A method and apparatus for detecting the flatness of an underwater structure

Abstract

Embodiments of the present application provide a flatness detection method and device for underwater buildings, and relate to the technical field of detection. The method comprises: acquiring three-dimensional point cloud data collected by an underwater three-dimensional laser scanning device for a surface of a preset underwater building; acquiring point cloud data of a unit area from the three-dimensional point cloud data; determining point cloud data of a concave area from the point cloud data of the unit area; performing three-dimensional reconstruction on the point cloud data of the concave area and the point cloud data of the unit area respectively to obtain a surface three-dimensional mesh of the concave area and a surface three-dimensional mesh of the unit area; calculating a first area of the concave area according to the surface three-dimensional mesh of the concave area; obtaining a second area of the unit area according to the surface three-dimensional mesh of the unit area; and performing flatness detection on the surface of the preset underwater building according to the first area and the second area. Thus, the present application can perform flatness detection on the surface of the preset underwater building based on three-dimensional point cloud data.

Description

Technical Field

[0001] This application relates to the field of testing technology, and more specifically, to a method and equipment for testing the flatness of underwater structures. Background Technology

[0002] Hydropower projects are crucial strategic infrastructure supporting the efficient development of hydropower and the comprehensive utilization of water resources. Affected by natural degradation, high-speed water flow, and frequent disasters, regular testing and quantitative evaluation of hydraulic concrete have become critical for ensuring the safe operation of hydropower projects. However, some important water diversion and spillway structures operate submerged for extended periods, lacking the conditions for drainage testing or incurring high drainage costs, leaving this portion of hydraulic concrete in a blind spot for safety risk management. Therefore, studying defects in hydraulic concrete, including cracks, erosion, and leakage, in underwater environments is essential for the stable operation of hydropower projects. In particular, due to the underwater concrete structure, the unevenness of the concrete surface caused by high-speed water flow exacerbates localized cavitation and erosion, which can worsen concrete defects and requires focused monitoring during routine maintenance. However, due to technological limitations, there is currently a lack of accurate and objective testing methods and application cases for the flatness of hydraulic concrete.

[0003] In existing technologies, underwater robots (such as multibeam sonar systems (such as rESON Seabat8125) and GPSrTK technology) are used to collect image data of hydraulic concrete to detect the smoothness of hydraulic concrete. This technology has been applied in practice to measure the unevenness of the upstream side of the dams of a power station in Northeast China and the Fengman Hydropower Station.

[0004] However, the aforementioned multibeam detection system can only operate at a distance of more than 5 meters from the shore underwater. This results in a blind spot in the measured data of the hydraulic concrete images acquired by the multibeam detection system. Furthermore, the hydraulic concrete images acquired by the multibeam detection system are mainly two-dimensional information without depth data, making it impossible to judge the flatness of the hydraulic concrete based on the acquired images. Summary of the Invention

[0005] The purpose of this application includes, for example, providing a method and device for detecting the flatness of an underwater structure, which can acquire three-dimensional point cloud data and perform flatness detection on the surface of a preset underwater structure based on the three-dimensional point cloud data, so as to realize the flatness detection of the surface of the preset underwater structure with different degrees of damage.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0007] In a first aspect, embodiments of this application provide a method for detecting the flatness of underwater structures, the method comprising:

[0008] Acquire 3D point cloud data of the surface of a pre-designed underwater structure using an underwater 3D laser scanning device;

[0009] Obtain point cloud data for a unit region from the three-dimensional point cloud data;

[0010] Determine the point cloud data of the concave region from the point cloud data of the unit region;

[0011] Three-dimensional reconstruction is performed based on the point cloud data of the recessed area and the point cloud data of the unit area to obtain the surface three-dimensional mesh of the recessed area and the surface three-dimensional mesh of the unit area.

[0012] Calculate the first area of ​​the recessed region based on the surface three-dimensional mesh of the recessed region;

[0013] The second area of ​​the unit region is obtained based on the surface three-dimensional mesh of the unit region;

[0014] The surface flatness of the preset underwater structure is tested based on the first area and the second area.

[0015] Optionally, before obtaining the point cloud data of a unit region from the three-dimensional point cloud data, the method further includes:

[0016] Noisy point cloud data is filtered out from the three-dimensional point cloud data to obtain the target point cloud data;

[0017] The step of obtaining point cloud data for a unit region from the three-dimensional point cloud data includes:

[0018] Obtain the point cloud data of the unit region from the target point cloud data.

[0019] Optionally, the step of filtering out noisy point cloud data from the three-dimensional point cloud data to obtain target point cloud data includes:

[0020] Calculate the Euclidean distance between any two point cloud data in the three-dimensional point cloud data;

[0021] Based on the Euclidean distance between any two point cloud data, point cloud data whose Euclidean distance is less than a first preset distance threshold is determined from the three-dimensional point cloud data as the noisy point cloud data;

[0022] The noisy point cloud data in the three-dimensional point cloud data is filtered out to obtain the target point cloud data.

[0023] Optionally, the step of filtering out noisy point cloud data from the three-dimensional point cloud data to obtain target point cloud data includes:

[0024] The number of point cloud data within a preset range centered on each point cloud data in the three-dimensional point cloud data is determined as the number of point cloud data corresponding to each point cloud data.

[0025] Calculate the average number of points based on the number of points corresponding to the multiple point cloud data;

[0026] The standard deviation of the point cloud for each point cloud data is determined based on the number of point clouds corresponding to each point cloud data and the average number of point clouds.

[0027] Based on the point cloud standard deviation of each point cloud data and a preset standard deviation threshold, point cloud data with a point cloud standard deviation less than the preset standard deviation threshold are identified as noisy point cloud data from the three-dimensional point cloud data.

[0028] The noisy point cloud data in the three-dimensional point cloud data is filtered out to obtain the target point cloud data.

[0029] Optionally, determining the point cloud data of the depression region from the point cloud data of the unit region includes:

[0030] The point cloud data of the unit region is fitted with a plane to obtain the target virtual plane;

[0031] Based on the target virtual plane, the point cloud data that is not on the target virtual plane from the target point cloud data is the point cloud data of the concave region.

[0032] Optionally, the step of performing planar fitting on the point cloud data of the unit region to obtain the target virtual plane includes:

[0033] At least three point cloud data points are randomly selected from the point cloud data of the unit region to generate an initial virtual plane;

[0034] The number of interior points of the initial virtual plane is determined based on the point cloud data of the unit region and the second preset distance threshold.

[0035] At least three point cloud data points are randomly selected from the point cloud data of the unit region to generate a new virtual plane, and the number of interior points of the new virtual plane is determined until the number of interior points is maximized. The virtual plane with the largest number of interior points is then determined as the target virtual plane.

[0036] Optionally, determining the number of interior points of the initial virtual plane based on the point cloud data of the unit region and the second preset distance threshold includes:

[0037] Determine the distance between other point cloud data in the point cloud data of the unit region and the initial virtual plane;

[0038] The number of interior points of the initial virtual plane is determined based on the number of point cloud data whose distance is less than the second preset distance threshold in the other point cloud data, and the number of randomly selected point cloud data.

[0039] Optionally, calculating the first area of ​​the recessed region based on the surface three-dimensional mesh of the recessed region includes:

[0040] The first area of ​​the recessed region is calculated based on the sum of the areas of all triangles in the surface three-dimensional mesh of the recessed region.

[0041] The step of obtaining the second area of ​​the unit region based on the surface three-dimensional mesh of the unit region includes:

[0042] The second area of ​​the unit region is calculated based on the sum of the areas of all triangles in the surface three-dimensional mesh of the unit region.

[0043] Optionally, the step of performing a flatness test on the surface of the preset underwater structure based on the first area and the second area includes:

[0044] The uneven area ratio is calculated based on the ratio of the first area to the second area; the uneven area ratio is used to characterize the smoothness of the unit area on the surface, and the larger the uneven area ratio, the worse the smoothness of the unit area.

[0045] Secondly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor can execute the machine-executable instructions to implement the underwater structure flatness detection method described in any of the first aspects.

