Simulation model grid division method and device, electronic equipment and computer program product

Abstract

The invention discloses a grid division method and device for a simulation model, electronic equipment and a computer program product. The method comprises the steps that a three-dimensional scanning model of a to-be-simulated object is acquired, and the three-dimensional scanning model is subjected to grid division based on preset grid density; performing multi-physical field modeling on the three-dimensional scanning model to obtain a simulation model to be evaluated; performing simulation calculation on the to-be-evaluated simulation model, and determining calculation time for performing simulation calculation on the to-be-evaluated simulation model and a stability index for measuring a simulation calculation result; performing iterative adjustment on the preset grid density to obtain a target grid density which enables the calculation time to be not greater than a preset time threshold value and the stability index to be not less than a preset index threshold value; and determining the to-be-evaluated simulation model subjected to grid division based on the target grid density as a target simulation model. According to the method, the technical problem that the grid density cannot be accurately selected under the condition of multi-physical modeling in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional scanning, and more particularly, to a method, apparatus, electronic device, and computer program product for meshing a simulation model. Background Art

[0002] Multi-physics field modeling refers to the process of coupling different physical fields for modeling and simulation. It takes into account the interactions and influences between multiple physical phenomena, providing more comprehensive and accurate simulation results. Multi-physics field modeling can help engineers and scientists better understand and predict complex physical systems, can be used for optimizing designs and improving performance, and can be used for fault diagnosis and fault prediction. The advantage of multi-physics field modeling is that it can provide more accurate and comprehensive simulation results. Compared with separately modeling each physical field, multi-physics field modeling can take into account the interactions and influences of different physical phenomena, thus more realistically reflecting the actual situation. By performing coupled analysis in a unified model, the process of repeated calculations and trial-and-error can be reduced, improving work efficiency and cost-effectiveness. Multi-physics field modeling has a wide range of applications in many fields, including but not limited to engineering design, aerospace, materials science, biomedicine, and earth science, etc.

[0003] Simulation calculation refers to the process of simulating and analyzing an actual system through mathematical models and computer programs. It can help engineers and scientists better understand and predict the behavior and performance of complex systems, optimize designs and improve processes, and enhance the reliability and safety of products. The advantage of simulation calculation is that it can provide accurate and comprehensive simulation results. Compared with traditional test methods, simulation calculation can eliminate the interference and errors of many factors, and can more conveniently and economically perform multiple simulation analyses. In addition, simulation calculation can also help explore and verify new theories and methods. By performing simulation calculations on an actual system, the understanding of its internal mechanism and behavior can be deepened, providing support and verification for the development of new theories and methods. The application scenarios of simulation calculation are very extensive, for example: analyzing vehicle collisions, fuel efficiency, vibration and noise, analyzing circuit performance, signal processing, electromagnetic radiation, etc.

[0004] 3D scanning refers to the process of capturing the geometric shape and appearance information of the surface of an actual object using optical, laser, or other sensing technologies and converting it into a digital three-dimensional model. By scanning and measuring the object from all directions, accurate information such as the size, shape, and texture of the object can be obtained. 3D scanning technology demonstrates its unique value in multiple fields. In rapid modeling and design, it can efficiently generate accurate three-dimensional models. In the field of reverse engineering, it can help engineers extract detailed data from existing objects. In addition, 3D scanning is widely used in fields such as cultural relic protection, digital art, and virtual reality, bringing unprecedented innovation and possibilities to these fields. The advantage of 3D scanning is that it can provide highly accurate and realistic three-dimensional models. Compared with traditional measurement methods, 3D scanning can capture more detailed and shape information and can complete the acquisition and processing of a large amount of data in a short time. In addition, 3D scanning can also help reduce human errors and improve work efficiency. Through automated scanning and data processing processes, the cost and time of manual measurement can be reduced.

[0005] Although there are numerous research and inventions in multi-physics modeling, simulation calculations, and 3D scanning currently, the effective combination of the three is still relatively scarce. In the work process of CAE engineers, modeling and meshing often take up a large amount of time, especially when dealing with complex models, the time spent is significantly increased. In addition, to ensure the accuracy of simulation results, CAE engineers also need to face the challenge of selecting an appropriate mesh size (i.e., mesh density), which is both time-consuming and laborious.

[0006] Regarding the problem that in the case of multi-physics modeling of the above-mentioned existing technologies, the mesh density cannot be accurately selected, no effective solution has been proposed yet. Summary of the Invention

[0007] Embodiments of the present invention provide a method, device, electronic device, and computer program product for meshing a simulation model to at least solve the technical problem that in the case of multi-physics modeling of the existing technology, the mesh density cannot be accurately selected.

[0008] According to one aspect of an embodiment of the present invention, a method for meshing a simulation model is provided, including: obtaining a three-dimensional scan model of an object to be simulated, wherein the three-dimensional scan model is meshed based on a preset grid density; performing multi-physical field modeling on the three-dimensional scan model to obtain a simulation model to be evaluated; performing simulation calculations on the simulation model to be evaluated, and determining the calculation time for performing simulation calculations on the simulation model to be evaluated and a stability index for measuring the stability of the simulation calculation results, wherein the preset grid density is positively correlated with the calculation time, and the preset grid density is positively correlated with the stability index; iteratively adjusting the preset grid density to obtain a target grid density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold; and determining the simulation model to be evaluated meshed based on the target grid density as the target simulation model.

[0009] Optionally, the method further includes: determining the preset grid density as the target grid density when the calculation time is not greater than the preset time threshold and the stability index is not less than the preset index threshold.

[0010] Optionally, iteratively adjusting the preset grid density to obtain a target grid density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold includes: calculating an index difference between the stability index of the simulation model to be evaluated and the preset index threshold during each iterative adjustment; when the index difference is negative, increasing the preset grid density by a first density step length and re-establishing the simulation model to be evaluated until the stability index of the simulation model is less than the preset index threshold.

