Three-dimensional Model Profile Feature Extraction Method, Device and Equipment for Numerical Simulation

Abstract

The embodiments of the present disclosure provide a method for extracting contour features of a three-dimensional model for numerical simulation, which specifically includes the following steps: defining the major axis of the three-dimensional model based on the boundary point set of the three-dimensional model; establishing a spatial rectangular coordinate system based on the major axis to construct a segmentation space of the three-dimensional model; determining the cross-sectional contour from the segmentation space, extracting the characteristic endpoints of the cross-sectional contour boundary based on the finite point set within the cross-sectional contour; performing interpolation calculation based on the characteristic endpoints to construct a spatial interpolation curve, and generating a complete parameterized contour curve of the three-dimensional model. In this way, by segmenting the three-dimensional model, the contour information of the three-dimensional model is identified and extracted based on the graphic axis transformation and the second-order Lagrangian interpolation curve. The extracted contour information not only retains the shape and topological relationship of the model but also contains the surface information of the model, which can effectively support the application scenarios of numerical simulation.

Description

Technical Field

[0001] The present disclosure relates to the technical field of three-dimensional models, and in particular to a method, device and equipment for extracting contour features of a three-dimensional model for numerical simulation. Background Art

[0002] Digital twin cities are a high-level goal in digital city development and a major innovation in intelligent urban management and services. The construction of digital twin cities requires modeling a wide variety of physical objects, employing diverse modeling methods. Three-dimensional models are a crucial component of digital twin cities. With their development, a vast database of 3D models has emerged, enabling real-time connectivity and data storage between the real and virtual worlds. 3D models inherently possess large data volumes and heterogeneous data formats, which complicates the extraction of effective information. The large volume of point data makes it difficult to uniformly sample and deform the model. Contour information from 3D models can concisely and effectively represent the geometric features of the model and provide valuable information for applications such as model simplification, mesh reconstruction, and numerical model construction. In response to diverse application needs and technological research, numerous contour extraction methods have emerged, such as approximating 3D curves with spline curves, point cloud interpolation, and obtaining complete contours by filtering characteristic curves. However, these current contour extraction methods are complex, requiring analysis and filtering of the geometric features of the mesh model. These methods are computationally intensive and sometimes fail to retain complete contour information, making them ineffective for numerical simulation applications. Summary of the Invention

[0003] The present disclosure provides a method, device and equipment for extracting contour features of three-dimensional models for numerical simulation. By segmenting the three-dimensional model, the contour information of the three-dimensional model is identified and extracted based on the graphic axis transformation and the second-order Lagrange interpolation curve. The extracted contour information retains the shape and topological relationship of the model and includes the surface information of the model, which can effectively support the application scenarios of numerical simulation.

[0004] According to a first aspect of the present disclosure, a method for extracting contour features of a three-dimensional model for numerical simulation is provided, which specifically includes the following steps: defining the major axis of the three-dimensional model based on the boundary point set of the three-dimensional model; establishing a spatial rectangular coordinate system based on the major axis to construct a subdivision space of the three-dimensional model; determining the cross-sectional contour from the subdivision space, and extracting the characteristic endpoints of the cross-sectional contour boundary based on the finite point set within the cross-sectional contour; performing interpolation calculation based on the characteristic endpoints to construct a spatial interpolation curve, and generating a complete parameterized contour curve of the three-dimensional model.

[0005] According to a second aspect of the present disclosure, a three-dimensional model contour feature extraction device is provided, which includes: a major axis definition module, used to define the major axis of the three-dimensional model based on the boundary point set of the three-dimensional model; a model segmentation module, used to establish a spatial rectangular coordinate system based on the major axis and construct a segmentation space of the three-dimensional model; an endpoint extraction module, used to determine the cross-sectional contour based on the segmentation space, and extract the characteristic endpoints of the cross-sectional contour boundary based on the finite point set within the cross-sectional contour; and a contour generation module, used to perform interpolation calculation based on the characteristic endpoints to construct a spatial interpolation curve and generate a complete parameterized contour curve of the three-dimensional model.

