A method and system for adaptively constructing automated three-dimensional modeling data

Abstract

The present invention belongs to the technical field of 3D modeling data processing, and specifically provides a method and system for the adaptive construction of automated 3D modeling data. The method mainly includes: obtaining initial data of the target object; performing feature extraction and rule derivation on the initial data to generate feature priority rules and constraint action rules; preprocessing the initial data according to the feature priority rules and constraint action rules; and extracting geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data. The present application can achieve intelligent control of modeling accuracy in different areas, improve the restoration of complex structural details, avoid wasting computing resources, and at the same time strengthen the linkage of topological constraints to ensure the geometric accuracy and structural integrity of the model. It 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 in particular relates to an automated three-dimensional modeling data adaptive construction method and system. Background Art

[0002] In the field of three-dimensional modeling, data is processed based on fixed rules, such as uniform density grid division or simple splicing of contour lines, lacking differentiated management of geometric features, making it difficult to strike a balance between modeling accuracy and efficiency in different areas.

[0003] Currently, it is difficult for 3D modeling to dynamically adjust processing strategies for areas with high curvature changes and flat areas, which often leads to the loss of complex structural details or waste of computing resources. In addition, the processing of topological relationships and geometric constraints lacks linkage. When faced with complex modeling scenarios containing multi-source data, unclear feature priorities and the lack of constraint transmission mechanisms can easily cause model geometric distortion or structural conflicts, making it difficult to meet the requirements of industrial-grade modeling for accuracy and efficiency. Summary of the Invention

[0004] This application provides an automated three-dimensional modeling data adaptive construction method and system, which effectively solves the problem in the existing technology that it is difficult to dynamically adjust the processing strategy for three-dimensional modeling in areas with high curvature changes and flat areas. It realizes intelligent regulation of the modeling accuracy of different areas, improves the restoration of complex structural details, avoids waste of computing resources, and at the same time strengthens the linkage of topological constraints, ensures the geometric accuracy and structural integrity of the model, and is suitable for efficient and accurate modeling of multi-source data.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for adaptively constructing automated three-dimensional modeling data, comprising:

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

[0008] Perform feature extraction and rule deduction on the initial data to generate feature priority rules and constraint action rules.

[0009] The initial data is preprocessed according to feature priority rules and constraint action rules.

[0010] Geometric structure features, curvature change features, connection node features and boundary features are extracted from the preprocessed data.

[0011] The geometric structure features are fitted and matched with the curvature change features, and the connection node features are matched with the boundary features to generate a basic facet set and a constraint connection relationship set.

[0012] A three-dimensional model surface mesh is generated based on a set of basic facets, and an internal support structure is generated based on a set of constraint connection relationships.

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

[0014] Furthermore, the initial data includes: an unordered point cloud set, a two-dimensional contour line set, a topological relationship set, and a geometric constraint condition set.

[0015] Perform feature extraction and rule deduction on the initial data to generate feature priority rules and constraint action rules, including:

[0016] A feature priority rule is generated according to the unordered point cloud set and the two-dimensional contour line set; and a constraint action rule is generated according to the topological relationship set and the geometric constraint condition set.

[0017] Furthermore, generating a feature priority rule based on the unordered point cloud set and the two-dimensional contour line set includes:

[0018] The unordered point cloud set is spatially divided into blocks to obtain a plurality of cubic grid units. The number of point clouds in each cubic grid unit is counted to obtain the spatial density of the point cloud. The cubic grid units whose spatial density is greater than the average density of all cubic grid units are marked as high-density areas.

[0019] The extension direction of each line segment in the two-dimensional contour line set is extracted, the direction change angles between adjacent line segments are traversed, and a continuous line segment group whose direction change angle is less than a preset coverage threshold is marked as a coverage area of ​​continuity direction.

[0020] The high-density area is spatially superimposed on the coverage area. If the high-density area is completely contained in the coverage area, it is marked as a joint action area; if it is only partially overlapped, it is marked as a single action area.

[0021] Generating a feature priority rule includes: allocating a first modeling priority to the joint action area; and allocating a second modeling priority to the single action area.

[0022] Furthermore, generating constraint action rules according to the topological relationship set and the geometric constraint condition set includes:

[0023] The hierarchical depth of the topological relationship set is traversed to identify key hierarchical nodes containing more child nodes than the average, and marked as core topological nodes; the frequency of occurrence of each constraint type in the type distribution of the geometric constraint condition set is counted, and the constraint type with the highest frequency is marked as the dominant constraint type.

