be calculated is calculated.The adaptability, accuracy and efficiency of flatness detection technology are improved.

Description

Technical Field

[0001] This invention relates to the field of tunnel inspection technology, and in particular to a method and system for inspecting the flatness of tunnels. Background Technology

[0002] Over- or under-excavation of a tunnel directly affects the quality of excavation. Excessive over- or under-excavation makes it difficult for the tunnel to bear the load as designed, and it will also have a chain reaction on subsequent tunnel construction. Smoothness detection is a key link in tunnel construction.

[0003] Smoothness testing can evaluate the quality of shotcrete construction in the initial support of tunnels, take corresponding measures based on the test results, and help to carry out dynamic design of tunnel construction, so as to improve construction quality and enhance management and control.

[0004] In existing flatness testing technologies, "single-point" measurement is usually used, which has few measuring points and makes it difficult to reflect the flatness of the initial support of the tunnel.

[0005] In addition, some flatness detection technologies employ 3D scanning. These methods firstly, they underutilize the 3D reconstruction model and image feature information, lacking a complete set of methods from data acquisition, image processing, model reconstruction to detection and recognition. The data is a 3D point cloud, but the calculation methods remain 2D. Secondly, the accuracy of the final detection results obtained by these methods is related to the accuracy of the instrument, the accuracy of the station setup, and the processing methods. Low accuracy in any of these aspects will lead to larger errors. Furthermore, these methods take too long in data acquisition, processing, and evaluation. When non-measuring targets exist in the detection scene, they also need to be processed during data processing, increasing processing time. Therefore, existing 3D laser detection technology for tunnels is difficult to adapt to the complex environment of drill-and-blast tunnel construction and cannot meet the needs of efficient tunnel construction. Summary of the Invention

[0006] The purpose of this invention is to propose a scheme for detecting tunnel flatness, thereby forming a three-dimensional laser detection technology that is adapted to the complex environment of tunnel construction.

[0007] To address the aforementioned technical problems, this invention provides a method for detecting tunnel smoothness, comprising: obtaining three-dimensional point cloud information of the tunnel to be detected after initial support shotcrete construction of the target tunnel; preprocessing the three-dimensional point cloud information to obtain a three-dimensional point cloud to be calculated; constructing a design tunnel outline for the tunnel to be detected based on excavation construction information, and comparing the three-dimensional point cloud to be calculated with the design tunnel outline, thereby calculating the smoothness of each concave and convex part in the three-dimensional point cloud to be calculated.

[0008] Preferably, the step of comparing the three-dimensional point cloud to be calculated with the design tunnel outline, and calculating the flatness of each concave and convex part in the three-dimensional point cloud to be calculated, includes: traversing each point cloud point of the current concave and convex part, calculating the distance from each point cloud point to the design tunnel outline, selecting the flatness feature point of the current concave and convex part; and obtaining a flatness index for quantitatively evaluating the current concave and convex part based on the distance between the flatness feature point and the design tunnel outline, and the projected area of ​​the current concave and convex part on the design tunnel outline.

[0009] Preferably, the process of constructing the design tunnel profile includes: based on the excavation cross-sectional feature diagram indicated in the excavation construction information, combined with the starting point location of the target tunnel and the thickness of the initial support shotcrete, expanding the design profile of the tunnel to be tested to obtain the design profile.

[0010] Preferably, the preprocessing includes: sequential noise reduction and thinning processing, wherein the step of preprocessing the three-dimensional point cloud information to obtain the three-dimensional point cloud to be calculated includes: determining the average distance between each point in the three-dimensional point cloud information and its k nearest neighbors, and then, based on the average distance of each point, using a preset density threshold, filtering and removing outliers; performing three-dimensional meshing processing on the minimum bounding volume of the noise-reduced three-dimensional point cloud information, calculating the thinning processing feature points of each mesh containing point cloud points, and replacing the corresponding mesh with each thinning processing feature point to form the three-dimensional point cloud to be calculated.

[0011] Preferably, the following steps are used to denoise the 3D point cloud information: search for the k nearest neighbors of each point in the 3D point cloud information, and calculate the mean distance between each point and its k nearest neighbors to obtain the average distance of each point; calculate the mean distance of all point points based on the average distance of each point, thereby calculating the standard deviation of the average distance sequence formed by all point points, and based on this, determine the density threshold, which is the sum of the mean distance and the standard deviation of a preset multiple; compare the average distance of each point with the density threshold respectively, so that point points with an average distance greater than the density threshold are removed from the 3D point cloud information as outliers.