[0006] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the program.

[0007] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect and / or the second aspect of the present disclosure is implemented.

[0008] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0010] Figure 1 A flowchart of a method for extracting contour features of a three-dimensional model according to an embodiment of the present disclosure is shown;

[0011] Figure 2 A specific flow chart of the contour feature extraction method according to an embodiment of the present disclosure is shown;

[0012] Figure 3 A solid diagram illustrating a three-dimensional model of an embodiment of the present disclosure;

[0013] Figure 4 A long axis representation of a three-dimensional model of an embodiment of the present disclosure is shown;

[0014] Figure 5 A schematic diagram of a three-dimensional model of an embodiment of the present disclosure in a rectangular coordinate system is shown;

[0015] Figure 6A cross-sectional profile view of a three-dimensional model of an embodiment of the present disclosure is shown;

[0016] Figure 7 A medial axis mesh diagram showing a cross-sectional profile of a three-dimensional model according to an embodiment of the present disclosure;

[0017] Figure 8 A feature endpoint diagram showing a cross-sectional profile of a three-dimensional model of an embodiment of the present disclosure;

[0018] Figure 9 shows a spatial interpolation curve diagram of each cross section of a three-dimensional model according to an embodiment of the present disclosure;

[0019] Figure 10 An interpolation graph between different cross sections of the three-dimensional model of the present disclosure is shown;

[0020] Figure 11 A diagram showing a medial axis expression in a two-dimensional geometric body according to an embodiment of the present disclosure is shown;

[0021] Figure 12 shows a Voronoi diagram of an embodiment of the present disclosure;

[0022] Figure 13 A Delaunay triangulation diagram of an embodiment of the present disclosure is shown;

[0023] Figure 14 shows the Lagrange interpolation polynomial of an embodiment of the present disclosure;

[0024] Figure 15 A block diagram of a 3D model contour feature extraction device according to the present disclosure is shown;

[0025] Figure 16 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0026] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0027] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0028] In the present disclosure, a method, device and equipment for extracting contour features of a three-dimensional model for numerical simulation are proposed, and a high-precision three-dimensional model contour feature calculation method is proposed. By using the medial axis calculation of the graphics and the three-dimensional space Lagrangian interpolation to identify and process multiple contour sections, the data of the original model can be greatly compressed while retaining good structural information, and the geometric features of the irregular model can be retained without human intervention; the extracted contour data is not affected by the translation, rotation, scaling, etc. of the model, and has good geometric invariance, as well as topological invariance that is not affected by various format changes of the model; when processing the three-dimensional model, the extraction accuracy of the model contour can be well controlled, and the efficiency of the model contour extraction can be greatly improved by segmentation. At the same time, combined with the spatial high-order curve interpolation, the fitting accuracy of the contour boundary is improved under the same segmentation distance.

[0029] Reference below Figure 1 The present invention provides a method for extracting contour features of a three-dimensional model for numerical simulation, which specifically includes the following steps:

[0030] S101: defining the long axis of the three-dimensional model according to the boundary point set of the three-dimensional model;

[0031] S102: Establishing a spatial rectangular coordinate system based on the long axis to construct a subdivision space of the three-dimensional model;

[0032] S103: determining a cross-sectional profile from the segmented space, and extracting characteristic endpoints of the cross-sectional profile boundary based on a finite set of points within the cross-sectional profile;

[0033] S104: Performing interpolation calculation based on the feature endpoints to construct a spatial interpolation curve, thereby generating a complete parameterized contour curve of the three-dimensional model.

[0034] In the above embodiment, defining the long axis of the three-dimensional model includes: obtaining two points with the farthest Euclidean distance based on the Euclidean distance between each point in the boundary point set of the three-dimensional model, and determining the direction connecting the two points as the long axis of the three-dimensional model.

