A hull block splicing method based on a laser point cloud device

Abstract

The application discloses a ship body segmentation splicing method based on a laser point cloud device, and comprises the following steps: acquiring X partial point cloud data of a ship body segmentation; splicing the X partial point cloud data by using the spatial overlapping part and the same features of the point cloud data; deleting the point cloud data of a part which is not needed to be compared with a ship body segmentation design model, and reserving the ship body segmentation point cloud; generating a target point by the key position of the ship body segmentation point cloud data, performing coordinate conversion on the coordinates of the target point and the corresponding position of the ship body segmentation design model, converting the coordinate system of the ship body segmentation point cloud data into the same as the theoretical coordinate system of the ship body segmentation design model; outputting the required point cloud format, saving the complete ship body segmentation point cloud data, and realizing the ship body segmentation splicing. The precision and efficiency of the segmentation splicing directly determine the linear smoothness, structural strength, assembly stress, construction period and cost of the final ship body, and is one of the core key processes of ship construction.

Description

Technical Field

[0001] This invention belongs to the field of shipbuilding and marine engineering manufacturing technology, and relates to a method for assembling ship hull segments based on laser point cloud equipment. Background Technology

[0002] In the construction of modern large ships and marine engineering structures (such as FPSOs, drilling platforms, large container ships, LNG carriers, etc.), the section construction method is commonly used. Sectional construction is one of the core technologies of modern shipbuilding. It refers to the process of dividing the entire hull into several independent sections or blocks, prefabricating them separately on jigs or platforms, and then transporting them to the slipway / dock to assemble them into a complete hull.

[0003] Existing methods for assembling ship hull segments have several drawbacks. Due to the large size and complex structure of the hull segments, multi-site scanning can easily lead to small overlapping areas and indistinct features in the point cloud. Traditional ICP algorithms are sensitive to the initial position and are prone to mismatches or convergence failures. The on-site environment (such as reflective curved surfaces and welding spatter) can cause the point cloud to contain noise and outliers, requiring preprocessing such as statistical filtering and Gaussian filtering. However, excessive filtering may result in the loss of key features. Summary of the Invention

[0004] To solve the above problems, the technical solution adopted by the present invention is: a method for segmenting and splicing ship hulls based on laser point cloud equipment, comprising the following steps:

[0005] S1: Obtain point cloud data for X parts of the hull segment; S2: Using the spatial overlap and common features of point cloud data, stitch together X parts of point cloud data; S3: Delete the point cloud data of the parts of the stitching result that do not need to be compared with the hull section design model, and retain the point cloud of the hull section. S4: Generate target points from key locations in the hull section point cloud data, perform coordinate transformation between the target points and the corresponding coordinates of the hull section design model, and convert the coordinate system of the hull section point cloud data to be consistent with the theoretical coordinate system of the hull section design model. S5: Output the required point cloud format, save the complete point cloud data of the hull segments, and realize the splicing of hull segments.

[0006] Furthermore: the process of stitching together X parts of point cloud data by utilizing the spatial overlap and common features of point cloud data is as follows: Visual alignment is achieved by selecting two adjacent points in the X partial point cloud data; The sections of the hull were stitched together based on the same features scanned from different sections. By using the top view of the point cloud, the segmented point cloud is stitched together horizontally. The segmented point cloud is vertically stitched together using the side view of the point cloud. After adjusting the overlap of the two station cloud data in the dual-view configuration, the top and side views of the adjacent point clouds of the two overlapping stations are fine-tuned to achieve the maximum overlap rate. The two station cloud data are then stitched together, with the absolute average value of the stitching not exceeding N mm and the overlap percentage not less than Y. This step is repeated to complete the stitching between X stations.

[0007] Furthermore, the process of horizontally stitching together segmented point clouds using a top view of the point cloud is as follows: Using a reference point at the same stationary position that can be acquired from the point cloud top view of the hull section itself or from the point clouds of two surrounding stations, the overlapping parts of the horizontal point cloud outlines and the same feature points of the adjacent point clouds are translated and aligned. If there is an angular deviation, the overall angle of the point clouds is adjusted by rotating the point cloud direction to improve the horizontal overlap rate of the two point clouds. The horizontal deviation of the point cloud splicing is judged by observing whether the main feature points of the point clouds of the two hull sections overlap. If the main feature points of the point clouds of the two hull sections overlap, the horizontal deviation of the point cloud splicing is acceptable; otherwise, it is unacceptable. The main feature points include the outer contour of each segment and the intersection of two structural surfaces of the hull segment. The same feature points are the same contours at the same position in the same segment.