[0012] Preferably, the thinning feature point is the point cloud point within the corresponding grid that is closest to the centroid of all point cloud points.

[0013] Preferably, the method further includes: determining the integrity of the three-dimensional point cloud information of the tunnel to be detected; if there are occluded areas in the scanned information, then performing point cloud information compensation processing on the occluded areas.

[0014] Preferably, the point cloud compensation processing is selected from one of the following three methods: using the point cloud information of the occluded area in the design tunnel outline to compensate for the incomplete three-dimensional point cloud information; or compensating for the incomplete three-dimensional point cloud information based on the average distance between the scanned incomplete three-dimensional point cloud information and the design tunnel outline; or using the ratio of the area of ​​the occluded area to the area of ​​all scanned areas as the flatness index of the occluded area.

[0015] On the other hand, a system for detecting tunnel smoothness is provided, comprising: a three-dimensional point cloud acquisition module, used to obtain three-dimensional point cloud information of the tunnel to be detected after initial support shotcrete construction of the target tunnel; a point cloud preprocessing module, used to preprocess the three-dimensional point cloud information to obtain a three-dimensional point cloud to be calculated; and a smoothness calculation module, used to construct a design tunnel outline of the tunnel to be detected based on excavation construction information, and compare the three-dimensional point cloud to be calculated with the design tunnel outline, and based on this, calculate the smoothness of each concave and convex part in the three-dimensional point cloud to be calculated.

[0016] Preferably, the system further includes a point cloud compensation module, which is used to determine the integrity of the three-dimensional point cloud information of the tunnel to be detected, and if there are occluded areas in the scanned information, then point cloud information compensation processing is performed on the occluded areas.

[0017] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0018] This invention proposes a method and system for detecting tunnel smoothness. The method and system can complete the entire scanning and processing process with a clear workflow; it achieves automated processing and calculation, improving efficiency; and it combines three-dimensional scanning, three-dimensional data processing and calculation, and three-dimensional data evaluation. This invention can provide multi-view, multi-dimensional results images to meet the needs of all parties, providing a guarantee for information management, strengthening detection quality, and consolidating accountability systems.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0021] Figure 1 This is a step diagram of a method for detecting tunnel smoothness according to an embodiment of this application.

[0022] Figure 2 This is a schematic diagram illustrating the generation principle of the minimum inclusion volume in the point cloud thinning process of the method for detecting tunnel smoothness in this application embodiment.

[0023] Figure 3 This is a schematic diagram illustrating the generation principle of thinning feature points in the point cloud thinning process of the method for detecting tunnel smoothness in this application embodiment.

[0024] Figure 4 This is a schematic diagram illustrating the principle of comparing the three-dimensional point cloud to be calculated with the design contour in the method for detecting tunnel smoothness according to an embodiment of this application.

[0025] Figure 5 This is an example diagram of the concrete contour concave-convex point cloud in the method for detecting tunnel smoothness according to an embodiment of this application.

[0026] Figure 6 This is a schematic diagram illustrating the calculation principle of the smoothness index in the method for detecting tunnel smoothness according to an embodiment of this application.

[0027] Figure 7 This is an example diagram of incomplete three-dimensional point cloud information in the method for detecting tunnel smoothness according to an embodiment of this application.

[0028] Figure 8 This is a block diagram of a system for detecting tunnel smoothness according to an embodiment of this application. Detailed Implementation

[0029] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples, so that the process of how the present invention uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that, as long as there is no conflict, the various embodiments and features in the various embodiments of the present invention can be combined with each other, and the resulting technical solutions are all within the protection scope of the present invention.

[0030] Furthermore, the steps illustrated in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in a different order than that shown here.

[0031] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms “a” and “an” as used herein are also intended to include the plural. It should also be understood that the terms “comprising” and / or “including” as used herein specify the presence of the stated features, integers, steps, operations, units, and / or components, without excluding the presence or addition of one or more other features, integers, steps, operations, units, components, and / or combinations thereof.

[0032] Over- or under-excavation of a tunnel directly affects the quality of excavation. Excessive over- or under-excavation makes it difficult for the tunnel to bear the load as designed, and it will also have a chain reaction on subsequent tunnel construction. Smoothness detection is a key link in tunnel construction.