[0035] In the above embodiment, a spatial rectangular coordinate system is established based on the long axis to construct a subdivision space of the three-dimensional model, including: establishing a spatial rectangular coordinate system based on the long axis, selecting any plane including the long axis as the subdivision reference plane of the three-dimensional model, calculating the maximum distance of the boundary point set of the three-dimensional model to the reference plane along the direction of the normal of the reference plane, and subdividing the maximum distance according to different subdivision accuracies to obtain the subdivision space of the three-dimensional model.

[0036] In the above embodiment, the cross-sectional contour is determined by the subdivision space, and the characteristic endpoints of the cross-sectional contour boundary are extracted according to the finite point set within the cross-sectional contour, including: obtaining the cross-sectional contour according to the subdivision space of the three-dimensional model, performing medial axis grid calculation on the finite point set within the cross-sectional contour, and extracting the sampling points closest to the cross-sectional contour boundary in the medial axis grid; determining multiple groups of boundary points by selecting the point group with the smallest Euclidean distance among the sampling points; and extracting the boundary points corresponding to the minimum values as the characteristic endpoints of the cross-sectional contour boundary according to the minimum values of the distances of different boundary points from the finite point set within the cross-sectional contour.

[0037] In this embodiment, the medial axis transformation, as a mature geometric processing technology, can quickly calculate and express the medial axis grid of two-dimensional graphics. With the help of the medial axis transformation of geometric graphics, it is convenient to express the original model while retaining the structural information of the original model. At the same time, by calculating the information of the endpoints of the medial axis grid, the model data can be greatly compressed to obtain the corresponding contour boundary feature points.

[0038] In the above embodiment, interpolation calculation is performed based on the feature endpoints to construct a spatial interpolation curve to generate a complete parametric contour curve of the three-dimensional model, including: projection simplification based on the feature endpoints, determining the interpolation nodes and constructing the interpolation function based on the interpolation nodes, and constructing the spatial interpolation curve through the interpolation function to form a complete parametric contour curve of the three-dimensional model, thereby completing the extraction and parametric expression of the geometric contour information.

[0039] In the above embodiment, projection simplification is performed according to the characteristic endpoints, interpolation nodes are determined, and interpolation functions are constructed based on the interpolation nodes, including the following steps: selecting characteristic endpoints, converting three-dimensional data into two-dimensional data by projection, obtaining projection coordinates corresponding to the yz axis and xz axis, selecting interpolation nodes respectively using a second-order interpolation function, and constructing Lagrange interpolation functions of the yz axis and xz axis projection surfaces respectively according to the interpolation nodes.

[0040] In the above embodiment, the complete parameterized contour curve includes: interpolation functions for the characteristic endpoints of each layer of cross-sectional contours and interpolation functions for the characteristic endpoints of adjacent cross-sectional contours between each cross-section. The interpolation functions for the characteristic endpoints of adjacent cross-sectional contours between each cross-section are constructed by: selecting, for the characteristic endpoints between different cross-sectional contours, the combinations with the smallest Euclidean distance between the characteristic endpoints in the adjacent cross-sectional contours, grouping them to obtain interpolation nodes for each group; and constructing an interpolation curve for each group based on the interpolation nodes, connecting the characteristic endpoints of all cross-sectional contours to generate a complete parameterized contour curve for the three-dimensional model.

[0041] The present invention identifies and extracts the contour of a three-dimensional model based on graphic medial axis transformation and second-order Lagrange interpolation method, with the aim of improving the accuracy and efficiency of contour recognition and generating parameterized three-dimensional contour data.

[0042] The following combination Figure 2 As shown, a specific embodiment is described: by segmenting the three-dimensional model, converting the three-dimensional geometry into two-dimensional geometry for contour information extraction, and then using three-dimensional space Lagrangian interpolation to generate complete three-dimensional model contour feature parameters, the specific steps are as follows:

[0043] S201: Determine the three-dimensional model: determine the model and boundary point set, such as Figure 3 shown.