[0046] Compared with the prior art, this application has the following beneficial effects:

[0047] This application provides a method and apparatus for detecting the flatness of an underwater structure. The method involves acquiring three-dimensional point cloud data collected by an underwater three-dimensional laser scanning device on the surface of a preset underwater structure; obtaining point cloud data for a unit area from the three-dimensional point cloud data; determining point cloud data for a concave area from the point cloud data of the unit area; performing three-dimensional reconstruction based on the point cloud data of the concave area and the point cloud data of the unit area to obtain a surface three-dimensional mesh for the concave area and a surface three-dimensional mesh for the unit area; calculating a first area of ​​the concave area based on the surface three-dimensional mesh of the concave area; obtaining a second area of ​​the unit area based on the surface three-dimensional mesh of the unit area; and performing flatness detection on the surface of the preset underwater structure based on the first and second areas. Therefore, this application can calculate the surface three-dimensional mesh of the concave region and the surface three-dimensional mesh of the unit region separately to obtain the first area of ​​the concave region and the second area of ​​the unit region, and then effectively calculate the area of ​​the surface three-dimensional mesh formed by the three-dimensional point cloud data, so as to improve the quantitative evaluation of the flatness detection method of underwater structure provided by this application, and then perform flatness detection on the surface of the preset underwater structure based on the three-dimensional point cloud data, so as to realize the flatness detection of the surface of the preset underwater structure with different degrees of damage. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 A schematic diagram of the structure of an underwater structure flatness detection system provided in an embodiment of this application;

[0050] Figure 2 A schematic diagram of an underwater three-dimensional laser scanning device provided in an embodiment of this application;

[0051] Figure 3 A schematic diagram of a data acquisition device provided in an embodiment of this application;

[0052] Figure 4 The flowchart of a method for detecting the flatness of an underwater structure provided in this application embodiment Figure 1 ;

[0053] Figure 5 A schematic diagram illustrating the line structured light principle of an underwater three-dimensional laser scanning device provided in this application embodiment;

[0054] Figure 6A schematic diagram illustrating different stages of three-dimensional point cloud data provided in an embodiment of this application;

[0055] Figure 7 The flowchart of a method for detecting the flatness of an underwater structure provided in this application embodiment Figure 2 ;

[0056] Figure 8 This is a schematic diagram illustrating the processing result of filtering noise from three-dimensional point cloud data, provided in an embodiment of this application.

[0057] Figure 9 A schematic diagram illustrating the cropping result of target point cloud data provided in an embodiment of this application;

[0058] Figure 10 The flowchart of a method for detecting the flatness of an underwater structure provided in this application embodiment Figure 3 ;

[0059] Figure 11 The flowchart of a method for detecting the flatness of an underwater structure provided in this application embodiment Figure 4 ;

[0060] Figure 12 The flowchart of a method for detecting the flatness of an underwater structure provided in this application embodiment Figure 5 ;

[0061] Figure 13 The flowchart of a method for detecting the flatness of an underwater structure provided in this application embodiment Figure 6 ;

[0062] Figure 14 The flowchart of a method for detecting the flatness of an underwater structure provided in this application embodiment Figure 7 ;

[0063] Figure 15 A schematic diagram illustrating the processing result of a target virtual plane provided in an embodiment of this application;

[0064] Figure 16 The flowchart of a method for detecting the flatness of an underwater structure provided in this application embodiment Figure 8 ;

[0065] Figure 17 A schematic diagram illustrating the unevenness of four preset point cloud data sets provided in an embodiment of this application;

[0066] Figure 18 A schematic diagram of the structure of an underwater structure flatness detection device provided in an embodiment of this application;

[0067] Figure 19This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0069] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0070] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0071] In the description of this application, it should be noted that if terms such as "upper," "lower," "inner," or "outer" are used to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is usually placed during use, they are only for the convenience of describing this application 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 this application.

[0072] Furthermore, the terms "first" and "second" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0073] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0074] Before providing a detailed explanation of the embodiments of this application, the application scenarios of these embodiments will be described first.

[0075] Due to defects in hydraulic concrete, serious damage can easily occur to major hydropower stations and underwater energy dissipation structures such as Wuqiangxi. Therefore, the acquisition of high-quality data and quantitative safety analysis of hydraulic concrete are of vital importance to the stable operation of hydropower projects.

[0076] For example, in a field application and verification of the stilling basin guide wall of a power station, an engineering application device was built to acquire three-dimensional point cloud data on the stilling basin guide wall. Based on the three-dimensional point cloud data processing library in the computer equipment, the acquired three-dimensional point cloud data was processed and analyzed. Then, based on the data processing and analysis results, the surface flatness of the preset underwater structure was detected, so as to realize the flatness detection of the surface of the preset underwater structure with different degrees of damage.

[0077] To clearly describe the method for detecting the flatness of underwater structures provided in the embodiments of this application, an exemplary underwater structure flatness detection system will be described in detail with reference to the accompanying drawings. Figure 1 This is a schematic diagram of a flatness detection system for underwater structures provided in an embodiment of this application. Figure 1 As shown, the underwater structure flatness detection system 100 may include: a data acquisition device 110 and a computer device 120.

[0078] The data acquisition device 110 can be communicatively connected to the computer device 120 to transmit the data collected in the data acquisition device 110 to the computer device 120 for data processing.

[0079] The data acquisition device 110 is a device equipped with an underwater 3D laser scanning device 105 that can be used in engineering applications, thereby acquiring underwater 3D point cloud data through the data acquisition device 110. This data acquisition device 110 can also be referred to as an engineering application device. The underwater 3D laser scanning device 105 is a scanner that selects line structured light based on the principle of triangulation. The model of the underwater 3D laser scanning device 105 can be selected according to actual needs; for example, the model of the underwater 3D laser scanning device 105 can be selected as ULS-200.

[0080] like Figure 2 As shown, Figure 2 This is a schematic diagram of an underwater three-dimensional laser scanning device provided in an embodiment of this application. The underwater three-dimensional laser scanning device 105 may include: a laser projection unit 105-1 and a camera imaging unit 105-2.

[0081] The laser projection unit 105-1 and the camera imaging unit 105-2 are fixedly connected. The laser projection unit 105-1 can project a 50° line structured light to scan the object 109 (such as the surface of a pre-designed underwater structure) for inspection. The underwater 3D laser scanning device 105 has 480 measurement points per line. Through the 360° rotating scanning head of the camera imaging unit 105-2, a full-coverage scan of the 3D space can be achieved. The scanning range of the camera imaging unit 105-2 can be selected according to the actual situation. For example, the scanning range of the camera imaging unit 105-2 can be selected from 0.36-2.5m, where the 2.5m scanning range corresponds to a resolution of 0.6mm.

[0082] Furthermore, the data acquisition device 110 may consist of a hoisting module 111, a detection module 112, and an operation module 113. The detection module 112 is fixedly connected to the hoisting module 111, and the operation module 113 is connected to both the fixedly connected detection module 112 and the hoisting module 111.

[0083] The hoisting module 111 consists of a boom platform 101, a winch 102, and an encoder 103. The boom platform 101 provides stable vertical movement to facilitate the movement of the underwater 3D laser scanning equipment. The dimensions of the boom platform 101 can be selected according to actual conditions; for example, the dimensions of the boom platform 101 can be selected as 2.2m high, 1.4m long, and 1m wide. The detection module 112 consists of a square frame 104 and an underwater 3D laser scanning equipment 105. The underwater 3D laser scanning equipment 105 is fixedly mounted on the square frame 104, which is fixed to the hoisting module 111. The square frame 104 provides a fixed positional relationship between the underwater 3D laser scanning equipment 105 and the guide wall of the preset underwater structure. The operation module 113 mainly consists of a power supply unit 106, a hoisting and remote control unit 107, and a scanner acquisition software 108. The hoisting and remote control unit 107 can be used to manually control the movement of the underwater 3D laser scanning device 105, and through the feedback of the encoder 109 in the operation module 113, the underwater 3D laser scanning device 105 can be deployed to a preset specific position underwater. The scanner acquisition software 108 can be used to set the acquisition parameters of the underwater 3D laser scanning device 105, and can also acquire underwater 3D point cloud data.

[0084] like Figure 3 As shown, Figure 3 This is a schematic diagram of a data acquisition device provided in an embodiment of this application. The data acquisition device 110 is equipped with an underwater three-dimensional laser scanning device 105.

[0085] In one possible implementation, when the data acquisition device 110 acquires 3D point cloud data, the engineering operator can remotely operate the operation module 113 from the work platform. First, the underwater 3D laser scanning device 105 in the detection module 112 is deployed to a predetermined specific underwater position via the hoisting module 111. After the underwater 3D laser scanning device 105 reaches the predetermined specific underwater position, it scans and transmits the scanned data to the scanner acquisition software 108 in the operation module 113 to acquire 3D point cloud data. After the 3D point cloud data acquisition is completed, the underwater 3D laser scanning device 105 is then vertically moved to the next predetermined position to acquire 3D point cloud data again. This process is repeated to achieve full coverage 3D point cloud data acquisition of the underwater portion of the surface of the predetermined underwater structure (such as a guide wall).

[0086] The computer device 120 can be an embedded edge computing device or a computing device with data processing capabilities; there are no restrictions, and it can be configured according to the actual situation. For example, the computer device 120 can be a computer device based on a 3D point cloud data processing library with a preset processing algorithm. The preset algorithm can be selected as a 3D point cloud data processing algorithm based on Open3D. The version of the 3D point cloud data processing library based on the preset processing algorithm can be arbitrarily selected; for example, the version of the 3D point cloud data processing library based on the preset processing algorithm can be selected as 0.16.0.

