Parametric 3D Modeling Method and System for Ship Section Construction

Abstract

This invention relates to the field of parametric modeling technology, specifically to a parametric 3D modeling method and system for ship segment construction. The method includes the following steps: based on 3D laser equipment, analyzing the spatial coordinates of the ship segments and integrating a 3D point matrix; extracting the spatial point matrix distribution characteristics; calculating the neighborhood distribution and change trends; screening disturbed areas; obtaining disturbed boundary characteristic parameters; analyzing and correcting modeling parameters; formatting and outputting the modeling instruction set; and obtaining the 3D modeling matching degree. This invention achieves efficient identification and automatic parameter correction of local changes in ship segments by fusing real-time acquired spatial point matrix features and dynamic disturbance parameters. In the modeling process, spatial distribution, disturbance differentiation, and parameter compensation are carried out simultaneously. Changes in boundary areas are converted into model adjustment signals in real time. The continuity of 3D structural constraints is naturally maintained during the generation process, and the modeling output directly matches the on-site data, achieving adaptive and refined segment modeling for the manufacturing site.

Description

Technical Field

[0001] This invention relates to the field of parametric modeling technology, and in particular to a parametric 3D modeling method and system for ship section construction. Background Technology

[0002] Parametric modeling involves using preset parameters or rules to control the structure and form of a geometric model, thereby enabling rapid model construction, modification, and reuse. Its applications are wide-ranging, covering multiple engineering and technical fields such as mechanical design, architectural modeling, product appearance design, and industrial manufacturing. Among these, the traditional parametric 3D modeling method for ship segmented construction refers to a modeling approach where the entire ship is divided into several structural units according to predetermined segments during the shipbuilding process, and a 3D model is constructed based on the design parameters of each segment. This method primarily addresses the problem of frequent changes in dimensions, layout, and component configuration between different segments in ship structural design.

[0003] Existing parametric modeling methods rely solely on static design parameters for geometric modeling during the ship section manufacturing process. This fails to proactively identify spatial changes during section processing and assembly. The external shape scanning results do not effectively participate in parameter adjustments, and on-site structural errors can only be corrected manually. The model generation lacks real-time feedback on the complex curved surfaces and continuous changes of the sections, resulting in imprecise geometric mapping. This makes it difficult to guarantee the accuracy of the overall structure and assembly consistency after section splicing, thus affecting the reliability of digital ship manufacturing. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a parametric 3D modeling method and system for ship section construction. The technical solution is as follows: On the one hand, a parametric 3D modeling method for ship section construction is provided, including the following steps: S1: Based on a three-dimensional laser device, collect spatial coordinate data of ship hull segments, screen reflection intensity and ranging synchronization, compare data continuity to determine valid segments, adjust data buffer order, and obtain spatial point distribution characteristics; S2: Based on the spatial point matrix distribution characteristics, calculate the neighborhood distribution of the target point, analyze the direction change trend, determine the angle offset, statistically analyze the neighborhood distance change, group and filter the disturbance performance, determine the boundary range, and obtain the disturbance boundary characteristic parameters. S3: Based on the disturbance boundary characteristic parameters, determine the impact of surface changes on modeling parameters, analyze the relationship between disturbance and target parameters, compare disturbance differences, adjust modeling control parameters, and use data interpolation to correct the expression to obtain the correction amount of modeling parameters; S4: Based on the modeling parameter correction amount, determine the contribution of the parameters to the positioning path, angle step size and surface offset, identify the priority path, perform instruction protocol formatting, and obtain the modeling execution instruction set; S5: Based on the modeling execution instruction set, compare the pairing of the three-dimensional mesh nodes with the spatial point matrix distribution characteristics, statistically analyze the spatial deviation, calculate the mean square error, determine the error performance, optimize the output format, and obtain the three-dimensional modeling matching degree.

[0005] On the other hand, the spatial lattice distribution characteristics include three-dimensional distribution range, surface coverage and structural uniformity; the disturbance boundary characteristic parameters include boundary positioning information, disturbance partition number and change area label; the modeling parameter correction amount includes curvature correction factor, plane adjustment factor and modeling compensation term; the modeling execution instruction set includes path control sequence, step size adjustment instruction and offset execution command; and the three-dimensional modeling matching degree includes spatial overlap degree, error evaluation parameters and model consistency index.

[0006] On the other hand, the steps for obtaining the spatial point matrix distribution features are as follows: S101: Based on a three-dimensional laser device, analyze the collected spatial coordinate data of the ship hull segments, match the time series labels in the reflection intensity data frame and the ranging data frame item by item, determine whether the time series labels between each data item are consistent, sort and aggregate the data frames that pass the consistency judgment to obtain a synchronous continuous point cloud. S102: Based on the synchronous continuous point cloud set, calculate the spatial distance change between adjacent coordinate points in each point cloud segment, identify data segments whose spatial distance changes are consistent, and aggregate the point cloud segments that meet the consistency after filtering to obtain a structurally consistent point sequence. S103: Based on the structural coherent point sequence, analyze the spatial coordinate distribution, construct a three-dimensional cache sequence according to the spatial location, optimize the spatial sorting of point cloud data within the cache sequence, and obtain the spatial point matrix distribution characteristics.

[0007] On the other hand, the steps for obtaining the disturbance boundary characteristic parameters are as follows: S201: Based on the spatial point matrix distribution characteristics, calculate the three-dimensional distance distribution between each center point and surrounding points, determine the spatial correspondence between adjacent points and center points, identify the set of adjacent points with dense arrangement and directional continuity, and obtain a local spatial association sequence. S202: Based on the local spatial correlation sequence, analyze the spatial relationship between neighboring points and the center point, calculate the direction vector from the neighboring points to the center point, compare the angle changes of each direction vector, filter out regions with direction changes and accompanying distance distribution differences, identify spatial segments with disturbance characteristics, and obtain the disturbance response segment index. S203: Based on the disturbance response fragment index, determine the set of boundary points in the spatial region, analyze the continuity and geometric orientation of the boundary point distribution, identify boundary fragments with continuous spatial variation characteristics, classify the valid boundary data, and obtain the disturbance boundary characteristic parameters.

