Automatic three-dimensional modeling data self-adaptive construction method and system

Abstract

The invention belongs to the technical field of three-dimensional modeling data processing, and particularly provides an automatic three-dimensional modeling data self-adaptive construction method and system, and the method mainly comprises the steps: obtaining the initial data of a target object; performing feature extraction and rule derivation on the initial data to generate a feature priority rule and a constraint effect rule; preprocessing the initial data according to a feature priority rule and a constraint effect rule; geometric structure features, curvature change features, connection node features and boundary features are extracted from the preprocessed data; according to the method, the modeling precision of different regions can be intelligently regulated and controlled, the detail reduction degree of a complex structure is improved, the waste of computing resources is avoided, meanwhile, the topological constraint linkage is strengthened, the geometric precision and structural integrity of the model are guaranteed, and the method is suitable for efficient and accurate modeling of multi-source data.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-dimensional modeling data processing, and particularly relates to an automated three-dimensional modeling data adaptive construction method and system. Background Art

[0002] In the field of three-dimensional modeling, processing data based on fixed rules, such as uniformly dividing grids by density or simply splicing contour lines, lacks differential management of geometric features and is difficult to balance the modeling accuracy and efficiency of different regions.

[0003] Currently, three-dimensional modeling is difficult to dynamically adjust processing strategies for high-curvature change regions and flat regions, often resulting in the loss of complex structure details or waste of computing resources. Moreover, the processing of topological relationships and geometric constraints lacks linkage. In the face of complex modeling scenarios involving multi-source data, it is easy to cause geometric distortion or structural conflicts of the model due to unclear feature priorities and lack of constraint conduction mechanisms, making it difficult to meet the requirements of industrial-level modeling for accuracy and efficiency. Summary of the Invention

[0004] This application effectively solves the problem in the prior art that three-dimensional modeling is difficult to dynamically adjust processing strategies for high-curvature change regions and flat regions by providing an automated three-dimensional modeling data adaptive construction method and system, realizes intelligent regulation of modeling accuracy for different regions, improves the restoration degree of complex structure details, avoids waste of computing resources, and at the same time strengthens the linkage of topological constraints to ensure the geometric accuracy and structural integrity of the model, and is applicable to efficient and accurate modeling of multi-source data.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In the first aspect, this application provides an automated three-dimensional modeling data adaptive construction method, including:

[0007] Obtain the initial data of the target object.

[0008] Extract features and derive rules from the initial data to generate feature priority rules and constraint action rules.

[0009] Preprocess the initial data according to the feature priority rules and constraint action rules.

[0010] Extract geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data.

[0011] Fit and match the geometric structure features with the curvature change features, and the connection node features with the boundary features respectively to generate a basic patch set and a constraint connection relationship set.

[0012] Generate the surface mesh of the 3D model according to the set of basic patches, and generate the internal support structure according to the set of constraint connection relationships.

[0013] Generate a 3D solid model based on the surface mesh of the 3D model and the internal support structure.

[0014] Furthermore, the initial data includes: an unordered point cloud set, a set of 2D contour lines, a set of topological relationships, and a set of geometric constraint conditions.

[0015] Perform feature extraction and rule derivation on the initial data to generate a feature priority rule and a constraint application rule, including:

[0016] Generate a feature priority rule according to the unordered point cloud set and the set of 2D contour lines; generate a constraint application rule according to the set of topological relationships and the set of geometric constraint conditions.

[0017] Furthermore, generating a feature priority rule according to the unordered point cloud set and the set of 2D contour lines includes:

[0018] Perform spatial partitioning on the unordered point cloud set to obtain multiple cube grid cells, count the number of point clouds in each cube grid cell to obtain the spatial density of the point cloud; mark the cube grid cells with a spatial density greater than the average density of all cube grid cells as high-density regions.

[0019] Extract the extension directions of each line segment in the set of 2D contour lines, traverse the angle of direction change between adjacent line segments, and mark the continuous line segment group with an angle of direction change less than a preset coverage threshold as a coverage area with a continuous direction.

[0020] Perform spatial superposition of the high-density region and the coverage region. If the high-density region is completely contained in the coverage region, mark it as a combined action area; if there is only partial overlap, mark it as a single action area.

[0021] Generate a feature priority rule, including: assigning a first modeling priority to the combined action area; assigning a second modeling priority to the single action area.

[0022] Furthermore, generating a constraint application rule according to the set of topological relationships and the set of geometric constraint conditions includes:

[0023] Traverse the hierarchical depth of the set of topological relationships, identify the key hierarchical nodes with the number of child nodes exceeding the average, and mark them as core topological nodes; count the occurrence frequencies of each constraint type in the type distribution of the set of geometric constraint conditions, and mark the constraint type with the highest frequency as the dominant constraint type.

[0024] Logically match the core topology nodes with the dominant constraint type: If the hierarchical depth of the core topology nodes corresponds to the applicable level of the dominant constraint type, establish a strong constraint binding relationship; if the hierarchical depths do not match but there are associated child nodes, establish a weak constraint binding relationship.

[0025] Generate constraint application rules, including: Assign the highest conduction priority to the strong constraint binding relationship and assign the secondary conduction priority to the weak constraint binding relationship.

[0026] Furthermore, preprocess the initial data according to the feature priority rules and constraint application rules, including:

[0027] Perform gradient denoising processing on the unordered point cloud set according to the feature priority rules to generate a denoised point cloud set; complete the breakpoints of the two-dimensional contour line set according to the continuity direction to generate a closed contour line set; use the constraint application rules to eliminate redundant nodes in the topology relationship set to generate a simplified topology relationship set; standardize the geometric constraint condition set to generate a constraint strength set.