[0011] Optionally, the method further includes: when the index difference is not negative, detecting whether the calculation time is greater than the preset time; when the calculation time is greater than the preset time, decreasing the preset grid density by a second density step length and re-establishing the simulation model to be evaluated until the calculation time is not greater than the preset time threshold.

[0012] Optionally, the method further includes: detecting whether the number of iterative adjustments to the preset grid density reaches a preset number threshold; when the number of iterative adjustments reaches the preset number threshold, determining the preset grid density obtained through the last iterative adjustment as the target grid density.

[0013] Optionally, obtaining the three-dimensional scan model of the object to be simulated includes: collecting the depth map of the object to be simulated through a binocular camera; collecting the three-dimensional point cloud of the object to be simulated through a lidar; registering the depth map and the three-dimensional point cloud of the same object to be simulated to obtain the three-dimensional reconstruction model of the object to be simulated; performing mesh division on the three-dimensional reconstruction model according to the preset mesh density to obtain the three-dimensional scan model.

[0014] Optionally, performing multi-physical field modeling on the three-dimensional scan model to obtain the simulation model to be evaluated includes: adding a material property definition to the three-dimensional scan model according to the material properties of the object to be simulated, where the material property definition is at least used to represent the physical properties and mechanical properties of the material of the object to be simulated; adding a working condition requirement definition to the three-dimensional scan model according to the working condition requirements of the object to be simulated, where the working condition requirement definition is at least used to represent the load, constraint, and boundary conditions of the object to be simulated; setting the physical fields to be simulated for the three-dimensional scan model according to the application environment of the object to be simulated, where the physical fields are used to select at least one physical model required for physical simulation of the object to be simulated; using at least one of the physical models to analyze the material property definition and the working condition requirement definition to obtain the simulation model to be evaluated.

[0015] According to another aspect of the embodiments of the present invention, there is also provided a mesh division device for a simulation model, including: an acquisition module, configured to acquire a three-dimensional scan model of an object to be simulated, where the three-dimensional scan model is mesh-divided based on a preset mesh density; a modeling module, configured to perform multi-physical field modeling on the three-dimensional scan model to obtain a simulation model to be evaluated; a calculation module, configured to perform simulation calculation on the simulation model to be evaluated, and determine the calculation time for performing simulation calculation on the simulation model to be evaluated, and a stability index for measuring the simulation calculation result, where the preset mesh density is positively correlated with the calculation time, and the preset mesh density is positively correlated with the stability index; an adjustment module, configured to iteratively adjust the preset mesh density to obtain a target mesh density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold; and a determination module, configured to determine the simulation model to be evaluated mesh-divided based on the target mesh density as the target simulation model.

[0016] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to execute the above-mentioned mesh division method for the simulation model through the computer program.

[0017] According to another aspect of the embodiments of the present invention, there is also provided a computer program product, including computer instructions, which when executed by a processor, implement the steps of the above grid division method of the simulation model.

[0018] In the embodiments of the present invention, a three-dimensional scanning model of an object to be simulated is obtained, wherein the three-dimensional scanning model is divided into grids based on a preset grid density; a multi-physical field modeling is performed on the three-dimensional scanning model to obtain a simulation model to be evaluated; a simulation calculation is performed on the simulation model to be evaluated, and the calculation time for performing the simulation calculation on the simulation model to be evaluated and a stability index for measuring the simulation calculation result are determined, wherein the preset grid density is positively correlated with the calculation time, and the preset grid density is positively correlated with the stability index; the preset grid density is iteratively adjusted to obtain a target grid density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold; the simulation model to be evaluated divided into grids based on the target grid density is determined as the target simulation model. Thus, before determining the target simulation model, a simulation calculation is pre-performed on the simulation model to be evaluated obtained by multi-physical field modeling, and according to the calculation time and the stability index obtained from the simulation calculation, the grid density used by the simulation model to be evaluated is flexibly adjusted to a target grid density that can meet the usage requirements, achieving the technical effect of accurately selecting the grid density, and further solving the technical problem that in the prior art, the grid density cannot be accurately selected in the case of multi-physical modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0020] Figure 1 is a flowchart of a grid division method of a simulation model according to an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of a 3D scanning device based on multi-physical field modeling and simulation calculation according to an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of a 3D scanning process based on multi-physical field modeling and simulation calculation according to an embodiment of the present invention;

[0023] Figure 4 is a schematic diagram of a three-dimensional reconstruction process according to an embodiment of the present invention;

[0024] Figure 5 is a schematic diagram of a grid division device of a simulation model according to an embodiment of the present invention;

[0025] Figure 6It is a structural block diagram of a computer terminal according to an embodiment of the present invention. Detailed implementation manners

[0026] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] According to an embodiment of the present invention, an embodiment of a method for meshing a simulation model is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0029] Figure 1 It is a flowchart of a method for meshing a simulation model according to an embodiment of the present invention. As Figure 1 shown, the method includes the following steps:

[0030] Step S102, obtain a three-dimensional scan model of the object to be simulated, where the three-dimensional scan model is meshed based on a preset grid density;

[0031] Step S104, perform multi-physical field modeling on the three-dimensional scan model to obtain a simulation model to be evaluated;

[0032] Step S106, perform simulation calculation on the simulation model to be evaluated, and determine the calculation time for performing simulation calculation on the simulation model to be evaluated and a stability index for measuring the simulation calculation result, where the preset grid density is positively correlated with the calculation time, and the preset grid density is positively correlated with the stability index;

[0033] Step S108: Iteratively adjust the preset grid density to obtain a target grid density such that the calculation time is not greater than the preset time threshold and the stability index is not less than the preset index threshold.

[0034] Step S110: Determine that the simulation model to be evaluated for mesh generation based on the target grid density is the target simulation model.

