A multi-scale component model finite element grid generation method and device and storage medium

Abstract

A multi-scale component model finite element grid generation method and device and storage medium, the method comprises the steps of: obtaining a multi-scale component model data file; a cyclic traversal list is established for the multi-scale component model data file; the cyclic traversal list is cyclically traversed; the cyclic traversal data is obtained; the feature grid parameters are set according to the cyclic traversal data; the multi-scale component model is divided into grids according to the feature grid parameters. The application can quickly generate different orders of magnitude geometric region grids for complex multi-scale component models, and connect the grids between different scales uniformly according to the geometric index control function. Compared with the traditional adaptive grid drawing method, the overall model grid quantity can be reduced, the design cycle can be greatly shortened, and the product design efficiency can be improved. The application can be applied to a complex model with different scales, and the grids between different scales are uniformly transitioned, so that the grids between scales do not change abruptly, and the grid quality is good.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of finite element mesh generation, and particularly relates to a multi-scale component model finite element mesh generation method and device and a storage medium. BACKGROUND

[0002] In the professional fields of aviation, aerospace, vehicles, ships, machinery and the like, when a complex multi-scale component model is subjected to CAE (Computer Aided Engineering) finite element preprocessing mesh division, if small mesh sizes are used to adapt to the size feature requirements in the small-scale direction, the number of generated meshes will be huge, affecting the subsequent calculation efficiency; if large mesh sizes are used to adapt to the size feature requirements in the large-scale direction, long and narrow elements will be generated in the small-scale direction, which cannot meet the subsequent calculation accuracy requirements. Therefore, high-quality and high-precision meshes are particularly important for the calculation results.

[0003] At present, for a model with complex multi-scale sizes, the general finite element preprocessing mesh division is as follows: the whole model is meshed by using existing commercial software or open source software, and finally the complex small-scale region is encrypted. However, this process is complicated and prone to errors, and the experience requirement for the mesh division worker is also relatively high, and the time and labor cost is expensive. SUMMARY

[0004] In view of the above problems, the application provides a multi-scale component model finite element mesh generation method, device and storage medium which can overcome the above problems or at least partially solve the above problems.

[0005] To solve the above technical problems, the application provides a multi-scale component model finite element mesh generation method, which comprises the following steps:

[0006] obtaining a multi-scale component model data file;

[0007] establishing a loop traversal list for the multi-scale component model data file;

[0008] looping through the loop traversal list;

[0009] obtaining loop traversal data;

[0010] setting feature mesh parameters according to the loop traversal data;

[0011] dividing the multi-scale component model into meshes according to the feature mesh parameters.

[0012] Preferably, the step of obtaining the multi-scale component model data file comprises the following steps:

[0013] obtaining a point data file of a multi-scale component model;

[0014] characterizing the point data file using a point characterization formula;

[0015] obtaining an edge data file of a multi-scale component model;

[0016] characterizing the edge data file using an edge characterization formula;

[0017] obtaining a face data file of a multi-scale component model;

[0018] characterizing the face data file using a face characterization formula.

[0019] Preferably, the establishing a circular traversal list for the multi-scale component model data file comprises the steps of:

[0020] normalizing the multi-scale component model data file;

[0021] establishing a circular traversal list for the multi-scale component model data file in a preset order;

[0022] setting the first data point of the circular traversal list as a starting point;

[0023] setting the left point of the starting point as a starting reference point;

[0024] obtaining all detection points of the circular traversal list;

[0025] setting the left point of all the detection points as detection reference points;

[0026] labeling the starting point, the starting reference point, the detection points and the detection reference points with geometric features.

[0027] Preferably, the circularly traversing the circular traversal list comprises the steps of:

[0028] obtaining the starting point, the starting reference point, the detection points and the detection reference points of the circular traversal list;

[0029] scanning the eight neighborhood data points of the starting point in a counterclockwise direction from the starting reference point;

[0030] scanning the eight neighborhood data points of each detection point in a counterclockwise direction from the detection reference point;

[0031] obtaining the scanning sequence of all the detection points.

[0032] Preferably, the obtaining a circular traversal data comprises the steps of:

[0033] obtaining a start point geometric feature corresponding to a start point in the cyclic traversal list;

[0034] obtaining a detection point geometric feature corresponding to a detection point in the cyclic traversal list;

[0035] calculating geometric feature correlations between all the detection point geometric features and the start point geometric feature;

[0036] setting the detection point geometric features with geometric feature correlations meeting preset values as feature vectors;

[0037] assembling the feature vectors and obtaining a feature space matrix.