[0008] On the other hand, the specific steps for obtaining the modeling parameter correction amount are as follows: S301: Based on the disturbance boundary characteristic parameters, determine the changing trend between the surface normal and the surrounding point set within each boundary point area, compare the spatial continuity and curvature change state, identify areas where there are surface turning points or direction adjustment phenomena, and obtain the surface disturbance influence factor. S302: Based on the surface disturbance influence factor, compare the control parameters of the modeling target corresponding to the disturbance region, determine the changes of curvature, surface orientation and control step size in the disturbance range, identify control terms with fluctuating parameter responses, and obtain parameter drift mapping data; S303: Based on the parameter drift mapping data, determine the distribution state of the control parameters, analyze the interpolation continuity of the parameter points in the surface space, adjust the control terms corresponding to the disturbance segments in the parameter expression, optimize the distribution performance of the control parameters in the space, and obtain the modeling parameter correction amount.

[0009] On the other hand, the specific steps for obtaining the modeling execution instruction set are as follows: S401: Based on the modeling parameter correction amount, determine the influence of each parameter on the positioning path, angle step size and surface offset adjustment process, compare the adjustment effect of parameter changes on each modeling link, screen the parameter items that affect the control process, and obtain the key factors of path control. S402: Based on the key factors for path regulation, analyze the control sections and parameter types, calculate the spatial node sequence corresponding to the modeling path, determine the directional continuity and parameter adaptation characteristics of the path content, identify path combinations with priority, and obtain the preferred path grouping index. S403: Based on the preferred path grouping index, analyze the hierarchical structure of modeling control parameters and spatial operation instructions, optimize the protocol field format of control instructions, and obtain the modeling execution instruction set.

[0010] On the other hand, the specific steps for obtaining the 3D modeling matching degree are as follows: S501: Based on the modeling execution instruction set, analyze the pairing of the three-dimensional mesh nodes and the spatial point matrix distribution characteristics, calculate the three-dimensional coordinate differences between each mesh node and the spatial point matrix, determine the validity of the spatial pairing of the nodes and the point matrix, filter point pairs with consistent pairing structures, and obtain the node spatial coupling structure. S502: Based on the node spatial coupling structure, calculate the spatial deviation between each pair of paired points, determine the consistency of the distribution of each deviation among all paired points, filter the data segments with deviation fluctuations, and obtain the error distribution statistical factor. S503: Based on the error distribution statistical factor, determine the relationship between the error range and the preset reference range, optimize the structure output format of the paired data, and obtain the 3D modeling matching degree.

[0011] On the other hand, the spatial coordinate data refers to the X, Y, and Z coordinate information of each sampling point in space collected by the three-dimensional laser device, the reflection intensity refers to the strength of the laser signal reflected back at each sampling point, and the distance measurement data refers to the distance data from each point to the origin of the device measured by the three-dimensional laser device.

[0012] On the other hand, the angle offset refers to the change in the angle between each direction vector in the neighborhood and the central reference direction, the neighborhood distance change refers to the change in the distance from each point in the neighborhood to the center point, and the boundary range refers to the boundary of the region in the point cloud that exhibits continuous geometric changes.

[0013] On the other hand, a parametric 3D modeling system for ship section construction is provided. This system is applied to parametric 3D modeling methods for ship section construction, including: The point cloud acquisition module is based on a 3D laser device to collect spatial coordinate data of ship hull segments, screen reflection intensity and ranging synchronization, compare data continuity to determine valid segments, adjust data buffer order, and obtain spatial point distribution characteristics. Based on the spatial point matrix distribution characteristics, the feature analysis module calculates the neighborhood distribution of the target point, analyzes the trend of directional change, judges the angle offset, statistically analyzes the neighborhood distance change, groups and filters the disturbance performance, determines the boundary range, and obtains the disturbance boundary characteristic parameters. Based on the disturbance boundary characteristic parameters, the parameter correction module determines the impact of surface changes on modeling parameters, analyzes the relationship between disturbance and target parameters, compares disturbance differences, adjusts modeling control parameters, and uses data interpolation to correct and express the correction amount of modeling parameters. Based on the modeling parameter correction amount, the instruction generation module determines the contribution of the parameters to the positioning path, angle step size and surface offset, identifies the priority path, performs instruction protocol formatting, and obtains the modeling execution instruction set. The matching evaluation module compares the pairing of the 3D mesh nodes with the spatial point matrix distribution features based on the modeling execution instruction set, calculates the spatial deviation, determines the mean square error, optimizes the output format, and obtains the 3D modeling matching degree.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By integrating real-time acquired spatial lattice features and dynamic disturbance parameters, the system achieves efficient identification of local changes in hull sections and automatic parameter correction. In the modeling process, spatial distribution, disturbance differentiation, and parameter compensation are carried out simultaneously. Changes in the boundary region are converted into model adjustment signals in real time. The continuity of 3D structural constraints is naturally maintained during the generation process. The modeling output directly matches the on-site data, enabling adaptive and refined segmented modeling for the manufacturing site. Attached Figure Description

[0015] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart of steps S1 of the present invention; Figure 3 This is a flowchart of steps S2 of the present invention; Figure 4 This is a flowchart of steps S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of steps S5 of the present invention; Figure 7 This is a system block diagram of the present invention. Detailed Implementation

[0016] 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. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0018] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0019] This invention provides a parametric 3D modeling method for ship section construction, such as... Figure 1 As shown, it includes the following steps: S1: Based on three-dimensional laser equipment, analyze the collected spatial coordinate data of ship hull segments, screen the synchronization time sequence of reflection intensity and distance measurement data, determine the effective segments by comparing data continuity, reorder the collected segments, optimize the data caching logic, integrate all spatial points to form a three-dimensional point array, and obtain the spatial point array distribution characteristics. S2: Based on the spatial point matrix distribution characteristics, calculate the spatial neighborhood distribution of any point, analyze the trend of vector change in each direction within the neighborhood, compare the angular offset between each neighborhood direction and the center point, count the changes in all neighborhood distances, group and filter regions by perturbation behavior, determine the continuously changing boundary range, and obtain the perturbation boundary characteristic parameters. S3: Based on the disturbance boundary characteristic parameters, determine the impact of spatial surface changes on the modeling parameter settings, analyze the relationship between disturbance changes and target modeling parameters, compare the differences between disturbance performance and modeling targets, implement corresponding adjustments to modeling control parameters, and obtain the modeling parameter correction amount by correcting parameter expressions through data interpolation; S4: Based on the modeling parameter correction amount, determine the contribution of each parameter to the modeling positioning path, angle step size and surface offset adjustment link, identify the path content with execution priority, and format all instructions according to the protocol to obtain the modeling execution instruction set; S5: Based on the modeling execution instruction set, compare the pairing of 3D mesh nodes with the spatial point matrix distribution characteristics, statistically analyze the spatial deviation between paired points, calculate the mean square error of paired points, make a judgment based on the error performance and reference range, optimize the output format of matching data, and obtain the 3D modeling matching degree.