[0035] In the embodiment of the present invention, a three-dimensional scan model of the object to be simulated is obtained, where the three-dimensional scan model is meshed based on a preset grid density; a multi-physics field model is established for the three-dimensional scan model to obtain a simulation model to be evaluated; the simulation model to be evaluated is subjected to simulation calculation, and the calculation time for performing the simulation calculation on the simulation model to be evaluated and the stability index for measuring the simulation calculation result are determined, where the preset grid density is positively correlated with the calculation time and the preset grid density is positively correlated with the stability index; the preset grid density is iteratively adjusted to obtain a target grid density such that the calculation time is not greater than the preset time threshold and the stability index is not less than the preset index threshold; and the simulation model to be evaluated meshed based on the target grid density is determined as the target simulation model. Thus, before determining the target simulation model, the simulation model to be evaluated obtained by multi-physics field modeling is pre-simulated, and the grid density used by the simulation model to be evaluated is flexibly adjusted according to the calculation time and stability index obtained from the simulation calculation, so that the target grid density that can meet the usage requirements is achieved, and the technical effect of accurately selecting the grid density is realized, thereby solving the technical problem that the grid density cannot be accurately selected in the prior art in the case of multi-physics modeling.

[0036] In the above step S102, the object to be simulated can be a physical object that needs to be analyzed by multi-physics fields. By performing a three-dimensional scan on the object, a three-dimensional scan model of the physical object can be obtained, and the three-dimensional contour of the physical object is represented by the three-dimensional scan model. Furthermore, based on the three-dimensional scan model, a multi-physics field model can be established to simulate the physical characteristics of the physical object under the action of multi-physics fields.

[0037] In the above step S102, on the surface of the three-dimensional scan model of the object to be simulated, meshing can be performed according to the preset grid density, and each grid can be used as a unit for which simulation analysis needs to be performed. The deformation of the object to be simulated under the action of multi-physics fields can be represented by the meshed grids.

[0038] It should be noted that the preset grid density on the three-dimensional scanning model is used to limit the grid size drawn on the surface of the three-dimensional scanning model. Among them, the smaller the divided grid, the greater the preset grid density of the three-dimensional scanning model, the more content needs to be analyzed in the simulation calculation, the greater the computing power occupied, the longer the computing time consumed, and the more stable the simulation calculation result; the larger the divided grid, the smaller the preset grid density of the three-dimensional scanning model, the less content needs to be analyzed in the simulation calculation, the smaller the computing power occupied, the shorter the computing time consumed, and the more unstable the simulation calculation result.

[0039] In the above step S104, for multi-physics field modeling of the three-dimensional scanning model, multiple sets of equations representing physical field models can be added to the three-dimensional scanning model. Each set of equations is a mathematical representation of a physical field. The multiple physical fields of the three-dimensional scanning model at least include: the physical field representing the material properties of the object to be simulated, the physical field representing the working condition requirements of the object to be simulated, and the physical field representing the application environment of the object to be simulated.

[0040] In the above step S106, after establishing the simulation model to be evaluated, the simulation model to be evaluated can be subjected to simulation calculation to obtain the simulation calculation result. Furthermore, the time waiting for the simulation calculation result is the calculation time of the simulation result; based on the simulation calculation result, a stability index for stability measurement is determined.

[0041] In the above step S106, the stability index can be the Jacobian ratio.

[0042] Optionally, the value range of the Jacobian ratio is [0, 1], and the closer it is to 1, the better the calculation effect; if the Jacobian ratio is negative, it indicates that the grid of the simulation model to be simulated is distorted and the simulation calculation result is distorted.

[0043] In the above step S108, for iterative adjustment of the preset grid density, in each iteration process, it can be adjusted according to a preset density step size to avoid excessive adjustment amplitude and missing the optimal target grid density.

[0044] In the above step S108, in the case of iterative adjustment of the preset grid density each time, the three-dimensional scanning model of the object to be simulated can be re-meshed, and after multi-physics field modeling based on the three-dimensional scanning model with the re-meshed grid, the simulation calculation is performed until the computing time consumed by the simulation calculation and the stability index of the simulation calculation result meet the requirements.

[0045] As an optional embodiment, the method further includes: when the computing time is not greater than a preset time threshold and the stability index is not less than a preset index threshold, determining the preset grid density as the target grid density.

[0046] In the above embodiments of the present application, after initially establishing the simulation model to be evaluated, if the calculation time obtained by performing simulation calculations based on the simulation model to be evaluated established this time is not greater than the preset time threshold and the stability index is not less than the preset index threshold, the preset grid density can be directly determined as the target grid density without the need for iterative adjustment of the preset grid density.

[0047] It should be noted that the purpose of simulation calculation is to obtain the true result of the object to be simulated under the action of the physical field. Therefore, the requirement for the stability of the simulation calculation result takes precedence over the requirement for the calculation time. So when iteratively adjusting the preset grid density, the compliance of the stability index can be compared first, and then the compliance of the calculation time can be compared.

[0048] As an alternative embodiment, iteratively adjusting the preset grid density to obtain a target grid density such that the calculation time is not greater than the preset time threshold and the stability index is not less than the preset index threshold includes: during each iterative adjustment, calculating the index difference between the stability index of the simulation model to be evaluated and the preset index threshold; in the case where the index difference is negative, increasing the preset grid density by the first density step and re-establishing the simulation model to be evaluated until the stability index of the simulation model is less than the preset index threshold.

[0049] In the above embodiments of the present application, a preset index threshold for measuring the stability of the simulation calculation result is preset. If the stability index of the simulation calculation result is less than the preset index threshold, it indicates that the simulation calculation result does not meet the requirements, and it is necessary to increase the grid density to improve the stability index of the simulation calculation result. Among them, the comparison between the stability index and the preset index threshold can be achieved by calculating the index difference between the stability index and the preset index threshold. If the index difference is negative, it indicates that the stability index is less than the preset index threshold, and then the preset grid density can be increased by the first density step and the simulation model to be evaluated can be re-established until the stability index of the simulation model is less than the preset index threshold.