[0028] Furthermore, extract geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data, including:

[0029] Extract geometric structure features according to the spatial distribution of the denoised point cloud set; extract curvature change features according to the continuity direction of the closed contour line set; extract connection node features according to the hierarchical depth of the simplified topology relationship set; extract boundary features according to the type distribution of the constraint strength set.

[0030] Furthermore, generate the surface mesh of the 3D model according to the basic patch set, including:

[0031] Perform triangulation on the basic patch set, locally encrypt the triangulation result according to the curvature change features, integrate the boundary features into the encrypted mesh, optimize the vertex distribution of the mesh along the continuity direction, adjust the mesh density according to the spatial density, and output the surface mesh of the 3D model.

[0032] Furthermore, generate the internal support structure according to the constraint connection relationship set, including:

[0033] Generate a basic node network based on the connection node features, transform the constraint connection relationship set into a structural connection, generate a multi-level support framework according to the hierarchical depth of the core topology nodes, map the constraint strength set to structural parameters, use the simplified topology relationship set to verify the integrity of the structural connection, and adjust the structural type according to the type distribution of the constraint strength set, and output the support structure.

[0034] Furthermore, generate a 3D solid model according to the surface mesh of the 3D model and the internal support structure, including:

[0035] Establish the binding relationship between the surface mesh vertices and the internal support structure nodes, convert the set of constraint connection relationships into physical constraint entities; inject the geometric elements of the boundary features into the surface mesh to generate an enhanced surface mesh.

[0036] Construct a tetrahedral mesh based on the binding relationship, embed the constraint entities into the mesh nodes, and locally encrypt the enhanced surface mesh according to the feature priority rules to generate a three-dimensional solid model containing geometric details and topological constraints.

[0037] In a second aspect, the present application provides an automated three-dimensional modeling data adaptive construction system, which is characterized in that it includes:

[0038] Initial data acquisition module: acquire the initial data of the target object.

[0039] Feature and rule generation module: perform feature extraction and rule derivation on the initial data to generate feature priority rules and constraint action rules.

[0040] Data preprocessing module: preprocess the initial data according to the feature priority rules and constraint action rules.

[0041] Feature extraction module: extract geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data.

[0042] Feature fitting and matching module: respectively fit and match the geometric structure features with the curvature change features, and the connection node features with the boundary features to generate a set of basic patches and a set of constraint connection relationships.

[0043] Model structure construction module: generate a three-dimensional model surface mesh according to the set of basic patches, and generate an internal support structure according to the set of constraint connection relationships.

[0044] Solid model generation module: generate a three-dimensional solid model according to the three-dimensional model surface mesh and the internal support structure.

[0045] In a third aspect, the present application provides an automated three-dimensional modeling data adaptive construction device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the steps of the automated three-dimensional modeling data adaptive construction method as described in the first aspect when executing the computer program.

[0046] In a fourth aspect, the present application provides a storage medium, in which computer program instructions are stored, and when the computer program instructions are read and run by a processor, the steps of the automated three-dimensional modeling data adaptive construction method as described in the first aspect are executed.

[0047] Advantages of the present invention:

[0048] This application adopts feature extraction and rule derivation technologies, combines multi-feature fitting and matching such as geometric structure and curvature change, dynamically generates basic patches and support structures, effectively solves the problem in the prior art that it is difficult to dynamically adjust the processing strategy for high-curvature change regions and flat regions in 3D modeling, realizes intelligent control of the modeling accuracy of different regions, improves the reduction degree of complex structure details, avoids waste of computing resources, strengthens the topological constraint linkage at the same time, ensures the geometric accuracy and structural integrity of the model, and is applicable to the efficient and accurate modeling of multi-source data.

[0049] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structure pointed out in the specification and the drawings. Brief Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0051] Figure 1 Shows a schematic flow diagram of an automated 3D modeling data adaptive construction method of the present invention;

[0052] Figure 2 Shows a schematic module diagram of an automated 3D modeling data adaptive construction system of the present invention. Detailed Embodiments

[0053] In order to solve the problems proposed in the background art, this application adopts feature extraction and rule derivation technologies, combines multi-feature fitting and matching such as geometric structure and curvature change, dynamically generates basic patches and support structures, realizes intelligent control of the modeling accuracy of different regions, improves the reduction degree of complex structure details, avoids waste of computing resources, strengthens the topological constraint linkage at the same time, ensures the geometric accuracy and structural integrity of the model, and is applicable to the efficient and accurate modeling of multi-source data.

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0055] In some embodiments, such as Figure 1 shown, the present application provides an automated three-dimensional modeling data adaptive construction method, including:

[0056] S1. Obtain the initial data of the target object.

[0057] S2. Extract features and derive rules from the initial data to generate feature priority rules and constraint action rules.

[0058] S3. Preprocess the initial data according to the feature priority rules and constraint action rules.

[0059] S4. Extract geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data.

[0060] S5. Fit and match the geometric structure features with the curvature change features, and the connection node features with the boundary features respectively to generate a basic patch set and a constraint connection relationship set.

[0061] S6. Generate a three-dimensional model surface mesh according to the basic patch set, and generate an internal support structure according to the constraint connection relationship set.

[0062] S7. Generate a three-dimensional solid model according to the three-dimensional model surface mesh and the internal support structure.