[0020] The spatial lattice distribution characteristics include the three-dimensional distribution range, surface coverage and structural uniformity; the perturbation boundary characteristic parameters include boundary location information, perturbation partition number and change area label; the modeling parameter corrections include curvature correction factor, plane adjustment factor and modeling compensation term; the modeling execution instruction set includes path control sequence, step size adjustment instruction and offset execution command; and the three-dimensional modeling matching degree includes spatial overlap degree, error evaluation parameters and model consistency index.

[0021] In S1, the 3D laser equipment refers to a 3D laser scanner that performs spatial scanning on the hull or sections to acquire point cloud data, including laser emission, reflection, reception, and ranging systems; spatial coordinate data refers to the X, Y, and Z coordinate information of each sampling point in space acquired by the 3D laser equipment, used to reconstruct the shape of the object; reflection intensity refers to the strength of the laser signal reflected back at each sampling point, used to determine material properties or surface conditions and filter noise; ranging data refers to the distance data from each point measured by the 3D laser equipment to the equipment origin (or reference point), which is the basis for generating spatial coordinates; valid segments refer to the part of point cloud data that, after time sequence and continuity screening, is identified as truly complete and continuously reflecting the actual surface morphology of the hull sections, excluding invalid data such as noise and breakpoints; data caching logic refers to the data management method adopted in the stages of point cloud data acquisition, transmission, storage, and sorting to ensure the efficiency and accuracy of data processing; and stereo point array refers to the processed and integrated 3D spatial point cloud set, which in terms of morphology presents a spatial structure grid or dense point cloud array of segmented surface shapes.

[0022] In S2, spatial neighborhood distribution refers to the set of all surrounding points within a set spatial distance range centered on a certain point, which is a common local structure analysis method in point cloud processing; vector change trend refers to the overall change law of the spatial direction vectors from each point in the neighborhood to the center point in three-dimensional space, used to reflect local curvature and surface morphology changes; neighborhood direction refers to the spatial direction formed from the center point to each point in the neighborhood, reflecting local surface orientation; angular offset refers to the change of the angle between each direction vector in the neighborhood and the central reference direction, used to identify local geometric features such as surface curvature and corners; neighborhood distance change refers to the change in the distance from each point in the neighborhood to the center point, used to characterize the surface undulation or flatness; perturbation performance refers to the comprehensive analysis of parameters such as spatial direction and distance, reflecting the drastic changes in the distribution of points within the neighborhood, revealing abrupt changes or discontinuities in the surface; filtered region refers to the local point cloud region selected as representative or with concentrated changes after grouping according to perturbation performance; boundary range refers to the boundary of the region in the point cloud that exhibits continuous geometric changes, often used to segment different curved surfaces or structural units.

[0023] In S3, spatial surface variation refers to the complex changes in continuous curvature, undulation, and bending of segmented external surfaces in space; disturbance variation refers to the calculated degree or trend of local geometric disturbance, directly manifested as discontinuities, abrupt changes, or anomalies in the point cloud structure; target modeling parameters refer to pre-set parameters (such as curvature, normal, node position, etc.) that reflect the ideal hull surface or structure in parametric 3D modeling; modeling control parameters refer to parameters used to adjust, control, and correct surface and structure generation and modification during 3D modeling, such as fitting step size and smoothing parameters; and correction parameter expression refers to the new parameter expression form formed after real-time adjustment of the original modeling parameters based on information such as disturbance changes.

[0024] In S4, the modeling positioning path refers to the control path used in the 3D modeling system to locate and generate nodes of surfaces or structures, affecting the spatial layout of the overall modeling; the angle step size refers to the step size of the angle change between nodes of surfaces or line segments when generating the model during the 3D modeling process, used to control the model resolution and smoothness; the surface offset adjustment refers to the operation of offsetting and adjusting the local position or node of the model surface, used to fine-tune the model to fit the actual point cloud surface; the path content refers to the positioning or generation function paths identified during the modeling process that need to be executed or adjusted first; the protocol formatting refers to converting all modeling control parameters, instructions, etc., into the standard format of the hardware communication protocol for easy transmission and recognition.

[0025] In S5, 3D mesh nodes refer to the key points in the generated 3D structure, typically the node coordinates of a mesh, surface, or skeleton model; spatial deviation refers to the difference in 3D spatial coordinates between the original point cloud points and the modeling mesh nodes, reflecting the modeling accuracy; mean square error refers to the average of the squared spatial deviations between all paired points, a commonly used error metric; error performance refers to the error distribution characteristics of the entire model in terms of spatial position relative to the original point cloud; and reference range refers to the set allowable error or quality assessment criteria, serving as the basis for judging modeling error matching.

[0026] like Figure 2 As shown, the specific steps for obtaining the spatial point matrix distribution characteristics are as follows: S101: Based on a three-dimensional laser device, analyze the collected spatial coordinate data of the ship hull segments, match the time series labels in the reflection intensity data frame and the ranging data frame item by item, determine whether the time series labels between each data item are consistent, sort and aggregate the data frames that pass the consistency judgment to obtain a synchronous continuous point cloud. Laser scanning is performed on the surface of the ship's hull sections. A laser beam is emitted and the reflected signal is received. Combined with the time-of-flight information, corresponding spatial coordinates are generated. The laser receiver synchronously records the signal intensity and the time of reflection at each reflection point, and adds a time-series tag to each data frame. During the acquisition phase, the time-tagged reflection intensity data frames and ranging data frames are archived and stored separately. In the post-processing phase, each frame of data is extracted sequentially, and each frame is compared based on the difference in time tag values. If the difference in time tag values ​​between two frames is less than the set time synchronization judgment standard, they are considered a synchronized data pair; otherwise, they are discarded. In practice, the time judgment standard can be set to 0.5 milliseconds. When the actual label difference is 0.3 milliseconds, the data frame can be judged as valid. The above method yields a set of preliminarily paired data frames. Based on this, the frames are arranged in ascending order of time labels to construct a data time linked list, forming a continuous time series. During the aggregation stage, a cache area is constructed with a fixed number of points, for example, 2000 points are collected per second, and the cache capacity is set to 10000 points. After the collection is full, the cached data is written to a temporary storage area to facilitate fast access and sorting and updating of the point cloud data in subsequent operations. Through the above processing flow, the scattered and disordered original scan data can be transformed into a structurally continuous and time-synchronized point cloud set, thus obtaining a synchronous continuous point cloud set.

