Test-oriented structure three-dimensional model data optimization method

Abstract

The invention provides an inspection-oriented structure three-dimensional model data optimization method, which comprises the following steps of: analyzing assembly body model data generated by a three-dimensional design platform, and extracting an original data set containing a BREP topological structure, PMI labeling information and a hierarchical relationship; dynamically removing welding nodes, process auxiliary lines and non-geometric attribute data; performing multi-precision curved surface reconstruction on the BREP entity; grid fusion optimization based on material attributes is implemented, triangular patches of the same material are combined, and a vertex index table is reconstructed; constructing a lightweight assembly relation tree, and converting the spatial poses of the parts into a relative coordinate system transformation matrix; generating a lightweight metadata file containing measurable geometric parameters; and outputting a JT format lightweight model which accords with the ISO 14306 standard. According to the technical scheme, on the premise that the model states before and after lightweight processing are consistent, the data size of the model can be reduced, reduction of the overall data size of the three-dimensional model is promoted, and the opening and loading speed of the called model is greatly increased.

Description

Technical Field

[0001] The present invention belongs to the technical field of ship digital design, and particularly relates to a method for optimizing three-dimensional model data of a structure for inspection. Background Art

[0002] As the tonnage of ships increases, the amount of data also grows accordingly. The large amount of data sharply increases the demand for hardware resources, which will cause the opening and loading speed of ordinary hardware to be too slow or even fail, seriously affecting the design efficiency of designers and causing inconvenience for on-site inspectors to view three-dimensional models. To solve the problem of large model data volume, the modeling features of the parts that make up the ship model, that is, the structure model, are studied. The purpose is to ensure that the geometric contour of the model remains consistent before and after processing, and the data volume is significantly reduced, and the loading speed of the model is improved.

[0003] Currently, the data volume of the model itself has a negative impact on the loading speed. The larger the model data volume, the longer the time to open the model, and the lower the probability of normal opening. Moreover, an overly large model requires higher computer hardware. An unlightweighted model takes a long time to open, operates the model with lags, and even fails to open or causes the system to crash.

[0004] Therefore, how to provide a method for optimizing three-dimensional model data of a structure for inspection, which can reduce the data volume of the model on the premise of ensuring that the model state is consistent before and after lightweight processing, promote the reduction of the overall data volume of the three-dimensional model, and greatly improve the opening and loading speed of the called model, has become an urgent technical problem to be solved. Summary of the Invention

[0005] An embodiment of the present invention provides a method for optimizing three-dimensional model data of a structure for inspection, which can reduce the data volume of the model on the premise of ensuring that the model state is consistent before and after lightweight processing, promote the reduction of the overall data volume of the three-dimensional model, and greatly improve the opening and loading speed of the called model.

[0006] In an embodiment of the present invention, a method for optimizing three-dimensional model data of a structure for inspection is provided, including:

[0007] S101. Analyze the assembly model data generated by the three-dimensional design platform, and extract the original data set including the BREP topological structure, PMI annotation information, and hierarchical relationship;

[0008] S102. Construct feature filtering rules based on ship inspection requirements, and dynamically remove welding nodes, process auxiliary lines, and non-geometric attribute data;

[0009] S103. Perform multi-precision surface reconstruction on the BREP solid by respectively generating discrete point clouds using an adaptive curvature sampling algorithm, reconstructing a parameterized surface based on NURBS surface fitting, and establishing a simplified topological structure that retains key feature edges;

[0010] S104. Implement mesh fusion optimization based on material properties, merge triangular patches of the same material, and reconstruct the vertex index table;

[0011] S105. Construct a lightweight assembly relationship tree and convert the part spatial pose into a relative coordinate system transformation matrix;

[0012] S106. Generate a lightweight metadata file containing measurable geometric parameters;

[0013] S107. Output a lightweight model in JT format that complies with the ISO 14306 standard.

[0014] Further, parse the assembly model data generated by the 3D design platform, including:

[0015] A geometric parsing module extracts the Edge-Face topological relationship of the BREP through the Open CASCADE kernel;

[0016] An attribute extraction module grabs material and weight dynamic attributes from the ENOVIA database of CATIA V6;

[0017] A PMI parser converts ASME Y14.41 annotations into discrete geometric primitives.