[0063] In some embodiments, the initial data in S1 includes: an unordered point cloud set, a two-dimensional contour line set, a topological relationship set, and a geometric constraint condition set.

[0064] The unordered point cloud set represents a dataset of the surface space coordinates of the target object obtained by three-dimensional laser scanning or structured light imaging technology. The data structure is a set containing a specified number of three-dimensional coordinate points (x, y, z).

[0065] The two-dimensional contour line set represents a planar contour dataset exported by CAD software or generated by digitizing engineering drawings. The data structure is a closed or open contour composed of multiple line segments, and each line segment contains the start / end point coordinates and the extension direction.

[0066] The topological relationship set represents the hierarchical connection relationships of the parts in the assembly. The data structure is a tree-like node relationship table that records the hierarchical depth and connection type of each node.

[0067] The geometric constraint condition set represents the assembly fit relationships between parts. The data type is constraint type (concentric / parallel / distance, etc.) + action object, and the attributes include the applicable hierarchical level and action scope of the constraint.

[0068] Exemplarily, in the modeling of an automotive engine block, the surface of the engine block is laser scanned to obtain 5 million spatial coordinate points. For example, the point clouds in areas such as the inner wall of the cylinder and the cooling water channels are dense, resulting in an unordered point cloud set.

[0069] Extract contour lines from the CAD drawing, such as the circular contour of the cylinder bore and the polygonal contour of the bolt hole, including the extension direction of the line segment. For example, the contour direction of the cylinder bore is continuous, resulting in a set of two-dimensional contour lines.

[0070] The topological relationship set includes a tree-like hierarchical structure. For example, the root node is the block body, and the child nodes include the cylinder bore, oil passage, water jacket, etc. The depth of the cylinder bore node is 2, and it contains 12 child nodes.

[0071] The geometric constraint condition set is the distribution of constraint types. For example, the concentric constraint accounts for 60%, and the parallel constraint accounts for 30%. The applicable level is from depth 1 to 3.

[0072] In S2, feature extraction and rule derivation are performed on the initial data to generate feature priority rules and constraint action rules, including:

[0073] S2a. Generate feature priority rules according to the unordered point cloud set and the two-dimensional contour line set.

[0074] S2b. Generate constraint action rules according to the topological relationship set and the geometric constraint condition set.

[0075] In some embodiments, generating feature priority rules according to the unordered point cloud set and the two-dimensional contour line set in S2a includes:

[0076] S2a1. Perform spatial partitioning on the unordered point cloud set to obtain multiple cubic grid cells, count the number of point clouds in each cubic grid cell to obtain the spatial density of the point cloud; mark the cubic grid cells with a spatial density greater than the average density of all cubic grid cells as high-density regions.

[0077] Partition the unordered point cloud set into cubic grid cells of equal size. The side length of the cubic grid cell is determined by the following steps:

[0078] S2a11. Calculate the maximum span values of the point cloud in the X, Y, and Z directions, denoted as , , 。

[0079] S2a12. Calculate the cube root value K of the total number of point clouds N, 。

[0080] Take the side length 。

[0081] Count the number of point clouds in each cube , calculate the average value of the point cloud quantity of all cubes , if , then mark this cube grid cell as a high-density area, otherwise mark it as a low-density area.

[0082] Exemplarily, divide the point cloud into 1 cube grid, calculate the average value of the point cloud quantity is 50 points / cell, the point cloud density of the inner wall of the cylinder is 120, which is a high-density area, and the boss on the top of the cylinder block is 120, which is a low-density area.

[0083] S2a2. Extract the extension directions of each line segment in the set of two-dimensional contour lines, traverse the angle of direction change between adjacent line segments, and mark the continuous line segment group with an angle of direction change less than the preset coverage threshold as the coverage area of the continuous direction.

[0084] The starting point coordinates of the line segment ( , ) to the ending point coordinates ( , ) is the extension direction , and the angle of direction change is the included angle between the extension vectors of adjacent line segments

[0085]

[0086] Among them, and represent the direction vector of the i-th line segment and the direction vector of the (i + 1)-th line segment respectively.

[0087] When all adjacent of the continuous line segment group are less than the preset coverage threshold, it is marked as the coverage area. The coverage threshold can be determined according to the modeling requirements of the degree of direction change of adjacent line segments.

[0088] Exemplarily, the angle of direction change of the contour line segment of the cylinder hole , is less than the preset coverage threshold of 5°, and it is marked as the coverage area of the continuous direction. The contour of the boss , is marked as a discontinuous area.

[0089] S2a3. Perform a spatial superposition of the high-density area and the coverage area. If the high-density area is completely contained in the coverage area, it is marked as the combined action area; if there is only partial overlap, it is marked as the single action area.

[0090] Specifically, if all the center points of the cubes in the high-density area are located inside the polygon of the coverage area, it is marked as the combined action area.

[0091] If the high-density area intersects with the coverage area but is not completely contained, it is marked as a single-action area.

[0092] S2a4 generates feature priority rules, including: assigning a first modeling priority to the combined-action area; assigning a second modeling priority to the single-action area.

[0093] Exemplarily, if the high-density area of the inner wall of the cylinder is completely contained in the contour coverage area, it is a combined-action area, and a first modeling priority is assigned; if the high-density area of the boss only partially overlaps with the contour, it is a single-action area, and a second modeling priority is assigned.