[0024] The core topology node is logically matched with the dominant constraint type: if the hierarchical depth of the core topology node corresponds to the applicable level of the dominant constraint type, a strong constraint binding relationship is established; if the hierarchical depth does not match but there is a child node association, a weak constraint binding relationship is established.

[0025] Generate constraint action rules, including: assigning the highest transmission priority to the strong constraint binding relationship, and assigning the secondary transmission priority to the weak constraint binding relationship.

[0026] Furthermore, the initial data is preprocessed according to the feature priority rules and constraint action rules, including:

[0027] The unordered point cloud set is subjected to gradient denoising processing according to the feature priority rule to generate a denoised point cloud set; the breakpoints of the two-dimensional contour line set are completed according to the continuity direction to generate a closed contour line set; the redundant nodes of the topological relationship set are eliminated using the constraint action rule to generate a simplified topological relationship set; the geometric constraint condition set is standardized to generate a constraint strength set.

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

[0029] Geometric structure features are extracted based on the spatial distribution of the denoised point cloud set; curvature change features are extracted based on the continuity direction of the closed contour line set; connection node features are extracted based on the hierarchical depth of the simplified topological relationship set; and boundary features are extracted based on the type distribution of the constraint strength set.

[0030] Furthermore, generating a surface mesh of a three-dimensional model according to the basic facet set includes:

[0031] The basic face set is triangulated, the triangulation result is locally encrypted according to the curvature change characteristics, the boundary features are integrated into the encrypted mesh, the vertex distribution of the mesh is optimized along the continuity direction, the mesh density is adjusted according to the spatial density, and the 3D model surface mesh is output.

[0032] Furthermore, an internal support structure is generated based on the constraint connection relationship set, including:

[0033] Based on the connection node characteristics, a basic node network is generated, and the constraint connection relationship set is converted into structural connection. A multi-level support framework is generated according to the hierarchical depth of the core topological node. The constraint strength set is mapped to structural parameters. The integrity of the structural connection is verified using a simplified topological relationship set. The structure type is adjusted according to the type distribution of the constraint strength set, and the support structure is output.

[0034] Furthermore, a three-dimensional solid model is generated based on the surface mesh and internal support structure of the three-dimensional model, including:

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

[0036] A tetrahedral mesh is constructed based on the binding relationship, the constraint entity is embedded in the mesh node, and the enhanced surface mesh is locally encrypted according to the feature priority rule 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, characterized in that it includes:

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

[0039] Feature and rule generation module: extracts features and derives rules from the initial data, and generates feature priority rules and constraint action rules.

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

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

[0042] Feature fitting and matching module: fits and matches geometric structure features with curvature change features, connection node features with boundary features, and generates a basic facet set and a constrained connection relationship set.

[0043] Model structure construction module: Generates a three-dimensional model surface mesh based on a set of basic facets, and generates an internal support structure based on a set of constraint connection relationships.

[0044] Solid model generation module: Generates a 3D solid model based on the surface mesh and internal support structure of the 3D model.

[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; and 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 storing computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the automated three-dimensional modeling data adaptive construction method as described in the first aspect are executed.

[0047] Beneficial effects of the present invention:

[0048] This application adopts feature extraction and rule deduction technology, combined with multi-feature fitting and matching such as geometric structure and curvature change, to dynamically generate basic facets and supporting structures, effectively solving the problem in existing technologies that it is difficult to dynamically adjust the processing strategy for three-dimensional modeling in areas with high curvature changes and flat areas, and realizes intelligent regulation of modeling accuracy in different areas, improves the restoration of complex structural 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, which is suitable for efficient and accurate modeling of multi-source data.

[0049] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 A schematic diagram showing a process flow of an automated three-dimensional modeling data adaptive construction method of the present invention is shown;

[0052] Figure 2 A module schematic diagram of an automated three-dimensional modeling data adaptive construction system of the present invention is shown. DETAILED DESCRIPTION

[0053] In order to solve the problems raised by the background technology, this application adopts feature extraction and rule deduction technology, combined with multi-feature fitting and matching such as geometric structure and curvature change, to dynamically generate basic facets and supporting structures, thereby realizing intelligent control of modeling accuracy in different areas, improving the restoration of complex structural details, avoiding waste of computing resources, and strengthening the linkage of topological constraints to ensure the geometric accuracy and structural integrity of the model, which is suitable for efficient and accurate modeling of multi-source data.

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0055] In some embodiments, as Figure 1 As shown, the present application provides a method for adaptively constructing automated 3D modeling data, comprising:

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

[0057] S2. Perform feature extraction and rule deduction on the initial data to generate feature priority rules and constraint action rules.

