A method for evaluating post-welding geometric deviation of a ship space based on a three-dimensional scanning point cloud, an electronic device, and a program product
By using 3D scanning point cloud technology, and employing the L1 median iterative algorithm and B-spline surface fitting method, the problems of low efficiency and insufficient accuracy of traditional measurement methods are solved, and automated evaluation and high-precision measurement of geometric deviations after welding of ship pipelines are realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI JIAOTONG UNIV
- Filing Date
- 2026-03-13
- Publication Date
- 2026-05-29
Smart Images
Figure CN122115776A_ABST
Abstract
Description
Technical Field
[0001] This disclosure pertains to the field of shipbuilding technology, and specifically relates to a method, electronic equipment, and program product for evaluating post-weld geometric deviations of ship space pipes based on three-dimensional scanning point clouds. Background Technology
[0002] In shipbuilding, welded pipes are numerous and have complex spatial arrangements, forming a crucial component of the hull structure and electromechanical systems. Because ship piping typically has a high slenderness ratio, thin cross-section, and high degree of spatial curvature, its welding process is easily affected by uneven heat input and residual stress, resulting in discrepancies between the actual finished pipe and the design shape. These discrepancies include angular deviations in bends, changes in cross-sectional shape, and localized deformation. To ensure the quality of piping welding and assembly accuracy, it is necessary to perform geometric measurements on the welded pipes to obtain their true forming state and conduct post-weld geometric deviation assessments.
[0003] Currently, shipyards still primarily rely on traditional measuring tools such as rulers, calipers, and squares for inspecting welded pipe fittings. These methods are inefficient and easily influenced by operator experience and reading methods, resulting in poor stability and consistency of results. More importantly, manual measurement can only obtain a limited amount of discrete, local dimensional information, making it difficult to reflect the complete three-dimensional morphology of the pipe's outer surface. This is insufficient for a comprehensive analysis of post-weld geometric changes and cannot meet the high-precision quality control requirements of modern shipbuilding. Currently, the following are the commonly used measurement methods:
[0004] (1) Most shipyards still mainly use traditional measuring tools such as rulers, calipers and squares when measuring welded pipe fittings. Operators usually perform manual positioning and reading at typical locations to obtain basic geometric information such as the length, bending angle and local cross-sectional dimensions of the pipe fittings.
[0005] (2) Some shipyards use spatial coordinate measuring equipment such as total stations to place reflective targets at several characteristic positions of the welded pipe fittings and measure their spatial coordinate points using a total station. Based on the relative positional relationship between the characteristic points, parameters such as the section length and bending angle of the pipe fittings can be calculated to characterize the main geometric features of the welded pipe fittings.
[0006] (3) With the development of 3D scanning technology, high-density point cloud data covering the outer surface of pipe fittings can be acquired relatively quickly on site. Operators usually manually select individual points, cross-sectional points or local area point clouds in the point cloud, and combine them with fitting tools to extract relevant geometric parameters to assist in analyzing the morphological changes of the pipe fittings after welding. Summary of the Invention
[0007] One aspect of this disclosure is a method for evaluating post-weld geometric deviations of ship space pipes based on three-dimensional scanned point clouds, comprising the following steps:
[0008] Acquire three-dimensional point cloud data of the outer surface of the ship's space tube;
[0009] Based on the three-dimensional point cloud data, the central skeleton point sequence is extracted using the L1 median iterative algorithm, and the skeleton point sequence is centered and corrected by fitting the cross-sectional ellipse features to obtain the axis point sequence reflecting the direction of the pipeline center.
[0010] Several slice sections are constructed along the axis point sequence. The point cloud data at each slice section is processed by arc length parameterized interpolation and angle uniform sampling to generate a topologically consistent two-dimensional point matrix.
[0011] Based on the two-dimensional point matrix, the digital three-dimensional model of the ship's space tube is reconstructed using the B-spline surface fitting method.