[0094] In some embodiments, in S2b, constraint action rules are generated according to the topological relationship set and the geometric constraint condition set, including:

[0095] S2b1. Traverse the hierarchical depth of the topological relationship set, identify the key hierarchical nodes with the number of child nodes exceeding the average, and mark them as core topological nodes; count the occurrence frequencies of each constraint type in the type distribution of the geometric constraint condition set, and mark the constraint type with the highest frequency as the dominant constraint type.

[0096] Perform a tree structure traversal on the topological relationship set and record the hierarchical depth values of each node.

[0097] Calculate the average number of child nodes of all non-leaf nodes , identify the nodes with the number of child nodes as key hierarchical nodes and mark them as core topological nodes.

[0098] Exemplarily, if the average number of child nodes of a non-leaf node , and the number of child nodes of the cylinder hole node , then it is marked as a core topological node.

[0099] Count the occurrence frequencies of each constraint type in the geometric constraint condition set , represents the occurrence frequency of the j-th constraint type, and the constraint types include parallel, perpendicular, concentric, etc. Mark max( ) as the dominant constraint type.

[0100] Exemplarily, if the frequency proportion of the concentric constraint is 60%, then mark concentric as the dominant constraint type.

[0101] S2b2. Logically match the core topological nodes with the dominant constraint type: if the hierarchical depth of the core topological node corresponds to the applicable level of the dominant constraint type, establish a strong constraint binding relationship; if the hierarchical depths do not match but there are child node associations, establish a weak constraint binding relationship.

[0102] If the hierarchical depth of the core topological node Satisfy: , where respectively represent the minimum and maximum values of the applicable hierarchical range of the dominant constraint type, then a strong constraint binding relationship is established.

[0103] If the hierarchical depth of the core topology node is not within the applicable hierarchical range, but there is a child node association, then a weak constraint binding relationship is established.

[0104] The determination condition for child node association can be: the depth of the associated child node and the depth of the core topology node The absolute value of the difference is less than or equal to the association threshold. For example, the association threshold can be 2, and the association threshold can be determined according to the influence degree of the topological node hierarchical difference on the constraint conduction.

[0105] S2b3. Generate constraint application rules, including: assigning the highest conduction priority to the strong constraint binding relationship and assigning the secondary conduction priority to the weak constraint binding relationship.

[0106] Exemplarily, the cylinder bore node is at the second hierarchical depth and contains 12 child nodes. This quantity exceeds the average of 8 nodes and is determined as the core topology node. Constraint statistics show that the concentric constraint accounts for 60%, which is determined as the dominant constraint type. Its applicable depth range is 1 - 3. When the constraint is matched, the depth of the cylinder bore node is within the applicable range, and a strong constraint binding relationship is established. The depth of the boss node is at level 4 and exceeds the range, but the depth of its child node is at level 5, and the difference is only 1, so a weak constraint binding relationship is established. Finally, the strong constraint binding obtains the highest conduction priority, and the weak constraint binding obtains the secondary conduction priority.

[0107] In some embodiments, in S3, the initial data is preprocessed according to the feature priority rule and the constraint application rule, including:

[0108] Perform gradient denoising processing on the unordered point cloud set according to the feature priority rule to generate a denoised point cloud set.

[0109] Specifically, the Gaussian filtering algorithm is used to process the point cloud data in the joint action area, and the median filtering algorithm is used to process the point cloud data in the single action area to generate a denoised point cloud set including denoised point coordinates, area markers (joint action area / single action area), and spatial density attributes.

[0110] Perform breakpoint completion on the two-dimensional contour line set according to the continuity direction to generate a closed contour line set.

[0111] Specifically, connect the intersection points of the extension lines of adjacent line segments in the covered area; construct arcs in the non-covered area, such as an arc with a radius equal to 0.3 times the average line segment length; perform a closed-loop detection on the start and end points of the contour line: measure the distance between the start point and the end point of the contour line , when the distance between the start point and the end point of the contour line is greater than the preset distance threshold, add a new straight line connection segment; generate a set of closed contour lines including the complete closed contour coordinate sequence and the direction continuity marker for each line segment; among them, the distance threshold can be determined according to the accuracy requirements of the two-dimensional contour line closed-loop detection.

[0112] Use the constraint action rule to eliminate the redundant nodes in the topological relationship set and generate a simplified topological relationship set.

[0113] Specifically, eliminating redundant nodes includes: retaining all nodes with strong constraint binding relationships, weak constraint binding nodes with a hierarchical depth difference less than or equal to 2, deleting unbound nodes, and reconstructing the parent-child relationship hierarchy to generate a simplified topological relationship set including: a node parent-child relationship connection table and a node constraint binding type marker.

[0114] Standardize the geometric constraint condition set to generate a constraint strength set.

[0115] Specifically, assign a reference strength value to the strong constraint binding relationship, such as 1.0; assign a reference strength value to the weak constraint binding relationship, such as 0.6; apply a correction factor according to the part material type, such as a correction factor of 1.0 for structural steel parts and a correction factor of 0.8 for aluminum alloy parts; calculate the final strength value of each constraint condition, and the product of the reference strength value and the material correction factor can be used as the final strength value. Generate a constraint strength set including constraint geometric parameters, final constraint strength values, and constraint type distribution markers.

[0116] Exemplarily, in the point cloud denoising process, the Gaussian filtering algorithm is used to generate a smooth point cloud in the combined action area of the inner wall of the cylinder, the median filtering process is used in the single action area of the boss, the contour line closing operation connects the breakpoints in the continuous contour area of the cylinder hole to form a complete closed loop, and in the boss area, the breakpoints are complemented by adding an arc segment with a radius of 2 mm. In the topological simplification stage, 17 unconstrained nodes are deleted, and only the strong constraint nodes related to the cylinder hole are retained. In the constraint standardization process, the concentric constraint strength of the cylinder hole is set to the highest value of 1.0, and the boss constraint strength is set to 0.6.