[0058] S3. Preprocess the initial data according to 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 facet set and a constraint connection relationship set.

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

[0062] S7. Generate a 3D solid model based on the 3D model surface mesh and 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] An unordered point cloud collection represents a dataset of spatial coordinates of the surface of a target object obtained through 3D laser scanning or structured light imaging technology. The data structure is a collection of a specified number of 3D coordinate points (x, y, z).

[0065] A 2D contour line set represents a plane contour dataset derived from CAD software or generated by digitizing engineering drawings. The data structure is a closed or open contour consisting of multiple line segments, each of which contains the start / end point coordinates and extension direction.

[0066] The topological relationship set represents the hierarchical connection relationship of each part 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 set represents the assembly and fitting relationship between parts. The data type is the constraint type (concentric / parallel / distance, etc.) + the object of action. The attributes include the constraint application level and scope.

[0068] For example, in the modeling of an automobile engine cylinder, a laser is used to scan the surface of the engine cylinder 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 channel are dense, and an unordered point cloud set is obtained.

[0069] Extract contour lines from CAD drawings, such as the circular contour of a cylinder hole and the polygonal contour of a bolt hole, including the extension direction of the line segments. For example, if the contour direction of a cylinder hole is continuous, a two-dimensional contour line set is obtained.

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

[0071] The geometric constraint condition set is distributed by constraint type, such as concentric constraints accounting for 60%, parallel constraints accounting for 30%, and the applicable level is depth 1-3.

[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 based on the unordered point cloud set and the 2D contour line set.

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

[0075] In some embodiments, generating a feature priority rule based on the unordered point cloud set and the two-dimensional contour line set in S2a includes:

[0076] S2a1. Spatially partition the unordered point cloud set into multiple cubic grid units. Count the number of point clouds in each cubic grid unit to obtain the spatial density of the point cloud. Mark the cubic grid units with a spatial density greater than the average density of all cubic grid units as high-density areas.

[0077] The unordered point cloud collection is divided into equal-sized cubic grid cells. The side length of the cubic grid cell is determined by the following steps:

[0078] S2a11. Calculate the maximum span 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] S2a13. Take the side length .

[0081] Count the number of point clouds in each cube , calculate the average number of point clouds of all cubes ,like , then the cube grid unit is marked as a high-density area, otherwise it is marked as a low-density area.

[0082] For example, the point cloud is divided into 1 Cube grid, calculate the average value of point cloud The point cloud density of the cylinder inner wall is 50 points / unit. 120, which is a high-density area, the top boss of the cylinder It is 120, which is a low-density area.

[0083] S2a2. Extract the extension direction of each line segment in the two-dimensional contour line set, traverse the direction change angles between adjacent line segments, and mark the continuous line segment group whose direction change angle is less than a preset coverage threshold as the coverage area of ​​the continuity direction.

[0084] Coordinates of the starting point of the line segment ( , ) to the end point coordinates( , ) is the extension direction , the direction change angle is the angle between the extension vectors of adjacent line segments

[0085]

[0086] in, and They 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 segments of a continuous line group When the coverage is less than a preset coverage threshold, it is marked as a coverage area. The coverage threshold can be determined based on the modeling requirements of the degree of change in the direction of adjacent line segments.

[0088] For example, the cylinder bore contour line segment direction change angle , less than the preset coverage threshold of 5°, marked as the continuity direction coverage area, boss contour , marked as non-contiguous areas.

[0089] S2a3. Spatially superimpose the high-density area and the covered area. If the high-density area is completely contained in the covered area, it is marked as a joint action area; if it only partially overlaps, it is marked as a single action area.

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

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

[0092] S2a4 generates feature priority rules, including: allocating a first modeling priority to the joint action area; and allocating a second modeling priority to the single action area.

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

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

[0095] S2b1. Traverse the hierarchical depth of the topological relationship set, identify the key hierarchical nodes with a number of child nodes exceeding the average, and mark them as core topological nodes; count the frequency of occurrence 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] Traverse the tree structure of the topological relationship set and record the hierarchical depth value of each node.

[0097] Calculate the average number of child nodes of all non-leaf nodes , identify the number of child nodes The nodes are key hierarchical nodes and are marked as core topological nodes.

[0098] For example, if the average number of child nodes of non-leaf nodes is , the number of child nodes of the cylinder hole node , it is marked as a core topology node.