[0050] Optionally, if the index difference is zero or positive, it indicates that the stability index is not less than the preset index threshold, and then it can be further detected whether the calculation time of the simulation calculation meets the requirements.

[0051] As an alternative embodiment, the method further includes: in the case where the index difference is not negative, detecting whether the calculation time is greater than the preset time; in the case where the calculation time is greater than the preset time, decreasing the preset grid density by the second density step and re-establishing the simulation model to be evaluated until the calculation time is not greater than the preset time threshold.

[0052] In the above embodiments of the present application, when the index difference is not negative, it indicates that the stability of the simulation calculation result meets the requirements. Then, it is possible to further detect whether the calculation time of the simulation calculation meets the requirements. Furthermore, when the calculation time is greater than the preset time, it indicates that the calculation time is too long. Then, the preset grid density can be reduced according to the second density step length to improve the calculation speed of the simulation calculation, reduce the calculation time, and obtain the target grid density that can balance the stability of the simulation calculation result and the calculation time.

[0053] Optionally, the first density step length and the second density step length can be the same.

[0054] Optionally, the second density step length can be smaller than the second density step length, so as to achieve precise adjustment of the grid density and avoid the stability index of the simulation calculation result being less than the preset index threshold due to too large an adjustment amplitude.

[0055] As an alternative embodiment, the method further includes: detecting whether the number of iterations for iteratively adjusting the preset grid density reaches a preset number threshold; when the number of iterations reaches the preset number threshold, determining the preset grid density obtained through the last iterative adjustment as the target grid density.

[0056] In the above embodiments of the present application, on the basis of adjusting the preset grid density according to the calculation time and the stability index, the number of iterations for iterative adjustment is limited by the preset number threshold. When the number of iterations reaches the preset number threshold, the preset grid density obtained through the last iteration can be used as the target grid density to avoid the iterative process falling into an infinite loop.

[0057] As an alternative embodiment, obtaining the three-dimensional scan model of the object to be simulated includes: collecting the depth map of the object to be simulated through a binocular camera; collecting the three-dimensional point cloud of the object to be simulated through a lidar; registering the depth map and the three-dimensional point cloud of the same object to be simulated to obtain the three-dimensional reconstruction model of the object to be simulated; dividing the three-dimensional reconstruction model into grids according to the preset grid density to obtain the three-dimensional scan model.

[0058] In the above embodiments of the present application, the three-dimensional scan model of the object to be simulated can be comprehensively obtained based on the scan results of multiple scanning devices. For example, by collecting the depth map of the object to be simulated through a binocular camera and collecting the three-dimensional point cloud of the object to be simulated through a lidar, and registering the depth map and the three-dimensional point cloud of the same object to be simulated, the purpose of combining the scan results of multiple scanning devices for the same object to be simulated is achieved, and a three-dimensional reconstruction model that can more accurately reflect the three-dimensional contour of the object to be simulated can be obtained. Furthermore, by dividing the surface of the three-dimensional reconstruction model into grids according to the preset grid density, the three-dimensional scan model of the object to be simulated can be obtained, realizing the acquisition of the three-dimensional scan model of the object to be simulated.

[0059] As an alternative embodiment, multi-physics field modeling is performed on a three-dimensional scan model to obtain a simulation model to be evaluated, including: adding a material property definition to the three-dimensional scan model according to the material properties of the object to be simulated, where the material property definition is at least used to represent the physical properties and mechanical properties of the material of the object to be simulated; adding a working condition requirement definition to the three-dimensional scan model according to the working condition requirements of the object to be simulated, where the working condition requirement definition is at least used to represent the load, constraints, and boundary conditions of the object to be simulated; setting the physical fields to be simulated for the three-dimensional scan model according to the application environment of the object to be simulated, where the physical fields are used to select at least one physical model required for physical simulation of the object to be simulated; and using at least one physical model to analyze the material property definition and the working condition requirement definition to obtain the simulation model to be evaluated.

[0060] In the above embodiment of the present application, under the multi-physics field modeling of the three-dimensional scan model, by adding a material property definition and a working condition requirement definition to the three-dimensional scan model, the definition of the self-attributes and boundary conditions of the object to be simulated is realized. Furthermore, according to the application environment of the object to be simulated, the physical fields to be simulated are set for the three-dimensional scan model, and the change of the physical properties of the object to be simulated in the real application environment can be simulated to obtain the simulation model to be evaluated of the object to be simulated.

[0061] The present invention also provides a preferred embodiment, which provides a 3D scanning method based on multi-physics field modeling and simulation calculation. This method combines sensor devices and can achieve high-precision and high-efficiency three-dimensional scanning of physical objects. At the same time, the technology of multi-physics field modeling and simulation calculation provides an important digital design method for fields such as industrial manufacturing and medical imaging. Through this method for 3D scanning, the preliminary modeling work of CAE development engineers is greatly reduced, and the R & D efficiency is improved.

[0062] Figure 2 is a schematic diagram of a 3D scanning device based on multi-physics field modeling and simulation calculation according to an embodiment of the present invention, as Figure 2 shown, including the following modules:

[0063] Sensor data acquisition module. Responsible for acquiring various data of real-world objects. This sensor data acquisition module includes 2 high-definition cameras and 1 lidar sensor.

[0064] Three-dimensional reconstruction module, which uses the acquired data for three-dimensional reconstruction and performs mesh generation.

[0065] Multi-physics field modeling module, which uses the model that has been meshed for multi-physics field modeling.

[0066] The simulation calculation module performs simulation calculations on the established multi-physical field model to estimate the required time and the Jacobian Ratio.