[0087] In one example, a display device may be integrated on the computer device 120. In another example, the computer device 120 may also be connected to an external display device. The external display device may be, for example, a monitoring monitor, a projector, a mobile phone, a tablet computer, a laptop computer, a terminal device, a virtual reality (VR) device, an augmented reality (AR) device, a digital TV, or a desktop computer, etc. There are no restrictions here, and it can be set according to the actual situation.

[0088] This application proposes a flatness detection system for underwater structures. Through a communication connection between a data acquisition device and a computer, underwater 3D point cloud data collected by the data acquisition device is transmitted to the computer, enabling the computer to analyze and process the collected 3D point cloud data, thereby performing flatness detection and analysis of the underwater structure. Therefore, this application's underwater structure flatness detection system can achieve the function of flatness detection and analysis of underwater structures using only a data acquisition device and a computer, reducing the complexity of the system while providing high accuracy and efficient, quantitative analysis.

[0089] The following is a detailed explanation and description of the underwater structure flatness detection method provided in the above embodiments of this application, with reference to the accompanying drawings. Figure 4 Example flow chart of a method for detecting the flatness of an underwater structure provided in this application embodiment. Figure 1 This method can be derived from, for example... Figure 1 The underwater structure flatness detection system 100 shown uses a computer device 120 in conjunction with a data acquisition device 110 to perform the detection. For example... Figure 4 As shown, the method for detecting the flatness of underwater structures includes the following steps:

[0090] S201. Acquire three-dimensional point cloud data collected by an underwater three-dimensional laser scanning device on the surface of a preset underwater structure.

[0091] In one possible implementation method, through Figure 1 The underwater 3D laser scanning device 105 in the data acquisition device 110 performs high-precision 3D point cloud data on the surface of the preset underwater structure. This process is also known as the data acquisition stage.

[0092] Specifically, such as Figure 2 As shown, the camera imaging unit 105-2 in the underwater three-dimensional laser scanning device 105 collects the line structure light emitted by the laser projection unit 105-1 on the object under test 109, and then collects and calculates the three-dimensional point cloud data of the object under test 109 (such as the surface of a preset underwater building) based on the principle of triangulation.

[0093] like Figure 5 As shown, Figure 5 This is a schematic diagram illustrating the line structured light principle of an underwater three-dimensional laser scanning device provided in this application embodiment. The line structured light emitted by the laser projection unit 105-1 projects a laser outline (e.g., the surface of a pre-set underwater structure) onto the object being measured 109 (such as the surface of a pre-set underwater structure). Figure 5 Point P (x) in w ,y w ,z w The location of the laser (e.g., point p) is used to form the laser plane, and the equation of the light plane projected by the laser (e.g., point p) is calculated using the following formula (1).

[0094] ax w +by w +cz w =d Formula (1)

[0095] Where a, b, c, and d are constants that determine the light plane.

[0096] Since the projection point of point P in the laser profile onto camera imaging unit 150-2 is P′(x c ,y c,z c The optical center of point P′ on camera imaging unit 150-2 is located at position O. c (0,0,0), then the optical center O c The projection point on the camera imaging unit 150-2 is O1(x1,y1,z1). The spatial line O is calculated using the following formula (2). c The equation of the line P′.

[0097]

[0098] Then, based on the above formulas (1) and (2), the coordinates (x, y) of point P are calculated using the following formula (3). w ,y w ,z w ).

[0099]

[0100]

[0101]

[0102] The coordinates (x, y) of point P of the line structured light were obtained according to the above formula (3). w ,y w ,z w Furthermore, since the camera imaging unit 105-2 of the underwater three-dimensional laser scanning device 105 can rotate the scanning head 360°, it can obtain three-dimensional point cloud data of a line structured light each time the scanning head rotates, thereby obtaining all three-dimensional point cloud data of the laser contour.

[0103] To facilitate a detailed explanation and description of the underwater structure flatness detection method provided in the above embodiments of this application, as follows: Figure 6 As shown, Figure 6 This is a schematic diagram of different stages of three-dimensional point cloud data provided in an embodiment of this application. Figure 6 (a) is a cross-sectional schematic diagram of the surface of a pre-designed underwater structure (such as hydraulic concrete). Figure 6 (b) is a schematic diagram of the laser profile of hydraulic concrete obtained by the three-dimensional laser scanner 105.

[0104] S202. Obtain point cloud data of a unit area from 3D point cloud data.

[0105] In one possible implementation, noise processing is performed on the acquired 3D point cloud data, such as... Figure 6 As shown in (c), by limiting the three-dimensional point cloud data to a preset range in the three axes (such as the XYZ axes), the point cloud data outside the preset range is clipped, retaining the coordinates that satisfy xn ∈(x min ,x max ),y n ∈(y min ,y max ),z n ∈(z min ,z max The point cloud data is used to remove point cloud data outside the effective detection area (unit area), i.e., to obtain the point cloud data of the unit area, such as... Figure 6 As shown in (d), the point cloud data of this unit region is valid point cloud data. The preset range is the unit region, which satisfies (0,0,0)~(x... n y n , z n ).

[0106] S203. Determine the point cloud data of the concave region from the point cloud data of the unit region.

[0107] In one possible implementation, a virtual plane is fitted based on the point cloud data of a unit region, and the result of the virtual plane fitting is used as follows: Figure 6 As shown in (e), the point cloud data of the concave region in the point cloud data of the unit region is determined, such as... Figure 6 As shown in (f).

[0108] Among them, the point cloud data of the recessed area is the main feature of the uneven area on the surface of the preset underwater structure.

[0109] S204. Perform three-dimensional reconstruction based on the point cloud data of the concave region and the point cloud data of the unit region to obtain the surface three-dimensional mesh of the concave region and the surface three-dimensional mesh of the unit region.

[0110] In one possible implementation, a preset contour reconstruction algorithm is used to calculate and 3D reconstruct the point cloud data of the concave region, resulting in a point cloud data consisting of polygons, which forms a 3D mesh corresponding to the surface of the concave region. Similarly, a preset contour reconstruction algorithm is used to calculate and 3D reconstruct the point cloud data of a unit area, resulting in a point cloud data consisting of polygons, which forms a 3D mesh corresponding to the surface of the unit area. The preset contour reconstruction algorithm can be selected according to the actual situation; for example, the preset contour reconstruction algorithm can be selected as the Alpha-Shapes algorithm.

[0111] It should be noted that the preset contour reconstruction algorithm for 3D reconstruction of point cloud data of the concave region and point cloud data of the unit region can be the same or different, and there is no restriction here.

[0112] S205. Calculate the first area of ​​the recessed region based on the surface three-dimensional mesh of the recessed region.

[0113] In one possible implementation, a mesh statistical analysis is performed on the surface 3D mesh of the recessed region, and then the first area of ​​the recessed region, Area, is calculated based on the statistical results. Unflatness The first area Unflatness This indicates the area of ​​the uneven region after the surface of a pre-designed underwater structure is damaged.

[0114] S206. Based on the surface three-dimensional mesh of the unit region, obtain the second area of ​​the unit region.

[0115] In one possible implementation, a mesh statistical analysis is performed on the surface 3D mesh of a unit region, and then the second area of ​​the unit region, Area, is calculated based on the statistical results. reference The second area reference This represents the area of ​​the study region when the surface of the preset underwater structure remains intact; that is, the projected area of ​​the point cloud data onto the target virtual plane.

[0116] S207. Based on the first area and the second area, perform a flatness test on the surface of the preset underwater structure.

[0117] In one possible implementation, based on the first area... Unflatness Second Area reference The surface flatness of a pre-designed underwater structure is tested, making the flatness test of the pre-designed underwater structure's surface quantifiable.

[0118] For example, if the second area under study is... reference Given a fixed area, when the surface defects of the pre-designed underwater structure cause minimal damage, the first area is [area]. Unflatness Smaller; when the surface defects of the pre-designed underwater structure are significantly damaged, the first area... Unflatness Larger. If damage occurs to the surface of the pre-designed underwater structure (such as hydraulic concrete), i.e., the first area... Unflatness Given a given situation, when the second area... reference The larger the area, the less flat the surface of the underwater structure will be; when the second area... reference The smaller the value, the greater the flatness of the surface of the underwater structure.