[0008] Furthermore, the process of vertically stitching together segmented point clouds using a side view of the point cloud is as follows: Using a point cloud side view as a reference point that can be collected from the same stationary position of the hull section itself or two surrounding point clouds, the overlapping parts and feature points of the vertical point cloud outlines of the two point clouds are aligned vertically. If there is an angular deviation, the overall angle of the point cloud is adjusted by rotating the point cloud direction to improve the vertical overlap rate of the two point clouds. By observing whether the main feature points of the two hull section point clouds overlap, it is determined whether the vertical deviation of the point cloud splicing is qualified. If the main feature points of the two hull section point clouds overlap, the vertical deviation of the point cloud splicing is qualified; otherwise, it is unqualified.

[0009] Furthermore: the process of deleting the point cloud data of parts that do not need to be compared with the hull section design model from the stitching result, and retaining the point cloud of the hull section, is as follows: Adjust the stitched point cloud to the top view using the point cloud group view. In the top view, delete the point cloud data other than the hull segment point cloud. Delete the point cloud data other than the hull segment point cloud structure in the order of coarse deletion first and fine deletion later. First, coarse deletion retains the hull segment point cloud and the point cloud within a certain range around it. Then, fine deletion only retains the hull segment point cloud. Switch to the side view and delete the sections sequentially as in the top view, keeping only the hull segment point cloud. If deletion cannot be performed through the top or side view, rotate and translate the hull segment point cloud to a suitable angle for deletion.

[0010] Furthermore: The process of generating target points from the key intersection positions of the two structural surfaces of the hull segment using the point cloud data of the hull segment, and performing coordinate transformation between the target points and the corresponding position coordinates of the hull segment design model to convert the coordinate system of the hull segment point cloud data to be consistent with the theoretical coordinate system of the hull segment design model is as follows: In the hull section point cloud, M target points are generated at the locations of the M target centers, where M ≥ 3, using identifiable target centers. The coordinates of the M target centers are exported, and the position coordinates of the key structural points of the hull corresponding to the M target centers are found. The coordinates of the design points corresponding to the M target centers in the hull section design model are imported and applied to the hull section point cloud in the corresponding order. By applying the target point coordinates from the M different hull section design models, the coordinate system of the hull section point cloud data is converted to be consistent with the theoretical coordinate system of the hull section design model.

[0011] A hull segment splicing device based on laser point cloud equipment includes: Acquisition module: used to acquire point cloud data of X parts of the hull section; The stitching module is used to stitch together X parts of point cloud data by utilizing the spatial overlap and common features of point cloud data. The deletion module is used to delete the point cloud data of the parts of the splicing result that do not need to be compared with the hull section design model, while retaining the point cloud of the hull section. The conversion module generates target points from key locations in the hull section point cloud data, performs coordinate transformation between the target points and their corresponding locations in the hull section design model, and converts the coordinate system of the hull section point cloud data to be consistent with the theoretical coordinate system of the hull section design model. Save module: Used to output the required point cloud format, save the complete point cloud data of the hull segment, and realize the splicing of hull segments.

[0012] A computer device includes: a processor and a memory, the memory storing a program module, characterized in that the program module runs on the processor to implement the method as described in any one of the claims.

[0013] This invention provides a method for splicing ship hull sections based on laser point cloud equipment, which relates to high-precision measurement, positioning and assembly technology in ship section construction, and particularly to a method for obtaining point cloud data of ship hull sections using three-dimensional laser scanning technology, and achieving accurate matching and splicing of spatial point clouds of sections through efficient point cloud splicing and point cloud data processing.

[0014] This method divides the massive overall structure into several hull sections that are prefabricated in the workshop, and then performs final assembly (combining small sections into larger sections) and final assembly (joining large sections or sections into the whole ship) in the dock or slipway. The accuracy and efficiency of the section splicing directly determine the final hull's smoothness, structural strength, assembly stress, as well as the construction cycle and cost, and is one of the core key processes in shipbuilding.