[0067] The structure optimization module adjusts the mesh division size and remodels according to whether the obtained estimated time and Jacobian Ratio meet the design requirements. If not, it will re-model.

[0068] Figure 3 It is a schematic diagram of a 3D scanning process based on multi-physical field modeling and simulation calculation according to an embodiment of the present invention. As Figure 3 shown, it includes the following steps:

[0069] Step S301, sensor data acquisition.

[0070] Step S302, three-dimensional reconstruction.

[0071] Step S303, multi-physical field modeling.

[0072] Step S304, simulation calculation, that is, performing simulation calculations on the established multi-physical field model to estimate the required time and the Jacobian Ratio.

[0073] Step S305, structure optimization, to determine whether the requirements are met, that is, whether the estimated time and the Jacobian Ratio meet the design requirements. If not, return to Step S302 to adjust the mesh division size and re-model.

[0074] In the above Step S301, it can be realized by the sensor data acquisition module. The sensor data acquisition module can use binocular vision and lidar to simultaneously perform 3D scanning on real-world objects. To ensure the accuracy of the obtained data, the scanner of the sensor data acquisition module is moved to perform a scan every 60 degrees.

[0075] Figure 4 It is a schematic diagram of a three-dimensional reconstruction process according to an embodiment of the present invention. As Figure 4 shown, the above Step S302 three-dimensional reconstruction can be realized by the three-dimensional reconstruction module. The three-dimensional reconstruction module is used to implement the following steps:

[0076] Step S401, stereo matching and depth map generation. By performing stereo matching on the binocular vision images, the disparity between the two cameras is calculated, and the depth value corresponding to each pixel is calculated according to the disparity to obtain the depth map of the object surface.

[0077] Step S402, point cloud conversion, converting the point cloud data collected by the lidar into the coordinate information of three-dimensional points.

[0078] Step S403, Data Fusion and Registration: Use a registration algorithm to fuse the data from binocular vision and lidar. By registering the binocular vision images and the lidar point cloud data, align them in three-dimensional space by calculating the rigid body transformation matrix between the two types of data to obtain a complete and high-precision three-dimensional model.

[0079] Step S404, Model Processing and Repair: Process and repair the obtained complete three-dimensional model to eliminate possible noise, defects, or incompleteness.

[0080] Step S405, Mesh Generation: Mesh the processed and repaired three-dimensional model to facilitate subsequent CAE calculations.

[0081] It should be noted that during the mesh generation process, an appropriate mesh density needs to be selected to ensure that the results obtained from CAE calculations have sufficient accuracy and precision. Generally speaking, the higher the mesh density, the higher the accuracy of the calculation results, but at the same time, it will also increase the calculation time and consumption of calculation resources. When performing three-dimensional reconstruction for the first time, use the preset mesh density, and subsequently use the mesh density provided by the structural optimization module for meshing.

[0082] The above-mentioned step S303 can be implemented through a multi-physics field modeling module, and this multi-physics field modeling module includes the following functions:

[0083] Function S11, Material Property Definition: Define and assign materials according to the physical and mechanical properties of the actual materials.

[0084] Function S12, Load and Boundary Condition Definition: Define loads, constraints, and boundary conditions according to the design requirements and actual working conditions.

[0085] Function S13, Physical Field Setting: According to the physical fields to be simulated, select appropriate physical models and solution methods, and make corresponding settings for the model. For example, for fluid dynamics simulations, parameters such as the density and viscosity of the fluid need to be set.

[0086] The above-mentioned step S304 can be implemented through a simulation calculation module, and this simulation calculation module includes the following functions:

[0087] Function S21, Calculate the estimated time to solve the entire model.

[0088] Function S22, Calculate the Jacobian Ratio of the model.

[0089] It should be noted that the Jacobian Ratio is the condition number of the matrix Jacobian matrix and is usually used to measure the numerical stability of solving non-linear equations.

[0090] Optionally, calculating the Jacobian ratio includes:

[0091] Step S1: Calculate the Jacobian matrix according to the system of equations of the multi-physics model.

[0092] For example, in finite element analysis, the Jacobian matrix is assembled from the element stiffness matrices.

[0093] Step S2: Singular value decomposition (SVD). Perform singular value decomposition on the Jacobian matrix to obtain its maximum singular value and minimum singular value.

[0094] Step S3: Calculate the Jacobian ratio, where the Jacobian ratio is the ratio of the maximum singular value to the minimum singular value.

[0095] The above step S304 can be implemented by the structure optimization module. The structure optimization module needs to make the Jacobian ratio as close to 1 as possible within the allowable time range. Therefore, it is necessary to set the maximum allowable calculation time t and the maximum allowable Jacobian ratio e. When t and e cannot be fully satisfied, the structure optimization module will return the process to step S302 and generate the mesh density according to the sign of the difference between the Jacobian ratio and the maximum allowable Jacobian ratio.

[0096] It should be noted that the closer the Jacobian ratio is to 1, the more regular the mesh elements of the simulation model are and the smaller the deformation is; the closer the ratio is to 0, the more distorted the mesh elements of the simulation model are, which may lead to an increase in numerical errors.

[0097] Optionally, when the Jacobian ratio of the simulation model is positive, reduce the mesh density; when the Jacobian ratio of the simulation model is negative, increase the mesh density.

[0098] Optionally, to avoid an infinite loop caused by the absence of an optimal solution within the set t and e, it is also necessary to set a maximum number of structure optimization times c. When the number of structure optimization times is greater than c, it means that the model meets the requirements.

[0099] According to an embodiment of the present invention, there is also provided an embodiment of a mesh generation device for a simulation model. It should be noted that the mesh generation device for the simulation model can be used to execute the mesh generation method for the simulation model in the embodiment of the present invention, and the mesh generation method for the simulation model in the embodiment of the present invention can be executed in the mesh generation device for the simulation model.