[0038] Preferably, the setting of the feature mesh parameters according to the cyclic traversal data comprises the steps of:

[0039] obtaining a first feature vector of the feature space matrix;

[0040] assigning a quantitative attribute to the first feature vector;

[0041] setting a region feature mesh parameter according to the quantitative attribute;

[0042] setting a surface curvature feature mesh parameter according to the quantitative attribute;

[0043] setting a small geometric size feature mesh parameter according to the quantitative attribute;

[0044] setting a chamfer roundness feature mesh parameter according to the quantitative attribute.

[0045] Preferably, the mesh division of the multi-scale component model according to the feature mesh parameters comprises the steps of:

[0046] obtaining the region feature mesh parameter, the surface curvature feature mesh parameter, the small geometric size feature mesh parameter and the chamfer roundness feature mesh parameter;

[0047] dividing the multi-scale component model according to the feature mesh parameters and obtaining a finite element mesh model;

[0048] transition connecting the finite element mesh model by using a geometric exponential growth mode.

[0049] The application further provides a multi-scale component model finite element mesh generation device, which comprises:

[0050] a multi-scale component model data file obtaining module, configured to obtain a multi-scale component model data file;

[0051] a cyclic traversal list establishing module, configured to establish a cyclic traversal list for the multi-scale component model data file;

[0052] The loop traversal list loop module is configured to loop through the loop traversal list.

[0053] The loop traversal data acquisition module is configured to acquire loop traversal data.

[0054] The feature mesh parameter setting module is configured to set feature mesh parameters according to the loop traversal data.

[0055] The mesh division module is configured to divide a multi-scale component model into meshes according to the feature mesh parameters.

[0056] The present application also provides an electronic device, characterized in that the electronic device comprises:

[0057] at least one processor; and

[0058] a memory in communication with the at least one processor; wherein

[0059] the memory stores instructions executable 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 multi-scale component model finite element mesh generation method.

[0060] The present application also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the multi-scale component model finite element mesh generation method.

[0061] The one or more technical solutions in the embodiments of the present application have at least the following technical effects or advantages: the multi-scale component model finite element mesh generation method, device and storage medium provided by the present application can quickly generate different orders of magnitude geometric region meshes for a complex multi-scale component model, and connect the meshes between different scales uniformly according to a geometric index control function. Compared with a traditional adaptive mesh generation method, the present application can reduce the number of meshes of the overall model, greatly shorten the design cycle, and improve the product design efficiency. The present application can be applied to a complex model with different scales, and the meshes between different scales are uniformly transitioned, which ensures that the meshes between scales will not be abruptly changed, and the mesh quality is good. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0063] Figure 1 is a flowchart of a multi-scale component model finite element grid generation method provided by an embodiment of the present application;

[0064] Figure 2 is a structural diagram of a multi-scale component model finite element grid generation device provided by an embodiment of the present application;

[0065] Figure 3 is a structural diagram of an electronic device provided by the present application;

[0066] Figure 4 is a structural diagram of a non-transitory computer readable storage medium provided by the present application;

[0067] Figure 5 is a plan view of a smart phone chip mold model in a multi-scale component model finite element grid generation method provided by an embodiment of the present application;

[0068] Figure 6 is a mark point of different regions of a smart phone chip mold model in a multi-scale component model finite element grid generation method provided by an embodiment of the present application;

[0069] Figure 7 is a grid division diagram of a smart phone chip mold model generated by a multi-scale component model finite element grid generation method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0070] The advantages and various effects of the present application will be more clearly presented hereinafter with specific embodiments and examples. Those skilled in the art should understand that these specific embodiments and examples are used to illustrate the present application, not to limit the present application.

[0071] Throughout the specification, unless otherwise specifically indicated, the terms used herein are to be understood as having the meanings commonly used in the art. Therefore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the present application belongs. If there is a contradiction, the present specification takes precedence.

[0072] Unless otherwise specifically stated, the various raw materials, reagents, instruments and equipment used in the present application can be purchased from the market or can be prepared by existing methods.

[0073] As Figure 1 In the embodiments of the present application, a multi-scale component model finite element grid generation method is provided, which comprises the following steps:

[0074] S1: obtaining a multi-scale component model data file;

[0075] In the embodiments of the present application, the step of obtaining the multi-scale component model data file comprises the steps of:

[0076] obtaining a point data file of the multi-scale component model;

[0077] characterizing the point data file using a point characterization formula;

[0078] obtaining an edge data file of the multi-scale component model;

[0079] characterizing the edge data file using an edge characterization formula;

[0080] obtaining a face data file of the multi-scale component model;

[0081] characterizing the face data file using a face characterization formula.