[0033] Smoothness testing can evaluate the excavation quality of shotcrete construction for initial support in tunnels, allow for appropriate remedial measures based on test results, and facilitate dynamic design of tunnel construction to improve construction quality and enhance management and control.

[0034] In existing flatness testing technologies, "single-point" measurement is usually used, which has few measuring points and makes it difficult to reflect the flatness of the initial support of the tunnel.

[0035] In addition, some flatness detection technologies employ 3D scanning. These methods firstly, they underutilize the 3D reconstruction model and image feature information, lacking a complete set of methods from data acquisition, image processing, model reconstruction to detection and recognition. The data is a 3D point cloud, but the calculation methods remain 2D. Secondly, the accuracy of the final detection results obtained by these methods is related to the accuracy of the instrument, the accuracy of the station setup, and the processing methods. Low accuracy in any of these aspects will lead to larger errors. Furthermore, these methods take too long in data acquisition, processing, and evaluation. When non-measuring targets exist in the detection scene, they also need to be processed during data processing, increasing processing time. Therefore, existing 3D laser detection technology for tunnels is difficult to adapt to the complex environment of drill-and-blast tunnel construction and cannot meet the needs of efficient tunnel construction.

[0036] Therefore, to address one or more of the aforementioned technical problems, this application proposes a method and system for detecting tunnel smoothness. This method and system, through the combination of three-dimensional scanning technology, three-dimensional data processing methods, and three-dimensional data evaluation methods, improves the adaptability, accuracy, and efficiency of smoothness detection technology.

[0037] Figure 1 This is a step diagram of a method for detecting tunnel smoothness according to an embodiment of this application. Figure 1 This is a step diagram illustrating a method for detecting tunnel smoothness according to an embodiment of this application. Figure 1As shown in the embodiment of the present invention, in the method for detecting tunnel smoothness (hereinafter referred to as the "smoothness detection method"), firstly, after the initial support shotcrete construction of the target tunnel is carried out in step S110, three-dimensional point cloud information of the tunnel to be detected is obtained. Then, step S120 preprocesses the three-dimensional point cloud information obtained in step S110 to obtain a three-dimensional point cloud to be calculated. Next, step S130 constructs a design tunnel outline for the tunnel to be detected based on the excavation construction information, and compares the three-dimensional point cloud to be calculated obtained in step S120 with the design tunnel outline. Based on this, the smoothness of each concave and convex part in the three-dimensional point cloud to be calculated is calculated.

[0038] The following is for reference. Figure 1 The specific process of the over-excavation and under-excavation detection method described in the embodiments of the present invention will be explained.

[0039] After the shotcrete is applied to the target tunnel where the tunnel to be inspected is located, step S110 uses three-dimensional laser scanning technology to perform a three-dimensional scan of the initial support flatness of the railway tunnel to be inspected, thereby obtaining the three-dimensional point cloud information of the tunnel to be inspected, and then proceeds to step S120.

[0040] In one embodiment, the preprocessing includes sequential noise reduction and thinning. In step S120, the 3D point cloud information obtained in step S110 is subjected to noise reduction and thinning processes sequentially to obtain the 3D point cloud to be calculated.

[0041] First, the noise reduction process in step S120 will be explained. Specifically, the average distance between each point in the 3D point cloud information obtained in step S110 and its k nearest neighbors is first determined. Then, based on the average distance of each point, outliers are filtered and removed using a preset density threshold.

[0042] Therefore, this embodiment of the invention identifies outliers by statistically analyzing the distribution density of points within an input point cloud region. The denser the point cloud, the higher the distribution density; conversely, the lower the density, the less dense the point cloud. Furthermore, the average distance between each point and its k nearest neighbors is defined as a density metric. If a point in the point cloud is less than a certain density threshold, it is considered an outlier and is removed from the list.

[0043] Specifically, the noise reduction process is as follows:

[0044] The first step is to search for the k nearest neighbors of each point in the 3D point cloud information and calculate the average distance between each point and all its k nearest neighbors, thereby obtaining the average distance of each point.