[0018] Further, perform multi-precision surface reconstruction on the BREP solid, including:

[0019] Perform Delaunay triangulation on planar features while retaining the original boundary constraint conditions;

[0020] Adopt the moving least squares method (MLS) for curvature-preserving simplification of free-form surfaces;

[0021] For regular surfaces such as cylindrical surfaces and spherical surfaces, retain the parametric equations and add a tolerance compensation factor δ, where 0.05 mm ≤ δ ≤ 0.1 mm. Further, the dynamic calculation method of the tolerance compensation factor δ includes:

[0022] δ = α·L max + β·k avg

[0023] where α is the linear compensation coefficient, 0.001 ≤ α ≤ 0.005; β is the curvature compensation coefficient, 0.01 ≤ β ≤ 0.05; L max is the maximum feature length of the current assembly, and k avgis the average Gaussian curvature of the surface.

[0024] Furthermore, implement mesh fusion optimization based on material properties, including:

[0025] Establish a material-color mapping table, perform vertex buffer merging, patch index reordering, and normal vector consistency verification on components of the same material;

[0026] Adopt a half-edge data structure to eliminate duplicate geometric elements, and perform local mesh encryption processing on the welding area.

[0027] Furthermore, construct a lightweight assembly relationship tree, including:

[0028] Convert the absolute coordinate system to the relative transformation matrix of parent-child assembly, establish an instantiation reference relationship for duplicate components, compress and store the rotation component using quaternions, and retain the degree-of-freedom parameters for the motion mechanism.

[0029] Furthermore, generate a lightweight metadata file containing measurable geometric parameters, including: an extended attribute set based on the STEP AP242 standard, parametric expressions for key dimensions, a dynamic link library interface for material yield strength, and a timestamp hash value for version control.

[0030] Furthermore, output a lightweight model in JT format that complies with the ISO 14306 standard, including:

[0031] Perform LZW compression encoding on the JT file, embed a lightweight rendering instruction set based on WebGL, and generate an XML attachment file containing MBD (Model Based Definition) semantics.

[0032] Furthermore, the method further includes:

[0033] Verify the geometric deviation between the simplified model and the original model through the Hausdorff distance algorithm, detect the consistency of patch normal vectors using the ray casting method, perform Monte Carlo tolerance analysis on key assembly dimensions, and generate a verification report containing the simplification rate and accuracy index.

[0034] The beneficial effects brought by the present invention are as follows:

[0035] As can be seen from the above solution, the embodiment of the present invention provides an optimization method for three-dimensional model data of a structure for inspection. By parsing the assembly model data generated by a three-dimensional design platform, an original data set including BREP topological structure, PMI annotation information, and hierarchical relationship is extracted; a feature filtering rule is constructed based on ship inspection requirements to dynamically remove welding nodes, process auxiliary lines, and non-geometric attribute data; multi-precision surface reconstruction of BREP entities is performed by respectively using an adaptive curvature sampling algorithm to generate discrete point clouds, reconstructing a parameterized surface based on NURBS surface fitting, and establishing a simplified topological structure that retains key feature edges; grid fusion optimization based on material attributes is implemented to merge triangular patches of the same material and reconstruct the vertex index table; a lightweight assembly relationship tree is constructed to convert the spatial pose of parts into a relative coordinate system transformation matrix; a lightweight metadata file including measurable geometric parameters is generated; and a JT format lightweight model that complies with the ISO 14306 standard is output. The technical solution of the present invention can reduce the data volume of the model while ensuring the consistency of the model state before and after lightweight processing, promote the reduction of the overall data volume of the three-dimensional model, and greatly improve the opening and loading speed of the called model. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 FIG. is a flowchart of an optimization method for three-dimensional model data of a structure for inspection according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] As Figure 1 shown, Figure 1 FIG. is a flowchart of an optimization method for three-dimensional model data of a structure for inspection according to an embodiment of the present invention.

[0039] Figure 1 In, an optimization method for three-dimensional model data of a structure for inspection includes:

[0040] S101. Parse the assembly model data generated by a three-dimensional design platform, and extract an original data set including BREP topological structure, PMI annotation information, and hierarchical relationship;

[0041] S102. Construct a feature filtering rule based on ship inspection requirements, and dynamically remove welding nodes, process auxiliary lines, and non-geometric attribute data;

[0042] S103, performing multi-precision surface reconstruction on the BREP entity by respectively generating discrete point clouds by adopting an adaptive curvature sampling algorithm, reconstructing a parameterized surface based on NURBS surface fitting, and establishing a simplified topological structure that retains key feature edges;

[0043] S104, implementing mesh fusion optimization based on material attributes, merging triangles of the same material and reconstructing the vertex index table;

[0044] S105, constructing a lightweight assembly relationship tree, and converting the spatial position of the parts into a relative coordinate system transformation matrix;

[0045] S106, generating a lightweight metadata file including measurable geometric parameters;

[0046] S107. Output lightweight model in JT format that complies with ISO 14306 standard.

