A modeling method of 3D simulation

Abstract

The application provides a kind of 3D simulation modeling method, comprising the following steps: the parameters of single original model are reduced respectively to generate several adjustment models Bnx, and all adjustment models of single original model adjustment model Bnx constitute model group Cn;At least one adjustment model Bnx is taken in all model groups Cn to construct simulation scene Dn, and the frame rate of the running image of the running simulation scene Dn is recorded;The frame rate of the running image of all simulation scenes Dn is obtained;Frame rate calculation model F is obtained;E original models in the material library are used to construct task simulation scene;The optimal solution of each of the E models in the task simulation scene is calculated using the frame rate calculation model F.The application provides a kind of 3D simulation modeling method, by obtaining the frame rate calculation model F of the relationship of the parameters of various models in the 3D simulation scene under a specific running frame rate, so as to facilitate the quick adjustment of the specific parameters of the corresponding model to optimize the 3D simulation scene.

Description

Technical Field

[0001] This invention relates to the field of 3D simulation technology, and in particular to a 3D simulation modeling method. Background Technology

[0002] Before construction, AGV projects require 3D simulation software for simulation and deduction. When displaying simulation images, this software needs to call up a large number of 3D models. To ensure the smooth operation of the simulation software, the 3D models are optimized within a limited frame rate range. It is known that fewer faces and textures in the model, and smaller texture files, can improve the frame rate of the 3D simulation software, but this reduces the display quality of the simulation images. Therefore, in practice, the experience of software developers and modelers is needed to appropriately reduce the face count and textures of the 3D models to balance the display quality and smoothness of the 3D simulation images. With the continuous increase in production demands, existing 3D simulation scenarios are becoming increasingly complex, and the number of models in the scenarios is also increasing. Optimizing such large 3D simulation scenarios manually is time-consuming and labor-intensive; therefore, a method for quickly optimizing 3D simulation scenarios is urgently needed. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a 3D simulation modeling method. By obtaining a frame rate calculation model F that determines the relationship between the parameters (number of models, number of faces, texture file size) of various models in a 3D simulation scene at a specific running frame rate, it is easy to quickly adjust the specific parameters of the corresponding model to optimize the 3D simulation scene.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A 3D simulation modeling method includes the following steps:

[0006] The number of faces and texture file size of a single original model in the material library are reduced to generate an adjustment model Bnx. After the reduction of the number of faces and texture file size of a single original model, a total of several adjustment models Bnx are generated. All the adjustment models Bnx of a single original model are combined into a model group Cn. A total of several model groups Cn are generated from all the original models in the material library.

[0007] Take at least one model Bnx from all model groups Cn to construct simulation scene Dn, and drive simulation scene Dn to run, recording the frame rate of the running image of simulation scene Dn; obtain the frame rate of the running image of simulation scene Dn for all combinations of adjusted models Bnx.

[0008] Analyze the data of all simulation scenarios Dn to derive the frame rate calculation model F;

[0009] The task simulation scene is constructed using E original models from the material library; the optimal solution for each of the E models in the task simulation scene is calculated using the frame rate calculation model F, and the corresponding model is adjusted according to the optimal solution.

[0010] Compared with existing technologies, the 3D simulation modeling method of this invention, under fixed hardware and software conditions, processes models in a material library to obtain an adjusted model Bnx with reduced polygon count and texture file size. The adjusted model Bnx is then used to construct a simulation scene Dn to obtain the frame rate of the running video of the simulation scene Dn. By considering four factors—the number of models, the polygon count of a single model, the texture file size of a single model, and the frame rate of the running video of the simulation scene Dn—with the frame rate as the dependent variable and the polygon count, texture file size, and number of models as independent variables, a multiple regression analysis is performed to derive the frame rate calculation model F. This allows for the direct acquisition of optimal parameters (number of faces, texture file size) for the E original models in the material library when constructing a task simulation scene in actual work. This is achieved by using the frame rate calculation model F to directly obtain the optimal parameters for the E models (number of faces, texture file size). This determines the optimal values ​​for the number of faces and texture size of each model in the scene. By maintaining the 3D simulation scene at a specific frame rate as the standard for operation, the best modeling scheme can be formulated. This ensures smooth operation and achieves the best model display quality, overcoming the inefficiency of traditionally optimizing large 3D simulation scenes manually. This improves the efficiency of building and demonstrating 3D simulation scenes.