[0117] In some embodiments, in S4, geometric structure features, curvature change features, connection node features, and boundary features are extracted from the preprocessed data, including:

[0118] Extract geometric structure features according to the spatial distribution of the denoised point cloud set.

[0119] Specifically, identify the three-dimensional coordinate distribution patterns of each point cloud in the denoised point cloud set, identify the spatial positions and ranges of high-density regions, use a surface reconstruction algorithm for the point clouds in the joint action area, and use a convex hull algorithm for the point clouds in a single action area to generate geometric structure features including the coordinates of key geometric elements, region division markers, and spatial topological relationships. Among them, the coordinates of key geometric elements such as the surface boundary points and corner point positions, the region division markers are inherited from the region types of the denoised point cloud set, and the spatial topological relationship is the topological structure formed by the point cloud density gradient.

[0120] Extract the curvature change features according to the continuity direction of the closed contour line set.

[0121] Specifically, uniformly sample points on the line segments marked as continuous in the continuity direction, and calculate the change amount of the steering angle between adjacent sampling points; for the contour segments in the area covered by the continuity direction, use the three-point circle method to calculate the local curvature radius, and for the contour segments in the discontinuous area, record the direction change angle value; generate curvature change features including a curvature change gradient map, the coordinates of high-curvature change points, and direction continuous segment markers; among them, the curvature change gradient map is a position-curvature value mapping table.

[0122] Extract the connection node features according to the hierarchical depth of the simplified topological relationship set.

[0123] Specifically, recursively scan the child nodes starting from the root node, record the depth value and the number of directly connected child nodes of each node, mark the core topological nodes and associated child nodes, and generate connection node features including a topological graph of node connection relationships, a set of core node coordinates, and a node hierarchical depth table.

[0124] Extract the boundary features according to the type distribution of the constraint strength set.

[0125] Specifically, group and statistically analyze the strength distribution according to the constraint type and identify the regions corresponding to strong constraint binding relationships and the regions corresponding to weak constraint binding relationships. For the regions corresponding to strong constraint binding relationships, apply the least squares boundary fitting algorithm to identify the boundary features, and for the regions corresponding to weak constraint binding relationships, apply the convex hull boundary extraction algorithm to identify the boundary features, and generate boundary features including boundary parametric equations, a constraint type-boundary mapping table, and boundary continuity markers.

[0126] Exemplarily, in the geometric structure feature extraction stage, obtain the accurate surface boundary point coordinates through a surface reconstruction algorithm for the point cloud of the inner wall of the cylinder, sample points every 0.5 millimeters on the cylinder hole contour to calculate the curvature, and determine the coordinate positions of high-curvature points. In the node feature extraction stage, obtain the complete connection topology of the core node coordinates of the cylinder hole and its 12 child nodes. In the boundary feature processing, use the least squares method to fit the boundary equation for the strong constraint area and extract the convex hull boundary polygon for the weak constraint area.

[0127] In some embodiments, the method for generating the set of basic surface patches in S5 includes:

[0128] Locate the coordinates of the key geometric elements in the geometric structure features as the control points of the surface, and at the same time determine the connection mode of these control points according to the spatial topological relationship in the geometric structure features.

[0129] Use the curvature change gradient map in the curvature change feature to determine the surface subdivision scheme. For example, set a high-density subdivision in the high-curvature change region where the curvature change rate is greater than the specified value, and set a low-density subdivision in the region where the curvature change rate is not greater than the specified value.

[0130] Perform local encryption processing on the surface patches at the coordinate positions of the high-curvature change points marked in the curvature change feature to ensure the accurate expression of the surface deformation details.

[0131] Keep the surface patches continuously transition in the marked direction continuous segment area in the curvature change feature to avoid the appearance of discontinuous boundaries.

[0132] Generate surface patches for the regions marked as the combined action areas in the geometric structure features using the bicubic B-spline surface reconstruction algorithm.

[0133] For the regions marked as single action areas in the geometric structure features, use the bilinear plane fitting method to generate simplified surface patches.

[0134] Associate and integrate the generated set of surface patches with the region division marks in the geometric structure features, and output the set of basic surface patches including all the control point parameters, connection topology relationships, and priority marks of the surface.

[0135] The method for generating the set of constraint connection relationships in S5 includes:

[0136] Perform spatial position matching between the set of core node coordinates in the connection node feature and the boundary parameter equation in the boundary feature.

[0137] Calculate the spatial distance d from each core node in the connection node feature to the nearest boundary in the boundary feature.

[0138] Determine the connection type according to the calculated distance d. For example, when d ≤ 0.05 mm, establish a rigid connection, representing a fixed degree of freedom constraint; when 0.05 mm < d ≤ 0.5 mm, establish a flexible connection, representing allowing elastic deformation; when d > 0.5 mm, no connection relationship is established.

[0139] Determine the connection strength value with reference to the constraint type-boundary mapping table in the boundary feature. For example, assign the highest strength value of 1.0 to the boundary region corresponding to the strong constraint binding relationship; assign the medium strength value of 0.6 to the boundary region corresponding to the weak constraint binding relationship.

[0140] Control the displacement constraint continuity using the boundary continuity marker in the application boundary features, such as:

[0141] When the continuity marker is true, force the displacement to be completely continuous. When the continuity marker is false, allow a small amount of slip deformation.