[0119] This application provides a method for detecting the flatness of an underwater structure. The method involves acquiring three-dimensional point cloud data collected by an underwater three-dimensional laser scanning device on the surface of a preset underwater structure; obtaining point cloud data for a unit area from the three-dimensional point cloud data; determining point cloud data for a concave area from the point cloud data of the unit area; performing three-dimensional reconstruction based on the point cloud data of the concave area and the point cloud data of the unit area to obtain a surface three-dimensional mesh for the concave area and a surface three-dimensional mesh for the unit area; calculating a first area of ​​the concave area based on the surface three-dimensional mesh of the concave area; obtaining a second area of ​​the unit area based on the surface three-dimensional mesh of the unit area; and performing flatness detection on the surface of the preset underwater structure based on the first and second areas. Therefore, this application can calculate the surface three-dimensional mesh of the concave region and the surface three-dimensional mesh of the unit region separately to obtain the first area of ​​the concave region and the second area of ​​the unit region, and then effectively calculate the area of ​​the surface three-dimensional mesh formed by the three-dimensional point cloud data, so as to improve the quantitative evaluation of the flatness detection method of underwater structure provided by this application, and then perform flatness detection on the surface of the preset underwater structure based on the three-dimensional point cloud data, so as to realize the flatness detection of the surface of the preset underwater structure with different degrees of damage.

[0120] The following is a detailed explanation and description of the underwater structure flatness detection method provided in the above embodiments of this application, with reference to the accompanying drawings. Figure 7 Example flow chart of a method for detecting the flatness of an underwater structure provided in this application embodiment. Figure 2 .like Figure 7 As shown, before obtaining the point cloud data of a unit region from the 3D point cloud data in the above method, the method may further include:

[0121] S301. Filter out noisy point cloud data from the 3D point cloud data to obtain the target point cloud data.

[0122] In one possible implementation, since the three-dimensional point cloud data is easily affected by impurities in the water (such as silt, plankton, aquatic plants, etc.) during the acquisition process, and the three-dimensional point cloud data is disordered, the features of the measured object 109 (such as the surface of the preset underwater building) cannot be directly extracted. Therefore, it is necessary to perform data filtering processing on the underwater three-dimensional point cloud data acquired by the underwater three-dimensional laser scanning device 105 to identify abnormal three-dimensional point cloud data (such as noise point cloud data) generated in the preset water body, and then filter out the noise point cloud data from the three-dimensional point cloud data to obtain the damage features of the surface of the preset underwater building (such as the surface of hydraulic concrete), i.e., the target point cloud data.

[0123] For example, such as Figure 8 As shown, Figure 8This is a schematic diagram illustrating the processing result of filtering noise from three-dimensional point cloud data, as provided in an embodiment of this application. Figure 8 The gray point cloud data in the image represents noisy point cloud data, while the pure black point cloud data represents valid point cloud data.

[0124] Obtain point cloud data for a unit region from 3D point cloud data, including:

[0125] S302. Obtain point cloud data of a unit area from the target point cloud data.

[0126] In one possible implementation, based on the target point cloud data obtained above, and by limiting the target point cloud data to a preset range in three axes (such as the XYZ axes), point cloud data outside the preset range is cropped, retaining coordinates that satisfy x n ∈(x min ,x max ),y n ∈(y min ,y max ),z n ∈(z min ,z max The point cloud data is used to remove point cloud data outside the valid detection area (unit area), thus obtaining the point cloud data of the unit area. This unit area's point cloud data is considered valid. The preset range is the unit area, which satisfies (0,0,0)~(x... n y n , z n ).

[0127] For example, such as Figure 9 As shown, Figure 9 This is a schematic diagram illustrating the cropping result of target point cloud data provided in an embodiment of this application. Figure 9 The gray point cloud data in the diagram represents noisy point clouds, while the pure black point cloud data represents valid point cloud data. Furthermore, due to the limitations of the underwater 3D laser scanning equipment 105's measurement range—specifically, the influence of the rope barrier on the hoisting module 111 in the data acquisition device 110—the acquired target point cloud data includes point cloud data from areas outside the guide wall. To retain the most valid data within the target point cloud data, a unit area of ​​x is used. n ∈(0.05,0.48), y n ∈(0.34,0.5), z n ∈(0.18,0.2), the filtered target point cloud data is cropped to remove point cloud data outside the effective detection area (unit area), that is, to obtain the point cloud data of the unit area.

[0128] The underwater structure flatness detection method provided in this application filters out noisy point cloud data from three-dimensional point cloud data to obtain target point cloud data, and then obtains point cloud data of a unit area from the target point cloud data. Therefore, this application performs data filtering processing on the obtained three-dimensional point cloud data to identify noisy point cloud data generated in a preset water body, and then filters out the noisy point cloud data from the three-dimensional point cloud data to obtain the surface damage characteristics of the preset underwater structure, i.e., the target point cloud data, thereby ensuring the data validity of the target point cloud data.

[0129] exist Figure 7 Based on the above, the method for detecting the flatness of underwater structures provided in the embodiments of this application will be explained and described in detail below with reference to the accompanying drawings. Figure 10 Example flow chart of a method for detecting the flatness of an underwater structure provided in this application embodiment. Figure 3 .like Figure 10 As shown, the method described above for filtering out noisy point cloud data from 3D point cloud data to obtain target point cloud data may include:

[0130] S401. Calculate the Euclidean distance between any two point cloud data in a 3D point cloud dataset.

[0131] In one possible implementation method, it can be achieved through... Figure 5 The underwater 3D laser scanning device 105 acquires the laser contour of the surface of a preset underwater structure (such as hydraulic concrete), and then obtains 3D point cloud data, which can be represented as P = {p1, p2, p3, ..., p n}, where any point cloud data p in the 3D point cloud data n It can be represented as p n ={x n ,y n ,z n}, where n is a positive integer greater than or equal to 1.

[0132] Since the three-dimensional point cloud data is easily affected by impurities in the water during the acquisition process, the acquired three-dimensional point cloud data contains noise. The following formula (4) can be used to calculate the Euclidean distance dist between any two points in the three-dimensional point cloud data.

[0133]

[0134] Where n and m are the indices of any two points in the 3D point cloud data.

[0135] S402. Based on the Euclidean distance between any two point cloud data, determine the point cloud data whose Euclidean distance is less than a first preset distance threshold from the three-dimensional point cloud data as noisy point cloud data.

[0136] The first preset distance threshold can be selected according to the actual situation. For example, the first preset distance threshold can be selected as 5.

[0137] In one possible implementation, any point cloud data such as p can be selected. n And using point cloud data such as p n Let the center of the sphere be a preset radius r (e.g., 0.005), then calculate the point cloud data as shown in p. n The Euclidean distance dist between the point cloud data and other point cloud data is calculated, and the point cloud data is statistically analyzed as p. n The number N of all point cloud data whose Euclidean distance dist between them and other point cloud data is less than a preset radius r (e.g., 0.005) is considered to be p when the number N of all point cloud data is less than a first preset distance threshold (e.g., 5). n This is noisy point cloud data.

[0138] It should be noted that noisy point cloud data can include isolated point clouds and invalid point clouds. An isolated point cloud is a neighborhood where the point cloud data exists, and there are no other point cloud data in the neighborhood besides the point cloud data. An invalid point cloud is a point cloud data that is less than a preset density value, in which case the point cloud is invalid.

[0139] S403. Filter out noisy point cloud data in the 3D point cloud data to obtain the target point cloud data.

[0140] In one possible implementation, noisy point cloud data (such as isolated point clouds and invalid point clouds) in the 3D point cloud data are screened and filtered out in order to extract valid point cloud data from the 3D point cloud data, that is, to obtain the target point cloud data.

[0141] The underwater structure flatness detection method provided in this application calculates the Euclidean distance between any two point cloud data in three-dimensional point cloud data; based on the Euclidean distance between any two point cloud data, it identifies point cloud data whose Euclidean distance is less than a first preset distance threshold as noisy point cloud data; and it filters out the noisy point cloud data in the three-dimensional point cloud data to obtain target point cloud data. Therefore, this application can identify point cloud data with an Euclidean distance less than the first preset distance threshold as noisy point cloud data from three-dimensional point cloud data and filter them out, so that the obtained target point cloud data is valid.

[0142] Optionally, in Figure 7 Based on the above, the method for detecting the flatness of underwater structures provided in the embodiments of this application will be explained and described in detail below with reference to the accompanying drawings. Figure 11 Example flow chart of a method for detecting the flatness of an underwater structure provided in this application embodiment. Figure 4 .like Figure 11As shown, the method described above for filtering out noisy point cloud data from 3D point cloud data to obtain target point cloud data may include:

[0143] S501. Determine the number of point cloud data within a preset range centered on each point cloud data in the three-dimensional point cloud data, which is the number of point cloud data corresponding to each point cloud data.

[0144] The preset range is a preset radius with any point cloud data point as the center. This preset radius can be selected according to the actual situation. For example, the preset radius can be 3.