[0099] Count the frequency of occurrence of each constraint type in the geometric constraint set , Represents the frequency of occurrence of the jth constraint type, which includes parallel, vertical, concentric, etc. ) is marked as the dominant constraint type.

[0100] For example, if the concentric constraint frequency accounts for 60%, concentricity is marked as the dominant constraint type.

[0101] S2b2. Logically match the core topology node with the dominant constraint type: if the hierarchical depth of the core topology node corresponds to the applicable level of the dominant constraint type, a strong constraint binding relationship is established; if the hierarchical depth does not match but there is a child node association, a weak constraint binding relationship is established.

[0102] If the level depth of the core topology node satisfy: ,in, The minimum and maximum values ​​of the applicable level range of the dominant constraint type are respectively represented, and a strong constraint binding relationship is established.

[0103] If the level depth of the core topology node If it is not within the applicable hierarchy range but has child node associations, a weak constraint binding relationship is established.

[0104] The judgment condition of child node association can be: the depth of the associated child node Depth of core topology nodes The absolute value of the difference Less than or equal to the association threshold, for example, the association threshold may be 2. The association threshold may be determined based on the degree of influence of topological node level differences on constraint conduction.

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

[0106] For example, the cylinder hole node is located at the second level depth and contains 12 child nodes, which exceeds the average of 8 nodes and is determined to be a core topology node. Constraint statistics show that the concentric constraint accounts for 60%, which is determined to be the dominant constraint type. Its applicable depth range is 1-3. When the constraints are matched, the cylinder hole node depth is within the applicable range, and a strong constraint binding relationship is established. The boss node depth is level 4, which is out of range, but the child node depth is 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, S3 pre-processes the initial data according to the feature priority rules and the constraint action rules, including:

[0108] Gradient denoising is performed on the disordered 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 labels (joint action area / single action area), and spatial density attributes.

[0110] The two-dimensional contour line set is completed according to the continuity direction to generate a closed contour line set.

[0111] Specifically, connect the intersection points of the extended lines of adjacent segments in the coverage area; construct arcs in the non-coverage area, such as an arc with a radius equal to 0.3 times the average length of the segment; perform closed-loop detection on the beginning and end points of the contour line: measure the distance between the beginning and end points of the contour line , when the distance between the first and last endpoints of the contour line When the spacing is greater than a preset threshold, a new straight line segment is added; a closed contour line set is generated, including a complete closed contour coordinate sequence and a continuity mark for each line direction; wherein the spacing threshold can be determined according to the accuracy requirements of the two-dimensional contour line closed loop detection.

[0112] The constraint action rules are used to eliminate 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] The set of geometric constraints is normalized to generate a set of constraint strengths.

[0115] Specifically, a baseline strength value, such as 1.0, is assigned to strong constraint bindings; a baseline strength value, such as 0.6, is assigned to weak constraint bindings. A correction factor is applied based on the part material type, such as 1.0 for structural steel parts and 0.8 for aluminum alloy parts. The final strength value for each constraint condition is calculated by multiplying the baseline strength value by the material correction factor. This generates a constraint strength set that includes the constraint geometry parameters, the final constraint strength value, and a constraint type distribution marker.

[0116] For example, in the point cloud denoising process, a Gaussian filtering algorithm is used to generate a smooth point cloud for the joint action area of ​​the cylinder inner wall, and a median filtering algorithm is used for the single action area of ​​the boss. The contour closing operation connects the breakpoints in the continuous contour area of ​​the cylinder hole to form a complete closed loop, and the breakpoints are completed in the boss area by adding 2 mm radius arc segments. In the topology simplification stage, 17 unconstrained nodes are deleted, and only the cylinder hole-related strong constraint nodes are retained. In the constraint normalization process, the cylinder hole concentricity constraint strength is set to the maximum value of 1.0, and the boss constraint strength is set to 0.6.

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

[0118] Extract geometric structure features based on the spatial distribution of denoised point cloud collection.

[0119] Specifically, the 3D coordinate distribution pattern of each point cloud in the denoised point cloud collection is identified, and the spatial location and range of high-density areas are identified. A surface reconstruction algorithm is applied to the joint action area point cloud, and a convex hull algorithm is applied to the single action area point cloud. These algorithms generate geometric structural features including the coordinates of key geometric elements, region division markers, and spatial topological relationships. The coordinates of key geometric elements include surface boundary points and corner point locations, the region division markers are inherited from the region type of the denoised point cloud collection, and the spatial topological relationship is the topological structure formed by the point cloud density gradient.