[0012] Based on the digital three-dimensional model, the geometric deviation parameters of the welded pipe fittings are calculated, thereby realizing the automated evaluation of the post-weld geometric deviation.
[0013] In one aspect of this disclosure, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor running the computer program to implement the above-described method for evaluating post-weld geometric deviations of ship space tubes based on three-dimensional scanned point clouds.
[0014] In one aspect of this disclosure, a computer program product includes a computer program that is executed by a processor to implement the above-described method for evaluating post-weld geometric deviations of ship space pipes based on three-dimensional scanned point clouds. Attached Figure Description
[0015] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which:
[0016] Figure 1 A schematic diagram of the process for evaluating post-weld geometric deviations of ship space pipes based on three-dimensional scanning point clouds, according to one embodiment of this disclosure.
[0017] Figure 2 A schematic diagram of pipe skeleton point extraction according to one embodiment of the present disclosure.
[0018] Figure 3 A schematic diagram of skeleton point centering correction according to one embodiment of the present disclosure.
[0019] Figure 4 A schematic diagram of a three-dimensional model of a pipeline surface according to one embodiment of the present disclosure. Detailed Implementation
[0020] This disclosure, through a study of existing solutions, identifies the following shortcomings in the current methods for geometric measurement and deviation assessment of shipboard space piping:
[0021] (1) Traditional measuring tools can only obtain local dimensional information of a small number of discrete locations, which is difficult to reflect the overall three-dimensional shape of the pipe fitting after welding. Moreover, the measurement results are easily affected by the operator's experience and reading method, and the reliability and consistency are difficult to guarantee.
[0022] (2) Manual measurement is limited by the size, posture and accessibility of the pipe fittings. In actual operation, the position of the measuring point often needs to be adjusted repeatedly, which is inefficient and not suitable for the inspection needs of various types of welded pipe fittings.
[0023] (3) Total station measurements rely on target layout, resulting in sparse points. They are only suitable for calculating main parameters such as length and direction, and are insufficient for quantifying post-weld features such as cross-sectional deviation and local deformation.
[0024] (4) Although three-dimensional scanning can obtain high-density point clouds, the existing point cloud processing methods mainly rely on manual point selection and manual fitting, which cannot automatically generate a digital model that reflects the post-weld state and make it difficult to form stable and consistent geometric analysis results.
[0025] This disclosure addresses the aforementioned problems by proposing a method for evaluating the dimensional accuracy of ship space pipes after welding based on three-dimensional scanning point clouds. This method acquires spatial point cloud data of the pipe's outer surface through scanning, reconstructs a three-dimensional digital model of its actual forming state, and thus achieves accurate evaluation of dimensional and morphological changes after welding.
[0026] According to one or more embodiments, a method for evaluating post-weld geometric deviations of ship space pipes based on three-dimensional scanned point clouds includes the following steps:
[0027] Acquire three-dimensional point cloud data of the outer surface of the ship's space tube;
[0028] Based on the three-dimensional point cloud data, the central skeleton point sequence is extracted using the L1 median iterative algorithm, and the skeleton point sequence is centered and corrected by fitting the cross-sectional ellipse features to obtain the axis point sequence reflecting the direction of the pipeline center.
[0029] Several slice sections are constructed along the axis point sequence. The point cloud data at each slice section is processed by arc length parameterized interpolation and angle uniform sampling to generate a topologically consistent two-dimensional point matrix.
[0030] Based on the two-dimensional point matrix, the digital three-dimensional model of the ship's space tube is reconstructed using the B-spline surface fitting method.
[0031] Based on the digital three-dimensional model, the geometric deviation parameters of the welded pipe fittings are calculated, thereby realizing the automated evaluation of the post-weld geometric deviation.
[0032] The extraction of the central skeleton point sequence using the L1 median iterative algorithm includes:
[0033] Principal component analysis is performed on the three-dimensional point cloud data to determine the diagonal direction of the three-dimensional bounding box. The data is then projected and sorted along this direction, and the initial sampling point sequence is extracted with a fixed step size.