[0142] Allocate the constraint conduction priority according to the node hierarchy depth table in the connection node features, such as: Nodes with a smaller hierarchy depth are given the highest conduction priority, and nodes with a larger hierarchy depth are given a secondary conduction priority.

[0143] Integrate the generated connection relationships with the node connection relationship topology diagram in the connection node features, and output a set of constraint connection relationships including the constraint relationships between nodes, connection types, strength values, and continuity markers.

[0144] Exemplarily, in the surface generation stage, create a high-precision surface with a control point spacing of 0.2 mm on the inner wall of the cylinder, and create a simplified plane on the boss. Measure that the distance from the cylinder hole node to the boundary is 0.03 mm, and establish a rigid connection; the distance from the boss node to the boundary is 0.2 mm, and establish a flexible connection allowing 0.1 mm of deformation.

[0145] In some embodiments, generating the three-dimensional model surface mesh according to the basic patch set in S6 includes:

[0146] S6a1. Perform triangulation on the basic patch set.

[0147] Specifically, the Delaunay triangulation algorithm can be used to discretize the surface patches in the basic patch set into an initial mesh of triangular patches, and the surface control points in the basic patch set are directly converted into mesh vertices.

[0148] S6a2. Locally refine the triangulation result according to the curvature change characteristics.

[0149] Specifically, perform mesh subdivision at the coordinate positions of the high-curvature change points marked in the curvature change characteristics. For example, divide the maximum curvature by a preset reference threshold, round up the obtained quotient, and the resulting value is the number of subdivision times. The reference threshold can be determined according to the influence degree of the curvature change characteristics on the mesh refinement, such as 0.05.

[0150] Perform adaptive mesh refinement in the high-curvature change regions marked on the curvature change gradient diagram in the curvature change characteristics, and keep the mesh evenly transition within the marked region of the direction continuous segment in the curvature change characteristics.

[0151] S6a3. Incorporate the boundary features into the refined mesh.

[0152] Specifically, the geometric boundary described by the boundary parameter equation in the boundary feature is forcibly converted into a mesh boundary edge, continuous vertex distributions are set at positions where the boundary continuity marked in the boundary feature is true, and the constraint type-boundary mapping table in the boundary feature is used to control the boundary hardening degree.

[0153] S6a4. Optimize the vertex distribution of the mesh along the continuity direction.

[0154] Specifically, within the region where the direction continuous segment in the curvature change feature is marked as true, re-parameterize the vertex coordinates according to the main direction of the continuity direction, and force the vertices to be evenly distributed along the main direction; keep the original vertex distribution in the discontinuous region.

[0155] S6a5. Adjust the mesh density according to the spatial density.

[0156] Specifically, according to the spatial distribution map of the spatial density, densify the mesh in the high-density region and sparsify the mesh in the low-density region, and maintain the mesh density difference corresponding to the region division mark in the geometric structure feature.

[0157] S6a6. Output the surface mesh of the three-dimensional model.

[0158] Specifically, generate the surface mesh of the three-dimensional model including vertex coordinates, patch connection relationships, and region density marks.

[0159] In S6, an internal support structure is generated according to the set of constraint connection relationships, including:

[0160] S6b1. Generate a basic node network based on the connection node feature.

[0161] Use the node connection relationship topology graph and the core node coordinate set in the connection node feature to establish a basic node network. For example, arrange key nodes at the positions marked in the core node coordinate set, connect adjacent nodes according to the node connection relationship topology graph to form an initial network skeleton, and use the node hierarchy depth table to determine the network hierarchy relationship.

[0162] S6b2. Convert the set of constraint connection relationships into structural connections.

[0163] Convert the connection relationship topology table in the set of constraint connection relationships into engineering structural connections. For example, set fixed support constraints for rigid connection types and elastic hinge constraints for flexible connection types, and inherit the connection strength value as the structural stiffness coefficient.

[0164] S6b3. Generate a multi-level support framework according to the hierarchical depth of the core topology nodes.

[0165] Construct a support frame hierarchy based on the hierarchical depth of the core topology nodes. For example, a core node with a hierarchical depth of 0 generates a primary frame at the first level, a child node with a hierarchical depth difference of 1 generates a secondary branch frame, and a child node with a hierarchical depth difference of 2 generates a tertiary auxiliary frame.

[0166] S6b4. Map the constraint strength set to structural parameters.

[0167] Traverse all the constraint conditions in the constraint strength set, and set the corresponding structural parameters according to the constraint strength value corresponding to each constraint condition. For example, a strength value of 1.0 corresponds to steel structure parameters, and a strength value of 0.6 corresponds to aluminum alloy parameters. Assign the converted material parameters to the structural elements associated with the corresponding constraint conditions to ensure that each structural connection obtains material property parameters matching its constraint strength.

[0168] S6b5. Verify the integrity of the structural connections using the simplified topology relation set.

[0169] Verify the connections through the parent-child relationship chain of nodes in the simplified topology relation set, such as verifying whether the child nodes establish valid connections with the parent nodes, detecting unconnected isolated nodes, and repairing the connection relationships with broken hierarchies.

[0170] S6b6. Adjust the structural type according to the type distribution of the constraint strength set.

[0171] For example, a truss structure is adopted in the area dominated by strong constraint binding relationships, a reticulated shell structure is adopted in the area dominated by weak constraint binding relationships, and a rigid frame composite structure is adopted in the mixed constraint area.

[0172] S6b7. Output the support structure.

[0173] Generate an internal support structure including beam element topology, material parameters, and connection constraints.