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

[0121] Specifically, sample points evenly on the line segments marked as continuous in the continuity direction, and calculate the change in the steering angle of adjacent sampling points; for the contour segments of the continuous direction coverage area, use the three-point circle method to calculate the local curvature radius, and for the contour segments of the discontinuous area, record the direction change angle value; generate a curvature change feature including a curvature change gradient map, coordinates of high curvature change points, and direction continuous segment marks; wherein the curvature change gradient map is a position-curvature value mapping table.

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

[0123] Specifically, starting from the root node, recursively scan the child nodes, 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 the connection node features including the node connection relationship topology graph, the core node coordinate set, and the node level depth table.

[0124] Boundary features are extracted based on the type distribution of constraint strength sets.

[0125] Specifically, the statistical strength distribution is grouped by constraint type and the areas corresponding to strong constraint binding relationships and weak constraint binding relationships are identified. For the areas corresponding to strong constraint binding relationships, the least squares boundary fitting algorithm is applied to identify boundary features. For the areas corresponding to weak constraint binding relationships, the convex hull boundary extraction algorithm is applied to identify boundary features, and boundary features including boundary parameter equations, constraint type-boundary mapping tables, and boundary continuity marks are generated.

[0126] For example, in the geometric structure feature extraction stage, the surface reconstruction algorithm is performed on the cylinder inner wall point cloud to obtain the precise coordinates of the surface boundary points, the curvature is calculated at every 0.5 mm sampling point of the cylinder hole contour, and the coordinate positions of the high curvature points are determined. In the node feature extraction stage, the complete connection topology of the cylinder hole core node coordinates and its 12 child nodes is obtained. In the boundary feature processing, the least squares method is used to fit the boundary equation of the strong constraint area, and the convex hull boundary polygon is extracted 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 key geometric elements in the geometric structure features as the control points of the surface, and at the same time determine the connection method 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, such as setting high-density subdivision in the high-curvature change area where the curvature change rate is greater than the specified value, and setting low-density subdivision in the area 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 continuous transition of the surface patches within the marked direction continuous segment area in the curvature change feature to avoid the appearance of discontinuous boundaries.

[0132] For the areas marked as the combined action areas in the geometric structure features, generate surface patches using the bicubic B-spline surface reconstruction algorithm.

[0133] For the areas 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 area division marks in the geometric structure features, and output the set of basic surface patches containing 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 allowable elastic deformation; when d > 0.5 mm, no connection relationship is established.

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

[0140] Use the boundary continuity flag in the boundary feature to control the displacement constraint continuity, such as:

[0141] When the continuity flag is true, the displacement is forced to be completely continuous, and when the continuity flag is false, a small amount of slip deformation is allowed.

[0142] The constraint conduction priority is assigned according to the node hierarchy depth table in the connection node feature, such as: nodes with a small hierarchy depth are given the highest conduction priority, and nodes with a large hierarchy depth are given the secondary conduction priority.

[0143] The generated connection relationship is integrated with the node connection relationship topology graph in the connection node feature, and a constraint connection relationship set containing the constraint relationship between nodes, connection type, strength value and continuity mark is output.

[0144] For example, during the surface generation phase, a high-precision surface with a control point spacing of 0.2 mm was created on the cylinder inner wall, and a simplified plane was created on the boss. The cylinder bore nodes were measured to be 0.03 mm from the boundary, establishing a rigid connection; the boss nodes were measured to be 0.2 mm from the boundary, establishing a flexible connection that allowed 0.1 mm of deformation.

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

[0146] S6a1. Triangulate the basic face set.

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

[0148] S6a2. Locally encrypt the segmentation results according to the curvature change characteristics.

[0149] Specifically, the coordinate positions of the high curvature change points indicated in the curvature change characteristics are meshed, such as dividing the maximum curvature value by a preset reference threshold, and rounding up the quotient to obtain the integer. The result is the number of subdivisions. The reference threshold can be determined based on the degree of influence of the curvature change characteristics on mesh encryption, such as 0.05.

[0150] Adaptive mesh encryption is performed on the high curvature change area marked on the curvature change gradient map in the curvature change feature, and the mesh transition is maintained uniformly in the direction continuous segment mark area in the curvature change feature.

[0151] S6a3. Incorporate 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 distribution is set at the position where the boundary continuity flag indicated by the boundary feature is true, and the constraint type-boundary mapping table in the boundary feature is applied to control the degree of boundary hardening.

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

[0154] Specifically, in the area where the direction continuous segment in the curvature change feature is marked as true, the vertex coordinates are re-parameterized according to the main direction of the continuity direction, forcing the vertices to be evenly spaced along the main direction; the original vertex distribution is retained in the discontinuous area.