[0145] In one possible implementation, the 3D point cloud data P = {p1, p2, p3, ..., p n There are N points in the 3D point cloud data P. Any point in the N points, such as p... n ={x n ,y n ,z n}, then p n With point P as the center of a sphere and a preset radius of r (e.g., 0.005), the sphere of this 3D point cloud data P can include N. rq If there are N points, then the N rq The number of points is p. n The neighborhood of a point with radius r is a point whose radius r is p. n The domain of the points, and according to the above formula (4), statistically analyze any one point in the three-dimensional point cloud data P (such as p). n The Euclidean distance (dist) from other point cloud data is less than that of any single point (e.g., p). n The number of point cloud data corresponding to a preset radius r (e.g., 0.005). That is, to count the number of points in the point cloud data corresponding to any given point (e.g., p). n The number of point cloud data inside the sphere corresponding to the preset radius r (e.g., 0.005).

[0146] It should be noted that the minimum number of point cloud data in the domain can be set to 17 to ensure effective removal of noisy point cloud data.

[0147] S502. Calculate the average number of point clouds based on the number of point clouds corresponding to multiple point cloud data.

[0148] In one possible implementation, the average number of point clouds is calculated using the following formula (5) based on the number of point clouds corresponding to the above multiple point cloud data.

[0149]

[0150] S503. Determine the standard deviation of the point cloud for each point cloud data based on the number of point clouds and the average number of point clouds corresponding to each point cloud data.

[0151] In one possible implementation, based on each point cloud data p n Corresponding number of points and average number of points The following formula (6) is used to calculate each point cloud data p. n The standard deviation of the point cloud σ p .

[0152]

[0153] S504. Based on the standard deviation of each point cloud data and the preset standard deviation threshold, identify point cloud data whose standard deviation is less than the preset standard deviation threshold as noisy point cloud data from the three-dimensional point cloud data.

[0154] Among them, the preset standard deviation threshold σ max This can be selectively chosen based on actual circumstances; for example, the preset standard deviation threshold σ. max The value can be set to 0.5.

[0155] In one possible implementation, based on each point cloud data p n The standard deviation of the point cloud σ p and the preset standard deviation threshold σ max And determine each point cloud data p n The standard deviation of the point cloud σ p and the preset standard deviation threshold σ max The size of a point cloud data p n The standard deviation of the point cloud σ p Greater than the preset standard deviation threshold σ max Then the point cloud data p n Given noisy point cloud data, if a certain point cloud data p n The standard deviation of the point cloud σ p Less than or equal to the preset standard deviation threshold σ max Then the point cloud data p n For effective point cloud data.

[0156] S505. Filter out noisy point cloud data in the 3D point cloud data to obtain the target point cloud data.

[0157] In one possible implementation, for a certain point cloud data p in the 3D point cloud data... n The standard deviation of the point cloud σ p Greater than the preset standard deviation threshold σ max The corresponding point cloud data is filtered and removed to extract valid point cloud data from the 3D point cloud data, thus obtaining the target point cloud data.

[0158] The underwater structure flatness detection method provided in this application determines the number of point cloud data points within a preset range centered on each point cloud data point in the three-dimensional point cloud data as the number of point cloud data points corresponding to each point cloud data point; calculates the average number of point cloud data points based on the number of point cloud data points corresponding to multiple point cloud data points; determines the standard deviation of the point cloud data point for each point cloud data point based on the number of point cloud data points corresponding to each point cloud data point and the average number of point cloud data points; identifies point cloud data point with a standard deviation less than the preset standard deviation threshold as noisy point cloud data from the three-dimensional point cloud data point based on the standard deviation of the point cloud data point for each point cloud data point and a preset standard deviation threshold; and filters out the noisy point cloud data point in the three-dimensional point cloud data point to obtain the target point cloud data. Therefore, this application can identify point cloud data point with a standard deviation less than the preset standard deviation threshold as noisy point cloud data point in the three-dimensional point cloud data point and filter them out, so that the obtained target point cloud data point data point is valid.

[0159] The following is a detailed explanation and description of the underwater structure flatness detection method provided in the above embodiments of this application, with reference to the accompanying drawings. Figure 12 Example flow chart of a method for detecting the flatness of an underwater structure provided in this application embodiment. Figure 5 .like Figure 12 As shown, the method described above for determining the point cloud data of the depression region from the point cloud data of a unit region may include:

[0160] S601. Perform plane fitting on the point cloud data of a unit area to obtain the target virtual plane.

[0161] In one possible implementation, in order to obtain the surface of a pre-defined underwater structure (such as a hydraulic concrete plane) before destruction, a pre-defined plane fitting algorithm is used to fit the point cloud data of a unit area to obtain the target virtual plane.

[0162] The preset plane fitting algorithm can be selected according to the actual situation. For example, the preset plane fitting algorithm can be selected as the RANSAC algorithm.

[0163] S602. Based on the target virtual plane, the point cloud data that is not on the target virtual plane from the target point cloud data is the point cloud data of the concave region.

[0164] In one possible implementation, based on the distance relationship between the target virtual plane and the target point cloud data, the point cloud data that is not on the target virtual plane is identified as the point cloud data of the recessed area. The point cloud data of the recessed area is also called the point cloud data of the outer point. The point cloud data of the outer point represents the surface of the preset underwater structure with different degrees of damage.

[0165] The underwater structure flatness detection method provided in this application performs plane fitting on point cloud data of a unit area to obtain a target virtual plane; based on the target virtual plane, point cloud data that are not on the target virtual plane are identified as point cloud data of concave areas. Thus, this application performs plane fitting on point cloud data of a unit area to obtain a target virtual plane, and confirms that point cloud data outside the target virtual plane are concave areas with varying degrees of damage, providing a basis for subsequent analysis of the flatness of the surface of the preset underwater structure.

[0166] exist Figure 12 Based on the above, the method for detecting the flatness of underwater structures provided in the embodiments of this application will be explained and described in detail below with reference to the accompanying drawings. Figure 13 Example flow chart of a method for detecting the flatness of an underwater structure provided in this application embodiment. Figure 6 .like Figure 13 As shown, the above method performs plane fitting on the point cloud data of a unit area to obtain the target virtual plane, which may include:

[0167] S701. Randomly select at least three point cloud data from the point cloud data of the unit area to generate an initial virtual plane.

[0168] In one possible implementation, at least three point cloud data points are randomly selected from the point cloud data of a unit region, such as p1(x1,y1,z1), p2(x2,y2,z2), and p3(x3,y3,z3). The vector is then calculated using the following formula (7). The vector is calculated using the following formula (8).

[0169]

[0170] Based on formulas (7) and (8), the vector is calculated using the following formula (9). sum vector cross product

[0171]

[0172] Among them, coordinate A can be represented as (x2-x1)*(x3-x1); coordinate B can be represented as (y2-y1)*(y3-y1); coordinate C can be represented as (z2-z1)*(z3-z1).

[0173] Based on formula (9), the constant d is calculated using the following formula (10).

[0174] d=-(A*x1+B*y1+C*z1) Formula (10)

[0175] Based on formula (10), the initial virtual plane is obtained by using the following formula (11).

[0176] Ax+By+Cz+d=0 Formula (11)

[0177] S702. Determine the number of interior points of the initial virtual plane based on the point cloud data of the unit area and the second preset distance threshold.

[0178] Wherein, the second preset distance threshold d max The second preset distance threshold d can be selected according to the actual situation. max You can choose 0.003.

[0179] In one possible implementation, the distance d from the point cloud data of a unit region to the initial virtual plane is used. p and the second preset distance threshold d max The size relationship between them determines the number of interior points of the initial virtual plane. If the distance d from the point cloud data of a unit area to the initial virtual plane is... p Less than the second preset distance threshold d max Then the number of point cloud data corresponding to the unit area is determined to be the number of interior points of the initial virtual plane; if the distance d from the point cloud data of the unit area to the initial virtual plane is... p Greater than or equal to the second preset distance threshold d max Then the number of point cloud data corresponding to the unit area is determined to be the number of external points of the initial virtual plane.

[0180] S703. Randomly select at least three point cloud data from the point cloud data of the unit area to generate a new virtual plane, and determine the number of interior points of the new virtual plane until the number of interior points is maximized, and determine the virtual plane with the largest number of interior points as the target virtual plane.

[0181] In one possible implementation, at least three point cloud data points are randomly selected from the point cloud data of the unit region, and a new virtual plane is regenerated according to step S701, based on the distance d from the point cloud data of the unit region to the new virtual plane. p and the second preset distance threshold d max The number of interior points of the new virtual plane is redefined. Then, the relationship between the number of interior points of the new virtual plane and the number of interior points of the previous virtual plane (such as the initial one) is calculated. The process continues until the number of interior points of the virtual plane is maximized, and the virtual plane with the largest number of interior points is identified as the target virtual plane.