[0082] In the embodiments of the present application, when the multi-scale component model data file is obtained, the point data file, the edge data file and the face data file of the multi-scale component model are obtained respectively, and are characterized in turn using the point characterization formula, the edge characterization formula and the face characterization formula, wherein the obtained point characterization formula is:

[0083] Node_n (n = 1, 2, 3…), the obtained edge characterization formula is: edge_i (i = 1, 2, 3…), and the obtained face characterization formula is: face_j (j = 1, 2, 3…).

[0084] S2: establishing a circular traversal list for the multi-scale component model data file;

[0085] In the embodiments of the present application, the step of establishing the circular traversal list for the multi-scale component model data file comprises the steps of:

[0086] performing normalization preprocessing on the multi-scale component model data file;

[0087] establishing a circular traversal list for the multi-scale component model data file in a preset order;

[0088] setting the first data point of the circular traversal list as a starting point;

[0089] setting the left point of the starting point as a starting reference point;

[0090] obtaining all detection points of the circular traversal list;

[0091] setting the left point of all the detection points as detection reference points;

[0092] performing geometric feature marking on the starting point, the starting reference point, the detection points and the detection reference points.

[0093] In the embodiment of the present application, when the circular traversal list is established for the multi-scale component model data file, first, the multi-scale component model data file is normalized and pretreated, then the multi-scale component model data file is traversed from left to right and from top to bottom to establish the circular traversal list, the first data point of the circular traversal list is found as the starting point and is marked as a0, the left point of the point is taken as the starting reference point and is marked as b0, and meanwhile all the detection points in the circular traversal list and their corresponding left points are taken as detection reference points and are marked with corresponding geometric features.

[0094] S3: circularly traversing the circular traversal list;

[0095] In the embodiment of the present application, the circular traversal of the circular traversal list comprises the steps of:

[0096] obtaining the starting point, the starting reference point, the detection points and the detection reference points of the circular traversal list;

[0097] scanning the eight neighborhood data points of the starting point in a counterclockwise direction from the starting reference point;

[0098] scanning the eight neighborhood data points of the detection points in a counterclockwise direction from each detection reference point;

[0099] obtaining the scanning sequences of all the detection points.

[0100] In the embodiment of the present application, when the circular traversal list is circularly traversed, all the detection points in the circular traversal list need to be circularly traversed and scanned, and the scanning starts from the starting point. Specifically, the eight neighborhood data points of the starting point are scanned in a counterclockwise direction from the starting reference point, and then the eight neighborhood data points of the detection points are scanned in a counterclockwise direction from each detection reference point, that is, for any one detection point, 8 adjacent points of the same scale and 9*2 points of adjacent scales above and below are scanned, that is, a total of 26 points are scanned, and the scanning sequences obtained by scanning are sequentially marked as x = [x1 x2 … x n ] T wherein the element x i is a descriptor, and n represents the number of descriptors.

[0101] S4: obtaining circular traversal data;

[0102] In the embodiment of the present application, the obtaining of the circular traversal data comprises the steps of:

[0103] obtaining the starting point geometric features corresponding to the starting point in the circular traversal list;

[0104] obtaining geometric features of the detection points corresponding to the detection points in the loop traversal list;

[0105] calculating geometric feature correlations between all the geometric features of the detection points and the geometric feature of the starting point;

[0106] setting the geometric features of the detection points with geometric feature correlations meeting a preset value as feature vectors;

[0107] assembling the feature vectors to obtain a feature space matrix.

[0108] In the embodiments of the present application, when the loop traversal list is looped, the geometric feature correlations between all the geometric features of the detection points and the geometric feature of the starting point are calculated when the loop stops, the geometric features of the detection points with geometric feature correlations meeting a preset value (for example, the geometric feature correlations are within a preset range) are set as feature vectors, and the feature vectors are assembled to obtain a feature space matrix.