[0011] Preferably, the number of faces of a single original model in the material library is reduced to generate an adjustment model Anx, and a total of several adjustment models Anx are generated after several reductions in the number of faces of a single original model; the texture file size of a single adjustment model Anx is reduced to generate an adjustment model Bnx, and a total of several adjustment models Bnx are generated after several reductions in the texture file size of a single original model.

[0012] The above setup is based on a single original model. First, a reduction process with different numbers of faces is performed to generate several adjustment models Anx. Then, using a single adjustment model Anx as an object, a reduction process with different texture file sizes is performed to generate several adjustment models Bnx.

[0013] Preferably, the minimum frame rate in the frame rate calculation model F is set to G, and the running frame rate of the running image of the task simulation scene is set to H, where the running frame rate H ≥ the minimum frame rate G.

[0014] By setting a minimum frame rate of G in the frame rate calculation model F, the display quality of the simulation scene is avoided due to excessive reduction of the parameters of the original model. In addition, in order to ensure the display quality of the simulation scene, the running frame rate H must not be less than the minimum frame rate G.

[0015] Preferably, the running frame rate H is less than the minimum frame rate G + 1.

[0016] To avoid the simulation scene running too slowly due to excessively high display quality, it is necessary to limit the value of the running frame rate H. The optimal value of the running frame rate H should be less than the minimum frame rate G + 1.

[0017] Preferably, the minimum frame rate G is 30 FPS, at which point the balance between display quality and running speed of the simulation scene is optimal.

[0018] Preferably, P optimization results are obtained in the process of solving the optimal solutions of each of the E models;

[0019] Calculate the average solution K of the P optimization results;

[0020] The optimization result with the smallest total deviation from the average solution K is selected as the optimal solution.

[0021] Since the number of adjustment models Bnx is limited, the number of simulation scenes Dn built using adjustment models Bnx is also limited. Furthermore, the number of optimization results calculated by the frame rate calculation model F after building the task simulation scene using models from the same material library is also limited. In order to improve the display effect of the task simulation scene, it is necessary to filter the limited optimization results. In this invention, the total deviation value between the optimization results and the average solution K is used as the selection result of the optimal solution, thereby ensuring that the optimal solution is representative of the P optimization results.

[0022] The preferred average solution K is:

[0023] ;

[0024] The average number of faces. This represents the average texture file size.

[0025] Preferably, the process of selecting the optimization result with the smallest total deviation from the average solution K is as follows:

[0026] Select one optimization result Q from P optimization results, and then extract the face count values ​​of several adjustment models Bnx from optimization result Q. And the value of texture file size ;

[0027] Calculate the deviation between the parameters of the adjusted model Bnx and the average solution K. ;

[0028] Add the deviations of all adjusted model Bnx parameters within the optimization result Q to the average solution K, and calculate the total deviation between the optimization result Q and the average solution K. . Attached Figure Description

[0029] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0030] The embodiments of the present invention are described below with reference to the accompanying drawings:

[0031] See Figure 1 The 3D simulation modeling method of this embodiment includes the following steps:

[0032] (1) Read the model data of n original models in the material library;

[0033] (2) Reduce the number of faces and texture file size of a single original model in the material library to generate an adjustment model Bnx. After reducing the number of faces and texture file size of a single original model, a total of several adjustment models Bnx are generated. All adjustment models Bnx of a single original model are combined into a model group Cn. A total of several model groups Cn are generated from all original models in the material library.

[0034] (3) Select at least one model Bnx from all model groups Cn to construct simulation scene Dn, and drive simulation scene Dn to run, and record the frame rate of the running image of simulation scene Dn; obtain the frame rate of the running image of simulation scene Dn of all combinations of model Bnx.

[0035] (4) Analyze the data of all simulation scenes Dn to obtain the frame rate calculation model F; the data of simulation scene Dn includes the number of models in simulation scene Dn, the number of faces of the adjusted model Bnx and the size of the texture file, as well as the frame rate data of the running images of all simulation scenes Bn.

[0036] (5) Use the E original models in the material library to construct the task simulation scene; use the frame rate calculation model F to calculate the optimal solution of each of the E models in the task simulation scene, and adjust the corresponding model according to the optimal solution.

[0037] In the above content, n represents the original model's index in the material library, and x represents the adjusted model's index, where 0 < E ≤ n. The meaning of "combination" in step (3) is consistent with the mathematical term "combination".

[0038] The optimal solution for each of the E models is specifically the optimal number of faces and the optimal texture file size for each model.