[0174] Exemplarily, in surface mesh processing, triangular partitioning is performed on the inner wall of the cylinder, and it is refined to 0.1 mm in the high-curvature area to optimize the vertex continuity distribution. A steel structure primary frame is established with the cylinder hole nodes as the core, an aluminum alloy reticulated shell structure is established in the boss area, and the integrity of the connection points is verified.

[0175] In some embodiments, in S7, a three-dimensional solid model is generated based on the surface mesh of the three-dimensional model and the internal support structure, including:

[0176] S71. Establish the binding relationship between the surface mesh vertices and the internal support structure nodes.

[0177] Establish a one-to-one correspondence between the vertex coordinates of the surface mesh and the spatial positions of the nearest nodes in the internal support structure, and determine the binding priority according to the node hierarchy depth table in the connection node characteristics. For example, nodes with a hierarchy depth difference ≤ 2 are rigidly bound, and nodes with a hierarchy depth difference > 2 are elastically bound.

[0178] S72. Convert the set of constraint connection relationships into physical constraint entities.

[0179] Extract all connection relationship topology tables in the set of constraint connection relationships, generate fixed constraint entities for each rigid connection type, and generate elastic constraint entities for each flexible connection type.

[0180] S73. Inject the geometric elements of the boundary feature into the surface mesh to generate an enhanced surface mesh.

[0181] Convert the boundary parameter equation in the boundary feature into a mesh constraint edge, force the mesh vertices to be continuously distributed at positions where the boundary continuity is marked as true, and set different boundary hardening levels according to the constraint type-boundary mapping table. For example, the boundary with a strong constraint binding relationship is set as a fully fixed boundary, and the boundary with a weak constraint binding relationship is set as allowing tangential slip.

[0182] S74. Construct a tetrahedral mesh based on the binding relationship.

[0183] Using the enhanced surface mesh as the outer surface and the nodes of the internal support structure as the internal connection points, tetrahedral elements can be generated according to the Delaunay criterion. For example, the element size in the combined action area is 0.5 times the minimum point distance, and the element size in the single action area is 2 times the minimum point distance.

[0184] S75. Embed the constraint entities into the mesh nodes.

[0185] Convert the physical constraint entities into node constraint conditions, and apply displacement constraint boundary conditions to the corresponding mesh nodes. For example, the rigid constraint entity fixes all degrees of freedom, and the elastic constraint entity applies a normal spring constraint.

[0186] S76. Locally encrypt the enhanced surface mesh according to the feature priority rules.

[0187] In the combined action area marked as the first modeling priority in the feature priority rules, for example, the element size is reduced to 0.5 times the original size, and adaptive node encryption is performed. In the single action area with the second modeling priority, the basic element size is maintained.

[0188] S77. Generate a three-dimensional solid model containing geometric details and topological constraints.

[0189] Integrate the encrypted tetrahedral mesh, constraint boundary conditions, and material parameters, and output a three-dimensional solid model compatible with CAE software. The model contains complete geometric feature and assembly constraint information.

[0190] Exemplarily, the surface vertices of the cylinder region 589,000 are rigidly connected to 127 support nodes. 17 rigid connections are converted into fixed constraints, 8 flexible connections are converted into spring constraints, and the boundary parameters of the cylinder bore are incorporated into the digital model. To create 2.5 million tetrahedral elements with a fully fixed boundary, the cylinder region is encrypted to 0.1 mm. Finally, an integrated mesh structure, material parameters including steel and aluminum alloy, and 36 assembly constraints are output to generate a complete model that can be directly imported into simulation software.

[0191] In some embodiments, the present application provides an automated three-dimensional modeling data adaptive construction system, which includes:

[0192] Initial data acquisition module: to acquire the initial data of the target object.

[0193] Feature and rule generation module: to perform feature extraction and rule derivation on the initial data to generate feature priority rules and constraint action rules.

[0194] Data preprocessing module: to preprocess the initial data according to the feature priority rules and constraint action rules.

[0195] Feature extraction module: to extract geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data.

[0196] Feature fitting and matching module: to fit and match the geometric structure features with the curvature change features, and the connection node features with the boundary features respectively to generate a set of basic patch sets and a set of constraint connection relationships.

[0197] Model structure construction module: to generate a three-dimensional model surface mesh according to the set of basic patch sets, and generate an internal support structure according to the set of constraint connection relationships.

[0198] Solid model generation module: to generate a three-dimensional solid model according to the three-dimensional model surface mesh and the internal support structure.

[0199] In some embodiments, the present application provides an automated three-dimensional modeling data adaptive construction device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the steps of the automated three-dimensional modeling data adaptive construction method as described in the first aspect when executing the computer program.

[0200] In some embodiments, the present application provides a storage medium, in which computer program instructions are stored. When the computer program instructions are read and run by a processor, the steps of the automated three-dimensional modeling data adaptive construction method as described in the first aspect are executed.

[0201] Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0202] It should be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0203] Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automated three-dimensional modeling data adaptive construction method, characterized in that Including: Obtain the initial data of the target object; Extract features and deduce rules from the initial data to generate feature priority rules and constraint effect rules; Preprocess the initial data according to the feature priority rules and constraint effect rules; Extract geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data; Fit and match the geometric structure features with the curvature change features, and the connection node features with the boundary features respectively to generate a basic patch set and a constraint connection relationship set; Generate a surface mesh of the 3D model according to the basic patch set, and generate an internal support structure according to the constraint connection relationship set; Generate a 3D solid model according to the surface mesh of the 3D model and the internal support structure.