[0182] For example, suppose that in the point cloud data of a unit area, each newly selected interior point has point cloud data of p1, p2, ..., pn. Then, using the point cloud data of these n interior points, we can establish a system of linear equations Ax = b. Here, A is a matrix composed of the coordinates of the n points. Parameters of the plane equation Solving the linear system of equations Ax = b will yield the equation of the virtual plane, thus forming a new virtual plane.

[0183] The underwater structure flatness detection method provided in this application involves randomly selecting at least three point cloud data points from a unit area of ​​point cloud data to generate an initial virtual plane; determining the number of interior points of the initial virtual plane based on the point cloud data of the unit area and a second preset distance threshold; randomly selecting at least three point cloud data points again from the unit area of ​​point cloud data to generate a new virtual plane, and determining the number of interior points of the new virtual plane, until the number of interior points is maximized, and determining the virtual plane with the maximum number of interior points as the target virtual plane. Therefore, this application can generate a new virtual plane based on randomly selecting at least three point cloud data points and determine the number of interior points of the new virtual plane, thereby determining the effective point cloud data of the target point cloud data and improving the effectiveness of the target point cloud data.

[0184] exist Figure 10 Based on the above, the method for detecting the flatness of underwater structures provided in the embodiments of this application will be explained and described in detail below with reference to the accompanying drawings. Figure 14 Example flow chart of a method for detecting the flatness of an underwater structure provided in this application embodiment. Figure 7 .like Figure 14 As shown, the method described above, which determines the number of interior points of the initial virtual plane based on the point cloud data of a unit region and a second preset distance threshold, may include:

[0185] S701. Determine the distance between other point cloud data and the initial virtual plane in the point cloud data of the unit area.

[0186] In one possible implementation, the distances between other point cloud data within a unit region and the initial virtual plane are calculated. For example, any point cloud data p(x) within the unit region's point cloud data... p ,y p ,z p Based on the above formulas (4), (10), and (11), the distance d from the point cloud data p to the initial virtual plane is calculated using formula (12). p .

[0187]

[0188] S702. Determine the number of interior points of the initial virtual plane based on the number of point cloud data whose distance is less than the second preset distance threshold from other point cloud data, and the number of randomly selected point cloud data.

[0189] In one possible implementation, the distance d between other point cloud data and the initial virtual plane is calculated using the above formula (12). p If the distance d p Less than the second preset distance threshold d max Then the number of point clouds corresponding to other point cloud data is the number of interior points of the initial virtual plane. Since the initial virtual plane is determined by randomly selecting at least three point cloud data such as p1(x1,y1,z1), p2(x2,y2,z2) and p3(x3,y3,z3), the number of randomly selected at least three point cloud data is also the number of interior points of the initial virtual plane.

[0190] It should be noted that, in addition to randomly selecting at least three point cloud data sets and other point cloud data sets whose distance is less than the second preset distance threshold d, the above also includes... max Apart from the point cloud data, the remaining point cloud data corresponds to the number of external points of the initial virtual plane.

[0191] For example, such as Figure 15 As shown, Figure 15 This is a schematic diagram illustrating the processing result of a target virtual plane provided in an embodiment of this application. For example... Figure 15 The black point cloud data represents the point cloud data of the interior points, and the gray point cloud data represents the point cloud data of the exterior points.

[0192] To distinguish concave regions in the target point cloud data, the RANSAC algorithm was used to fit the target virtual plane. The second distance threshold was set to 0.003, and the number of iterations was set to 10000. After multiple runs, 0.4114x + 0.9113y - 0.0171z - 0.53614 = 0 was selected as the target virtual plane. Point cloud data within 0.003 of this plane were considered interior points, and other points were considered exterior points.

[0193] The underwater structure flatness detection method provided in this application determines the distance between other point cloud data and an initial virtual plane in the point cloud data of a unit area; based on the number of point cloud data whose distance is less than a second preset distance threshold and the number of randomly selected point cloud data, the number of interior points of the initial virtual plane is determined. Therefore, this application can determine the number of interior points of the initial virtual plane based on the number of point cloud data whose distance is less than the second preset distance threshold, thereby determining the effective point cloud data of the target point cloud data and improving the effectiveness of the target point cloud data.

[0194] The following is a detailed explanation and description of the underwater structure flatness detection method provided in the above embodiments of this application, with reference to the accompanying drawings. Figure 16 The flowchart of a method for detecting the flatness of an underwater structure provided in this application embodiment Figure 8 .like Figure 16 As shown, the method described above, which calculates the first area of ​​the recessed region based on the surface three-dimensional mesh of the recessed region, may include:

[0195] S801. Calculate the first area of ​​the concave region based on the sum of the areas of all triangles in the surface three-dimensional mesh of the concave region.

[0196] In one possible implementation, a preset contour reconstruction algorithm is used to calculate and 3D reconstruct the point cloud data of the concave region, resulting in a 3D surface mesh of the concave region. This 3D surface mesh is a triangular mesh formed by connecting multiple adjacent triangles. Each triangle is composed of the coordinates of a single point cloud data point; that is, the point cloud coordinates of each triangle are known. Therefore, the area of ​​each triangle can be calculated, and the areas of all triangles are added together to obtain the first area of ​​the concave region, Area. Unflatness That is, the area of ​​the uneven region.

[0197] Based on the surface 3D mesh of the unit region, the second area of ​​the unit region is obtained, including:

[0198] S802. Calculate the second area of ​​the unit region based on the sum of the areas of all triangles in the surface 3D mesh of the unit region.

[0199] In one possible implementation, a preset contour reconstruction algorithm is used to calculate and 3D reconstruct the point cloud data of a unit region, resulting in a surface 3D mesh for that unit region. This surface 3D mesh is a triangular mesh formed by connecting multiple adjacent triangles. Each triangle is composed of the coordinates of a single point cloud data point; that is, the point cloud coordinates of each triangle are known. Therefore, the area of ​​each triangle can be calculated, and the areas of all triangles are added together to obtain the second area of ​​the unit region, Area. reference That is, the area of ​​the study region.

[0200] The underwater structure flatness detection method provided in this application calculates a first area of ​​the concave region based on the sum of the areas of all triangles in the surface three-dimensional mesh of the concave region; and calculates a second area of ​​a unit region based on the sum of the areas of all triangles in the surface three-dimensional mesh of a unit region. Therefore, this application can calculate the first area of ​​the concave region and the second area of ​​the unit region based on the sum of the areas of all triangles in the surface three-dimensional mesh of the concave region and the unit region, respectively, thereby effectively calculating the area of ​​the surface three-dimensional mesh formed by three-dimensional point cloud data, and improving the quantitative evaluation of the underwater structure flatness detection method provided in this application.

[0201] Optionally, in some embodiments of this application, performing a flatness test on the surface of a preset underwater structure based on a first area and a second area may include:

[0202] According to the first area Unflatness Second Area reference The ratio is used to calculate the Unflatness Area Ratio (UAR).

[0203] Specifically, according to the first area Unflatness Second Area reference The ratio of the uneven area ratio (UAR) is calculated using the following formula (13).

[0204]

[0205] The Uneven Area Ratio (UAR) characterizes the smoothness of a unit area on a surface, i.e., the severity of unevenness defects occurring within that unit area. The UAR value ranges from (0, ∞).

[0206] In one possible implementation, if the second area under study is... reference Given a fixed area, when the surface defects of the pre-designed underwater structure cause minimal damage, the first area is [area]. Unflatness The smaller the area, the smaller the UAR value; when the surface defects of the underwater structure are large, the first area... Unflatness A larger value indicates a larger uneven area compared to the UAR value. This is relevant if surface defects (such as hydraulic concrete) of the underwater structure are pre-defined, meaning the first area (Area) is affected. Unflatness Given a given situation, when the second area... reference The larger the value, the smaller the area of ​​unevenness compared to the UAR value; when the second area... reference The smaller the value, the larger the uneven area ratio (UAR).

[0207] Therefore, the smaller the uneven area is compared to the UAR, the less damage it can represent to the surface of the pre-designed underwater structure within a unit area, i.e., the smoothness is very high; the larger the uneven area is compared to the UAR, the more damage it can represent to the surface of the pre-designed underwater structure within a unit area, i.e., the smoothness is very low.

[0208] To facilitate understanding of the above-mentioned method for detecting the flatness of underwater structures, this application also provides an embodiment of an application example of the method for detecting the flatness of underwater structures.

[0209] To verify the effectiveness of the underwater structure flatness detection method, a field demonstration application was conducted at a power station in Sichuan Province. This power station is a concrete gravity dam that was put into operation in 2014 and has two stilling basins: a bottom outlet and a surface outlet. For example, in the field application power station provided in this application embodiment, the application area may include the underwater portions of the left guide wall of the bottom outlet, the middle guide wall of the bottom outlet, and the middle guide wall of the surface outlet.