[0109] In the embodiments of the present application, the scanning sequence x = [x1 x2 … xn] obtained in step S3 is subjected to a principal component analysis (PCA) to obtain a feature space matrix. n ] T Correspondingly, it can be obtained that the mean vector of the feature vectors is m x = E{x}, and the covariance matrix is C x = E{(x-m x )(x-m x ) T}. Since x is an n-dimensional column vector, C x is an n × n matrix. C ii represents the variance of the i-th component of the vector x, and C ij represents the covariance of x i and x j in the vector x. If x i is correlated with x j , C ij ≠ 0, otherwise, C ij = 0. At this time, the first point in the mark sequence that is correlated with a0 is n k , and it can be obtained that the space of all the feature vectors is an n-dimensional feature space (feature space), and the expression is:

[0110]

[0111] S5: setting feature grid parameters according to the loop traversal data;

[0112] In the embodiments of the present application, the step of setting the feature grid parameters according to the loop traversal data includes the steps of:

[0113] obtaining the first feature vector of the feature space matrix;

[0114] assigning a quantitative attribute to the first eigenvector;

[0115] setting a region feature grid parameter according to the quantitative attribute;

[0116] setting a curved surface curvature feature grid parameter according to the quantitative attribute;

[0117] setting a small geometric size feature grid parameter according to the quantitative attribute;

[0118] setting a chamfer round feature grid parameter according to the quantitative attribute.

[0119] In the embodiment of the present application, when setting the feature grid parameters according to the cyclic traversal data, first, the first eigenvector of the feature space matrix is obtained, and a quantitative attribute is assigned to the first eigenvector, and then the region feature grid parameter, the curved surface curvature feature grid parameter, the small geometric size feature grid parameter and the chamfer round feature grid parameter are set according to the quantitative attribute.

[0120] In the embodiment of the present application, for the feature space obtained in step S4, the first eigenvector is assigned a quantitative attribute, denoted as c=c0, and the grid parameters are set according to the quantitative attribute of the eigenvector. Specifically, for different scale region features, the region feature grid parameter is h c =[L / n]; for curved surface curvature features, the curved surface curvature feature grid parameter is h c =2Rsin(φ / 2); for small geometric size features, the small geometric size feature grid parameter is h c =L AB , and for chamfer round features, the chamfer round feature grid parameter is h c =R A .

[0121] S6: meshing the multi-scale component model according to the feature grid parameters.

[0122] In the embodiment of the present application, the meshing the multi-scale component model according to the feature grid parameters comprises the steps of:

[0123] obtaining the region feature grid parameter, the curved surface curvature feature grid parameter, the small geometric size feature grid parameter and the chamfer round feature grid parameter;

[0124] meshing the multi-scale component model according to the feature grid parameters and obtaining a finite element grid model;

[0125] transition connecting the finite element grid model using a geometric exponential growth pattern.

[0126] In the embodiment of the present application, when the multi-scale component model is meshed according to the feature mesh parameters, the multi-scale component model is meshed according to each feature mesh parameter in step S5 to obtain a finite element mesh model, and then the finite element mesh model is transitionally connected by using a geometric exponential growth pattern.

[0127] In the embodiment of the present application, specifically, the geometric exponential growth pattern used is: n = δ0(1 + r) n-1 wherein δ0, δ n respectively represent the height of the first layer of mesh and the height of the n th layer of mesh, and r is a normal mesh distance variation rate.

[0128] The present application will be described in detail below with specific embodiments.

[0129] As shown in FIG. 1, it is a typical plane view of a smart phone chip molding model, which is a complex multi-scale assembly and can be divided into three parts, namely block_n (n = 0, 1, 2), the largest block in the uppermost layer is a copper heat sink, which is larger in size than the chip and has the same load applied to the four corners; the lower layer is the chip; and the thin layer in the middle is a softening layer material (the size is intentionally enlarged in the plane view for easy observation) connecting the upper heat sink and the bottom chip, and the thickness of the softening layer is much smaller than the size of the heat sink and the size of the chip. Figure 5 In the embodiment of the present application, the multi-scale component model data file corresponding to the smart phone chip molding model is:

[0130] edge = [1, 2; 2, 3; 3, 4; 4, 1; 5, 6; 6, 7; 7, 8; 5, 8; 9, 10; 10, 11; 11, 12; 12, 9];

[0131] face{1} = [1, 2, 3, 4];

[0132] face{2} = [5, 6, 7, 8];

[0133] face{3} = [9, 10, 11, 12];

[0134] In the embodiment of the present application, the method provided by the present application is performed on the smart phone chip molding model in

[0135] , and the following results can be obtained: Figure 5 Figure 6 ​The shown smart phone chip mold model different area mark points and Figure 7 The shown smart phone chip mold model meshing diagram.