[0044] S202: Define the long axis of the model: According to the Euclidean distance between each point in the boundary point set of the three-dimensional model, obtain the two points with the longest Euclidean distance, and determine the direction connecting the two points as the long axis of the three-dimensional model; Figure 4 As shown, the calculation method of the major axis is as follows:

[0045] Select any two points A from the boundary point set of the 3D model i (x1, y1, z1), B i (x2, y2, z2), calculate the Euclidean distance d(A between two points i , B i ):

[0046]

[0047] Traverse the distance between all the boundary points in the 3D model space and calculate d(A i , B i ) The largest group or groups, connect the A of one group with a straight line i ,B i The connection between them is the long axis of the three-dimensional model.

[0048] S203: Constructing the subdivision space: Taking the direction of the long axis of the three-dimensional model as the z axis, establish a spatial rectangular coordinate system (x, y, z). Figure 5 As shown in the figure, the specific construction process is: select any plane including the long axis as the subdivision reference plane A1B1 of the three-dimensional model, select the direction perpendicular to the reference plane A1B1 as the X-axis direction, the direction parallel to the reference plane A1B1 as the Y-direction, and the center of the long axis as the origin to establish a spatial rectangular coordinate system.

[0049] Along the direction of the normal line of the reference plane A1B1, the distance from the boundary point set of the three-dimensional model to the reference plane A1B1 is calculated, as follows: Figure 5 As shown, the calculation method is as follows: Assume that the expression of the reference plane A1B1 is: Ax+By+Cz+D=0, and the distance d from the boundary point set to the reference plane A1B1 is:

[0050]

[0051] According to the above formula, the maximum distance d1 and d2 of each surface point on both sides of the reference plane A1B1 to the reference plane A1B1 are calculated respectively, and the contour of the three-dimensional model is divided parallel to the reference plane A1B1 at a certain plane distance interval Δh, where Where n is the number of slices into which the 3D model is segmented. You can set different segmentation accuracies based on actual needs to segment the maximum distance and obtain the segmentation space of the 3D model. By setting the segmentation of the 3D model, you can control the segmentation accuracy of any model and effectively improve the accuracy of contour extraction.

[0052] S204: Calculation of medial axis of profile: Figure 6 As shown in the figure, the cross-section profile is obtained based on the subdivision space of the three-dimensional model. The medial axis mesh calculation is performed on the finite point set within the cross-section profile. The characteristic endpoints in the medial axis mesh closest to the cross-section profile boundary are extracted as sampling points and used as the geometric features of the cross-section profile. The medial axis mesh calculation is performed according to the definition of the Voronoi diagram. The calculation method is as follows:

[0053] Assume that the finite set of points within the cross-section is M, M={m0,m1,…,m n}, set the sampling density to Where m≦n; any point of the boundary point set of the profile boundary is e i , any point in the finite point set M within the profile is g, MAT is the point set within the profile, which can be expressed as MAT(Ω)={g∈M,d(g,e i )≤d(g,e j ),i≠j,i,j∈M}, where d(g,e i ) is the point g to element e i Euclidean distance; specifically, g(x1, y1, z1) and e i (x2, y2, z2), point g to element e i The calculation formula of the Euclidean distance is:

[0054]

[0055] The line formed by the point set in MAT is the medial axis grid of the 3D model section, such as Figure 7 As shown in the figure. The definition of medial axis expression shows that the sampling points in the medial axis mesh closest to the 3D model surface retain the boundary contour information of the 3D model. As a mature geometry processing technology, medial axis transformation can quickly calculate and express the medial axis mesh of 2D graphics. With the help of medial axis transformation of geometric graphics, it is convenient to express the original model while retaining its structural information. At the same time, by calculating the information of the medial axis mesh endpoints, the model data can be significantly compressed to obtain the corresponding contour boundary feature points.