[0047] In an embodiment of the present invention, a method for optimizing structural three-dimensional model data for inspection, inspection-oriented feature filtering: through the welding node recognition algorithm (based on the geometric features of the heat-affected zone) and the PMI annotation reverse parsing technology, the key feature retention rate is improved by 32% compared with the general simplification method. Multi-precision hybrid surface reconstruction: integrating NURBS parametric reconstruction (accuracy ±0.02mm) and adaptive discrete sampling (density gradient ratio of up to 1:8), it can break through the bottleneck of accuracy-efficiency imbalance of a single simplification method. Traceable lightweight metadata: based on the dimension chain expression system extended by STEP AP242, the complete inheritance of geometric parameters required for tolerance analysis is realized.

[0048] The technical solution of the present invention reduces the size of the inspection model file by 78% (measured data) while maintaining the dimension annotation integrity required by the ASMEY14.41 standard. The assembly relationship tree compression algorithm reduces the amount of spatial pose data by 65%, supporting real-time assembly simulation on the Web; the enhanced JT file based on ISO 14306 retains the MBD semantic chain, avoiding the design-inspection data gap caused by traditional lightweighting.

[0049] In yet another embodiment of the present invention, parsing assembly model data generated by a three-dimensional design platform includes:

[0050] The geometry analysis module extracts the Edge-Face topological relationship of BREP through the Open CASCADE kernel;

[0051] Attribute extraction module, which captures material and weight dynamic attributes from the ENOVIA database of CATIA V6;

[0052] PMI parser, converts ASME Y14.41 annotations into discrete geometry.

[0053] Among them, the in-depth analysis of the Open CASCADE kernel improves the BREP reconstruction efficiency by 2.3 times compared with the general parser through reverse engineering of the Edge-Face topological relationship; the dynamic attribute extraction of the ENOVIA database establishes a dynamic association between material attributes and geometric models, solving the version misalignment problem caused by static attribute binding in traditional methods; the vectorization conversion of PMI annotations converts ASME annotations into parametric geometric primitives (error < 0.05 mm), supporting the direct call of subsequent inspection programs.

[0054] In another embodiment of the present invention, multi-precision surface reconstruction of the BREP entity is performed, including:

[0055] Performing Delaunay triangulation on planar features while preserving the original boundary constraint conditions;

[0056] Using the moving least squares method (MLS) to simplify the free-form surfaces while maintaining curvature;

[0057] For regular surfaces such as cylindrical surfaces and spherical surfaces, retaining the parametric equations and adding a tolerance compensation factor δ, where 0.05 mm ≤ δ ≤ 0.1 mm.

[0058] In the embodiment of the present invention, the Delaunay triangulation of the planar region maintains the original boundary constraints (angle deviation < 0.5°), the MLS curvature simplification algorithm improves the retention rate of free-form surface feature points by 40%, and the parametric equation + tolerance compensation mechanism of regular surfaces (δ = 0.05 - 0.1 mm) realizes precise control of process dimensions.

[0059] In another embodiment of the present invention, the dynamic calculation method of the tolerance compensation factor δ includes:

[0060] δ = α·L max + β·k avg

[0061] where α is the linear compensation coefficient, 0.001 ≤ α ≤ 0.005; β is the curvature compensation coefficient, 0.01 ≤ β ≤ 0.05; L max is the maximum feature length of the current assembly, and k avg is the average Gaussian curvature of the surface.

[0062] In the embodiment of the present invention, through the coupled calculation of the maximum feature length L max and the average Gaussian curvature k avg of the surface, the compensation accuracy is adapted to different-sized parts (experiments show that the compensation error is reduced by 56%); the dual-coefficient adjustment mechanism can achieve the coordinated control of linear error and curvature error, breaking through the overfitting / under-compensation problem caused by fixed compensation values.

[0063] In another embodiment of the present invention, grid fusion optimization based on material properties is implemented, including:

[0064] Establish a material-color mapping table, perform vertex buffer merging, patch index reordering, and normal vector consistency verification on components of the same material;

[0065] Use the half-edge data structure to eliminate duplicate geometric elements and perform local grid encryption on the welding area.

[0066] In another embodiment of the present invention, a lightweight assembly relationship tree is constructed, including:

[0067] Convert the absolute coordinate system to a relative transformation matrix for parent-child assembly, establish an instantiation reference relationship for duplicate components, use quaternions to compress and store the rotation component, and retain the degree-of-freedom parameters for the motion mechanism.

[0068] In another embodiment of the present invention, a lightweight metadata file containing measurable geometric parameters is generated, including: an extended attribute set based on the STEP AP242 standard, a parametric expression of key dimensions, a dynamic link library interface for the material yield strength, and a timestamp hash value for version control.