2. The adaptive construction method of automated three-dimensional modeling data according to claim 1, characterized in that The initial data includes: an unordered point cloud set, a 2D contour line set, a topological relationship set, and a geometric constraint condition set; Extract features and deduce rules from the initial data to generate feature priority rules and constraint effect rules, including: Generate feature priority rules according to the unordered point cloud set and the 2D contour line set; generate constraint effect rules according to the topological relationship set and the geometric constraint condition set.

3. The automated three-dimensional modeling data adaptive construction method according to claim 2, wherein Generate feature priority rules according to the unordered point cloud set and the 2D contour line set, including: Perform spatial partitioning on the unordered point cloud set to obtain multiple cubic grid cells, count the number of point clouds in each cubic grid cell to obtain the spatial density of the point clouds; mark the cubic grid cells with a spatial density greater than the average density of all cubic grid cells as high-density regions; Extract the extension directions of each line segment in the 2D contour line set, traverse the direction change angles between adjacent line segments, and mark the continuous line segment group with a direction change angle less than a preset coverage threshold as a coverage area with a continuous direction; Perform spatial superposition on the high-density region and the coverage area. If the high-density region is completely contained in the coverage area, mark it as a joint action area; if only partially overlapped, mark it as a single action area; Generate feature priority rules, including: assign a first modeling priority to the joint action area; assign a second modeling priority to the single action area.

4. The automated three-dimensional modeling data adaptive construction method according to claim 3, wherein Generate constraint effect rules according to the topological relationship set and the geometric constraint condition set, including: Traverse the hierarchical depth of the topological relationship set, identify the key hierarchical nodes with the number of child nodes exceeding the average, and mark them as core topological nodes; count the occurrence frequencies of each constraint type in the type distribution of the geometric constraint condition set, and mark the constraint type with the highest frequency as the dominant constraint type; Perform logical matching between the core topological nodes and the dominant constraint type: if the hierarchical depth of the core topological node corresponds to the applicable level of the dominant constraint type, establish a strong constraint binding relationship; if the hierarchical depths do not match but there are child node associations, establish a weak constraint binding relationship; Generate constraint effect rules, including: assign the highest conduction priority to the strong constraint binding relationship, and assign a secondary conduction priority to the weak constraint binding relationship.

5. The automated three-dimensional modeling data adaptive construction method according to claim 3, wherein Preprocess the initial data according to the feature priority rules and constraint effect rules, including: Perform gradient denoising on the unordered point cloud set according to the feature priority rule to generate a denoised point cloud set; complete the breakpoints of the two-dimensional contour line set according to the continuity direction to generate a closed contour line set; eliminate the redundant nodes of the topological relationship set by using the constraint action rule to generate a simplified topological relationship set; standardize the geometric constraint condition set to generate a constraint strength set.

6. The automated three-dimensional modeling data adaptive construction method according to claim 5, wherein Extract geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data, including: Extract geometric structure features according to the spatial distribution of the denoised point cloud set; extract curvature change features according to the continuity direction of the closed contour line set; extract connection node features according to the hierarchical depth of the simplified topological relationship set; extract boundary features according to the type distribution of the constraint strength set.

7. The automated three-dimensional modeling data adaptive construction method according to claim 3, wherein Generate the surface mesh of the 3D model according to the basic patch set, including: Perform triangulation on the basic patch set, locally encrypt the triangulation result according to the curvature change feature, integrate the boundary feature into the encrypted mesh, optimize the vertex distribution of the mesh along the continuity direction, adjust the mesh density according to the spatial density, and output the surface mesh of the 3D model.

8. The automated three-dimensional modeling data adaptive construction method according to claim 5, characterized in that Generate the internal support structure according to the constraint connection relationship set, including: Generate a basic node network based on the connection node feature, convert the constraint connection relationship set into a structural connection, generate a multi-level support framework according to the hierarchical depth of the core topological nodes, map the constraint strength set to structural parameters, verify the integrity of the structural connection with the simplified topological relationship set, and adjust the structural type according to the type distribution of the constraint strength set, and output the support structure.

9. The automated three-dimensional modeling data adaptive construction method according to claim 1, characterized in that Generate a 3D solid model according to the surface mesh of the 3D model and the internal support structure, including: Establish the binding relationship between the surface mesh vertices and the internal support structure nodes, and convert the constraint connection relationship set into physical constraint entities; inject the geometric elements of the boundary feature into the surface mesh to generate an enhanced surface mesh; Construct a tetrahedral mesh based on the binding relationship, embed the constraint entities into the mesh nodes, and locally encrypt the enhanced surface mesh according to the feature priority rule to generate a 3D solid model containing geometric details and topological constraints.

10. An automated three-dimensional modeling data adaptive construction system, characterized in that, It includes: Initial data acquisition module: acquire the initial data of the target object; Feature and rule generation module: perform feature extraction and rule derivation on the initial data to generate a feature priority rule and a constraint action rule; Data preprocessing module: preprocess the initial data according to the feature priority rule and the constraint action rule; Feature extraction module: extract geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data; Feature fitting and matching module: respectively fit and match the geometric structure feature with the curvature change feature, and the connection node feature with the boundary feature to generate a basic patch set and a constraint connection relationship set; Model structure construction module: generate the surface mesh of the 3D model according to the basic patch set, and generate the internal support structure according to the constraint connection relationship set; Solid model generation module: generate a 3D solid model according to the surface mesh of the 3D model and the internal support structure.

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