[0027] S102: Based on synchronous continuous point cloud set, calculate the spatial distance change between adjacent coordinate points in each point cloud segment, identify data segments with coherent spatial distance changes, and aggregate the point cloud segments that meet the coherence after filtering to obtain a structurally coherent point sequence. The coordinate information in the point cloud is read point by point in the acquisition sequence, and the three-dimensional spatial distance between every two consecutive coordinate points is calculated item by item. After the distance value is calculated, the overall average value and its variation range are extracted from the set of distance differences between all adjacent points. Each distance value is compared with the average value. If the deviation of a distance value from the average value is within the allowable error range, the point pair can be judged as continuous structural data and continues to be recorded in the valid segment. During the continuous judgment process, if the distance between two points is found to exceed the set error range, it is considered that a structural break has occurred at that location, and it is marked as a segmentation point, and the process restarts. In practice, the determination and recording of a coherent segment is as follows: when the average point spacing is 4.2 mm and the error tolerance is set to ±0.75 mm, if the current point pair spacing is 4.1 mm, the condition is met and it is identified as a coherent point pair. Each segment judgment requires at least 5 consecutive points that meet the condition to be confirmed as a coherent structural sequence. If there are fewer than 5, they are automatically removed or classified as invalid data segments. Through the above operations, the entire point cloud data is grouped and filtered according to distance continuity. All points that meet the structural continuity are collected and stored in the structural segment sequence cache table, thereby forming a structural coherent point sequence with spatial geometric coherence.

[0028] S103: Based on the structurally coherent point sequence, analyze the spatial coordinate distribution, construct a three-dimensional cache sequence according to the spatial location, optimize the spatial sorting of point cloud data within the cache sequence, and obtain the spatial point matrix distribution characteristics. The system reads the coordinate range of all points in 3D space and divides the entire region into equal intervals according to a preset spatial size, forming volume division units. Each unit is numbered as a voxel region. After division, each point is assigned to the corresponding voxel number based on its spatial coordinate region, completing the association operation between points and spatial units. The number of points in each voxel unit is counted, the point density value per unit volume is calculated, and it is determined whether it is within a reasonable density range. For example, the normal point density range is set to 0.02 to 0.2 points per cubic centimeter. If the density value of a certain voxel unit is... If the value is 0.01 or higher than 0.25, it can be identified as an abnormal region and marked. After dividing the voxel units and constructing the point density index, the coordinate order of the points within each unit is optimized. The points are arranged in ascending order along the X-axis, and if they are the same, they are arranged in order along the Y-axis and Z-axis respectively. A spatial sorting priority list is constructed. The optimized point sequence is written into a unified spatial point lattice cache structure through buffer storage, completing the overall spatial reorganization and ordered arrangement processing. This ensures that the point data corresponding to each position in the space can be quickly retrieved and located, thereby forming a spatial point lattice distribution feature with complete three-dimensional distribution attributes.

[0029] like Figure 3 As shown, the specific steps for obtaining the perturbation boundary characteristic parameters are as follows: S201: Based on the spatial point matrix distribution characteristics, calculate the three-dimensional distance distribution between each center point and surrounding points, determine the spatial correspondence between adjacent points and center points, identify the set of adjacent points with dense arrangement and directional continuity, and obtain the local spatial association sequence. First, determine the three-dimensional coordinates of each center point. Extract all surrounding points within a fixed range from this center point from the point matrix data, setting the query radius to 10 mm. Calculate the linear distance between each point within this range and the center point, storing all distance values ​​in a local distance distribution array. Then, calculate the mean and maximum values ​​of each distance. By comparing the difference between the distance value of each surrounding point and the mean, determine whether the point and the center point form a valid spatial proximity relationship. If the distance difference is less than 20% of the mean, mark it as an adjacent point; otherwise, discard it. Continue iterating through all points around the center point, forming an adjacency set of points that meet the proximity condition. Then, based on the spatial arrangement of points within this set, calculate the arrangement direction by extracting the spatial coordinate difference between every two points. Compare whether the direction is within the three-dimensional coordinate range of the center point. In 3D space, there is a continuous trend of change. The amplitude of change of each pair of directions is grouped and statistically analyzed according to the included angle value. If more than 70% of the point pairs have an included angle change of less than 15 degrees, the point set is determined to have directional continuity. If it does not meet the requirement, the current point set is ignored and other point sets are judged. The adjacent point sets that pass the dual judgment of spatial density and directional continuity are recorded and marked, and their point numbers or spatial index numbers are written into the local spatial association index table. After all the center point data is processed at once, the local spatial structure is archived. In a certain section of the hull, if there are 12 points within a 10 mm radius around a center point, and the arrangement direction of 10 of these points changes by less than 15 degrees and the point distance is about 4.2 mm, then this point forms a valid local spatial association structure. Such standard point group structures can be archived sequentially to obtain a local spatial association sequence.

[0030] S202: Based on the local spatial correlation sequence, analyze the spatial relationship between neighboring points and the center point, calculate the direction vector from the neighboring points to the center point, compare the angle changes of each direction vector, filter out regions with direction changes and accompanying distance distribution differences, identify spatial segments with perturbation characteristics, and obtain the perturbation response segment index. The three-dimensional coordinates of each center point and its adjacent points are read one by one. The direction information from each adjacent point to the center point is extracted based on the coordinate difference. A set of direction vectors is constructed using the coordinate component differences. The angle relationship between any two vectors in this set is compared. All angle values ​​are statistically analyzed and compared with a set direction change judgment value. The angle change limit is set to 20 degrees. If the angle between any two direction vectors is greater than this value, the direction is considered to have a significant deviation, and the direction vector is marked as a direction abrupt change. Simultaneously, the distance between the adjacent points corresponding to this direction vector and the center point is recorded. The adjacent points associated with the direction abrupt change are then sorted in ascending order of distance value, and the top five distance values ​​are extracted. The distance difference between the last five points is used as an indicator of local distance difference change. If the difference is greater than 4 mm, it is considered that there is not only directional disturbance at the point, but also a sudden change in spatial distance distribution, which meets the judgment requirements of disturbance characteristics. Continue to traverse all adjacent point sets. In all point sets that meet the disturbance characteristics, spatial index number is assigned and added to the disturbance response fragment index table. In the scanning of complex curved surface areas of the ship's hull, if there are six pairs of vectors with an angle greater than 20 degrees in a point set, and the difference between the maximum and minimum distance values ​​is greater than 5 mm, then the area can be classified as a disturbance area. Such disturbance areas will become important reference fragments for model curvature adjustment or detail fitting in actual modeling, and the disturbance response fragment index is obtained.