[0136] As Figure 2 In the embodiments of the present application, the present application also provides a multi-scale component model finite element mesh generation device, the device comprises:

[0137] A multi-scale component model data file acquisition module 10 is configured to acquire a multi-scale component model data file.

[0138] A loop traversal list establishing module 20 is configured to establish a loop traversal list for the multi-scale component model data file.

[0139] A loop traversal list cycle module 30 is configured to cyclically traverse the loop traversal list.

[0140] A loop traversal data acquisition module 40 is configured to acquire loop traversal data.

[0141] A feature mesh parameter setting module 50 is configured to set feature mesh parameters according to the loop traversal data.

[0142] A meshing module 60 is configured to mesh a multi-scale component model according to the feature mesh parameters.

[0143] The multi-scale component model finite element mesh generation device provided by the present application can perform the multi-scale component model finite element mesh generation method provided by the above steps.

[0144] Reference will now be made to Figure 3 which shows a structural schematic diagram of an electronic device 100 suitable for implementing the embodiments of the present disclosure. The electronic device in the embodiments of the present disclosure can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 3 The shown electronic device is only an example and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.

[0145] As Figure 3As shown, the electronic device 100 can include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 101 that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 102 or loaded into a random access memory (RAM) 103 from a storage device 108. Various programs and data required for the operation of the electronic device 100 are also stored in the RAM 103. The processing device 101, the ROM 102, and the RAM 103 are connected to each other through a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0146] In general, the following devices can be connected to the I / O interface 105: input devices 106 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 107 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 108 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 109. The communication devices 109 can allow the electronic device 100 to communicate wirelessly or wired with other devices to exchange data. Although the electronic device 100 having various devices is shown in the figure, it should be understood that all of the shown devices are not required to be implemented or possessed. More or less devices can be alternatively implemented or possessed.

[0147] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication devices 109, or installed from the storage devices 108, or installed from the ROM 102. When the computer program is executed by the processing device 101, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

[0148] Reference is made below to Figure 4 which shows a structural schematic diagram of a computer readable storage medium suitable for being used to implement embodiments of the present disclosure, the computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the multi-scale component model finite element mesh generation method as claimed in any one of the above.

[0149] It should be noted that the computer-readable medium in the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 above. In the present disclosure, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present disclosure, the computer-readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or apparatus. The program code contained in the computer-readable medium can be transmitted by any suitable medium, including but not limited to a wire, a cable, an RF (radio frequency) or the like, or any suitable combination of the above.

[0150] The computer-readable medium described above can be contained in the electronic device described above; or can exist separately and not be assembled into the electronic device.

[0151] The computer-readable medium described above carries one or more programs, when the one or more programs are executed by the electronic device, the electronic device is caused to: obtain at least two Internet protocol addresses; send a node evaluation request including the at least two Internet protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet protocol address from the at least two Internet protocol addresses and returns; receive the Internet protocol address returned by the node evaluation device; wherein the obtained Internet protocol address indicates an edge node in a content distribution network.

[0152] Alternatively, the computer readable medium described above carries one or more programs, when the one or more programs are executed by the electronic device, the electronic device is caused to: receive a node evaluation request comprising at least two internet protocol addresses; select an internet protocol address from the at least two internet protocol addresses; and return the selected internet protocol address; wherein the received internet protocol address indicates an edge node in a content distribution network.

[0153] Computer program code for carrying out operations of the present disclosure can be written in any one or more of a variety of programming languages or combinations of languages, including an object-oriented programming language such as Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0154] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order than that noted in the figures. For example, two blocks noted in succession can in fact be executed substantially concurrently or in the opposite order, depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flow diagrams, and combinations of blocks in the block diagrams and / or flow diagrams, can be implemented by dedicated hardware-based systems that perform the specified functions or operations, or can be implemented by a combination of dedicated hardware-based systems and computer instructions.

[0155] The units described in the embodiments of the present disclosure can be implemented by software, or can be implemented by hardware. In some cases, the name of the unit does not constitute a limitation on the unit itself. For example, the first obtaining unit can also be described as a unit for obtaining at least two internet protocol addresses.

[0156] The application provides a multi-scale component model finite element grid generation method and device and a storage medium, which can quickly generate different orders of magnitude of geometric region grids for a complex multi-scale component model, and connect the grids of different scales in sequence according to a geometric index control function, thereby reducing the number of overall model grids, greatly shortening the design cycle, improving the product design efficiency, and being applicable to a complex model with different scales, and ensuring that the grids between different scales are uniformly transitioned and do not suddenly change, and the grid quality is good.