[0034] For each initial sampling point, a local neighborhood is constructed, and the position of the skeleton point is iteratively updated using the geometric median under the L1 norm until the global displacement convergence condition is met or the maximum number of iterations is reached.
[0035] The converged skeleton points are sorted using a minimum spanning tree to generate an ordered skeleton point sequence.
[0036] During the iterative update process, if the distance between adjacent skeleton points is detected to exceed a preset threshold, a new sampling point is inserted at the gap or endpoint and the iteration continues until no new sampling points are generated and the convergence condition is met.
[0037] The centering correction of the skeleton point sequence by combining the elliptical feature fitting of the combined cross section includes:
[0038] For any skeleton point on the axis, construct a tangent plane and select original point cloud data whose distance from the plane is less than a preset threshold, and orthogonally project it onto the tangent plane to obtain a two-dimensional point set;
[0039] The coordinates of the center of the ellipse are obtained by fitting the two-dimensional point set to an ellipse using a random sampling consensus algorithm.
[0040] The center of the ellipse is back-projected into three-dimensional space to replace the original skeleton points, thereby achieving geometric centering correction of the pipeline axis.
[0041] The process of performing arc-length parameterized interpolation and angle uniform sampling on the point cloud data at each slice section includes:
[0042] The point cloud data at the slice section is orthogonally projected onto the tangent plane and transformed to the local coordinate system to obtain a two-dimensional cross-sectional point set;
[0043] Calculate the cumulative curve arc length of each point in the two-dimensional cross-section point set relative to the starting point, and perform Akima interpolation based on the cumulative curve arc length to obtain the cross-sectional profile point set after interpolation enhancement.
[0044] The interpolated cross-sectional contour point set is converted into polar coordinate representation, and equidistant angle resampling is performed within a preset angle range to generate contour points that are uniformly distributed in the circumferential direction as shape value points.
[0045] The method of reconstructing the digital three-dimensional model of the ship's space tube using B-spline surface fitting includes:
[0046] The two-dimensional value point matrix is parameterized, and the control point grid of the B-spline surface is obtained by curve interpolation.
[0047] Periodic parameters are used in the circumferential direction to ensure the section is closed, and open node vectors are used in the axial direction to ensure the stability of the end shape.
[0048] A continuous three-dimensional surface model that satisfies interpolation constraints is generated based on the control point mesh.
[0049] This disclosure provides a method for automatically extracting the central skeleton of a space pipeline point cloud; a method for constructing local cross-sections along the skeleton direction and generating a topologically consistent two-dimensional contour point matrix; and a method for reconstructing a parametric model of the pipeline outer surface based on B-spline surfaces. These methods enable the reconstruction process of ship space pipeline structures to be logically coherent and structurally clear, and can be completed automatically without human intervention, providing a reliable digital basis for evaluating the geometric deviation of welded pipe fittings.
[0050] According to one or more embodiments, point cloud data of the outer surface of a ship's spatial pipe structure is obtained using 3D scanning technology. Based on reverse modeling methods, a digital geometric model of the spatial pipe is reconstructed, enabling accurate and efficient assessment of geometric deviations. This disclosure is applicable to the reconstruction of 3D spatial pipe models and the assessment of geometric deviations in ship pipelines composed of straight pipe sections and conventional bends. It can perform geometric extraction and modeling of pipelines with certain spatial curvature and directional changes. It should be noted that this disclosure is not applicable to pipe fittings with multiple branches or intersecting structures, such as intersecting pipes.