[0100] Figure 5 is a schematic diagram of a mesh generation device for a simulation model according to an embodiment of the present invention, as Figure 5As shown, the device may include: an acquisition module 51, configured to acquire a three-dimensional scan model of an object to be simulated, where the three-dimensional scan model is meshed based on a preset grid density; a modeling module 53, configured to perform multi-physics field modeling on the three-dimensional scan model to obtain a simulation model to be evaluated; a calculation module 55, configured to perform simulation calculations on the simulation model to be evaluated, and determine the calculation time for performing simulation calculations on the simulation model to be evaluated, and a stability index for measuring the stability of the simulation calculation results, where the preset grid density is positively correlated with the calculation time, and the preset grid density is positively correlated with the stability index; an adjustment module 57, configured to iteratively adjust the preset grid density to obtain a target grid density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold; and a determination module 59, configured to determine that the simulation model to be evaluated meshed based on the target grid density is the target simulation model.

[0101] It should be noted that the acquisition module 51 in this embodiment may be configured to execute step S102 in the embodiment of the present application, the modeling module 53 in this embodiment may be configured to execute step S104 in the embodiment of the present application, the calculation module 55 in this embodiment may be configured to execute step S106 in the embodiment of the present application, the adjustment module 57 in this embodiment may be configured to execute step S108 in the embodiment of the present application, and the determination module 59 in this embodiment may be configured to execute step S110 in the embodiment of the present application. The examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in the above embodiments.

[0102] In an embodiment of the present invention, a three-dimensional scan model of an object to be simulated is acquired, where the three-dimensional scan model is meshed based on a preset grid density; multi-physics field modeling is performed on the three-dimensional scan model to obtain a simulation model to be evaluated; simulation calculations are performed on the simulation model to be evaluated, and the calculation time for performing simulation calculations on the simulation model to be evaluated and a stability index for measuring the stability of the simulation calculation results are determined, where the preset grid density is positively correlated with the calculation time and the preset grid density is positively correlated with the stability index; the preset grid density is iteratively adjusted to obtain a target grid density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold; and the simulation model to be evaluated meshed based on the target grid density is determined as the target simulation model. Thus, before determining the target simulation model, simulation calculations are pre-performed on the simulation model to be evaluated obtained through multi-physics field modeling, and according to the calculation time and stability index obtained from the simulation calculations, the grid density used by the simulation model to be evaluated is flexibly adjusted to a target grid density that can meet the usage requirements, achieving the technical effect of accurately selecting the grid density, and further solving the technical problem that the prior art cannot accurately select the grid density in the case of multi-physics modeling.

[0103] As an alternative embodiment, the device further includes: a determination sub-module, configured to determine the preset grid density as the target grid density when the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold.

[0104] As an alternative embodiment, the adjustment module includes: a calculation unit, configured to calculate an index difference between the stability index of the simulation model to be evaluated and the preset index threshold during each iterative adjustment; a first adjustment unit, configured to increase the preset grid density by a first density step and re-establish the simulation model to be evaluated until the stability index of the simulation model is less than the preset index threshold when the index difference is negative.

[0105] As an alternative embodiment, the device further includes: a first detection unit, configured to detect whether the calculation time is greater than a preset time when the index difference is not negative; a second adjustment unit, configured to decrease the preset grid density by a second density step and re-establish the simulation model to be evaluated until the calculation time is not greater than the preset time threshold when the calculation time is greater than the preset time.

[0106] As an alternative embodiment, the device further includes: a second detection unit, configured to detect whether the number of iterations for iteratively adjusting the preset grid density reaches a preset number threshold; a third adjustment unit, configured to determine the preset grid density obtained through the last iterative adjustment as the target grid density when the number of iterations reaches the preset number threshold.

[0107] As an alternative embodiment, the acquisition module includes: a first acquisition unit, configured to acquire a depth map of the object to be simulated through a binocular camera; a second acquisition unit, configured to acquire a three-dimensional point cloud of the object to be simulated through a lidar; a registration unit, configured to register the depth map and the three-dimensional point cloud of the same object to be simulated to obtain a three-dimensional reconstruction model of the object to be simulated; a grid division unit, configured to perform grid division on the three-dimensional reconstruction model according to the preset grid density to obtain a three-dimensional scan model.

[0108] As an alternative embodiment, the modeling module includes: a first definition module for adding a material property definition to the 3D scan model according to the material properties of the object to be simulated, where the material property definition is at least used to represent the physical and mechanical properties of the material of the object to be simulated; a second definition module for adding an operating condition requirement definition to the 3D scan model according to the operating condition requirements of the object to be simulated, where the operating condition requirement definition is at least used to represent the load, constraints, and boundary conditions of the object to be simulated; a setting module for setting the physical field to be simulated for the 3D scan model according to the application environment of the object to be simulated, where the physical field is used to select at least one physical model required for physical simulation of the object to be simulated; and an analysis module for analyzing the material property definition and the operating condition requirement definition using at least one physical model to obtain the simulation model to be evaluated.

[0109] An embodiment of the present invention can provide an electronic device, which can be a computer terminal, and the computer terminal can be any one of the computer terminal devices in a computer terminal group. Optionally, in this embodiment, the above computer terminal can also be replaced with a terminal device such as a mobile terminal.

[0110] Optionally, in this embodiment, the above computer terminal can be located in at least one of multiple network devices in a computer network.