[0015] This method significantly improves shipbuilding quality, reduces assembly errors and rework; greatly enhances shipbuilding efficiency, shortens construction cycles, and reduces time spent on slipways / docks; effectively reduces construction costs, material waste, and labor costs; improves production safety and the working environment, with precision analysis reducing repair and rework operations in high-altitude, confined, and high-temperature environments (such as pyrotechnic straightening), thus reducing the risk of worker injury; and promotes technological advancements and digital transformation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the method; Figure 2 This is a site distribution map of X-stations for scanning ship hull sections using laser point cloud equipment; Figure 3 This is a diagram for selecting point clouds outside the point clouds of the hull sections; Figure 4 A diagram illustrating the process of deleting selected point clouds. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Figure 1 This is a flowchart of the method; A method for segmenting and assembling ship hulls based on laser point cloud equipment includes the following steps: S1: The hull is scanned using laser point cloud scanning technology based on laser point cloud equipment to obtain point cloud data of X parts of the hull segment; the model of the laser point cloud equipment used is: Leica RTC360. S2: Using the spatial overlap and common features of point cloud data, stitch together X parts of point cloud data; S3: Delete the point cloud data of the parts that do not need to be compared with the hull section design model from the splicing result, and retain the point cloud of the hull section; S4: Target points are generated from key locations in the point cloud data of hull sections. The coordinates of the target points and their corresponding locations in the hull section design model are transformed to convert the coordinate system of the hull section point cloud data to be consistent with the theoretical coordinate system of the hull section design model. S5: Output the required point cloud format, save the complete point cloud data of the hull segments, and realize the splicing of hull segments.

[0021] Steps S1 / S2 / S3 / S4 / S5 are executed sequentially.

[0022] The completed ship hull sections are inspected. Based on the laser point cloud equipment, X stations are planned to complete the collection of the required point cloud data. The visible features of the ship hull sections are scanned and collected station by station according to the planned stations using the laser point cloud equipment. After the laser point cloud equipment is set up, it emits lasers to the ship hull sections and receives the reflected information to record and save the ship hull sections in the form of spatial point cloud. The point cloud data is divided into X parts of point cloud data according to the actual number of stations set up by the equipment. Import the obtained point cloud data of the hull segments into the point cloud data stitching software Cyclone REGISTER 360PLUS; The laser point cloud device imports X portions of point cloud data into a workstation equipped with point cloud data stitching software via USB flash drive. The newly created project folder name must contain letters, numbers, or symbols and cannot contain Chinese characters. Open the point cloud data stitching software and create a new project. Disable automatic black-and-white target registration and automatic point cloud registration options, and select the options to import images and images without a background. Browse the imported point cloud data files and select .prj format files to import into the point cloud data stitching software.

[0023] Each station of the laser point cloud device generates a unique point cloud dataset, such as... Figure 2 A site distribution map of X-stations for scanning ship hull sections using a laser point cloud device; The process of stitching together X parts of point cloud data by utilizing the spatial overlap and common features of point cloud data is as follows: Visual alignment is achieved by selecting two adjacent points in the X partial point cloud data; The segments of the hull are stitched together according to the same features scanned in the hull section; during the scan, the outline edge of each segment and object scanned will be recorded as a point cloud outline. Two adjacent stations will definitely scan and record the same outline of the same segment at the same position, which will be used as the same features, specifically the shape of the segment, the straight line of the structural edge, line segment, etc. By using the top view of the point cloud, the segmented point cloud is stitched together horizontally. The segmented point cloud is vertically stitched together using the side view of the point cloud. After adjusting and overlapping the point clouds of both stations in the dual-view configuration, fine-tune (translation and rotation) the same contours at the same location in the same segment using the combine and optimize function to align them again. Then, fine-tune the top and side views of the adjacent point clouds of the two overlapping stations to achieve the maximum overlap rate. Finally, stitch the point cloud data of the two stations together. The absolute average value of the stitched data should not exceed N mm, and the overlap percentage should not be less than Y%. Repeat this step to complete the stitching between X stations. N is set to 5; Y is set to 30. After stitching, the point cloud error should not exceed 5mm, and the connection sequence of each point cloud with the preceding and following point clouds should be correct. Lock all point cloud connections using the connection locking function.