[0031] S203: Based on the disturbance response fragment index, determine the set of boundary points in the spatial region, analyze the continuity and geometric orientation of the boundary point distribution, identify boundary fragments with continuous spatial variation characteristics, classify valid boundary data, and obtain disturbance boundary characteristic parameters. Read the corresponding point set according to the segment number and summarize all spatial coordinate points within each segment. Calculate the position range of each point set in 3D space. Within each group, select points located at geometric edges or region boundaries as candidate boundary points. The criterion is that the average distance between a point and its three nearest neighbors exceeds 30% of the overall average distance within the segment. If this condition is met, it is included in the candidate boundary set. Establish connections between all candidate boundary points sequentially to construct boundary point chains. Review the arrangement order of all points within the chain, judging continuity based on whether the arrangement direction is a single, progressive direction. If the change in connection direction between boundary points does not exceed 30 degrees and the average point distance does not exceed the previously calculated average boundary distance... If the point spacing is 1.5 times, the boundary segment is considered to have a stable and continuous orientation. Further analysis is conducted to determine whether the spatial extension direction of the boundary segment remains consistent and whether its curvature change in the overall structure is within a stable range. For example, if the continuous length of the boundary segment exceeds 30 mm and the average point spacing is 4.5 mm, and the angle fluctuation of the arrangement direction is within 25 degrees, then the segment can be confirmed as a spatially continuous boundary. The point set with boundary stability and continuity is then classified under a unified identifier, and its three-dimensional position and number are recorded as the final boundary archiving output. If there are four boundary chains that meet the conditions in a certain perturbation segment, each with a length exceeding 25 mm, and the cumulative number of boundary points reaches 80, then it can be included in the effective boundary set, and the perturbation boundary characteristic parameters are constructed.

[0032] like Figure 4 As shown, the specific steps for obtaining the modeling parameter correction values ​​are as follows: S301: Based on the perturbation boundary characteristic parameters, determine the changing trend between the surface normal and the surrounding point set within each boundary point region, compare the spatial continuity and curvature change state, identify regions with surface turning or direction adjustment phenomena, and obtain the surface perturbation influence factor. The system reads the 3D coordinates of each boundary point and its spatial position relative to the surrounding point set, divides the area into regions, extracts six adjacent points within the neighborhood of each boundary point, and calculates their spatial differences to determine the local surface orientation. Then, it generates normal information sequentially using triangular facets composed of three points. After generating a reference normal direction at the central boundary point, it calculates the angle difference between this normal direction and the normal directions generated by the corresponding triangular facets of each adjacent point. The angle differences are sorted by value and stored in a record list. If a boundary point has three or more points in its neighborhood with a normal deviation greater than 25 degrees, the boundary point is considered to have a normal abrupt change. Simultaneously, it compares the spatial distances between the six points in the neighborhood pairwise and statistically analyzes all distance values. If the difference between the maximum and minimum values ​​exceeds 5 mm, the point is further marked as having a curvature abrupt change characteristic. All boundary point data are processed, and the location numbers of all locations where normal and distance changes coexist are recorded and classified by region. In the hull transition section region, if a boundary point has four normal change angles greater than 30 degrees among its six adjacent points, and its maximum neighbor distance is 12 mm and its minimum neighbor distance is 6 mm, then the point is recorded as a surface turning point. Then, using the local set of such points, points in all regions that exhibit drastic curvature changes and normal discontinuities are extracted as core point groups with significant surface disturbance effects. These are further archived by segment identifier numbers to obtain the surface disturbance influence factor.

[0033] S302: Based on the surface disturbance influence factor, compare the control parameters of the modeling target corresponding to the disturbance region, determine the changes of curvature, surface orientation and control step size in the disturbance range, identify control terms with fluctuations in parameter response, and obtain parameter drift mapping data; The modeling target parameter file corresponding to each disturbance region is read and associated, and the control parameters related to that region are extracted. Specifically, the curvature control factor, surface orientation marker value, and modeling path step size are extracted. These parameters are spatially paired with the disturbance point locations to construct a comparison list. For each point, the difference between the original modeling parameters and the average parameter value of the region before disturbance is recorded. Control parameters with curvature changes exceeding 0.08, step size changes exceeding 2 mm, or orientation offset angles exceeding 20 degrees are recorded as fluctuation items. In the actual example, if the original curvature is set to 0.12, and the corresponding… If the actual interpolated value of the disturbance point is 0.21 and the change range is 0.09, then this item is considered to have exceeded the set limit and needs to be judged as drift. Continue to compare and judge each control parameter. If a parameter item fluctuates above the set threshold for more than three consecutive points in the entire set of disturbance-affected points, then the control item is further marked as a structural drift control factor. Establish a list of fluctuation item numbers and record its coordinate range in the model area to form a drift control parameter index. Finally, write the mapping relationship between the point and the control item into the parameter drift mapping data table to obtain the parameter drift mapping data.

[0034] S303: Based on parameter drift mapping data, determine the distribution state of control parameters, analyze the interpolation continuity of parameter points in surface space, adjust the control terms corresponding to disturbance segments in parameter expression, optimize the distribution performance of control parameters in space, and obtain the modeling parameter correction amount; Read the coordinate distribution of each control item in space, sort all points with the same control parameter number according to their 3D coordinate values, and determine their arrangement in model space based on their distribution. For consecutive point sequences, determine whether their interpolation interval is within the set standard range. For example, if the maximum allowable interval is set to 10 mm, and the interval between points corresponding to a certain parameter number exceeds 12 mm consecutively, it is determined to be an interpolation discontinuity item. Local adjustments are made to this parameter, reading the control values ​​of the points before and after it, and redefining the interpolation point parameters using a linear interpolation method. In actual operation, if the control value for point A is 0.15, the control value for point B is 0.25, and no control parameter is set at the intermediate point, then the value is reset to 0.20 at the intermediate point, and the process continues. All parameter terms with discontinuities are processed, and then all control parameters with drift terms are grouped in space by region index. It is determined whether there is a clustering drift phenomenon of multiple control terms in a certain region. If three or more control parameter terms are marked as drift terms in a segment, the region is defined as a high-disturbance segment. The expression values ​​of all control parameters in the region are optimized and reconstructed. Curvature parameters are adjusted first, and then step size and direction terms are processed to ensure that the difference between control values ​​between adjacent points after reconstruction does not exceed the set error threshold. In a practical example, when the original curvature value in a disturbance segment is 0.18 and the curvature value of the adjacent point is 0.31, it is reset to 0.24 after adjustment. All corrected control parameters are integrated and the modeling parameter correction amount is output.