[0051] The method and process disclosed herein are as follows: Figure 1 As shown, a 3D scan of the ship's space pipe is first performed to obtain point cloud data covering the outer surface of the pipe. Then, the central skeleton points are extracted from the original point cloud using the L1 median method, and the skeleton point sequence is corrected by fitting cross-sectional features to accurately reflect the central direction of the pipeline. Based on this, several slice sections are constructed along the skeleton line, and a topologically consistent 2D shape point matrix is generated through interpolation and uniform angle sampling. Next, the shape points are parametrically reconstructed using the B-spline surface fitting method to generate a digital 3D model of the space pipeline. Based on this model, geometric deviation indicators such as the ellipticity, local outer surface deformation, and bending angle changes of the welded pipe can be further calculated, achieving automated evaluation of post-weld dimensional accuracy. Specific steps include:
[0052] (1) Acquisition of 3D point cloud of pipe fittings
[0053] The original point cloud of the space tube was obtained by scanning the structure of the ship's space tube using a 3D laser scanner. .
[0054] (2) Pipeline skeleton generation
[0055] The skeleton of a 3D object is a compact and intuitive representation of its geometric features, effectively capturing the morphological description and topological structure of the target object. It is widely used in shape analysis, 3D reconstruction, and other fields. In marine piping engineering, accurate extraction of the piping skeleton (i.e., the central axis) is crucial for achieving piping system positioning and reverse reconstruction. Based on this, this study utilizes the L1 median iterative algorithm for skeleton point extraction. The entire process is as follows.
[0056] First, initial skeleton points are selected. Principal Component Analysis (PCA) is performed to obtain its three principal directions, thereby determining its 3D bounding box and diagonal direction. Project along the diagonal direction and sort them, then extract the initial sampling point sequence with a fixed step size. ,in This represents the number of skeleton points.
[0057] Secondly, the L1 median algorithm is used to iteratively update the skeleton points, as follows.
[0058] The first step is to process each initial sampling point. Constructing the neighborhood range
[0059] (1)
[0060] in The neighborhood radius can be set based on the point cloud density and pipe diameter.
[0061] The second step is to update the positions of the skeleton points through iterative optimization. Let the first... The skeleton point set before the round of iteration is For each current skeleton point and its corresponding local neighborhood The geometric median under the L1 norm is used as the center point of this neighborhood:
[0062] (2)
[0063] In the formula, This represents the number of points in the neighborhood. Ultimately, a set of updated skeleton center points is obtained. .
[0064] The third step is to determine whether the algorithm has converged. (Global displacement) It is measured by calculating the maximum displacement of all skeleton points:
[0065] (3)
[0066] If global displacement Or the number of outer iterations reaches If the sequence converges, then the entire sequence is considered convergent. Otherwise, the new skeleton point sequence is... Return to step one as input for the next iteration.
[0067] During the iteration process, if the distance between adjacent skeleton points is detected to be too large, new sampling points can be inserted at the gaps or endpoints, and the algorithm returns to the second step to rebuild the neighborhood and participate in subsequent iterations. The algorithm terminates when no more new sampling points are generated and the convergence condition is met.
[0068] Finally, an initial ordered skeleton point is generated by sorting the skeleton points using a minimum spanning tree (MST), which serves as the geometric representation of the pipe's central axis, such as... Figure 2 As shown.
[0069] (3) Skeleton point correction
[0070] When the pipeline point cloud distribution is asymmetrical, the local median points obtained using the L1 median method are biased towards the complete side of the point cloud, causing the local pipeline axis to deviate from the true center. Therefore, post-processing of the skeleton points is required to achieve center correction based on elliptical feature fitting.
[0071] Specifically, for any skeleton point on the axis Construct a tangent plane, and on this plane, select points from the original point cloud whose distance from the plane is less than a threshold. The point set is orthogonally projected onto the cross-sectional plane to obtain a two-dimensional point set. Subsequently, the projection point set is elliptical-fitted in the cross-sectional plane coordinate system using the Random Sample Consensus (RANSAC) algorithm, and the ellipse equation is shown in equation (4):
[0072] (4)
[0073] In equation (4), The algebraic coefficients used for fitting the solution collectively determine the shape, orientation, and position of the ellipse at the cross section. Based on these fitting coefficients, the two-dimensional coordinates of the ellipse's center can be directly calculated using equation (5). .