[0045] The second step assumes that the density of all points in the input point cloud follows a Gaussian distribution determined by the mean and standard deviation. Therefore, based on the average distance of each point in the point cloud, the mean of the average distances of all point cloud points is calculated, thereby calculating the standard deviation of the average distance sequence formed by all point cloud points. Based on the mean of the average distances of all point cloud points and the standard deviation of the average distance sequence, a density threshold (d) is determined. In this embodiment, the density threshold d is the sum of the mean average distance and the standard deviation, which is a preset multiple. In a preferred embodiment, the density threshold d is the mean average distance plus 1 to 3 times the standard deviation.

[0046] The third step is to compare the average distance of each point cloud point with the density threshold mentioned above, thereby removing point cloud points with an average distance greater than the density threshold as outliers from the 3D point cloud information, thus completing the noise reduction process of the 3D point cloud information.

[0047] The noise reduction processing of the three-dimensional point cloud information described in this invention can filter out interference data in the point cloud and reduce the interference of the complexity of the target area and the randomness of the process on the calculation results of over-excavation and under-excavation.

[0048] Next, the thinning process in step S120 will be explained. Figure 3 This is a schematic diagram illustrating the generation principle of thinning feature points in the point cloud thinning process of the method for detecting tunnel smoothness according to an embodiment of this application. Figure 3 As shown, in the thinning process, firstly, based on the denoised 3D point cloud information, the smallest bounding body matching the actual scanned point cloud is determined; then, the current smallest bounding body is subjected to 3D meshing, and the thinning feature points of each mesh containing point cloud points are calculated; finally, each thinning feature point replaces the corresponding mesh, thereby forming the 3D point cloud to be calculated. The thinning feature point is the point cloud point within the corresponding mesh that is closest to the centroid of all point cloud points.

[0049] Figure 2 This diagram illustrates the generation principle of the minimum bounding volume in the point cloud thinning process of the method for detecting tunnel smoothness according to an embodiment of this application. Before the point cloud thinning process, it is necessary to determine the minimum bounding volume (e.g., the minimum bounding cuboid) of the space containing the point cloud, such as... Figure 2As shown, a minimum bounding cuboid is constructed with xyz coordinates. Here, x represents the tunnel span, y represents the tunnel vertical direction, and z represents the tunnel excavation direction. This minimum bounding cuboid uses the nearest integer mileage section outside the target area as its starting face (the section containing the nearest integer mileage in the opposite Z direction outside the target area) and its ending face (the section containing the nearest integer mileage in the forward Z direction outside the target area). The top face is the horizontal plane containing the highest point in the vertical direction (the plane perpendicular to the y-axis where the point cloud has the maximum y-value), and the bottom face is the horizontal plane containing the arch line. The sides of the tunnel span (left side – the plane perpendicular to the X-axis where the point cloud has the minimum X-value, right side – the plane perpendicular to the X-axis where the point cloud has the maximum X-value) are tangent to the point cloud contour, thus completing the construction of the minimum bounding cuboid space.

[0050] refer to Figure 3 The point cloud thinning process described in this embodiment of the invention uses voxel filtering to determine the centroid and selects the point closest to the centroid of the point cloud within the grid as the output point, discarding other points. Specifically, based on the preset resolution required for 3D mesh division (e.g., a cube with a maximum side length of 10mm), the space of the smallest enclosing cuboid is divided into a 3D grid, see [link to relevant documentation]. Figure 3 The process iterates through all grid cells. For grid cells containing multiple points, the centroid of all point cloud points within the current grid is calculated, and the grid cell is replaced with the output point (thinned feature point) that is closest to the centroid. Grid cells that do not contain any point cloud points are directly filtered out. Finally, all output points (thinned feature points) are used as the thinned point cloud to obtain the 3D point cloud to be calculated.

[0051] The thinning process described in this invention can reduce the hardware requirements for flatness calculation and reduce the calculation time, enabling the flatness detection algorithm described in this invention to find a balance between accuracy, efficiency and control difficulty.

[0052] Figure 4 This is a schematic diagram illustrating the principle of comparing the three-dimensional point cloud to be calculated with the design contour in the method for detecting tunnel smoothness according to an embodiment of this application. (Reference) Figure 4 In step S130 of the present invention, the design of the tunnel outline model is first established, and then the preprocessed three-dimensional point cloud information is compared with the design tunnel outline, so as to calculate the flatness of each concave and convex part in the three-dimensional point cloud to be calculated based on the comparison result.