[0024] Furthermore, the process of horizontally stitching together segmented point clouds using a top view of the point cloud is as follows: Using a point cloud top view as a reference point that can be captured from the same stationary position of the hull segment itself or two surrounding point clouds, the horizontal overlapping parts of the point cloud contours of the two point clouds (two point clouds refer to the two point clouds scanned by adjacent stations (station N and station N+1) when scanning sequentially from station 1 to station N) and the same feature points (the contour edges of each segment and object scanned during scanning are recorded as point cloud contours, and adjacent stations will definitely scan and record the same contour at the same position of the same segment, which is used as the same feature point) are translated and aligned. If there is an angular deviation, the overall angle of the point cloud is adjusted by rotating the point cloud direction to improve the horizontal overlap rate of the two point clouds. By observing the main feature points of the hull segment point clouds at the two stations (the main feature points include the outer contour of each surface of the segment and the intersection of the two structural surfaces of the hull segment), the horizontal overlap rate of the two point clouds is improved. Specifically, the horizontal deviation of the point cloud splicing is judged by whether the shape, structural edge straight lines, line segments, etc. of the segment overlaps. If the main feature points of the point clouds of the two ship hull segments overlap, the horizontal deviation of the point cloud splicing is qualified; otherwise, it is unqualified.

[0025] Furthermore, the process of vertically stitching together segmented point clouds using a side view of the point cloud is as follows: Using a point cloud side view as a reference point that can be collected from the same stationary position of the hull section itself or two surrounding point clouds, the overlapping parts of the vertical point cloud outlines and feature points (including the vertical outline of the section and the intersection of the two structural surfaces of the hull section) of the two point clouds are aligned vertically. If there is an angular deviation, the overall angle of the point cloud is adjusted by rotating the point cloud direction to improve the vertical overlap rate of the two point clouds. The vertical deviation of the point cloud splicing is judged by observing whether the main feature points of the two hull section point clouds overlap. If the main feature points of the two hull section point clouds overlap, the vertical deviation of the point cloud splicing is qualified; otherwise, it is unqualified.

[0026] Furthermore, the process of deleting the point cloud data of parts that do not need to be compared with the hull segment design model from the stitching result, and retaining the point cloud of the hull segment, is as follows: Adjust the stitched point cloud to the top view using the point cloud group view. In the top view, use the polygon selection and deletion function to delete the point cloud data outside the hull segment point cloud. You can use the polygon selection function to select a point cloud area first, and then delete the unwanted parts by selecting the option to delete the inside or the outside. To prevent accidental deletion of hull section point cloud data and ensure its integrity, point cloud data outside the hull section structure must be deleted in the order of coarse deletion followed by fine deletion. Coarse deletion should retain the hull section point cloud and the point cloud within a 500mm radius around it, while fine deletion should retain only the hull section point cloud. Switch to a top-view perspective and follow the deletion steps sequentially, retaining only the hull section point cloud. If deletion cannot be performed through the top or side views, the hull section point cloud can be rotated and translated to a suitable angle for deletion. Figure 3 To select point cloud operations outside the point cloud of the hull segment, Figure 4 The process of deleting selected point clouds.

[0027] The coarse deletion process is as follows: confirm the main body range of the segmented point cloud, zoom out to display the entire segmented point cloud body, use the selection function to select the entire segmented point cloud body, and use the delete function to delete the outer part of the selection. At this time, a lot of useless point clouds other than the segmented point cloud body are still retained within the selection range. The process of fine-tuning is as follows: Rotate the retained segmented main point cloud to a side view, front view, or top view position; select the useless point cloud outside the main segmented point cloud again; use the delete function to delete the selected useless point cloud; repeat this step to delete the useless point cloud at each position in each view. The process of generating target points from key locations in the hull segment point cloud data (the intersection of two structural surfaces of the hull segment), and then performing coordinate transformation between the target points and their corresponding locations in the hull segment design model to convert the coordinate system of the hull segment point cloud data to be consistent with the theoretical coordinate system of the hull segment design model is as follows: Before the laser point cloud device scans the hull section, a target needs to be placed at three or more key locations on the hull section. The center of the target corresponds to the key structural point of the hull section. The corresponding coordinates of this key structural point can be found on the hull section design model. The laser point cloud device will scan the target and the hull section together into the hull section point cloud data. The virtual target center generation function can be used to identify the target center in the point cloud of the hull section and generate M target points at the M target center positions in the point cloud of the hull section, where M≥3; How to generate target centers using the virtual target center generation function; The entire target can be seen in the point cloud. Zooming in on the point cloud can confirm the center position of the target. Using this function, you can select and mark the coordinates of each point in the point cloud, zoom in on the center of the target, and select the center point to display the three-dimensional coordinates (XYZ) of this point to obtain the center point of the target. Export the coordinates of M target points, and find the position coordinates of the key structural points of the ship hull corresponding to the centers of the M targets. Using the control point application function, the coordinates of the design points corresponding to the center points of the M targets in the hull section design model are imported and applied to the hull section point cloud in the corresponding order (the generated M target points can be sorted 1-M). By applying the coordinates of the design points corresponding to the center points of the targets in the M different hull section design models, the coordinate system of the hull section point cloud data is converted to be consistent with the theoretical coordinate system of the hull section design model.