[0074] (5)
[0075] The fitted circle center is back-projected into three-dimensional space to replace the original axis points. This allows for the geometric centering correction of the pipeline axis. Figure 3 This demonstrates that by centering the skeleton points, the central direction of the pipeline can be reflected more accurately.
[0076] (4) Solving the slice profile
[0077] This section includes solving for the contour points of the slice plane, and performing interpolation and angle homogenization on the contour point set to generate structurally continuous and uniformly sampled cross-sectional contour data.
[0078] At each skeleton point, construct tangent plane points, orthogonally project these points onto the tangent plane, and transform them into a local coordinate system based on this plane to obtain a two-dimensional cross-sectional point set. .
[0079] To address the potential sparsity or missing points in the original point cloud, this study performs arc-length parameterized interpolation enhancement on the projected 2D contour point set. First, the cumulative curve arc length of each point relative to the starting point is calculated. :
[0080] (6)
[0081] (7)
[0082] Select uniformly within the cumulative arc length interval Target sampling locations Considering the potential numerical oscillations that conventional polynomial interpolation may produce when dealing with boundary conditions or drastically changing regions, this study selects the Akima interpolation method. This method possesses excellent locality and smoothness, and functions with arc length as parameters are constructed respectively. and This allows us to determine the coordinates of the target location. , And obtain the cross-sectional profile interpolation point set after data augmentation and completion. .
[0083] To eliminate density differences in the spatial distribution of interpolation points, for dense... Angle resampling is performed on each interpolation point. The interpolation points are then converted into polar coordinates as shown in equation (8):
[0084] (8)
[0085] In equation (8), and The coordinates of the interpolation point in the local two-dimensional rectangular coordinate system of the cross section; and These represent the polar radius and polar angle after the conversion, respectively. Subsequently, in Divide the interval into equally spaced intervals There are several angles. For each discrete angle, the corresponding contour points are selected using a nearest neighbor search strategy to generate the final contour. A uniform set of contour points (i.e., the shape points participating in surface fitting) is generated. This step ensures the uniform distribution of contour points in the polar direction, significantly improving the convergence and stability of the subsequent geometric fitting algorithm. After processing, the resampled points are back-projected into the original 3D coordinate system using an inverse transformation.
[0086] After the above processing, the discrete point cloud data is reconstructed into a topologically isomorphic two-dimensional matrix of point values. This matrix can be represented as,
[0087] (9)
[0088] in For axial section index, This is the index for circumferential sampling points. 'n' refers to the number of skeleton points, i.e., the number of cross-sections, and 'm' refers to the number of shape value points on a single cross-section. Each element... Representing the The first section The individual point values not only establish a logical connection between the point cloud data, but also provide a standardized input data structure for subsequent inverse interpolation to solve for the control vertices.
[0089] (5) Reconstruction of three-dimensional model of pipeline surface
[0090] In order to obtain a three-dimensional outer surface that can accurately reflect the true forming state of the welded pipe fitting, this disclosure is based on the aforementioned topologically consistent value point set. A parametric 3D model of the pipeline was constructed using NURBS surface interpolation. Before building the surface, the shape points were first parametrically processed, and the control point mesh for NURBS representation was obtained through curve interpolation. As the core descriptive unit of the parametric surface, the spatial position of the control points is determined by the shape point constraint calculation, which can express the overall smooth morphology of the pipeline's outer surface in a compact mathematical form.
[0091] During surface reconstruction, periodic parameters are used in the circumferential direction to ensure cross-section closure, while open node vectors are used in the axial direction to ensure the stability of the end shape. Based on the final solved control point array, a continuous, smooth, and interpolation-constrained three-dimensional surface model can be generated, providing a reliable basis for subsequent quantitative analysis of post-weld geometric deviations. Figure 4 A schematic diagram of parametric modeling of the pipeline surface.