[0035] like Figure 5 As shown, the specific steps for obtaining the modeling execution instruction set are as follows: S401: Based on the modeling parameter correction, determine the influence of each parameter on the positioning path, angle step size and surface offset adjustment process, compare the adjustment effect of parameter changes on each modeling link, screen the parameter items that affect the control process, and obtain the key factors of path control. Curvature correction factor, angle step size adjustment value, and surface offset compensation value are extracted from the correction data. These are then clustered by segment region. The parameters are paired item by item with the positioning path parameters, angle setting parameters, and surface correction items in the control flow during modeling. For each positioning path node, its corresponding curvature parameter is read to determine if a correction input exists. If so, the curvature difference before and after correction at that path node is recorded. Then, the correction amount is subtracted from each angle step size setting to obtain the correction offset value. Whether the offset is greater than 1.5 degrees is used to determine if it has an actual impact on the angle setting. For the surface offset portion, the model mesh is extracted. The Z-axis offset between the original and corrected coordinates of a node is determined. If the offset is greater than the set reference value by 3 mm, it is considered a significant offset. All parameters involved in the control process are then processed. In the entire dataset, parameters with offsets exceeding the set reference value are categorized into the influencing item set, and their influencing locations and corresponding parameter categories are marked. In a real-world scenario, for example, if the original curvature value of a path node is 0.15 and the corrected value is 0.23, a difference of 0.08, then that point on the path will be recorded as a curvature-sensitive point. Through the above judgment and comparison process, all parameters that affect path settings, angle segmentation, or surface geometry are extracted, forming key factors for path control.

[0036] S402: Based on key factors of path regulation, analyze control sections and parameter types, calculate the spatial node sequence corresponding to the modeling path, determine the directional continuity and parameter adaptation characteristics of the path content, identify path combinations with priority, and obtain the preferred path grouping index. Each influencing parameter is divided according to its model control segment number. The frequency of occurrence of each parameter type in each segment is counted and a control frequency table is generated. Then, the spatial position sequence of all path nodes is extracted from the modeling data. The spatial distance and angular direction value between path points are calculated. The directional change angle of adjacent path segments is judged. If the directional angle between adjacent segments is less than 20 degrees, it is judged as a directional continuous segment and the path segment is added to the continuous path queue. Then, the path point position is matched with its parameter item one by one to determine whether each path segment has a complete combination of control parameters, including curvature, angle step size and surface offset. If all three parameters in a path segment are marked as key control factors, the path segment is defined as a highly responsive path segment. All continuous path segments with complete parameter control capabilities are prioritized and sorted. In the actual case, if the directional change of path segment A is 15 degrees, the average node spacing is 9 mm, and all three parameters have adjustment records, then the path segment is included in the preferred path set, and its start point number and end point number are recorded to form a preferred path grouping index.

[0037] S403: Based on the preferred path grouping index, analyze the hierarchical structure of modeling control parameters and spatial operation instructions, optimize the protocol field format of control instructions, and obtain the modeling execution instruction set; The system reads the control parameter set and node information corresponding to each path segment, and performs hierarchical analysis on the structure between parameter items and execution commands. First, the parameter items are classified according to the modeling stage into a path construction control layer, an angle refinement adjustment layer, and a surface fitting correction layer. Within each layer, the calling order of each parameter item is organized, and the corresponding control parameter values ​​are sequentially listed according to the path node number. At the same time, a control command template structure is constructed, and fixed fields are set in the template to identify the parameter type and parameter position. Then, all parameter values ​​are filled into the corresponding fields to form a unified command structure. Different types of control command fields are combined and spliced ​​according to the format requirements. A fixed format length is used to align the fields of each command, and a start and end character structure is introduced to ensure the integrity of command parsing. In the actual data construction of the path segment, if a path number is P104, the corresponding curvature correction factor is 0.09, the angle step size is 3 degrees, and the surface offset value is 2.7 mm, then the constructed control command should be filled with three values ​​according to the field and the parameter header identifier P104 should be added. The control information of all preferred path segments is converted into a unified format modeling execution command set.

[0038] like Figure 6 As shown, the specific steps for obtaining the 3D modeling matching degree are as follows: S501: Based on the modeling execution instruction set, analyze the pairing of 3D mesh nodes and spatial point lattice distribution characteristics, calculate the 3D coordinate differences between each mesh node and spatial point lattice, determine the validity of the spatial pairing of nodes and point lattice, filter point pairs with consistent pairing structures, and obtain the node spatial coupling structure. The system reads the spatial coordinate values ​​and modeling control parameters carried in each modeling path node instruction within the instruction set. It then sequentially imports the generated 3D mesh nodes into the comparison process, extracting the 3D coordinate information of each mesh node and spatially pairing it with the point cloud coordinate data in the spatial point matrix. A pairing distance threshold of 5 mm is set. The criterion is that the absolute values ​​of the 3D coordinate differences between the mesh node and a point in the point matrix in the X, Y, and Z dimensions do not exceed this threshold. If the condition is met, the point pair is marked as a valid pairing. The system continues to perform a nearest neighbor search within the point matrix for each mesh node. If multiple point cloud data points within a local area satisfy the above pairing conditions, the point with the smallest distance is selected as the pairing target point. Record the pairing relationship between the two. In the example of the midship section, when the coordinates of a node are (1560, 2080, 45), if there is a point in the lattice with coordinates (1563, 2082, 46), the three-dimensional difference is within 5 mm. The pairing is valid and added to the pairing list. After pairing, the structural consistency of all validly paired mesh nodes and lattice point sets is judged. According to the spatial arrangement order, the direction is compared according to the direction vector formed by three adjacent mesh nodes and lattice nodes. If the angle difference between more than 70% of the direction vectors is less than 10 degrees, the current mesh structure is determined to be consistent with the lattice structure. This type of node pair is classified into the list of structurally consistent point pairs. The node spatial coupling structure is constructed from all structurally consistent point pairs.