[0053] Furthermore, in this embodiment of the invention, based on the excavation cross-sectional feature diagram indicated in the excavation construction information, and in conjunction with the starting point location of the tunnel to be inspected and the thickness of the initial support shotcrete, the design contour of the tunnel to be inspected is expanded and simulated to obtain the design contour. See [link to relevant documentation]. Figure 4The left image shows the excavation cross-section feature diagram shown in the excavation construction information. The thickness of the initial support shotcrete is obtained, and then extended along the longitudinal axis of the tunnel to expand it into the three-dimensional tunnel outline for design.

[0054] When comparing the preprocessed 3D point cloud information with the designed tunnel outline, this embodiment of the invention first overlaps the actual point cloud outline (the preprocessed 3D point cloud information) with the designed 3D tunnel outline, and obtains the concrete outline convex and concave point cloud by comparing the actual outline with the designed outline. Figure 5 This is an example diagram of a concrete contour point cloud used in a method for detecting tunnel smoothness according to an embodiment of this application. By overlaying the pre-processed actual point cloud contour with the designed three-dimensional contour, the point cloud of the over-excavated or under-excavated portion can be obtained. See [link to relevant documentation]. Figure 5 This is to calculate the flatness index of each uneven part.

[0055] After obtaining the concrete contour point cloud, the concrete contour point cloud obtained in this embodiment of the invention can be divided into a three-dimensional mesh according to a preset precision (e.g., a cube with a side length of 50mm), and further divided into multiple concave and convex regions. Figure 5 This example demonstrates a concave-convex area formed by the actual concrete profile and the tunnel profile in the design.

[0056] In this embodiment of the invention, since the calculation methods for the flatness index of each concave and convex part are similar, this embodiment of the invention will only use the calculation method of the flatness index of a concave and convex area as an example for explanation.

[0057] Figure 6 This is a schematic diagram illustrating the calculation principle of the smoothness index in the method for detecting tunnel smoothness according to an embodiment of this application. (Reference) Figure 6 First, iterate through all point cloud points of the current uneven area and calculate the distance of each point cloud point to the design tunnel outline, then select the flatness feature point of the current uneven area. Specifically, the point cloud point in the current uneven area that is farthest from the design tunnel outline is taken as the flatness feature point of the current uneven area.

[0058] Then, based on the distance between the currently selected smoothness feature point and the design tunnel outline, and the projected area of ​​the current uneven part on the design tunnel outline, a smoothness index for quantitatively evaluating the current uneven part is obtained. The smoothness index of the current uneven part is calculated according to the following expression:

[0059]

[0060] Where γ represents the flatness index of the current uneven part, h maxThe distance between the current smoothness feature point and the designed tunnel outline is represented by S, and S represents the projected area of ​​the current uneven part on the designed tunnel outline. A higher smoothness index indicates worse smoothness; a lower smoothness index indicates better smoothness. Therefore, step S130 of this embodiment of the invention can obtain the smoothness index of uneven areas at different locations on the tunnel to be inspected, thereby enabling a quantitative evaluation of the smoothness distribution on the entire tunnel to be inspected.

[0061] Furthermore, in actual tunnel smoothness testing, the construction team may intentionally obscure parts of the testing area, resulting in incomplete 3D point cloud data collected in step S110. (See [link to relevant documentation]). Figure 7 ( Figure 7 This is an example diagram of incomplete three-dimensional point cloud information in the method for detecting tunnel smoothness according to an embodiment of this application. This results in partial occlusion of the scanned area, and after the point cloud data of the occluded areas is removed, a complete smoothness evaluation of the target area cannot be performed. Therefore, the smoothness detection method according to this embodiment further includes: determining the completeness of the three-dimensional point cloud information of the tunnel to be detected; if there are occluded areas in the scanned information, then performing point cloud information compensation processing on the occluded areas (areas lacking point cloud information).

[0062] In practical applications, the point cloud compensation processing described in this embodiment of the invention is selected from one of the following three methods.

[0063] The first point cloud compensation method involves simulating the design contour surface to form a point cloud for filling. Specifically, point cloud information located at the same position as the occluded area in the designed tunnel contour is used to compensate for the incomplete 3D point cloud information collected.

[0064] The second point cloud compensation method involves simulating the distance between the average over- or under-excavation point cloud of the unobstructed area and the design outline to fill in the gaps. Specifically, the incomplete 3D point cloud information is compensated based on the average distance between the scanned incomplete 3D point cloud information and the design tunnel outline.