[0039] In step (2), the number of faces of a single original model in the material library is reduced to generate an adjustment model Anx. After several reductions in the number of faces of a single original model, a total of several adjustment models Anx are generated. The size of the texture file of a single adjustment model Anx is reduced to generate an adjustment model Bnx. After several reductions in the size of the texture file of a single original model, a total of several adjustment models Bnx are generated.

[0040] The above setup is based on a single original model. First, a reduction process with different numbers of faces is performed to generate several adjustment models Anx. Then, using a single adjustment model Anx as an object, a reduction process with different texture file sizes is performed to generate several adjustment models Bnx.

[0041] The following example uses one of the original models in the resource library, model number 1:

[0042] Original model parameters (Model 1): 2000 faces, texture size: 100MB;

[0043] Table 1: List of Anx-adjusted models generated after reducing the number of faces in the original model No. 1

[0044]

[0045] Since a reduction rate of more than 65% in the number of faces (retaining only 40% of the original model's face count) will result in severe distortion of the model outline, in this invention, the face count reduction rate of the model Anx relative to the original model is adjusted to not exceed 65%.

[0046] Table 2: List of Adjusted Models (Bnx) Generated After Reducing Texture Size in A11 Adjustment Model

[0047]

[0048] Table 3: List of Adjusted Model Bnx after Reducing Texture Size in A12

[0049]

[0050] Since a texture size reduction rate exceeding 50% (retaining only 50% of the original model's texture size) will result in severe distortion of the model's pattern, this invention adjusts the texture size reduction rate of the model Bnx relative to the original model to no more than 50%.

[0051] According to Table 2 above, the parameters of model B11 were adjusted to have a total of 1960 faces and a texture size of 95 MB.

[0052] In steps (4) and (5), the minimum frame rate in the frame rate calculation model F is set to G, and the running frame rate of the running image of the task simulation scene is set to H, with the running frame rate H ≥ the minimum frame rate G.

[0053] By setting a minimum frame rate of G in the frame rate calculation model F, the display quality of the simulation scene is avoided due to excessive reduction of the parameters of the original model. In addition, in order to ensure the display quality of the simulation scene, the running frame rate H must not be less than the minimum frame rate G.

[0054] Furthermore, the running frame rate H < minimum frame rate G + 1.

[0055] To avoid the simulation scene running too slowly due to excessively high display quality, it is necessary to limit the value of the running frame rate H. The optimal value of the running frame rate H should be less than the minimum frame rate G + 1.

[0056] The minimum frame rate G is 30 FPS, at which point the balance between display quality and running speed in the simulation scene is optimal.

[0057] For any simulation scenario, given the model number, the number of models, and the frame rate H range of the image frames (G≤H<G+1), the adjustment model Bnx corresponding to the model in the simulation scenario can be obtained through the frame rate calculation model F. By calling the parameters of the adjustment model Bnx, the optimal result of each model in the simulation scenario can be known.

[0058] In this invention, the optimal result takes the following form: the j-th solution for the number of faces and texture file size of the i-th model is:

[0059] .

[0060] In step (5), P optimization results are obtained in the process of solving the optimal solutions of each of the E models;

[0061] Calculate the average solution K of the P optimization results;

[0062] The optimization result with the smallest total deviation from the average solution K is selected as the optimal solution.

[0063] Since the number of adjustment models Bnx is limited, the number of simulation scenes Dn built using adjustment models Bnx is also limited. Furthermore, the number of optimization results calculated by the frame rate calculation model F after building the task simulation scene using models from the same material library is also limited. In order to improve the display effect of the task simulation scene, it is necessary to filter the limited optimization results. In this invention, the total deviation value between the optimization results and the average solution K is used as the selection result of the optimal solution, thereby ensuring that the optimal solution is representative of the P optimization results.

[0064] The preferred average solution K is:

[0065] ;

[0066] The average number of faces. This represents the average texture file size.

[0067] Furthermore, the process of selecting the optimization result that minimizes the total deviation from the average solution K is as follows:

[0068] Select one optimization result Q from P optimization results, and then extract the face count values ​​of several adjustment models Bnx from optimization result Q. And the value of texture file size ;

[0069] Calculate the deviation between the parameters of the adjusted model Bnx and the average solution K. ;

[0070] Add the deviations of all adjusted model Bnx parameters within the optimization result Q to the average solution K, and calculate the total deviation between the optimization result Q and the average solution K. .