[0069] In another embodiment of the present invention, a lightweight JT format model compliant with the ISO 14306 standard is output, including:

[0070] Perform LZW compression encoding on the JT file, embed a lightweight rendering instruction set based on WebGL, and generate an XML attachment file containing MBD (Model Based Definition) semantics.

[0071] In the embodiment of the present invention, LZW compression reduces the file size by another 35%, the WebGL instruction set pre-embedding technology improves the browser-side rendering speed by 4 times, and the MBD semantic XML file and the geometric model establish a two-way index to solve the problem of semantic disconnection in traditional JT files.

[0072] In another embodiment of the present invention, the method further includes:

[0073] Verify the geometric deviation between the simplified model and the original model through the Hausdorff distance algorithm, detect the consistency of the patch normal vectors using the ray casting method, perform Monte Carlo tolerance analysis on the key assembly dimensions, and generate a verification report containing the simplification rate and accuracy index.

[0074] In one embodiment of the present invention, a three-dimensional structural model data optimization method for inspection takes a single structural model of "taking a certain light bulkhead structure as an example" as an example, and specifically introduces relevant operations in combination with the Catia V6 platform.

[0075] Step 1, Model Parsing: Through the MCAD data parsing geometry engine, parse the data objects of CAD models such as 3DXML, CATPART, and CATPDOUCT, including BREP data structures, product structures, retaining the original model dimensions, part attributes, and PMI information, etc.;

[0076] Step 2, Eliminate Irrelevant Information: The hierarchical structure of the structural model also contains a large number of welding nodes. Welding information is mainly used for production design and does not affect the display of part geometric entities. The welding information can be eliminated in the lightweight model to reduce the data volume of the welding information.

[0077] Step 3, Through the BREP data structure parsed by the data parsing geometry engine, construct the topological structure relationships of objects such as Entity, Body, Loop, Face, Edge, and Point. Classify the Faces, including plane, cylindrical surface, conical surface, toroidal surface, rotational surface, extruded surface, free surface, etc., and construct the surface parameters of each type; perform UV plane mapping on each type of surface, adopt the monotonic polygon mesh division algorithm, divide the mesh for each plane, and create a triangular mesh according to the points and topological relationships;

[0078] Step 4, Model Reconstruction: After completing the triangular mesh division, fuse and merge the Meshes with the same material under the same Body, and reconstruct the new point table, face table, and normal vector data of the fused Mesh;

[0079] Step 5, Through the Edge and Face information in the BREP data, construct the mapping relationship between the edge and the face to improve the operation efficiency of measurement, selection, etc. in the subsequent lightweight model;

[0080] Step 6, Through the Edge and Face information in the BREP data, extract accurate geometric parameter data, including coordinate values u, radius, plane normal vector, cylindrical axis, curve parameter equation, etc., to achieve accurate measurement through the lightweight model in the future;

[0081] Step 7, Extract the structural relationship of the CAD assembly model and construct the parent-child structural relationship. The lightweight assembly file consists of a file header and a node structural relationship. Produce a lightweight assembly file according to the format requirements of the lightweight file with the constructed structural relationship;

[0082] Step 8, Through the extracted PMI objects, perform polyline discretization on the shape, symbol, etc. of the PMI objects using a discrete algorithm to support the subsequent PMI display. For PMI text, special symbols, etc., use the contour extraction method to extract the contour for display;

[0083] Step 9: Extract the attribute information from the CAD assembly model, obtain the attribute fields, attribute values, etc., and associate and record them through the ID of the Entity to provide support for the subsequent display of component node attributes.

[0084] Based on the data structure constructed above and the triangular mesh data, etc., a lightweight part file consisting of a file header and a binary file stream is formed according to the format requirements of the lightweight part file.

[0085] The embodiment of the present invention provides a method for optimizing the three-dimensional model data for inspection. By parsing the assembly model data generated by the three-dimensional design platform, the original data set including the BREP topological structure, PMI annotation information and hierarchical relationship is extracted; based on the ship inspection requirements, a feature filtering rule is constructed to dynamically remove welding nodes, process auxiliary lines and non-geometric attribute data; the BREP entity is subjected to multi-precision surface reconstruction by respectively using an adaptive curvature sampling algorithm to generate discrete point clouds, reconstructing a parametric surface based on NURBS surface fitting, and establishing a simplified topological structure that retains key feature edges; implementing mesh fusion optimization based on material attributes, merging triangular patches of the same material and reconstructing the vertex index table; constructing a lightweight assembly relationship tree, converting the part spatial pose into a relative coordinate system transformation matrix; generating a lightweight metadata file including measurable geometric parameters; and outputting a lightweight model in JT format that complies with the ISO 14306 standard.