[0065] The third point cloud compensation method: No filling is performed; compensation is based on the occlusion ratio η. To aid in evaluating the quality of the work. Specifically, using the area S of the obscured region. 遮挡 The area S of the entire scanned region 扫描 The ratio of is directly used as an indicator of the flatness of the shaded area.

[0066] It should be noted that, in order to avoid intentional occlusion in practical applications, the embodiments of the present invention typically directly select the third point cloud compensation processing method to compensate for the three-dimensional point cloud information.

[0067] On the other hand, based on the above-mentioned flatness detection method, this embodiment of the invention also provides a system for detecting tunnel flatness (also called "flatness detection system"). Figure 8 This is a block diagram of a system for detecting tunnel smoothness according to an embodiment of this application.

[0068] like Figure 8 As shown, the flatness detection system of this embodiment includes: a three-dimensional point cloud acquisition module 81, a point cloud preprocessing module 82, and a flatness calculation module 83. Specifically, the three-dimensional point cloud acquisition module 81 is implemented according to the method described in step S110 above, configured to obtain the three-dimensional point cloud information of the tunnel to be detected after the initial support shotcrete construction of the target tunnel; the point cloud preprocessing module 82 is implemented according to the method described in step S120 above, configured to preprocess the three-dimensional point cloud information to obtain the three-dimensional point cloud to be calculated; the flatness calculation module 83 is implemented according to the method described in step S130 above, configured to construct a design tunnel outline for the tunnel to be detected based on the excavation construction information, compare the three-dimensional point cloud to be calculated with the design tunnel outline, and calculate the flatness of each concave and convex part in the three-dimensional point cloud to be calculated.

[0069] In addition, the flatness detection system described in this embodiment of the invention further includes a point cloud compensation module 84. The point cloud compensation module 84 determines the integrity of the three-dimensional point cloud information of the tunnel to be detected. If there are occluded areas in the scanned information, point cloud information compensation processing is performed on the occluded areas.

[0070] This invention discloses a method and system for detecting tunnel smoothness. The method and system can complete the entire scanning and processing process with a clear workflow; it achieves automated processing and calculation, improving efficiency; and it combines three-dimensional scanning, three-dimensional data processing and calculation, and three-dimensional data evaluation. This invention can provide multi-view, multi-dimensional results images to meet the needs of all parties, providing a guarantee for information management, strengthening detection quality, and consolidating accountability systems.

[0071] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0072] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0073] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0074] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0075] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0076] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for detecting tunnel smoothness, characterized in that, include: After carrying out the initial support shotcrete construction on the target tunnel, the three-dimensional point cloud information of the tunnel to be inspected was obtained. The three-dimensional point cloud information is preprocessed to obtain the three-dimensional point cloud to be calculated; Based on the excavation construction information, a design tunnel outline for the tunnel to be inspected is constructed, and the three-dimensional point cloud to be calculated is compared with the design tunnel outline. Based on this, the flatness of each concave and convex part in the three-dimensional point cloud to be calculated is calculated. The step of comparing the three-dimensional point cloud to be calculated with the designed tunnel contour, and calculating the flatness of each uneven part in the three-dimensional point cloud to be calculated, includes: Traverse each point cloud point of the current uneven part and calculate the distance of each point cloud point to the design tunnel outline. Select the flatness feature point of the current uneven part and take the point cloud point that is farthest from the design tunnel outline in the current uneven part as the flatness feature point of the current uneven part. Based on the distance between the smoothness feature points and the designed tunnel outline, and the projected area of ​​the current uneven part on the designed tunnel outline, a smoothness index for quantitatively evaluating the current uneven part is obtained. The three-dimensional point cloud to be calculated is overlaid with the designed tunnel outline, and the concrete outline uneven point cloud is obtained by comparing the actual outline with the designed outline. The obtained concrete outline uneven point cloud is divided into three-dimensional meshes according to a preset accuracy and divided into multiple uneven parts. The smoothness index of the current uneven part is calculated according to the following expression: ； in, This indicates the flatness index of the current uneven parts. This indicates the distance between the current flatness feature point and the designed tunnel outline. This indicates the projected area of ​​the current uneven part on the designed tunnel outline.