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

[0156] Specifically, according to the spatial distribution map of spatial density, the grid is encrypted in the high-density area and the grid is sparse in the low-density area, and the grid density difference corresponding to the regional division mark in the geometric structure feature is maintained.

[0157] S6a6. Output the 3D model surface mesh.

[0158] Specifically, a three-dimensional model surface mesh including vertex coordinates, facet connection relationships and area density marks is generated.

[0159] In S6, the internal support structure is generated based on the constraint connection relationship set, including:

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

[0161] Use the node connection relationship topology diagram and core node coordinate set in the connection node feature to establish a basic node network, such as arranging key nodes at the positions indicated by the core node coordinate set, connecting adjacent nodes according to the node connection relationship topology diagram to form an initial network skeleton, and applying the node hierarchy depth table to determine the network hierarchy relationship.

[0162] S6b2. Convert the constraint connection relationship set into a structural connection.

[0163] Convert the connection relationship topology table in the constraint connection relationship set to an engineering structure connection. For example, set a fixed support constraint for a rigid connection type, set an elastic hinge constraint for a flexible connection type, 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] The support framework hierarchy is constructed according to the hierarchical depth of the core topological nodes. For example, the core node with a hierarchical depth of 0 generates a first-level main framework, the child node with a hierarchical depth difference of 1 generates a second-level branch framework, and the child node with a hierarchical depth difference of 2 generates a third-level auxiliary framework.

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

[0167] Traverse all constraints in the constraint strength set and set the corresponding structural parameters based on the constraint strength value of each constraint, such as a strength value of 1.0 for steel structural parameters and a strength value of 0.6 for aluminum alloy parameters. Assign the converted material parameters to the structural elements associated with the corresponding constraints to ensure that each structural connection obtains material property parameters that match its constraint strength.

[0168] S6b5. Use a simplified topological relationship set to verify the integrity of structural connections.

[0169] Verify connections by simplifying the parent-child relationship chain of nodes in the topological relationship set, such as verifying whether child nodes have established valid connections with parent nodes, detecting unconnected isolated nodes, and repairing hierarchical broken connection relationships.

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

[0171] For example, the truss structure is used in the area where strong constraint binding relationships dominate, the lattice shell structure is used in the area where weak constraint binding relationships dominate, and the rigid frame composite structure is used in the mixed constraint area.

[0172] S6b7. Output support structure.

[0173] Generate internal support structures that include beam element topology, material parameters, and connection constraints.

[0174] For example, during surface meshing, the cylinder inner wall was triangulated, subdivided to 0.1 mm in areas of high curvature, and vertex continuity was optimized. A steel main frame was constructed with the cylinder bore node as the core, and an aluminum alloy lattice shell structure was created in the boss area, with the integrity of the connection points verified.

[0175] In some embodiments, generating a three-dimensional solid model according to the surface mesh of the three-dimensional model and the internal support structure in S7 includes:

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

[0177] A one-to-one correspondence is established between the coordinates of each vertex of the surface mesh and the spatial position of the nearest node in the internal support structure. The binding priority is determined according to the node level depth table in the connection node feature. For example, rigid binding is established for nodes with a level depth difference ≤ 2, and elastic binding is established for nodes with a level depth difference > 2.

[0178] S72. Convert the constraint connection relationship set into a physical constraint entity.

[0179] All connection relationship topology tables in the constraint connection relationship set are extracted, and a fixed constraint entity is generated for each rigid connection type, and an elastic constraint entity is generated for each flexible connection type.

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

[0181] Convert the boundary parameter equations in the boundary feature into mesh constraint edges, force the mesh vertices to be distributed continuously at the position where the boundary continuity mark is true, and set different boundary hardening levels according to the constraint type-boundary mapping table, such as setting the strong constraint binding relationship boundary to a completely fixed boundary and the weak constraint binding relationship boundary to allow tangential slip.

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

[0183] With the enhanced surface mesh as the outer surface and the internal support structure nodes as the internal connection points, tetrahedral elements can be generated according to the Delaunay criterion, such as: the unit size of the joint action area is 0.5 times the minimum point spacing, and the unit size of the single action area is 2 times the minimum point spacing.

[0184] S75. Embed the constraint entity into the mesh node.

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

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

[0187] In the joint action area marked as the first modeling priority by the feature priority rule, the element size is reduced to 0.5 times the original size, and adaptive node encryption is performed, while the basic element size is maintained in the single action area of ​​the second modeling priority.

