A three-dimensional model lightweight processing method, device and electronic equipment

Abstract

The application discloses a three-dimensional model lightweight processing method and device and electronic equipment, and relates to the technical field of model lightweight processing, and comprises the following steps: acquiring original three-dimensional models in multiple formats; performing multi-step lightweight processing on the original three-dimensional models to obtain standard three-dimensional models; calling target parameters, optimizing the standard three-dimensional models according to the target parameters to obtain target three-dimensional models; and the target parameters comprise target platform hardware configuration parameters and model application scene parameters. The application can acquire original three-dimensional models in multiple formats, perform multi-step lightweight processing on the original three-dimensional models, optimize model display effects for different application scenes and ensure the quality on the basis that the loading and rendering speeds of three-dimensional models in various different formats are not affected, thereby bringing more efficient and sustainable solutions for ground engineering projects.

Description

Technical Field

[0001] This invention relates to the field of lightweight model processing technology, and in particular to a method, apparatus, and electronic device for lightweight processing of three-dimensional models. Background Technology

[0002] 3D modeling for ground engineering is a widely used technique in civil engineering, urban planning, and transportation engineering. Traditional two-dimensional design methods can no longer meet the needs of modern engineering projects, hence the increasing application of 3D modeling technology. Through 3D modeling, the design of ground engineering projects can be displayed and analyzed more intuitively, enabling more accurate visualization and simulation.

[0003] In existing technologies, the creation and processing of 3D models often consumes significant computing resources and storage space. In practical applications, the large and complex nature of 3D model files leads to long rendering times and slow loading speeds, consequently impacting the efficiency and feasibility of engineering projects. These problems are particularly pronounced in resource-constrained environments, such as mobile devices and low-configuration computers.

[0004] The currently published invention patent, titled "Method, System, Storage Medium, and Image Processing Device for Lightweighting 3D Models" (publication number CN112734930B), processes the original model by deleting duplicate vertices, repairing seams, and removing overlapping parts to obtain a basic optimized model. This basic optimized model is then mesh-normalized to obtain a pre-optimized mesh model, which is then output. While this invention patent shortens the 3D model lightweighting processing time and improves the effect of the lightweighted model, it does not consider that different lightweighting parameters are needed for different scenarios to achieve the best display effect.

[0005] The currently published invention patent, titled "Automatic Lightweighting Method for 3D Models" with publication number CN118628690A, simplifies and evaluates the original model, iteratively adjusting the lightweighting parameters until a target is met. While this invention patent optimizes the display effect of the 3D model by adjusting the lightweighting parameters, it does not consider cross-platform compatibility and performance optimization, resulting in some impact on the loading and rendering speed of 3D models in different formats.

[0006] There is currently no effective solution to the above problems. Summary of the Invention

[0007] This specification provides a method, apparatus, and electronic device for lightweight processing of 3D models, in order to solve the problem that existing technologies cannot optimize 3D models for different scenarios without affecting the loading and rendering speed of 3D models of different formats.

[0008] Firstly, embodiments of this specification provide a method for lightweighting a three-dimensional model, the method comprising:

[0009] Obtain original 3D models in multiple formats;

[0010] The original 3D model is subjected to a multi-step lightweighting process to obtain a standard 3D model;

[0011] The target parameters are retrieved, and the standard 3D model is optimized based on the target parameters to obtain the target 3D model. The target parameters include the target platform hardware configuration parameters and the model application scenario parameters.

[0012] In some embodiments, the multi-step lightweighting process of the original 3D model to obtain a standard 3D model includes:

[0013] Metadata is obtained by parsing the original 3D model, and a first-level 3D model is constructed based on the metadata. The metadata includes geometric data, material data, and texture data. The geometric data includes mesh vertices. The metadata of the original 3D model and the metadata of the first-level 3D model are the same.