2. The method according to claim 1, characterized in that, The process of constructing the tunnel outline for the design includes: Based on the excavation cross-sectional feature diagram indicated in the excavation construction information, and combined with the starting point location of the target tunnel and the thickness of the initial support shotcrete, the design profile of the tunnel to be tested is extended and simulated to obtain the design tunnel profile.

3. The method according to claim 1, characterized in that, The preprocessing includes sequential noise reduction and thinning processes, wherein the step of preprocessing the 3D point cloud information to obtain the 3D point cloud to be calculated includes: The average distance between each point in the three-dimensional point cloud information and its k nearest neighbors is determined. Then, based on the average distance of each point, outliers are filtered and removed using a preset density threshold. The minimum bounding volume of the noise-reduced 3D point cloud information is subjected to 3D meshing. The thinning feature points of each mesh containing point cloud points are calculated, and each thinning feature point replaces the corresponding mesh to form the 3D point cloud to be calculated.

4. The method according to claim 3, characterized in that, The following steps are used to perform noise reduction on the 3D point cloud information: Search for the k nearest neighbors of each point in the three-dimensional point cloud information, and calculate the average distance between each point and its k nearest neighbors to obtain the average distance of each point. Based on the average distance of each point cloud point, the mean distance of all point cloud points is calculated, and the standard deviation of the average distance sequence formed by all point cloud points is calculated. Based on this, the density threshold is determined, which is the sum of the mean distance and the standard deviation of a preset number of times. The average distance of each point cloud point is compared with the density threshold, and point cloud points whose average distance is greater than the density threshold are removed from the three-dimensional point cloud information as outliers.

5. The method according to claim 3, characterized in that, The thinning process is performed on the point cloud point that is closest to the centroid of all point cloud points within the corresponding grid.

6. The method according to claim 5, characterized in that, The method further includes: The integrity of the three-dimensional point cloud information of the tunnel to be detected is determined. If there are occluded areas in the scanned information, point cloud information compensation processing is performed on the occluded areas.

7. The method according to claim 6, characterized in that, Point cloud compensation processing is selected from one of the following three methods: The point cloud information of the occluded area in the tunnel outline used in the design is used to compensate for the incomplete 3D point cloud information; or The incomplete 3D point cloud information is compensated based on the average distance between the scanned incomplete 3D point cloud information and the designed tunnel outline; or The ratio of the area of ​​the obscured region to the area of ​​the entire scanned region is used directly as the flatness index of the obscured region.

8. A system for detecting tunnel smoothness, characterized in that, include: The 3D point cloud acquisition module is used to obtain the 3D point cloud information of the tunnel to be inspected after the initial support shotcrete construction of the target tunnel is carried out. A point cloud preprocessing module is used to preprocess the three-dimensional point cloud information to obtain the three-dimensional point cloud to be calculated. The flatness calculation module is used to construct a design tunnel outline for the tunnel to be inspected based on the excavation construction information, and compare the three-dimensional point cloud to be calculated with the design tunnel outline. Based on this, the flatness of each concave and convex part in the three-dimensional point cloud to be calculated is calculated. The flatness calculation module is also used to: traverse each point cloud point of the current concave and convex part, calculate the distance of each point cloud point to the design tunnel outline, select the flatness feature point of the current concave and convex part, and take the point cloud point that is farthest from the design tunnel outline in the current concave and convex part as the flatness feature point of the current concave and convex part. Based on the distance between the smoothness feature points and the designed tunnel outline, and the projected area of ​​the current uneven part on the designed tunnel outline, a smoothness index for quantitatively evaluating the current uneven part is obtained. The three-dimensional point cloud to be calculated is overlaid with the designed tunnel outline, and the concrete outline uneven point cloud is obtained by comparing the actual outline with the designed outline. The obtained concrete outline uneven point cloud is divided into three-dimensional meshes according to a preset accuracy and divided into multiple uneven parts. The smoothness index of the current uneven part is calculated according to the following expression: ； in, This indicates the flatness index of the current uneven parts. This indicates the distance between the current flatness feature point and the designed tunnel outline. This indicates the projected area of ​​the current uneven part on the designed tunnel outline.

9. The system according to claim 8, characterized in that, The system also includes: The point cloud compensation module is used to determine the integrity of the three-dimensional point cloud information of the tunnel to be detected. If there are occluded areas in the scanned information, point cloud information compensation processing is performed on the occluded areas.

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