[0188] S77. Generate 3D solid models that include geometric details and topological constraints.

[0189] Integrate the encrypted tetrahedral mesh, constraint boundary conditions and material parameters to output a 3D solid model compatible with CAE software. The model contains complete geometric features and assembly constraint information.

[0190] For example, 589,000 surface vertices in the cylinder area are rigidly connected to 127 support nodes, 17 rigid connections are converted into fixed constraints, and 8 flexible connections are converted into spring constraints. The cylinder hole boundary parameters are integrated into the digital model to achieve a completely fixed boundary to create 2.5 million tetrahedral units, which are encrypted to 0.1 mm in the cylinder area. The final output integrates the mesh structure, material parameters including steel and aluminum alloy, and 36 assembly constraints 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: obtains the initial data of the target object.

[0193] Feature and rule generation module: extracts features and derives rules from the initial data, and generates feature priority rules and constraint action rules.

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

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

[0196] Feature fitting and matching module: fits and matches geometric structure features with curvature change features, connection node features with boundary features, and generates a basic facet set and a constrained connection relationship set.

[0197] Model structure construction module: Generates a three-dimensional model surface mesh based on a set of basic facets, and generates an internal support structure based on a set of constraint connection relationships.

[0198] Solid model generation module: Generates a 3D solid model based on the surface mesh and internal support structure of the 3D model.

[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 of the first aspect when executing the computer program.

[0200] In some embodiments, the present application provides a storage medium storing computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the method for adaptively constructing automated three-dimensional modeling data as in the first aspect are executed.

[0201] Any reference to memory, storage, database, or other media used in the embodiments provided herein 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 document, relational terms such as "first" and "second" are used only 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 terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or elements that are inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device that includes the element.

[0203] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; 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. A method for adaptively constructing automated three-dimensional modeling data, characterized in that: include: Obtain initial data of the target object; Perform feature extraction and rule deduction on the initial data to generate feature priority rules and constraint action rules; Preprocess the initial data according to the feature priority rules and constraint action 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, the connection node features with the boundary features, and generate a basic facet set and a constraint connection relationship set; Generate a 3D model surface mesh based on a set of basic facets, and generate an internal support structure based on a set of constraint connection relationships; Generate a 3D solid model based on the surface mesh and internal support structure of the 3D model; The initial data includes: an unordered point cloud set, a two-dimensional contour line set, a topological relationship set, and a geometric constraint condition set; Perform feature extraction and rule deduction on the initial data to generate feature priority rules and constraint action rules, including: Generate a feature priority rule based on the unordered point cloud set and the two-dimensional contour line set; generate a constraint action rule based on the topological relationship set and the geometric constraint condition set; Generating a feature priority rule according to the unordered point cloud set and the two-dimensional contour line set includes: Spatially partitioning the unordered point cloud set into multiple cubic grid units, counting the number of point clouds in each cubic grid unit to obtain the spatial density of the point cloud; marking a cubic grid unit having a spatial density greater than the average density of all cubic grid units as a high-density area; Extracting the extension direction of each line segment in the two-dimensional contour line set, traversing the direction change angles between adjacent line segments, and marking a continuous line segment group whose direction change angle is less than a preset coverage threshold as a coverage area of ​​continuity direction; The high-density area is spatially superimposed on the coverage area. If the high-density area is completely contained in the coverage area, it is marked as a joint action area; if it is only partially overlapped, it is marked as a single action area; Generating a feature priority rule includes: allocating a first modeling priority to the joint action area; and allocating a second modeling priority to the single action area.

2. The method for adaptively constructing automated 3D modeling data according to claim 1, wherein: Generating constraint action rules according to the topological relationship set and the geometric constraint condition set includes: Traversing the hierarchical depth of the topological relationship set, identifying key hierarchical nodes with a number of child nodes exceeding the average, and marking them as core topological nodes; counting the frequency of occurrence of each constraint type in the type distribution of the geometric constraint condition set, and marking the constraint type with the highest frequency as the dominant constraint type; Logically matching the core topology node with the dominant constraint type: if the hierarchical depth of the core topology node corresponds to the applicable level of the dominant constraint type, a strong constraint binding relationship is established; if the hierarchical depth does not match but there is a child node association, a weak constraint binding relationship is established; Generate constraint action rules, including: assigning the highest transmission priority to the strong constraint binding relationship, and assigning the secondary transmission priority to the weak constraint binding relationship.