[0014] The first mesh vertex of the first-level 3D model is read, the surface defect is located through the first mesh vertex, and the area around the surface defect is reconstructed to obtain the second-level 3D model;

[0015] Read the second mesh vertices of the second-level 3D model, delete duplicate second mesh vertices, identify geometric surfaces based on the remaining second mesh vertices after deletion, merge adjacent geometric surfaces, and obtain the third-level 3D model;

[0016] Read the third mesh vertex and texture data of the third-level 3D model, identify the target region through the third mesh vertex, remove the texture data in the target region, and obtain the fourth-level 3D model. The target region includes invisible regions and regions with a distance greater than a preset threshold.

[0017] Different levels of detail are created on the aforementioned level four 3D model to obtain a level five 3D model;

[0018] Read the fifth mesh vertex and material data of the fifth-level 3D model, identify small meshes through the fifth mesh vertex, merge adjacent small meshes into large meshes, identify similar material data of each large mesh, merge the similar material data, and read the fifth mesh vertex of the fifth-level 3D model again. Based on the reread fifth mesh vertex, identify and delete hidden faces to obtain the sixth-level 3D model.

[0019] The six-level 3D model is compressed to obtain a standard 3D model.

[0020] In some embodiments, the extent reconstruction around the surface defect includes:

[0021] The mesh around the surface defect is subdivided to obtain the target mesh;

[0022] Extract the feature information of the target mesh, and use the feature information to construct a feature mesh to fill surface defects.

[0023] In some embodiments, the creation of different levels of detail includes:

[0024] Create different levels of detail using the following formula:

[0025]

[0026] Among them, LOD adjusted To create different levels of detail; LOD original The original level of detail; DistanceFactor is the distance factor, a constant between 0 and 1; DetailFactor is the detail factor, a positive integer; Distance is the distance between the observer and the model; QualityFactor is the quality factor, a constant between 0 and 1.

[0027] In some embodiments, identifying similar material data for each large mesh includes:

[0028] Extract texture features from each large grid, the texture features including color information, texture information, and reflectance information;

[0029] Determine the differences in color information, texture information, and reflectance within each large mesh;

[0030] When the differences in color information, texture information, and reflectivity between any two large meshes are all within the corresponding preset difference threshold range, the material data of the two large meshes are identified as similar material data.

[0031] In some embodiments, the method further includes:

[0032] The secondary 3D model is backed up to obtain a backup 3D model;

[0033] If an anomaly occurs during the multi-step lightweighting process of the original 3D model, the abnormal model is deleted and the backup 3D model is invoked.

[0034] In some embodiments, optimizing the standard 3D model based on the target parameters includes:

[0035] Read the target platform hardware configuration parameters and model application scenario parameters, and obtain optimization parameters based on the target platform hardware configuration parameters and application scenario parameters. The target platform hardware configuration parameters include working efficiency, memory capacity, and processing speed. The model application scenario parameters include lighting requirements, material requirements, and the amount of data in the standard 3D model.

[0036] The standard 3D model is adjusted according to the optimization parameters to obtain the 3D model to be tested. The adjustment of the standard 3D model includes texture data adjustment, geometry data adjustment, and material property adjustment.

[0037] A simulation platform is built based on the hardware configuration parameters of the target platform. The 3D model to be tested is tested on the simulation platform. The optimization parameters are adjusted based on the test results until the optimal optimization parameters are obtained.

[0038] The standard 3D model is adjusted based on the optimal optimization parameters to obtain the target 3D model.

[0039] Secondly, embodiments of this specification also provide a lightweight processing device for three-dimensional models, the device comprising:

[0040] The acquisition module is used to acquire original 3D models in various formats;

[0041] The lightweight processing module is used to perform multi-step lightweight processing on the original 3D model to obtain a standard 3D model.

[0042] The optimization module is used to retrieve target parameters and optimize the standard 3D model according to the target parameters to obtain the target 3D model. The target parameters include target platform hardware configuration parameters and model application scenario parameters.