[0056] S205: Identify the endpoints of the profile: select the point group d(g,e) with the minimum Euclidean distance among the sampling points. i ), determine multiple groups of boundary points e i ; From the point set of MAT, according to different boundary points e i The minimum value d(g,e) of any point g in the finite set of points within the profile i ), extract the minimum value d(g,e i ) corresponds to the boundary point e i The set P of m , then e m The set P of is the characteristic endpoint of the profile boundary, such as Figure 8 As shown; among them, e m The expression of the set P is as follows:

[0057] W(P)={d(g,e m )≤d(g,e i ),i≠m,i,g∈MAT}

[0058] S206: Constructing a spatial interpolation curve to generate a complete parametric contour: Projection simplification is performed based on the characteristic endpoints of the contour section, interpolation nodes are determined, and interpolation functions are constructed based on the interpolation nodes. The spatial interpolation curve is constructed by combining these interpolation functions to form a complete parametric contour curve for the 3D model, completing the extraction and parametric expression of the geometric contour information. The complete parametric contour curve includes the interpolation functions for the characteristic endpoints of each layer of the cross-sectional contour and the interpolation functions for the characteristic endpoints of the cross-sectional contours between adjacent cross-sectional sections.

[0059] Since the interpolation of feature endpoints is a three-dimensional space curve, direct interpolation is difficult to handle. It can be simplified with the help of projection, and the three-dimensional data can be converted into two-dimensional data to solve the problem. That is, the space curve F(x,y,z) is projected onto the y,z plane and the x,z plane. After projection, the space curve is converted into a space surface. Two projections generate two surfaces, and the two surfaces can determine one and only one space curve. Finally, the equations of the two space surfaces are combined to obtain the desired space interpolation curve, such as Figure 9 The specific steps are as follows:

[0060] Step 1: The characteristic endpoints of the profile section of the known 3D model are e1(x1,y1,z1), e2(x2,y2,z2), e3(x3,y3,z3)...e n (x n ,y n ,z n ), in the projection space, the corresponding yz-axis projection coordinate is p yz (y n ,z n), the corresponding xz axis projection coordinate is p xz (x n ,z n According to the definition of the Lagrange interpolation formula, a second-order interpolation function is used to select three adjacent interpolation nodes e1(y0,z0), e2(y1,z1), and e3(y2,z2) from the characteristic endpoints, and construct the interpolation function between the interpolation nodes for curve connection.

[0061] The interpolation nodes are selected as follows: based on the tangent value of the angle between the line connecting the maximum y coordinate of the feature endpoint and the remaining points and the horizontal line, the interpolation nodes e1(y0,z0), e2(y1,z1), and e3(y2,z2) are selected in order of the tangent value. The two end points of each data set are used as the starting or ending endpoints of the next data set, and the interpolation data is calculated until all data are traversed. If the interpolation node data is missing after traversal, the average of the data of the previous and next endpoints is taken as the interpolation data.

[0062] Step 2: Construct the Lagrange interpolation function based on the interpolation nodes composed of characteristic endpoints. The steps are as follows:

[0063] According to the definition of Lagrange interpolation, first construct the basis functions l0, l1, l2 as follows:

[0064]

[0065]

[0066]

[0067] The interpolation function composed of basis functions is: zz0l0(y)+z1l1(y)+z2l2(y)

[0068] Similarly, in the xz projection plane, the interpolation function is: xz0l0(x)+z1l1(x)+z2l2(x)

[0069] By combining the two curve equations above, we can obtain the spatial interpolation curve:

[0070]

[0071] Similarly, for the characteristic endpoints between different profiles, between adjacent profiles, select the combination with the smallest Euclidean distance between the characteristic endpoints in different profiles, group them, and obtain the interpolation nodes of each group; the two end characteristic endpoints in each group of data serve as the starting endpoints or ending endpoints of the next group of data until all data are traversed. Every three layers of profile data constitute the interpolation nodes between profiles. If the interpolation node data is missing after traversal, the average value of the data of the front and back endpoints is taken as the interpolation data. The second-order Lagrangian interpolation function is constructed again through the interpolation nodes, and the construction method is the same as above. Through the interpolation curves between groups, such as Figure 10 As shown in FIG, by connecting all feature endpoints on the cross-sectional contour of the 3D model through a second-order interpolation function, the extraction and parameterized expression of the geometric contour information of the entire 3D model can be completed.