[0111] In this embodiment, the above computer terminal can execute the program code of the following steps in the mesh generation method of the simulation model: obtaining a 3D scan model of the object to be simulated, where the 3D scan model is meshed based on a preset mesh density; performing multi-physics field modeling on the 3D scan model to obtain the simulation model to be evaluated; performing simulation calculations on the simulation model to be evaluated, and determining the calculation time for performing the simulation calculations on the simulation model to be evaluated and a stability index for measuring the simulation calculation results, where the preset mesh density is positively correlated with the calculation time, and the preset mesh density is positively correlated with the stability index; iteratively adjusting the preset mesh density to obtain a target mesh density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold; and determining the simulation model to be evaluated meshed based on the target mesh density as the target simulation model.

[0112] Figure 6 is a structural block diagram of a computer terminal according to an embodiment of the present invention, as Figure 6 shown, the computer terminal 60 may include: one or more (only one is shown in the figure) processors 62 and a memory 64.

[0113] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the mesh generation method and device of the simulation model in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, to implement the above-mentioned mesh generation method of the simulation model. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory may further include a memory remotely disposed relative to the processor, and these remote memories may be connected to the terminal 60 through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0114] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: obtaining a three-dimensional scan model of the object to be simulated, where the three-dimensional scan model is meshed based on a preset mesh density; performing multi-physics field modeling on the three-dimensional scan model to obtain a simulation model to be evaluated; performing simulation calculations on the simulation model to be evaluated, and determining the calculation time for performing the simulation calculations on the simulation model to be evaluated, and a stability index for measuring the stability of the simulation calculation results, where the preset mesh density is positively correlated with the calculation time, and the preset mesh density is positively correlated with the stability index; iteratively adjusting the preset mesh density to obtain a target mesh density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold; determining the simulation model to be evaluated meshed based on the target mesh density as the target simulation model.

[0115] Optionally, the above processor may further execute the program code of the following steps: when the calculation time is not greater than the preset time threshold and the stability index is not less than the preset index threshold, determining the preset mesh density as the target mesh density.

[0116] Optionally, the above processor may further execute the program code of the following steps: during each iterative adjustment, calculating the index difference between the stability index of the simulation model to be evaluated and the preset index threshold; when the index difference is negative, increasing the preset mesh density by a first density step and re-establishing the simulation model to be evaluated until the stability index of the simulation model is less than the preset index threshold.

[0117] Optionally, the above processor may further execute the program code of the following steps: when the index difference is not negative, detecting whether the calculation time is greater than the preset time; when the calculation time is greater than the preset time, decreasing the preset mesh density by a second density step and re-establishing the simulation model to be evaluated until the calculation time is not greater than the preset time threshold.

[0118] Optionally, the above-mentioned processor may also execute the program code of the following steps: detecting whether the number of iterations for iteratively adjusting the preset grid density reaches a preset number threshold; in the case where the number of iterations reaches the preset number threshold, determining the preset grid density obtained through the last iterative adjustment as the target grid density.

[0119] Optionally, the above-mentioned processor may also execute the program code of the following steps: collecting a depth map of the object to be simulated through a binocular camera; collecting a three-dimensional point cloud of the object to be simulated through a lidar; registering the depth map and the three-dimensional point cloud of the same object to be simulated to obtain a three-dimensional reconstruction model of the object to be simulated; performing mesh division on the three-dimensional reconstruction model according to the preset grid density to obtain a three-dimensional scan model.

[0120] Optionally, the above-mentioned processor may also execute the program code of the following steps: adding a material property definition to the three-dimensional scan model according to the material properties of the object to be simulated, where the material property definition is at least used to represent the physical properties and mechanical properties of the material of the object to be simulated; adding a working condition requirement definition to the three-dimensional scan model according to the working condition requirements of the object to be simulated, where the working condition requirement definition is at least used to represent the load, constraint, and boundary conditions of the object to be simulated; setting the physical field to be simulated for the three-dimensional scan model according to the application environment of the object to be simulated, where the physical field is used to select at least one physical model required for physical simulation of the object to be simulated; using at least one physical model to analyze the material property definition and the working condition requirement definition to obtain an evaluation simulation model.

[0121] Those of ordinary skill in the art can understand that Figure 6 The structure shown is only for illustration, and the computer terminal may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, and terminal devices such as Mobile Internet Devices (MID), PAD, etc. Figure 6 It does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 60 may further include more or fewer components (such as a network interface, a display device, etc.) than those shown Figure 6 in the figure, or have a different configuration from that shown Figure 6 in the figure.

[0122] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware of the terminal device through a computer program, and the computer program can be stored in a non-volatile medium. The non-volatile storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0123] An embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the above non-volatile storage medium can be used to store the program code executed by the mesh generation method of the simulation model provided in the above embodiment.

[0124] Optionally, in this embodiment, the above non-volatile storage medium can be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0125] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining a three-dimensional scan model of an object to be simulated, where the three-dimensional scan model is meshed based on a preset mesh density; performing multi-physics field modeling on the three-dimensional scan model to obtain a simulation model to be evaluated; performing simulation calculations on the simulation model to be evaluated, and determining the calculation time for performing the simulation calculations on the simulation model to be evaluated, and a stability index for measuring the stability of the simulation calculation results, where the preset mesh density is positively correlated with the calculation time, and the preset mesh density is positively correlated with the stability index; iteratively adjusting the preset mesh density to obtain a target mesh density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold; determining the simulation model to be evaluated meshed based on the target mesh density as the target simulation model.

[0126] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: when the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold, determining the preset mesh density as the target mesh density.

[0127] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: during each iterative adjustment, calculating an index difference between the stability index of the simulation model to be evaluated and the preset index threshold; when the index difference is negative, increasing the preset mesh density by a first density step and re-establishing the simulation model to be evaluated until the stability index of the simulation model is less than the preset index threshold.

[0128] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: when the index difference is not negative, detecting whether the calculation time is greater than a preset time; when the calculation time is greater than the preset time, decreasing the preset mesh density by a second density step and re-establishing the simulation model to be evaluated until the calculation time is not greater than the preset time threshold.