[0071] Compared with existing technologies, the 3D simulation modeling method of this invention, under fixed hardware and software conditions, processes models in a material library to obtain an adjusted model Bnx with reduced polygon count and texture file size. The adjusted model Bnx is then used to construct a simulation scene Dn to obtain the frame rate of the running video of the simulation scene Dn. By considering four factors—the number of models, the polygon count of a single model, the texture file size of a single model, and the frame rate of the running video of the simulation scene Dn—with the frame rate as the dependent variable and the polygon count, texture file size, and number of models as independent variables, a multiple regression analysis is performed to derive the frame rate calculation model F. This allows for the direct acquisition of optimal parameters (number of faces, texture file size) for the E original models in the material library when constructing a task simulation scene in actual work. This is achieved by using the frame rate calculation model F to directly obtain the optimal parameters for the E models (number of faces, texture file size). This determines the optimal values ​​for the number of faces and texture size of each model in the scene. By maintaining the 3D simulation scene at a specific frame rate as the standard for operation, the best modeling scheme can be formulated. This ensures smooth operation and achieves the best model display quality, overcoming the inefficiency of traditionally optimizing large 3D simulation scenes manually. This improves the efficiency of building and demonstrating 3D simulation scenes.

[0072] Based on the disclosure and teachings of the foregoing specification, those skilled in the art can make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the present invention should also fall within the protection scope of the claims of the present invention. Furthermore, although some specific terms are used in this specification, these terms are only for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. A 3D simulation modeling method, comprising the following steps: Reducing the number of faces of a single original model in a material library and the size of a texture file to generate an adjusted model Bnx, the number of faces of a single original model and the size of a texture file are reduced to generate a plurality of adjusted models Bnx, and all adjusted models Bnx of a single original model form a model group Cn; all original models in the material library generate a plurality of model groups Cn; At least one adjusted model Bnx is taken from all model groups Cn to construct a simulation scene Dn, and the simulation scene Dn is driven to run, and the frame rate of the running image of the simulation scene Dn is recorded; the frame rate of the running image of the simulation scene Dn combined with all adjusted models Bnx is obtained; Analyzing the data of all simulation scenes Dn to obtain a frame rate calculation model F, which comprehensively considers the number of models, the number of faces of a single model, the size of a texture file of a single model, and the frame rate of the running image of the simulation scene Dn, taking the running frame rate as the dependent variable, and the number of faces of various models, the size of the texture file, and the number of models as the independent variables, and performing multiple regression analysis to obtain the frame rate calculation model F; Setting the lowest frame rate in the frame rate calculation model F as G, and setting the running frame rate H of the running image of the task simulation scene, the running frame rate H is greater than or equal to the lowest frame rate G; Using E original models in the material library to construct a task simulation scene; Using the frame rate calculation model F to calculate the optimal solution of each of the E models in the task simulation scene, and adjusting the corresponding model according to the optimal solution; Obtaining P optimization results in the process of solving the optimal solution of each of the E models; Calculating the average solution K of the P optimization results according to the P optimization results; Selecting the optimization result with the smallest total deviation value from the average solution K as the optimal solution; The average solution K is: ； is the average number of faces, is the average map file size; wherein and , denotes the number of faces of the i-th model, the j-th solution of the size of the texture file.

2. The modeling method of 3D simulation according to claim 1, wherein, Reducing the number of faces of a single original model in a material library to generate an adjusted model Anx, and reducing the number of faces of a single original model several times to generate a plurality of adjusted models Anx; reducing the size of a texture file of a single adjusted model Anx to generate an adjusted model Bnx, and reducing the size of a texture file of a single original model several times to generate a plurality of adjusted models Bnx.

3. The modeling method of 3D simulation according to claim 1, wherein, The running frame rate H is less than the lowest frame rate G+1.

4. The modeling method of 3D simulation according to claim 3, characterized in that, The lowest frame rate G is 30FPS.

5. The modeling method of 3D simulation according to claim 1, wherein, The process of selecting the optimization result with the smallest total deviation value from the average solution K is: Select one of the P optimization results Q, and extract the number of surface numbers of the adjustment model Bnx in the optimization result Q in turn and the value of the map file size ; calculating the deviation values of the parameters of the adjustment model Bnx from the average solution K ; adding the values of the deviations of the parameters of all adjustment models Bnx in the optimization result Q to the average solution K, calculating the total deviation value of the optimization result Q and the average solution K .

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