[0092] (6) Geometric deviation assessment
[0093] After obtaining the parametric surface model of the spatial pipeline, the deviation of the post-weld geometry can be evaluated based on this model. By extracting several sections along the pipeline axis and calculating their characteristic parameters, key geometric information such as the cross-sectional shape, bend angle, and local spatial shape of the pipe fitting can be automatically obtained and compared with the design model to obtain the corresponding deviation value, which is used to determine whether the post-weld pipe fitting meets the standard requirements.
[0094] This disclosure constructs a digital 3D model of the welded pipe fitting based on 3D point cloud, which can obtain more complete and continuous structural morphology information. Compared with the traditional method that relies on manual reading or manual point selection, the reconstructed digital model can truly reflect the overall 3D state of the welded pipe fitting, providing a reliable basis for calculating key geometric deviation indicators such as ellipticity deviation, bending angle deviation, and local deformation, thereby significantly improving the reliability and consistency of welding dimensional accuracy assessment.
[0095] This disclosure employs the L1 median iterative algorithm to extract skeleton points, combined with elliptical feature fitting for centering correction, effectively overcoming the axis offset problem caused by point cloud asymmetry and obtaining an axis that accurately reflects the true geometric center of the pipeline. Since the L1 median algorithm uses the geometric median under the L1 norm as the neighborhood center point, it is more robust to outliers than the traditional mean method, and can stably converge to the central trend position of the point cloud distribution even with uneven point cloud density. When the pipeline point cloud is asymmetrically distributed due to scanning angle limitations or occlusion, the L1 median point will be biased towards the complete side of the point cloud, resulting in a systematic bias. This disclosure introduces elliptical feature fitting for post-processing correction, using the RANSAC algorithm to fit an ellipse to the cross-sectional projection point set, and replacing the original L1 median point with the center of the fitted ellipse as the true center point, fundamentally eliminating the influence of point cloud asymmetry on center positioning, ensuring that the axis accurately reflects the geometric center of the pipeline rather than the point cloud center.
[0096] Furthermore, this disclosure solves the problem of contour discontinuity caused by the sparseness or missing data in the original point cloud by employing arc-length parameterized Akima interpolation and angle-uniform sampling to generate a topologically consistent two-dimensional point matrix. This generates standardized cross-sectional data with continuous structure, uniform sampling, and consistent topology, providing high-quality input for surface reconstruction. This is because the original scanned point cloud often exhibits local sparseness or data loss due to surface reflection, occlusion, or scanning resolution limitations, resulting in uneven distribution and large density differences in the cross-sectional contour points obtained by direct projection. Arc-length parameterized Akima interpolation is used: an interpolation function is constructed using the cumulative curve arc length as a parameter. Akima interpolation has excellent locality and smoothness, effectively avoiding boundary oscillations and achieving reasonable data enhancement and completion in areas with missing data, restoring contour continuity. Angled uniform sampling converts the interpolated dense points into polar coordinates and resamples at equal angles within the interval, forcibly eliminating circumferential density differences and ensuring that each cross-section has the same number of uniformly distributed point values. The resulting two-dimensional value point matrix establishes a standardized topological structure in the axial and circumferential directions, enabling clear correspondences between points on different sections. This provides a regularized data structure for subsequent B-spline surface fitting, significantly improving the numerical stability and convergence of surface reconstruction.
[0097] It should be understood that in the embodiments of this disclosure, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.
[0098] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. 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.
[0099] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this disclosure, and these modifications or substitutions should all be covered within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.