[0072] In the above embodiment, as the medial axis expression is a mature geometric processing technology, the medial axis transformation can not only conveniently express the original model, but also greatly compress the model data and retain the structural information of the original model. Currently, it has important and mature applications in many engineering fields, such as finite element analysis, shape analysis, solid modeling, etc. Specifically, the two-dimensional medial axis is composed of a series of circle centers and radii. These circles must meet the requirements of being maximally inscribed in the model boundary and having at least two intersections with the boundary. For example, Figure 11 The medial axis expression in the two-dimensional geometry shown in the figure is combined with the Voronoi diagram to determine and extract the endpoints of the three-dimensional model contour.

[0073] In the above embodiment, the Voronoi diagram, also called Thiessen polygon, is composed of a set of continuous polygons consisting of perpendicular bisectors connecting two adjacent points, such as Figure 12 As shown. N distinct points on a plane divide the plane according to the nearest neighbor principle; each point is associated with its nearest neighbor region. A Delaunay triangle is a triangle formed by connecting related points that share an edge with adjacent Voronoi polygons, as shown in Figure 13 The center of the circumcircle of a Delaunay triangle is a vertex of the Voronoi polygon associated with the triangle.

[0074] For the point set {P0,P1,…,P n} in the seed point P k , its Voronoi region R k Defined as:

[0075] R k ={x∈X|d(x,P k )<d(x,P j ),j={0,1,2,…,n},j≠k}

[0076] In the above embodiment, in numerical analysis, Lagrange interpolation is a polynomial interpolation method. In the field of engineering, many practical problems use functions to represent certain internal connections or laws. For example, in a rectangular coordinate system, there are multiple isolated points. With the help of Lagrange interpolation, a polynomial can be found whose function value contains these isolated points, such as Figure 14 As shown. Such a polynomial is called a Lagrange interpolation polynomial. The specific composition of the Lagrange interpolation polynomial consists of interpolation nodes and basis functions, and the specific form is as follows:

[0077] First-order Lagrange interpolation: There are 2 interpolation nodes, and the distance between these two points is replaced by a straight line segment;

[0078] Second-order Lagrange interpolation: There are 3 interpolation nodes in total, and the three points are replaced by quadratic functions;

[0079] n-order Lagrange interpolation: There are n+1 interpolation nodes in total, and these n+1 points are replaced by n-order functions.

[0080] The general formula of the n-order interpolation formula is as follows:

[0081] Basis functions:

[0082]

[0083] Interpolation polynomial:

[0084]

[0085] By changing the parameters in the interpolation polynomial, the present invention can change the size and shape of the three-dimensional model outline, such as zooming in, zooming out, moving, deforming, etc. It has the characteristics of being easy to express and calculate, and can be conveniently used to construct numerical models for numerical simulation.

[0086] The present invention identifies and extracts the contours of three-dimensional models based on the medial axis transformation of graphics and the second-order Lagrange interpolation method, achieving the following technical effects:

[0087] 1. Rapidly extract model contour features: As a mature geometric processing technology, medial axis transformation can quickly calculate and express the medial axis grid of two-dimensional graphics. With the help of the medial axis transformation of geometric figures, it is convenient to express the original model while retaining the structural information of the original model. At the same time, by calculating the information of the medial axis grid endpoints, the model data can be greatly compressed to obtain the corresponding contour boundary feature points.

[0088] 2. Effectively improve the accuracy of contour extraction: By setting the segmentation of the three-dimensional model, the segmentation accuracy of any model can be controlled. At the same time, by constructing the interpolation function between the feature points with the help of the second-order curve in the three-dimensional space, the accuracy of the model contour can be further improved, and parameterized model contour data can be generated.