[0039] S502: Based on the node spatial coupling structure, calculate the spatial deviation between each pair of paired points, determine the consistency of the distribution of each deviation among all paired points, filter the data segments with deviation fluctuations, and obtain the error distribution statistical factor. Extract the 3D coordinate difference between the grid node and the corresponding lattice point in each pair of points. Record the absolute difference in the X, Y, and Z directions for each pair of differences, and calculate the total 3D distance deviation. Sort the spatial deviations of all pairing points by node number, and calculate the maximum, minimum, average, and standard deviation of the overall deviation. Then, divide all deviation values ​​into intervals, using the average value as the baseline to divide the deviation into upper and lower segments. If the deviation value of a pairing point exceeds the average value plus twice the standard deviation, it is considered a deviation fluctuation point. All such deviation fluctuation points are recorded separately in the abnormal deviation list. In the example, if the average spatial deviation of all paired points is 2.5 mm and the standard deviation is 0.8 mm, then the upper limit of deviation judgment is 4.1 mm. If the deviation of a certain point after pairing is 4.7 mm, exceeding this value, it is marked as a fluctuation point. Continue to analyze the deviation distribution trend of all paired points, and record the point sequence segment with multiple consecutive fluctuation points as error fluctuation segment. If 6 out of 10 consecutive points in a certain segment are fluctuation points, then the entire segment is marked as an error fluctuation segment. The segment number and its fluctuation characteristics are recorded in the error statistics index table to obtain the error distribution statistics factor.

[0040] S503: Based on the error distribution statistical factor, determine the relationship between the error range and the preset reference range, optimize the structural output format of the paired data, and obtain the 3D modeling matching degree; The system reads the error range data and point distribution of each segment in the statistical factors, and compares the relationship between the average error value of each segment and the preset reference error upper limit. The reference error upper limit is set to 4 mm. If the average deviation of a segment is 3.8 mm, it is determined to be within the reference range; if the average deviation is 4.5 mm, it exceeds the reference range and is classified as an unqualified segment. The system then calculates the proportion of matched points exceeding the error within each segment. If the proportion exceeds 40%, the segment is considered an abnormal match segment. The system continues to iterate through all segments, filtering out matched segments and abnormal match segments, and classifying them into two sets. When outputting the matching results data, the matched segments are... The paired data is output in a compact structure format, using triplet coordinate compression to retain only data fields with a difference of no more than 1 mm, while setting the remaining fields to zero to save storage. For mismatched segments, a redundant structure format is used to output the complete coordinates and error information of each pair of paired points, along with an additional matching status marker field. The structure output is organized by segment sequence numbering, and the output of each segment data forms a complete paired structure file. At the end of the file, a 3D modeling overall matching evaluation field is added to record the average total deviation, maximum deviation, and consistency index ratio. For example, the matching consistency rate is 83%, the maximum deviation is 5.2 mm, and the 3D modeling matching degree is output.

[0041] like Figure 7 As shown, a parametric 3D modeling system for ship section construction includes: The point cloud acquisition module is based on a 3D laser device to collect spatial coordinate data of ship hull segments, screen reflection intensity and ranging synchronization, compare data continuity to determine valid segments, adjust data buffer order, and obtain spatial point distribution characteristics. The feature analysis module calculates the neighborhood distribution of the target point based on the spatial point matrix distribution characteristics, analyzes the trend of directional change, judges the angular offset, statistically analyzes the neighborhood distance change, groups and filters the disturbance performance, determines the boundary range, and obtains the disturbance boundary characteristic parameters. The parameter correction module, based on the disturbance boundary characteristic parameters, judges the impact of surface changes on modeling parameters, analyzes the relationship between disturbance and target parameters, compares disturbance differences, adjusts modeling control parameters, and uses data interpolation correction to express and obtain the modeling parameter correction amount; The instruction generation module determines the contribution of the modeling parameter correction to the positioning path, angle step size and surface offset, identifies priority paths, performs instruction protocol formatting, and obtains the modeling execution instruction set. The matching evaluation module, based on the modeling execution instruction set, compares the pairing of 3D mesh nodes with the spatial point matrix distribution characteristics, statistically analyzes spatial deviations, calculates mean square error, determines error performance, optimizes the output format, and obtains the 3D modeling matching degree.

[0042] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0043] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0044] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0045] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0046] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0047] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A parametric 3D modeling method for ship section construction, characterized in that, The method includes: S1: Based on a three-dimensional laser device, collect spatial coordinate data of ship hull segments, screen reflection intensity and ranging synchronization, compare data continuity to determine valid segments, adjust data buffer order, and obtain spatial point distribution characteristics; S2: Based on the spatial point matrix distribution characteristics, calculate the neighborhood distribution of the target point, analyze the direction change trend, determine the angle offset, statistically analyze the neighborhood distance change, group and filter the disturbance performance, determine the boundary range, and obtain the disturbance boundary characteristic parameters. S3: Based on the disturbance boundary characteristic parameters, determine the impact of surface changes on modeling parameters, analyze the relationship between disturbance and target parameters, compare disturbance differences, adjust modeling control parameters, and use data interpolation to correct the expression to obtain the correction amount of modeling parameters; S4: Based on the modeling parameter correction amount, determine the contribution of the parameters to the positioning path, angle step size and surface offset, identify the priority path, perform instruction protocol formatting, and obtain the modeling execution instruction set; S5: Based on the modeling execution instruction set, compare the pairing of the three-dimensional mesh nodes with the spatial point matrix distribution characteristics, statistically analyze the spatial deviation, calculate the mean square error, determine the error performance, optimize the output format, and obtain the three-dimensional modeling matching degree.

2. The parametric 3D modeling method for ship section construction according to claim 1, characterized in that, The spatial point distribution characteristics include three-dimensional distribution range, surface coverage and structural uniformity; the disturbance boundary characteristic parameters include boundary positioning information, disturbance partition number and change area label; the modeling parameter corrections include curvature correction factor, plane adjustment factor and modeling compensation term; the modeling execution instruction set includes path control sequence, step size adjustment instruction and offset execution command; and the three-dimensional modeling matching degree includes spatial overlap degree, error evaluation parameters and model consistency index.