[0210] To analyze the differences in the unsmooth area ratio (UAR) of the surface of a pre-designed underwater structure under different degrees of damage, and to determine whether the unsmooth area ratio can characterize the level of flatness of the surface of the pre-designed underwater structure, four pre-designed point cloud datasets with large differences in guide wall damage were selected for flatness detection analysis, namely pre-designed point cloud datasets a, b, c, and d.

[0211] Figure 17 This is a schematic diagram illustrating the unevenness of four preset point cloud data sets provided in an embodiment of this application. For example... Figure 17 As shown, after the data processing stage (i.e., filtering, cropping, and fitting of the virtual plane), the pure black point cloud data represents the point cloud data of points within the target virtual plane, i.e., the point cloud data that has not been destroyed, while the gray point cloud data represents the point cloud data of points outside the target virtual plane, i.e., the point cloud data that has been destroyed. The degree of destruction of the destroyed point cloud data can be represented by the intensity of the color. The brighter the gray point cloud data, the deeper the degree of destruction, while the lighter the gray point cloud data, the shallower the degree of destruction.

[0212] It is possible Figure 17It can be seen that the degree of damage to the four preset point cloud data increases sequentially. Preset point cloud data a is basically undamaged, that is, there are no depressions or uneven areas, meaning the hydraulic concrete is relatively intact. Preset point cloud data b has slight damage in some areas, that is, there are shallow depressions (i.e., uneven areas), meaning the hydraulic concrete is generally intact with some shallow damage in some areas. Preset point cloud data c has small areas of damage, that is, there are deep depressions (i.e., uneven areas), meaning the hydraulic concrete has obvious and deep damage. Preset point cloud data d has large areas of damage, that is, there are large areas of depressions (i.e., uneven areas), meaning the hydraulic concrete is significantly depressed and severely damaged.

[0213] According to the above embodiments, surface 3D mesh reconstruction of the concave region is performed on four preset point cloud data respectively, and the first area of ​​the concave region can be calculated respectively. Unflatness That is, the area of ​​the uneven region. Unflatness Area Unflatness A = 0.0001m 2 Area Unflatness B = 0.0424m 2 Area Unflatness C = 0.0838m 2 Area Unflatness D = 0.1557m 2 And calculate the projected area of ​​the point cloud data of a unit region onto the target virtual plane, that is, the second area of ​​the unit region, Area. reference That is, the area of ​​the study region of the point cloud data is calculated as Area. reference =0.4168m 2 .

[0214] The uneven area ratio (UAR) of the four preset point cloud data is calculated according to the above formula (13). The results of the uneven area ratio (UAR) of the four preset point cloud data are shown in Table 1. Table 1 is a schematic diagram of the results of the uneven area ratio (UAR) of the four preset point cloud data provided in the embodiment of this application.

[0215] Serial Number <![CDATA[Area Unflatness (m 2 )]]> UAR Point cloud a 0.0001 <![CDATA[2.4×10 -4 ]]> Point cloud b 0.0424 0.1017 Point cloud c 0.0838 0.2011 Point cloud d 0.1557 0.3736

[0216] As shown in Table 1 above, firstly, as the surface smoothness of the preset underwater structure gradually changes from smooth to uneven, the Uneven Area Ratio (UAR) also gradually increases from zero. The UAR value can be used as a relative indicator to measure the smoothness of different areas of the preset underwater structure's surface. Secondly, the UAR value represents the degree of unevenness of the preset underwater structure's surface corresponding to the point cloud data per unit area, and has an average significance.

[0217] It should be noted that when selecting the study area of ​​the surface of the preset underwater structure, if the surface of the preset underwater structure has large random fluctuations in unevenness, it is necessary to consider the influence of the unit size of the study area on the determination of the UAR value of uneven area. Therefore, it is very important to select an appropriate study area (i.e., unit area) for quantitative evaluation.

[0218] Third, under the same test conditions, the uneven area ratio (UAR) can be used to quantitatively evaluate the surface of a pre-defined underwater structure (such as hydraulic concrete). The uneven area ratio (UAR) can be used for risk assessment. For example, the uneven area ratio (UAR) ∈ (0, 0.05) is defined as "no risk"; the uneven area ratio (UAR) ∈ (0.05, 0.15) is defined as "low risk"; the uneven area ratio (UAR) ∈ (0.15, 0.25) is defined as "medium risk"; and the uneven area ratio (UAR) ∈ (0.25, ∞) is defined as "high risk".

[0219] In summary, the hydraulic concrete structures such as guide walls and foundation slabs of hydropower projects operate submerged for extended periods. Traditional detection methods, such as physical contact measurement, acoustic measurement, and image measurement, have limitations in measurement accuracy and data acquisition density, making it impossible to accurately and objectively detect and evaluate the surface flatness of pre-designed underwater structures. The underwater structure flatness detection method provided in this application employs underwater 3D laser scanning technology to acquire high-precision 3D point cloud data. This data is then processed through data filtering, cropping, and virtual plane fitting. A non-flatness area ratio index is proposed to quantitatively evaluate the surface flatness of pre-designed underwater structures. The method was applied in a field study on the stilling basin guide wall of a power station in Sichuan Province. A field data acquisition device was built, and computer equipment using a 3D point cloud data processing library based on a pre-defined algorithm (such as Open3D) processed the data. The effectiveness of the underwater structure flatness detection method provided in this application was verified through data comparison and analysis of the uneven area ratio. Furthermore, the uneven area ratio can be used as a quantitative evaluation index to quantitatively analyze and judge the surface flatness of the underwater structure. Simultaneously, this underwater structure flatness detection method can be practically applied to major infrastructure projects such as hydropower hubs, giving it engineering significance.

[0220] It should be noted that, based on the underwater structure flatness detection method provided in this application, the stitching technology of three-dimensional point cloud data can be further integrated. This can transform the collected and dispersed three-dimensional point cloud data into a unified three-dimensional point cloud data, allowing for the analysis and comparison of the flatness of the surface of the underwater structure (such as hydraulic concrete) on a larger scale. At the same time, the preset intelligent defect recognition algorithm (such as the analysis and processing of three-dimensional point cloud data) on the computer equipment can be further improved, enabling the direct identification of defects in the three-dimensional point cloud data, thereby improving the efficiency of defect detection.

[0221] Based on the same inventive concept, this application also provides a device for detecting the flatness of underwater structures. Since the principle of the device in this application is similar to the method for detecting the flatness of underwater structures described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0222] Figure 18 This is a schematic diagram of a flatness detection device for underwater structures provided in an embodiment of this application. Figure 18 As shown, the flatness detection device 90 for the underwater structure includes:

[0223] The first acquisition module 91 is used to acquire three-dimensional point cloud data collected by the underwater three-dimensional laser scanning equipment on the surface of a preset underwater building;

[0224] The second acquisition module 92 is used to acquire point cloud data of a unit area from the three-dimensional point cloud data;

[0225] Module 93 is used to determine the point cloud data of the concave region from the point cloud data of the unit region;

[0226] The first module 94 is used to perform three-dimensional reconstruction based on the point cloud data of the concave region and the point cloud data of the unit region, respectively, to obtain the surface three-dimensional mesh of the concave region and the surface three-dimensional mesh of the unit region.

[0227] Calculation module 95 is used to calculate the first area of ​​the recessed region based on the surface three-dimensional mesh of the recessed region;

[0228] The second module 96 is used to obtain the second area of ​​the unit region based on the surface three-dimensional mesh of the unit region;

[0229] The detection module 97 is used to detect the flatness of the surface of a preset underwater structure based on the first area and the second area.

[0230] Optionally, in one implementation, the second acquisition module 92 is specifically used for:

[0231] Noisy point cloud data is filtered out from 3D point cloud data to obtain target point cloud data;

[0232] Obtain point cloud data for a unit region from 3D point cloud data, including:

[0233] Obtain point cloud data for a unit area from the target point cloud data.

[0234] Optionally, in one implementation, the second acquisition module 92 is specifically used for:

[0235] Calculate the Euclidean distance between any two point cloud data in a 3D point cloud dataset;

[0236] Based on the Euclidean distance between any two point cloud data, point cloud data with an Euclidean distance less than a first preset distance threshold are identified as noisy point cloud data from the 3D point cloud data.

[0237] Noisy point cloud data is filtered out from the 3D point cloud data to obtain the target point cloud data.

[0238] Optionally, in one implementation, the second acquisition module 92 is specifically used for:

[0239] The number of point cloud data points within a preset range centered on each point cloud data point in the 3D point cloud data is determined as the number of point cloud data points corresponding to each point cloud data point.

[0240] Calculate the average number of points based on the number of points corresponding to multiple point cloud data sets.

[0241] Determine the standard deviation of the point cloud for each point cloud data based on the number of point clouds and the average number of point clouds corresponding to each point cloud data.