[0089] 3. Can be used for numerical calculation model reconstruction: The complete contour data finally extracted by this method is a piecewise nonlinear Lagrange interpolation polynomial in a three-dimensional space coordinate system. By changing the parameters in the polynomial, the contour size and shape of the three-dimensional model can be changed, such as zooming in, zooming out, moving, deforming, etc. It has the characteristics of easy expression and calculation, and can be conveniently used to construct numerical models for numerical simulation.

[0090] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0091] The above is an introduction to the method embodiment. The following is a further explanation of the solution disclosed in the present disclosure through an apparatus embodiment.

[0092] Figure 15 FIG. 6 is a block diagram of a 3D model contour feature extraction apparatus 600 according to an embodiment of the present disclosure, comprising:

[0093] The major axis definition module 601 is used to define the major axis of the three-dimensional model according to the boundary point set of the three-dimensional model;

[0094] The model segmentation module 602 is used to establish a spatial rectangular coordinate system based on the long axis to construct a segmentation space of the three-dimensional model;

[0095] An endpoint extraction module 603 is used to determine a cross-sectional profile according to the segmented space, and extract characteristic endpoints of the cross-sectional profile boundary according to a finite set of points within the cross-sectional profile;

[0096] The contour generation module 604 is used to perform interpolation calculation based on the feature endpoints to construct a spatial interpolation curve and generate a complete parameterized contour curve of the three-dimensional model.

[0097] The three-dimensional model contour feature extraction device 600 of the present invention can quickly extract model contour features, effectively improve the contour extraction accuracy, and has the characteristics of being easy to express and calculate for changes in the contour size and shape of the three-dimensional model, such as enlargement, reduction, movement, deformation, etc., and can be conveniently used to construct numerical models for numerical simulation.

[0098] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0099] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0100] Figure 16 A schematic block diagram of an electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0101] The device 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the device 800 can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0102] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0103] The computing unit 801 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the 3D model contour feature extraction method. For example, in some embodiments, the 3D model contour feature extraction method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the 3D model contour feature extraction method described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the 3D model contour feature extraction method by any other suitable means (e.g., by means of firmware).

[0104] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0106] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0107] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0108] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0109] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0110] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0111] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for extracting contour features of a three-dimensional model for numerical simulation, comprising the following steps: defining a long axis of the three-dimensional model according to a set of boundary points of the three-dimensional model; Establishing a spatial rectangular coordinate system based on the long axis to construct a subdivision space of the three-dimensional model; wherein, the spatial rectangular coordinate system is established with the long axis, and any plane including the long axis is selected as a subdivision reference plane of the three-dimensional model; along the direction of the normal of the reference plane, the maximum distance from the boundary point set of the three-dimensional model to the reference plane is calculated; and the maximum distance is subdivided according to different subdivision accuracies to obtain a subdivision space of the three-dimensional model; A cross-sectional profile is determined by the subdivided space, and characteristic endpoints of the cross-sectional profile boundary are extracted based on a finite set of points within the cross-sectional profile; wherein, the cross-sectional profile is obtained based on the subdivided space of the three-dimensional model, a medial axis grid is calculated for the finite set of points within the cross-sectional profile, and sampling points in the medial axis grid closest to the cross-sectional profile boundary are extracted; multiple groups of boundary points are determined by selecting a group of points with the smallest Euclidean distance among the sampling points; and based on the minimum value of different boundary points from the finite set of points within the cross-sectional profile, the boundary points corresponding to the minimum value are extracted as characteristic endpoints of the cross-sectional profile boundary; Interpolation calculation is performed according to the characteristic endpoints to construct a spatial interpolation curve to generate a complete parameterized contour curve of the three-dimensional model; the method includes: performing projection simplification according to the characteristic endpoints, determining interpolation nodes and constructing interpolation functions based on the interpolation nodes, and constructing a spatial interpolation curve through the interpolation functions to form a complete parameterized contour curve of the three-dimensional model, thereby completing the extraction and parameterized expression of geometric contour information; wherein, performing projection simplification according to the characteristic endpoints, determining interpolation nodes and constructing interpolation functions based on the interpolation nodes includes the following steps: selecting characteristic endpoints, converting three-dimensional data into two-dimensional data through projection, obtaining projection coordinates corresponding to the yz axis and xz axis, selecting interpolation nodes respectively using a second-order interpolation function, and constructing Lagrangian interpolation functions of the yz axis and xz axis projection surfaces respectively according to the interpolation nodes; the complete parameterized contour curve includes: the interpolation functions of the characteristic endpoints of each layer of the cross-sectional contour and the interpolation functions of the characteristic endpoints of the adjacent cross-sectional contours between each cross-section.