[0028] After completing the coordinate transformation, publish the point cloud data of the hull section, select the desired LAS point cloud format, choose the storage path, and save the complete point cloud data of the hull section. Output a splicing deviation report.

[0029] The process of using the control point application function to import and apply the coordinates of the design points corresponding to the center points of M targets in the hull section design model to the hull section point cloud in a corresponding order is as follows: Record the 3D coordinates of 1-M points corresponding to the target center point in the design model, replace the exported M target center point coordinates, and re-import these M new coordinates into the segmented point cloud using the import file function. Then, use the control point application function to apply the imported M new coordinates to the positions of the original M target points.

[0030] A hull segment splicing device based on laser point cloud equipment includes: Acquisition module: used to acquire point cloud data of X parts of the hull section; The stitching module is used to stitch together X parts of point cloud data by utilizing the spatial overlap and common features of point cloud data. The deletion module is used to delete the point cloud data of the parts of the splicing result that do not need to be compared with the hull section design model, while retaining the point cloud of the hull section. The conversion module generates target points from key locations in the hull section point cloud data, performs coordinate transformation between the target points and their corresponding locations in the hull section design model, and converts the coordinate system of the hull section point cloud data to be consistent with the theoretical coordinate system of the hull section design model. Save module: Used to output the required point cloud format, save the complete point cloud data of the hull segment, and realize the splicing of hull segments.

[0031] A computer device includes: a processor and a memory, the memory storing a program module that runs on the processor to implement the method as described in any one of the claims.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for segmenting and assembling ship hulls based on laser point cloud equipment, characterized in that: Includes the following steps: S1: Obtain point cloud data for X parts of the hull segment; S2: Using the spatial overlap and common features of point cloud data, stitch together X parts of point cloud data; S3: Delete the point cloud data of the parts of the stitching result that do not need to be compared with the hull section design model, and retain the point cloud of the hull section. S4: Generate target points from key locations in the hull section point cloud data, perform coordinate transformation between the target points and the corresponding coordinates of the hull section design model, and convert the coordinate system of the hull section point cloud data to be consistent with the theoretical coordinate system of the hull section design model. S5: Output the required point cloud format, save the complete point cloud data of the hull segments, and realize the splicing of hull segments.

2. The method for segmenting and splicing ship hulls based on laser point cloud equipment according to claim 1, characterized in that: The process of stitching together X parts of point cloud data by utilizing the spatial overlap and common features of point cloud data is as follows: Visual alignment is achieved by selecting two adjacent points in the X partial point cloud data; The sections of the hull were stitched together based on the same features scanned from different sections. By using the top view of the point cloud, the segmented point cloud is stitched together horizontally. The segmented point cloud is vertically stitched together using the side view of the point cloud. After adjusting the overlap of the two station cloud data in the dual-view configuration, the top and side views of the adjacent point clouds of the two overlapping stations are fine-tuned to achieve the maximum overlap rate. The two station cloud data are then stitched together, with the absolute average value of the stitching not exceeding N mm and the overlap percentage not less than Y. This step is repeated to complete the stitching between X stations.