Claims
1. A method for evaluating post-weld geometric deviations of ship space pipes based on three-dimensional scanned point clouds, characterized in that, Includes the following steps: Acquire three-dimensional point cloud data of the outer surface of the ship's space tube; Based on the three-dimensional point cloud data, the central skeleton point sequence is extracted using the L1 median iterative algorithm, and the skeleton point sequence is centered and corrected by fitting the cross-sectional ellipse features to obtain the axis point sequence reflecting the direction of the pipeline center. Several slice sections are constructed along the axis point sequence. The point cloud data at each slice section is processed by arc length parameterized interpolation and angle uniform sampling to generate a topologically consistent two-dimensional point matrix. Based on the two-dimensional point matrix, the digital three-dimensional model of the ship's space tube is reconstructed using the B-spline surface fitting method. Based on the digital three-dimensional model, the geometric deviation parameters of the welded pipe fittings are calculated, thereby realizing the automated evaluation of the post-weld geometric deviation.
2. The method according to claim 1, characterized in that, The extraction of the central skeleton point sequence using the L1 median iterative algorithm includes: Principal component analysis is performed on the three-dimensional point cloud data to determine the diagonal direction of the three-dimensional bounding box. The data is then projected and sorted along this direction, and the initial sampling point sequence is extracted with a fixed step size. For each initial sampling point, a local neighborhood is constructed, and the position of the skeleton point is iteratively updated using the geometric median under the L1 norm until the global displacement convergence condition is met or the maximum number of iterations is reached. The converged skeleton points are sorted using a minimum spanning tree to generate an ordered skeleton point sequence.
3. The method according to claim 2, characterized in that, During the iterative update process, if the distance between adjacent skeleton points is detected to exceed a preset threshold, a new sampling point is inserted at the gap or endpoint and the iteration continues until no new sampling points are generated and the convergence condition is met.
4. The method according to claim 1, characterized in that, The centering correction of the skeleton point sequence by combining the elliptical feature fitting of the combined cross section includes: For any skeleton point on the axis, construct a tangent plane and select original point cloud data whose distance from the plane is less than a preset threshold, and orthogonally project it onto the tangent plane to obtain a two-dimensional point set; The coordinates of the center of the ellipse are obtained by fitting the two-dimensional point set to an ellipse using a random sampling consensus algorithm. The center of the ellipse is back-projected into three-dimensional space to replace the original skeleton points, thereby achieving geometric centering correction of the pipeline axis.
5. The method according to claim 1, characterized in that, The process of performing arc-length parameterized interpolation and angle uniform sampling on the point cloud data at each slice section includes: The point cloud data at the slice section is orthogonally projected onto the tangent plane and transformed to the local coordinate system to obtain a two-dimensional cross-sectional point set; Calculate the cumulative curve arc length of each point in the two-dimensional cross-section point set relative to the starting point, and perform Akima interpolation based on the cumulative curve arc length to obtain the cross-sectional profile point set after interpolation enhancement. The interpolated cross-sectional contour point set is converted into polar coordinate representation, and equidistant angle resampling is performed within a preset angle range to generate contour points that are uniformly distributed in the circumferential direction as shape value points.
6. The method according to claim 1, characterized in that, The method of reconstructing the digital three-dimensional model of the ship's space tube using B-spline surface fitting includes: The two-dimensional value point matrix is parameterized, and the control point grid of the B-spline surface is obtained by curve interpolation. Periodic parameters are used in the circumferential direction to ensure the section is closed, and open node vectors are used in the axial direction to ensure the stability of the end shape. A continuous three-dimensional surface model that satisfies interpolation constraints is generated based on the control point mesh.
7. The method according to claim 1, characterized in that, The geometric deviation parameters include at least one of ellipticity deviation, pipe bending angle deviation, and local outer surface deformation.
8. The method according to claim 1, characterized in that, The ship space pipe is a three-dimensional space pipeline composed of straight pipe sections and curved pipe sections, and does not include intersecting pipes or multi-branch intersection structures.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method as described in any one of claims 1-8.
10. A computer program product, comprising a computer program, characterized in that, The computer program is executed by a processor to implement the method according to any one of claims 1-8.