[0242] Based on the standard deviation of each point cloud data and the preset standard deviation threshold, point cloud data with a standard deviation less than the preset standard deviation threshold are identified as noisy point cloud data from the 3D point cloud data.

[0243] Noisy point cloud data is filtered out from the 3D point cloud data to obtain the target point cloud data.

[0244] Optionally, in one implementation, the determining module 93 is specifically used for:

[0245] Plane fitting is performed on the point cloud data of a unit area to obtain the target virtual plane;

[0246] Based on the target virtual plane, the point cloud data that is not on the target virtual plane from the target point cloud data is the point cloud data of the concave region.

[0247] Optionally, in one implementation, the determining module 93 is specifically used for:

[0248] Randomly select at least three point cloud data points from the point cloud data of a unit area to generate an initial virtual plane;

[0249] The number of interior points of the initial virtual plane is determined based on the point cloud data of the unit area and the second preset distance threshold.

[0250] At least three point cloud data points are randomly selected from the point cloud data of the unit area to generate a new virtual plane, and the number of interior points of the new virtual plane is determined until the number of interior points is maximized. The virtual plane with the largest number of interior points is then determined as the target virtual plane.

[0251] Optionally, in one implementation, the determining module 93 is specifically used for:

[0252] Determine the distances between other point cloud data and the initial virtual plane within the point cloud data of a unit region;

[0253] The number of interior points of the initial virtual plane is determined based on the number of point cloud data whose distance is less than the second preset distance threshold from other point cloud data, and the number of randomly selected point cloud data.

[0254] Alternatively, in one optional implementation, the calculation module 95 is specifically used for:

[0255] The first area of ​​the concave region is calculated based on the sum of the areas of all triangles in the surface 3D mesh of the concave region.

[0256] Alternatively, in one optional implementation, the second obtaining module 96 is specifically used for:

[0257] The second area of ​​a unit region is calculated by summing the areas of all triangles in the surface 3D mesh of that unit region.

[0258] Optionally, in one implementation, the detection module 97 is specifically used for:

[0259] The uneven area ratio is calculated based on the ratio of the first area to the second area. The uneven area ratio is used to characterize the smoothness of a unit area on the surface. The larger the uneven area ratio, the worse the smoothness of the unit area.

[0260] It should be noted that for details not disclosed in the underwater structure flatness detection device of this application embodiment, please refer to the details disclosed in the underwater structure flatness detection method of this application embodiment, which will not be repeated here.

[0261] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors, or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SoC).

[0262] Figure 19 This application provides a schematic diagram of the structure of a computer device, as shown in the embodiment of the present application. Figure 19 As shown, the computer device 1000 may include a processor 1001, a memory 1002, and a bus. The memory 1002 stores machine-readable instructions that can be executed by the processor 1001. When the computer device is running, the machine-readable instructions are executed. The processor 1001 and the memory 1002 communicate via the bus. The processor 1001 is used to execute the steps of the underwater structure flatness detection method in the above embodiment.

[0263] The memory 1002, processor 1001, and bus components are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The mobile storage device includes at least one software function module that can be stored in the memory 1002 as software or firmware or embedded in the operating system (OS) of a computer device. The processor 1001 is used to execute executable modules stored in the memory 1002, such as the software function modules and computer programs included in the method for detecting the flatness of underwater structures using mobile storage media.

[0264] The memory 1002 may be, but is not limited to, random access memory (rAM), read-only memory (rOM), programmable read-only memory (PrOM), erasable programmable read-only memory (EPrOM), electrically erasable programmable read-only memory (EEPrOM), etc.

[0265] Optionally, embodiments of this application also provide a computer-readable storage medium storing a computer program. When the computer program is run by a processor, the processor executes the steps of the underwater structure flatness detection method using a mobile storage medium described in the above embodiments. The specific implementation and technical effects are similar and will not be repeated here.

[0266] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0267] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0268] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0269] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0270] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting the flatness of underwater structures, characterized in that, The method includes: Acquire 3D point cloud data of the surface of a pre-designed underwater structure using an underwater 3D laser scanning device; Obtain point cloud data for a unit region from the three-dimensional point cloud data; Determine the point cloud data of the concave region from the point cloud data of the unit region; Three-dimensional reconstruction is performed based on the point cloud data of the recessed area and the point cloud data of the unit area to obtain the surface three-dimensional mesh of the recessed area and the surface three-dimensional mesh of the unit area. Calculate the first area of ​​the recessed region based on the surface three-dimensional mesh of the recessed region; The second area of ​​the unit region is obtained based on the surface three-dimensional mesh of the unit region; The surface flatness of the preset underwater structure is tested based on the first area and the second area.

2. The method for detecting the flatness of underwater structures according to claim 1, characterized in that, Before obtaining the point cloud data of a unit region from the three-dimensional point cloud data, the method further includes: Noisy point cloud data is filtered out from the three-dimensional point cloud data to obtain the target point cloud data; The step of obtaining point cloud data for a unit region from the three-dimensional point cloud data includes: Obtain the point cloud data of the unit region from the target point cloud data.

3. The method for detecting the flatness of underwater structures according to claim 2, characterized in that, The step of filtering out noisy point cloud data from the three-dimensional point cloud data to obtain target point cloud data includes: Calculate the Euclidean distance between any two point cloud data in the three-dimensional point cloud data; Based on the Euclidean distance between any two point cloud data, point cloud data whose Euclidean distance is less than a first preset distance threshold is determined from the three-dimensional point cloud data as the noisy point cloud data; The noisy point cloud data in the three-dimensional point cloud data is filtered out to obtain the target point cloud data.

4. The method for detecting the flatness of underwater structures according to claim 2, characterized in that, The step of filtering out noisy point cloud data from the three-dimensional point cloud data to obtain target point cloud data includes: The number of point cloud data within a preset range centered on each point cloud data in the three-dimensional point cloud data is determined as the number of point cloud data corresponding to each point cloud data. Calculate the average number of points based on the number of points corresponding to the multiple point cloud data; The standard deviation of the point cloud for each point cloud data is determined based on the number of point clouds corresponding to each point cloud data and the average number of point clouds. Based on the point cloud standard deviation of each point cloud data and a preset standard deviation threshold, point cloud data with a point cloud standard deviation less than the preset standard deviation threshold are identified as noisy point cloud data from the three-dimensional point cloud data. The noisy point cloud data in the three-dimensional point cloud data is filtered out to obtain the target point cloud data.

5. The method for detecting the flatness of underwater structures according to claim 1, characterized in that, The step of determining the point cloud data of the concave region from the point cloud data of the unit region includes: The point cloud data of the unit region is fitted with a plane to obtain the target virtual plane; Based on the target virtual plane, the point cloud data that is not on the target virtual plane from the target point cloud data is the point cloud data of the concave region.

6. The method for detecting the flatness of underwater structures according to claim 5, characterized in that, The step of performing planar fitting on the point cloud data of the unit region to obtain the target virtual plane includes: At least three point cloud data points are randomly selected from the point cloud data of the unit region to generate an initial virtual plane; The number of interior points of the initial virtual plane is determined based on the point cloud data of the unit region and the second preset distance threshold. At least three point cloud data points are randomly selected from the point cloud data of the unit region to generate a new virtual plane, and the number of interior points of the new virtual plane is determined until the number of interior points is maximized. The virtual plane with the largest number of interior points is then determined as the target virtual plane.

7. The method for detecting the flatness of underwater structures according to claim 6, characterized in that, The step of determining the number of interior points of the initial virtual plane based on the point cloud data of the unit region and the second preset distance threshold includes: Determine the distance between other point cloud data in the point cloud data of the unit region and the initial virtual plane; The number of interior points of the initial virtual plane is determined based on the number of point cloud data whose distance is less than the second preset distance threshold in the other point cloud data, and the number of randomly selected point cloud data.

8. The method for detecting the flatness of underwater structures according to claim 1, characterized in that, The step of calculating the first area of ​​the recessed region based on the surface three-dimensional mesh of the recessed region includes: The first area of ​​the recessed region is calculated based on the sum of the areas of all triangles in the surface three-dimensional mesh of the recessed region. The step of obtaining the second area of ​​the unit region based on the surface three-dimensional mesh of the unit region includes: The second area of ​​the unit region is calculated based on the sum of the areas of all triangles in the surface three-dimensional mesh of the unit region.

9. The method for detecting the flatness of underwater structures according to any one of claims 1-8, characterized in that, The step of performing a surface flatness test on the preset underwater structure based on the first area and the second area includes: The uneven area ratio is calculated based on the ratio of the first area to the second area; the uneven area ratio is used to characterize the smoothness of the unit area on the surface, and the larger the uneven area ratio, the worse the smoothness of the unit area.

10. A computer device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement the underwater structure flatness detection method according to any one of claims 1-9.

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