3. The method for segmenting and splicing ship hulls based on laser point cloud equipment according to claim 2, characterized in that: The process of horizontally stitching together segmented point clouds using a top view of the point cloud is as follows: Using a reference point at the same stationary position that can be acquired from the point cloud top view of the hull section itself or from the point clouds of two surrounding stations, the overlapping parts of the horizontal point cloud outlines and the same feature points of the adjacent point clouds are translated and aligned. If there is an angular deviation, the overall angle of the point clouds is adjusted by rotating the point cloud direction to improve the horizontal overlap rate of the two point clouds. The horizontal deviation of the point cloud splicing is judged by observing whether the main feature points of the point clouds of the two hull sections overlap. If the main feature points of the point clouds of the two hull sections overlap, the horizontal deviation of the point cloud splicing is acceptable; otherwise, it is unacceptable. The main feature points include the outer contour of each segment and the intersection of two structural surfaces of the hull segment. The same feature points are the same contours at the same position in the same segment.

4. The method for segmenting and splicing ship hulls based on laser point cloud equipment according to claim 2, characterized in that: The process of vertically stitching together segmented point clouds using a side view of the point cloud is as follows: Using a point cloud side view as a reference point that can be collected from the same stationary position of the hull section itself or two surrounding point clouds, the overlapping parts and feature points of the vertical point cloud outlines of the two point clouds are aligned vertically. If there is an angular deviation, the overall angle of the point cloud is adjusted by rotating the point cloud direction to improve the vertical overlap rate of the two point clouds. By observing whether the main feature points of the two hull section point clouds overlap, it is determined whether the vertical deviation of the point cloud splicing is qualified. If the main feature points of the two hull section point clouds overlap, the vertical deviation of the point cloud splicing is qualified; otherwise, it is unqualified.

5. The method for segmenting and splicing ship hulls based on laser point cloud equipment according to claim 1, characterized in that: The process of deleting point cloud data from parts of the stitching result that do not need to be compared with the hull section design model, and retaining the point cloud of the hull section, is as follows: Adjust the stitched point cloud to the top view using the point cloud group view. In the top view, delete the point cloud data other than the hull segment point cloud. Delete the point cloud data other than the hull segment point cloud structure in the order of coarse deletion first and fine deletion later. First, coarse deletion retains the hull segment point cloud and the point cloud within a certain range around it. Then, fine deletion only retains the hull segment point cloud. Switch to the side view and delete the sections in the same way as the top view, keeping only the point cloud of the hull segments. If deletion cannot be performed through the top or side view, the point cloud of the hull segment can be rotated and translated to a suitable angle for deletion.

6. The method for segmenting and splicing ship hulls based on laser point cloud equipment according to claim 1, characterized in that: The process of generating target points from the key intersection locations of two structural surfaces of the hull segment using point cloud data of the hull segment, and then performing coordinate transformation between the target points and their corresponding positions in the hull segment design model to convert the coordinate system of the hull segment point cloud data to be consistent with the theoretical coordinate system of the hull segment design model is as follows: In the hull section point cloud, M target points are generated using identifiable target centers at M target center locations, where M≥3. The coordinates of the M target center points are exported, and the position coordinates of the key structural points of the hull corresponding to the M target centers are found. The coordinates of the design points corresponding to the M target center points in the hull section design model are imported and applied to the hull section point cloud in the corresponding order. By applying the coordinates of the target center points in the M different hull section design models, the coordinate system of the hull section point cloud data is converted to be consistent with the theoretical coordinate system of the hull section design model.

7. A hull segment splicing device based on laser point cloud equipment, characterized in that: include: Acquisition module: used to acquire point cloud data of X parts of the hull section; The stitching module is used to stitch together X parts of point cloud data by utilizing the spatial overlap and common features of the point cloud data. The deletion module is used to delete the point cloud data of the parts of the splicing result that do not need to be compared with the hull section design model, while retaining the point cloud of the hull section. The conversion module generates target points from key locations in the hull section point cloud data, performs coordinate transformation between the target points and the corresponding coordinates of the hull section design model, and converts the coordinate system of the hull section point cloud data to be consistent with the theoretical coordinate system of the hull section design model. Save module: Used to output the required point cloud format, save the complete point cloud data of the hull segment, and realize the splicing of hull segments.

8. A computer device, comprising: A processor and a memory, the memory storing a program module, characterized in that the program module runs on the processor to implement claim 1. The method described in any one of the 6 methods.

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