2. The method according to claim 1, characterized in that Defining the long axis of the three-dimensional model includes: According to the Euclidean distance between each point of the boundary point set of the three-dimensional model, the two points with the farthest Euclidean distance are obtained, and the direction connecting the two points is determined to be the long axis of the three-dimensional model.

3. The method according to claim 1, characterized in that The construction of the interpolation function of the characteristic endpoints of the adjacent cross-sectional profiles between the cross-sectional profiles includes: For the characteristic endpoints between different cross-sectional profiles, between adjacent cross-sectional profiles, select the combination with the smallest Euclidean distance value between the characteristic endpoints in different cross-sectional profiles, group them, and obtain the interpolation nodes of each group; construct the interpolation curve of each group based on the interpolation nodes, connect the characteristic endpoints of all the cross-sectional profiles respectively, and generate a complete parameterized contour curve of the three-dimensional model.

4. A 3D model contour feature extraction device, comprising: A long axis definition module, configured to define the long axis of the three-dimensional model according to a set of boundary points of the three-dimensional model; a model segmentation module, configured to establish a spatial rectangular coordinate system based on the long axis and construct a segmentation space for the three-dimensional model; wherein the spatial rectangular coordinate system is established with the long axis, any plane containing the long axis is selected as a segmentation reference plane for the three-dimensional model, and the maximum distance from a set of boundary points of the three-dimensional model to the reference plane is calculated along the direction of the normal line of the reference plane, and the maximum distance is segmented according to different segmentation accuracies to obtain a segmentation space for the three-dimensional model; An endpoint extraction module is configured to determine a cross-sectional profile based on the subdivided space and extract characteristic endpoints of the cross-sectional profile boundary based on a finite set of points within the cross-sectional profile; wherein the cross-sectional profile is obtained based on the subdivided space of the three-dimensional model, a medial axis grid is calculated for the finite set of points within the cross-sectional profile, and sampling points in the medial axis grid closest to the cross-sectional profile boundary are extracted; multiple groups of boundary points are determined by selecting a group of points with the smallest Euclidean distance among the sampling points; and based on the minimum value of the distance between different boundary points and the finite set of points within the cross-sectional profile, the boundary points corresponding to the minimum value are extracted as characteristic endpoints of the cross-sectional profile boundary; A contour generation module is used to perform interpolation calculation based on the characteristic endpoints to construct a spatial interpolation curve and generate a complete parameterized contour curve of the three-dimensional model; the module includes: performing projection simplification based on the characteristic endpoints, determining interpolation nodes and constructing interpolation functions based on the interpolation nodes, and constructing a spatial interpolation curve through the interpolation functions to form a complete parameterized contour curve of the three-dimensional model, thereby completing the extraction and parameterized expression of geometric contour information; wherein, performing projection simplification based on the characteristic endpoints, determining interpolation nodes and constructing interpolation functions based on the interpolation nodes include the following steps: selecting characteristic endpoints, converting three-dimensional data into two-dimensional data by projection, obtaining projection coordinates corresponding to the yz axis and xz axis, selecting interpolation nodes respectively using a second-order interpolation function, and constructing Lagrange interpolation functions of the yz axis and xz axis projection surfaces respectively according to the interpolation nodes; the complete parameterized contour curve includes: the interpolation functions of the characteristic endpoints of each layer of the profile and the interpolation functions of the characteristic endpoints of the profiles of adjacent profiles between each profile.

5. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 3.

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