3. The parametric 3D modeling method for ship section construction according to claim 1, characterized in that, The specific steps for obtaining the spatial point matrix distribution features are as follows: S101: Based on a three-dimensional laser device, analyze the collected spatial coordinate data of the ship hull segments, match the time series labels in the reflection intensity data frame and the ranging data frame item by item, determine whether the time series labels between each data item are consistent, sort and aggregate the data frames that pass the consistency judgment to obtain a synchronous continuous point cloud. S102: Based on the synchronous continuous point cloud set, calculate the spatial distance change between adjacent coordinate points in each point cloud segment, identify data segments whose spatial distance changes are consistent, and aggregate the point cloud segments that meet the consistency after filtering to obtain a structurally consistent point sequence. S103: Based on the structural coherent point sequence, analyze the spatial coordinate distribution, construct a three-dimensional cache sequence according to the spatial location, optimize the spatial sorting of point cloud data within the cache sequence, and obtain the spatial point matrix distribution characteristics.

4. The parametric 3D modeling method for ship section construction according to claim 1, characterized in that, The specific steps for obtaining the disturbance boundary characteristic parameters are as follows: S201: Based on the spatial point matrix distribution characteristics, calculate the three-dimensional distance distribution between each center point and surrounding points, determine the spatial correspondence between adjacent points and center points, identify the set of adjacent points with dense arrangement and directional continuity, and obtain a local spatial association sequence. S202: Based on the local spatial correlation sequence, analyze the spatial relationship between neighboring points and the center point, calculate the direction vector from the neighboring points to the center point, compare the angle changes of each direction vector, filter out regions with direction changes and accompanying distance distribution differences, identify spatial segments with disturbance characteristics, and obtain the disturbance response segment index. S203: Based on the disturbance response fragment index, determine the set of boundary points in the spatial region, analyze the continuity and geometric orientation of the boundary point distribution, identify boundary fragments with continuous spatial variation characteristics, classify the valid boundary data, and obtain the disturbance boundary characteristic parameters.

5. The parametric 3D modeling method for ship section construction according to claim 1, characterized in that, The specific steps for obtaining the modeling parameter correction amount are as follows: S301: Based on the disturbance boundary characteristic parameters, determine the changing trend between the surface normal and the surrounding point set within each boundary point area, compare the spatial continuity and curvature change state, identify areas where there are surface turning points or direction adjustment phenomena, and obtain the surface disturbance influence factor. S302: Based on the surface disturbance influence factor, compare the control parameters of the modeling target corresponding to the disturbance region, determine the changes of curvature, surface orientation and control step size in the disturbance range, identify control terms with fluctuating parameter responses, and obtain parameter drift mapping data; S303: Based on the parameter drift mapping data, determine the distribution state of the control parameters, analyze the interpolation continuity of the parameter points in the surface space, adjust the control terms corresponding to the disturbance segments in the parameter expression, optimize the distribution performance of the control parameters in the space, and obtain the modeling parameter correction amount.

6. The parametric 3D modeling method for ship section construction according to claim 1, characterized in that, The specific steps for obtaining the modeling execution instruction set are as follows: S401: Based on the modeling parameter correction amount, determine the influence of each parameter on the positioning path, angle step size and surface offset adjustment process, compare the adjustment effect of parameter changes on each modeling link, screen the parameter items that affect the control process, and obtain the key factors of path control. S402: Based on the key factors for path regulation, analyze the control sections and parameter types, calculate the spatial node sequence corresponding to the modeling path, determine the directional continuity and parameter adaptation characteristics of the path content, identify path combinations with priority, and obtain the preferred path grouping index. S403: Based on the preferred path grouping index, analyze the hierarchical structure of modeling control parameters and spatial operation instructions, optimize the protocol field format of control instructions, and obtain the modeling execution instruction set.

7. The parametric 3D modeling method for ship section construction according to claim 1, characterized in that, The specific steps for obtaining the 3D modeling matching degree are as follows: S501: Based on the modeling execution instruction set, analyze the pairing of the three-dimensional mesh nodes and the spatial point matrix distribution characteristics, calculate the three-dimensional coordinate differences between each mesh node and the spatial point matrix, determine the validity of the spatial pairing of the nodes and the point matrix, filter point pairs with consistent pairing structures, and obtain the node spatial coupling structure. S502: Based on the node spatial coupling structure, calculate the spatial deviation between each pair of paired points, determine the consistency of the distribution of each deviation among all paired points, filter the data segments with deviation fluctuations, and obtain the error distribution statistical factor. S503: Based on the error distribution statistical factor, determine the relationship between the error range and the preset reference range, optimize the structure output format of the paired data, and obtain the 3D modeling matching degree.

8. The parametric 3D modeling method for ship section construction according to claim 1, characterized in that, The spatial coordinate data refers to the X, Y, and Z coordinate information of each sampling point in space collected by the three-dimensional laser device; the reflection intensity refers to the strength of the laser signal reflected back at each sampling point; and the distance measurement data refers to the distance data from each point to the origin of the device measured by the three-dimensional laser device.

9. The parametric 3D modeling method for ship section construction according to claim 1, characterized in that, The angle offset refers to the change in the angle between each direction vector in the neighborhood and the central reference direction; the neighborhood distance change refers to the change in the distance from each point in the neighborhood to the center point; and the boundary range refers to the boundary of the region in the point cloud that exhibits continuous geometric changes.

10. A parametric 3D modeling system for ship section construction, the system being used to implement the parametric 3D modeling method for ship section construction as described in any one of claims 1-9, characterized in that, The system includes: The point cloud acquisition module is based on a 3D laser device to collect spatial coordinate data of ship hull segments, screen reflection intensity and ranging synchronization, compare data continuity to determine valid segments, adjust data buffer order, and obtain spatial point distribution characteristics. Based on the spatial point matrix distribution characteristics, the feature analysis module calculates the neighborhood distribution of the target point, analyzes the trend of directional change, judges the angle offset, statistically analyzes the neighborhood distance change, groups and filters the disturbance performance, determines the boundary range, and obtains the disturbance boundary characteristic parameters. Based on the disturbance boundary characteristic parameters, the parameter correction module determines the impact of surface changes on modeling parameters, analyzes the relationship between disturbance and target parameters, compares disturbance differences, adjusts modeling control parameters, and uses data interpolation to correct and express the correction amount of modeling parameters. Based on the modeling parameter correction amount, the instruction generation module determines the contribution of the parameters to the positioning path, angle step size and surface offset, identifies the priority path, performs instruction protocol formatting, and obtains the modeling execution instruction set. The matching evaluation module compares the pairing of the 3D mesh nodes with the spatial point matrix distribution features based on the modeling execution instruction set, calculates the spatial deviation, determines the mean square error, optimizes the output format, and obtains the 3D modeling matching degree.

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