3. The method for adaptively constructing automated 3D modeling data according to claim 1, wherein: Preprocess the initial data according to the feature priority rules and constraint rules, including: The unordered point cloud set is subjected to gradient denoising processing according to the feature priority rule to generate a denoised point cloud set; the breakpoints of the two-dimensional contour line set are completed according to the continuity direction to generate a closed contour line set; the redundant nodes of the topological relationship set are eliminated using the constraint action rule to generate a simplified topological relationship set; the geometric constraint condition set is standardized to generate a constraint strength set.

4. The method for adaptively constructing automated 3D modeling data according to claim 3, wherein: Extract geometric structure features, curvature change features, connection node features, and boundary features from the preprocessed data, including: Geometric structure features are extracted based on the spatial distribution of the denoised point cloud set; curvature change features are extracted based on the continuity direction of the closed contour line set; connection node features are extracted based on the hierarchical depth of the simplified topological relationship set; and boundary features are extracted based on the type distribution of the constraint strength set.

5. The method for adaptively constructing automated 3D modeling data according to claim 1, wherein: Generating a 3D model surface mesh based on a set of basic facets includes: The basic face set is triangulated, the triangulation result is locally encrypted according to the curvature change characteristics, the boundary features are integrated into the encrypted mesh, the vertex distribution of the mesh is optimized along the continuity direction, the mesh density is adjusted according to the spatial density, and the 3D model surface mesh is output.

6. The method for adaptively constructing automated 3D modeling data according to claim 3, wherein: Generate internal support structures based on constraint connection relationships, including: Based on the connection node characteristics, a basic node network is generated, and the constraint connection relationship set is converted into structural connection. A multi-level support framework is generated according to the hierarchical depth of the core topological node. The constraint strength set is mapped to structural parameters. The integrity of the structural connection is verified using a simplified topological relationship set. The structure type is adjusted according to the type distribution of the constraint strength set, and the support structure is output.

7. The method for adaptively constructing automated 3D modeling data according to claim 1, wherein: Generate a 3D solid model based on the 3D model surface mesh and internal support structure, including: Establish binding relationships between surface mesh vertices and internal support structure nodes, convert constraint connection relationship sets into physical constraint entities; inject geometric elements of boundary features into the surface mesh to generate an enhanced surface mesh; A tetrahedral mesh is constructed based on the binding relationship, the constraint entity is embedded in the mesh node, and the enhanced surface mesh is locally encrypted according to the feature priority rule to generate a three-dimensional solid model containing geometric details and topological constraints.

8. An automated three-dimensional modeling data adaptive construction system, characterized in that: It includes: Initial data acquisition module: obtains the initial data of the target object; Feature and rule generation module: extracts features and derives rules from initial data, generating feature priority rules and constraint action rules; Data preprocessing module: preprocesses the initial data according to feature priority rules and constraint action rules; Feature extraction module: extracts geometric structure features, curvature change features, connection node features and boundary features from preprocessed data; Feature fitting and matching module: Fits and matches geometric structure features with curvature change features, connection node features with boundary features, and generates a basic facet set and a constraint connection relationship set; Model structure construction module: Generates 3D model surface mesh based on the basic facet set, and generates internal support structure based on the constraint connection relationship set; Solid model generation module: generates a 3D solid model based on the surface mesh and internal support structure of the 3D model; The initial data includes: an unordered point cloud set, a two-dimensional contour line set, a topological relationship set, and a geometric constraint condition set; Perform feature extraction and rule deduction on the initial data to generate feature priority rules and constraint action rules, including: Generate a feature priority rule based on the unordered point cloud set and the two-dimensional contour line set; generate a constraint action rule based on the topological relationship set and the geometric constraint condition set; Generating a feature priority rule according to the unordered point cloud set and the two-dimensional contour line set includes: Spatially partitioning the unordered point cloud set into multiple cubic grid units, counting the number of point clouds in each cubic grid unit to obtain the spatial density of the point cloud; marking a cubic grid unit having a spatial density greater than the average density of all cubic grid units as a high-density area; Extracting the extension direction of each line segment in the two-dimensional contour line set, traversing the direction change angles between adjacent line segments, and marking a continuous line segment group whose direction change angle is less than a preset coverage threshold as a coverage area of ​​continuity direction; The high-density area is spatially superimposed on the coverage area. If the high-density area is completely contained in the coverage area, it is marked as a joint action area; if it is only partially overlapped, it is marked as a single action area; Generating a feature priority rule includes: allocating a first modeling priority to the joint action area; and allocating a second modeling priority to the single action area.

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