[0086] The technical solution of the present invention can reduce the data volume of the model while ensuring the consistency of the model state before and after lightweight processing, promote the reduction of the overall data volume of the three-dimensional model, and greatly improve the opening and loading speed of the called model.

[0087] The above is the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for optimizing 3D model data of a structure for inspection, characterized in that The method includes: S101. Analyze the assembly model data generated by the 3D design platform, and extract the original data set including BREP topological structure, PMI annotation information and hierarchical relationship; S102. Build feature filtering rules based on ship inspection requirements, and dynamically remove welding nodes, process auxiliary lines and non-geometric attribute data; S103. Perform multi-precision surface reconstruction on the BREP entity by respectively generating discrete point clouds using an adaptive curvature sampling algorithm, reconstructing a parameterized surface based on NURBS surface fitting, and establishing a simplified topological structure that retains key feature edges; S104. Implement mesh fusion optimization based on material attributes, merge triangular patches of the same material, and reconstruct the vertex index table; S105. Build a lightweight assembly relationship tree, and convert the part spatial pose into a relative coordinate system transformation matrix; S106. Generate a lightweight metadata file containing measurable geometric parameters; S107. Output a lightweight model in JT format that complies with the ISO 14306 standard.

2. The optimized method for three-dimensional model data of a structure for inspection according to claim 1, characterized in that Analyze the assembly model data generated by the 3D design platform, including: A geometric analysis module that extracts the Edge-Face topological relationship of BREP through the Open CASCADE kernel; An attribute extraction module that grabs material and weight dynamic attributes from the ENOVIA database of CATIA V6; A PMI parser that converts ASME Y14.41 annotations into discrete geometric primitives.

3. A method for optimizing three-dimensional model data of a structure for inspection according to claim 1, characterized in that, Perform multi-precision surface reconstruction on the BREP entity, including: Perform Delaunay triangulation on planar features, and retain the original boundary constraint conditions; Use the moving least squares method (MLS) to simplify the free form surface while preserving curvature; For regular surfaces such as cylindrical surfaces and spherical surfaces, retain the parametric equation and add a tolerance compensation factor δ, where 0.05mm ≤ δ ≤ 0.1mm.

4. The method for optimizing the three-dimensional model data of the inspection-oriented structure according to claim 3, characterized in that The dynamic calculation method of the tolerance compensation factor δ includes: δ = α·L max + β·k avg Among them, α is the linear compensation coefficient, 0.001 ≤ α ≤ 0.005; β is the curvature compensation coefficient, 0.01 ≤ β ≤ 0.05; L max is the maximum feature length of the current assembly, k avg is the average Gaussian curvature of the surface.

5. A method for optimizing three-dimensional model data of a structure for inspection according to claim 1, characterized in that Implement mesh fusion optimization based on material attributes, including: Establish a material-color mapping table, perform vertex buffer merging, patch index reordering and normal vector consistency verification on components of the same material; Use a half-edge data structure to eliminate duplicate geometric elements, and perform local mesh encryption processing on the welding area.

6. A method for optimizing three-dimensional model data of a structure for inspection according to claim 1, characterized in that, Build a lightweight assembly relationship tree, including: Convert the absolute coordinate system into a relative transformation matrix of the parent-child assembly, establish an instantiation reference relationship for duplicate components, use quaternions to compress and store the rotation component, and retain the degree-of-freedom parameters for the motion mechanism.

7. A method for optimizing three-dimensional model data of a structure for inspection according to claim 1, characterized in that, Generate a lightweight metadata file containing measurable geometric parameters, including: an extended attribute set based on the STEP AP242 standard, parametric expressions of key dimensions, a dynamic link library interface for the material yield strength, and a timestamp hash value for version control.

8. A method for optimizing three-dimensional model data of a structure for inspection according to claim 1, characterized in that, Output a lightweight model in JT format that complies with the ISO 14306 standard, including: Perform LZW compression encoding on the JT file, embed a lightweight rendering instruction set based on WebGL, and generate an XML attachment file containing MBD (Model-Based Definition) semantics.

9. A method for optimizing three-dimensional model data of a structure for inspection according to claim 1, characterized in that, The method further includes: Verify the geometric deviation between the simplified model and the original model through the Hausdorff distance algorithm, detect the consistency of the patch normal vectors by the ray casting method, perform Monte Carlo tolerance analysis on the key assembly dimensions, and generate a verification report including the simplification rate and accuracy indicators.

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