[0129] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: detecting whether the number of iterations for iteratively adjusting the preset grid density reaches a preset number threshold; in the case where the number of iterations reaches the preset number threshold, determining the preset grid density obtained through the last iterative adjustment as the target grid density.

[0130] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: collecting a depth map of the object to be simulated through a binocular camera; collecting a three-dimensional point cloud of the object to be simulated through a lidar; registering the depth map and the three-dimensional point cloud of the same object to be simulated to obtain a three-dimensional reconstruction model of the object to be simulated; performing mesh division on the three-dimensional reconstruction model according to the preset grid density to obtain a three-dimensional scan model.

[0131] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: adding a material property definition to the three-dimensional scan model according to the material properties of the object to be simulated, where the material property definition is at least used to represent the physical properties and mechanical properties of the material of the object to be simulated; adding a working condition requirement definition to the three-dimensional scan model according to the working condition requirements of the object to be simulated, where the working condition requirement definition is at least used to represent the load, constraints, and boundary conditions of the object to be simulated; setting the physical field to be simulated for the three-dimensional scan model according to the application environment of the object to be simulated, where the physical field is used to select at least one physical model required for performing physical simulation on the object to be simulated; using at least one physical model to analyze the material property definition and the working condition requirement definition to obtain the simulation model to be evaluated.

[0132] An embodiment of the present invention further provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it implements the steps of the mesh division method of the simulation model provided in the above embodiment.

[0133] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0134] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0135] In several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0136] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0138] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned non-volatile storage medium includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs and other various media that can store program codes.

[0139] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for meshing a simulation model, characterized in that Including: Obtain a three-dimensional scan model of the object to be simulated, wherein the three-dimensional scan model is meshed based on a preset grid density; Perform multi-physics field modeling on the three-dimensional scan model to obtain a simulation model to be evaluated; Perform simulation calculations on the simulation model to be evaluated, and determine the calculation time for performing simulation calculations on the simulation model to be evaluated and a stability index for measuring the stability of the simulation calculation results, wherein the preset grid density is positively correlated with the calculation time, and the preset grid density is positively correlated with the stability index; Iteratively adjust the preset grid density to obtain a target grid density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold; Determine the simulation model to be evaluated meshed based on the target grid density as the target simulation model.

2. The method according to claim 1, wherein The method further includes: When the calculation time is not greater than the preset time threshold and the stability index is not less than the preset index threshold, determine the preset grid density as the target grid density.

3. The method according to claim 1, characterized in that Iteratively adjusting the preset grid density to obtain a target grid density such that the calculation time is not greater than a preset time threshold and the stability index is not less than a preset index threshold includes: During each iterative adjustment, calculate the index difference between the stability index of the simulation model to be evaluated and the preset index threshold; When the index difference is negative, increase the preset grid density by a first density step and re-establish the simulation model to be evaluated until the stability index of the simulation model is less than the preset index threshold.

4. The method according to claim 3, wherein The method further includes: When the index difference is not negative, detect whether the calculation time is greater than the preset time; When the calculation time is greater than the preset time, decrease the preset grid density by a second density step and re-establish the simulation model to be evaluated until the calculation time is not greater than the preset time threshold.

5. The method according to any one of claims 1, 3 and 4, characterized in that The method further includes: Detect whether the number of iterative adjustments to the preset grid density reaches a preset number threshold; When the number of iterations reaches the preset number threshold, determine the preset grid density obtained through the last iterative adjustment as the target grid density.

6. The method according to claim 1, characterized in that Obtaining a three-dimensional scan model of the object to be simulated includes: Collect a depth map of the object to be simulated through a binocular camera; Collect three-dimensional point clouds of the object to be simulated through a lidar; Register the depth map and the three-dimensional point clouds of the same object to be simulated to obtain a three-dimensional reconstruction model of the object to be simulated; Mesh the three-dimensional reconstruction model according to the preset grid density to obtain the three-dimensional scan model.

7. The method according to claim 1, characterized in that Performing multi-physics field modeling on the three-dimensional scan model to obtain a simulation model to be evaluated includes: According to the material properties of the object to be simulated, add material property definitions to the three-dimensional scan model, where the material property definitions are at least used to represent the physical and mechanical properties of the material of the object to be simulated; Adding a working condition requirement definition to the three-dimensional scanning model according to the working condition requirement of the object to be simulated, wherein the working condition requirement definition is used to at least represent the load, constraint and boundary conditions of the object to be simulated; According to the application environment of the object to be simulated, a physical field to be simulated is set for the three-dimensional scanning model, wherein the physical field is used to select at least one physical model required for performing physical simulation on the object to be simulated; The material property definition and the operating condition requirement definition are analyzed using at least one of the physical models to obtain the simulation model to be evaluated.

8. A meshing device for a simulation model, characterized in that: include: An acquisition module, configured to acquire a three-dimensional scanned model of the object to be simulated, wherein the three-dimensional scanned model is meshed based on a preset mesh density; A modeling module, configured to perform multi-physics field modeling on the three-dimensional scanning model to obtain a simulation model to be evaluated; a calculation module, configured to perform simulation calculation on the simulation model to be evaluated, and determine a calculation time for the simulation calculation on the simulation model to be evaluated, and a stability index for measuring the simulation calculation result, wherein the preset grid density is positively correlated with the calculation time, and the preset grid density is positively correlated with the stability index; an adjustment module, configured to iteratively adjust the preset grid density to obtain a target grid density such that the calculation time is no greater than a preset time threshold and the stability index is no less than a preset index threshold; A determination module is used to determine that the simulation model to be evaluated, which is meshed based on the target mesh density, is a target simulation model.

9. An electronic device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to execute the meshing method of the simulation model according to any one of claims 1 to 7 through the computer program.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by a processor, the steps of the meshing method of the simulation model described in any one of claims 1 to 7 are implemented.

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