[0157] It should be understood that the above specific embodiments of the present application are only used for illustrative or explanatory purposes of the principles of the present application, and do not constitute a limitation of the present application. Therefore, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present application shall be included in the protection scope of the present application. In addition, the appended claims of the present application are intended to cover all variations and modifications falling within the scope and boundary of the appended claims, or the equivalent forms of such scope and boundary.

Claims

1. A method for generating finite element mesh of a multi-scale component model, characterized in that, The method comprises the steps of: acquiring a multi-scale component model data file; establishing a cyclic traversal list for the multi-scale component model data file; cyclically traversing the cyclic traversal list; acquiring cyclic traversal data; setting feature mesh parameters according to the cyclic traversal data; dividing a mesh for a multi-scale component model according to the feature mesh parameters; wherein the step of dividing a mesh for a multi-scale component model according to the feature mesh parameters comprises the steps of: acquiring region feature mesh parameters, curved surface curvature feature mesh parameters, small geometric size feature mesh parameters, and chamfer and round feature mesh parameters; dividing a mesh for the multi-scale component model according to the feature mesh parameters to obtain a finite element mesh model; and transition connecting the finite element mesh model using a geometric exponential growth pattern.

2. The multi-scale component model finite element mesh generation method of claim 1, wherein, The step of acquiring a multi-scale component model data file comprises the steps of: acquiring a point data file for a multi-scale component model; characterizing the point data file using a point characterization formula; acquiring an edge data file for a multi-scale component model; characterizing the edge data file using an edge characterization formula; acquiring a face data file for a multi-scale component model; characterizing the face data file using a face characterization formula.

3. The multi-scale component model finite element mesh generation method of claim 1, wherein, The step of establishing a cyclic traversal list for the multi-scale component model data file comprises the steps of: performing normalization preprocessing on the multi-scale component model data file; establishing a cyclic traversal list for the multi-scale component model data file in a preset order; setting a first data point of the cyclic traversal list as a starting point; setting a left point of the starting point as a starting reference point; acquiring all detection points of the cyclic traversal list; setting left points of all the detection points as detection reference points; performing geometric feature marking on the starting point, the starting reference point, the detection points, and the detection reference points.

4. The multi-scale component model finite element mesh generation method of claim 1, wherein, The step of cyclically traversing the cyclic traversal list comprises the steps of: acquiring the starting point, the starting reference point, the detection points, and the detection reference points of the cyclic traversal list; scanning eight neighborhood data points of the starting point in a counterclockwise direction starting from the starting reference point; scanning eight neighborhood data points of the detection points in a counterclockwise direction starting from each detection reference point; acquiring a scanning sequence of all the detection points.

5. The multi-scale component model finite element mesh generation method of claim 1, wherein, The step of acquiring cyclic traversal data comprises the steps of: acquiring starting point geometric features corresponding to the starting point in the cyclic traversal list; acquiring detection point geometric features corresponding to the detection points in the cyclic traversal list; calculating geometric feature correlation degrees of all the detection point geometric features and the starting point geometric features; setting detection point geometric features with geometric feature correlation degrees meeting preset values as feature vectors; assembling the feature vectors to obtain a feature space matrix.

6. The multi-scale component model finite element mesh generation method of claim 1, wherein, The step of setting feature mesh parameters according to the cyclic traversal data comprises the steps of: acquiring a first feature vector of the feature space matrix; assigning a quantitative attribute to the first feature vector; setting region feature mesh parameters according to the quantitative attribute; setting curved surface curvature feature mesh parameters according to the quantitative attribute; setting small geometric size feature mesh parameters according to the quantitative attribute; setting chamfer and round feature mesh parameters according to the quantitative attribute.

7. A multi-scale component model finite element mesh generation apparatus for implementing the method of any one of claims 1 to 6, characterized by The device comprises: a multi-scale component model data file acquisition module, configured to acquire a multi-scale component model data file; a loop traversal list establishing module, configured to establish a loop traversal list for the multi-scale component model data file; a loop traversal list cycle module, configured to cyclically traverse the loop traversal list; a loop traversal data acquisition module, configured to acquire loop traversal data; a feature mesh parameter setting module, configured to set feature mesh parameters according to the loop traversal data; a mesh division module, configured to divide a multi-scale component model into meshes according to the feature mesh parameters.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable 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 multi-scale component model finite element mesh generation method of any one of preceding claims 1-6. 9.A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the multi-scale component model finite element mesh generation method of any one of preceding claims 1-6.

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