[0043] In some embodiments, the lightweight processing module includes a first processing tool, a second processing tool, a third processing tool, a fourth processing tool, a fifth processing tool, and a sixth processing tool;

[0044] The first processing tool is used to parse the original 3D model to obtain metadata, construct a first-level 3D model based on the metadata, read the first mesh vertex of the first-level 3D model, locate surface defects through the first mesh vertex, and perform range reconstruction around the surface defects to obtain a second-level 3D model.

[0045] The second processing tool is used to read the second mesh vertices of the secondary 3D model, delete duplicate second mesh vertices, identify geometric surfaces based on the remaining second mesh vertices after deletion, merge adjacent geometric surfaces, and obtain a tertiary 3D model.

[0046] The third processing tool is used to read the third mesh vertices and texture data of the third-level 3D model, identify the target region through the third mesh vertices, remove the texture data in the target region, and obtain the fourth-level 3D model.

[0047] The fourth processing tool is used to create different levels of detail on the four-level three-dimensional model to obtain a five-level three-dimensional model.

[0048] The fifth processing tool is used to read the fifth mesh vertex and material data of the fifth-level 3D model, identify small meshes through the fifth mesh vertex, merge adjacent small meshes into large meshes, identify similar material data of each large mesh, merge the similar material data, and read the fifth mesh vertex of the fifth-level 3D model again. Based on the reread fifth mesh vertex, it identifies and deletes hidden faces to obtain a sixth-level 3D model.

[0049] The sixth processing tool is used to compress the level 6 3D model to obtain a standard 3D model.

[0050] Thirdly, embodiments of this specification also provide an electronic device, including a memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to implement the steps of the above-described three-dimensional model lightweight processing method.

[0051] Fourthly, embodiments of this specification also provide a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the above-described three-dimensional model lightweighting processing method.

[0052] This specification provides a method, apparatus, and electronic device for lightweighting 3D models. First, it acquires original 3D models in various formats. Then, it performs multi-step lightweighting processing on the original 3D models to obtain a standard 3D model. Finally, it retrieves target parameters and optimizes the standard 3D model based on these parameters to obtain a target 3D model. The target parameters include target platform hardware configuration parameters and model application scenario parameters. This approach optimizes model display effects and ensures quality for different application scenarios without affecting the loading and rendering speed of various 3D model formats, providing a more efficient and sustainable solution for ground engineering projects. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0054] Figure 1 This is a flowchart illustrating a method for lightweighting a three-dimensional model provided in the embodiments of this specification.

[0055] Figure 2 This is a schematic diagram of an embodiment of the lightweight 3D model processing method provided in this specification, applied in a scenario example.

[0056] Figure 3 This is a schematic diagram of the structural composition of a lightweight three-dimensional model processing device provided in the embodiments of this specification;

[0057] Figure 4 This is a schematic diagram of the structural composition of the electronic device provided in the embodiments of this specification. Detailed Implementation

[0058] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0059] As mentioned earlier, existing technologies for lightweighting 3D models do not take into account the need for different lightweighting parameters for different scenarios to achieve the best display effect. They also do not consider the cross-platform compatibility and performance optimization of 3D models, resulting in a certain impact on the loading and rendering speed of 3D models of different formats.

[0060] To address the aforementioned issues, this specification provides a method for lightweighting 3D models. First, original 3D models in various formats are acquired. Then, the original 3D models undergo multi-step lightweighting processing to obtain a standard 3D model. Finally, target parameters are retrieved, and the standard 3D model is optimized based on these target parameters to obtain a target 3D model. These target parameters include target platform hardware configuration parameters and model application scenario parameters.

[0061] The embodiments in this specification employ the aforementioned lightweight 3D model processing method. By performing multi-step lightweight processing on the original 3D model, it transforms the original 3D model into a standard 3D model. This standard 3D model can serve as the underlying model. When a specific format 3D model file needs to be output to adapt to a target platform, this invention performs targeted optimization on this underlying model, thereby converting it into a target 3D model adapted to the specific target platform. This not only ensures the flexibility and customizability of the 3D model but also improves its compatibility and performance in different application scenarios. By retrieving target parameters to optimize the standard 3D model, the optimized model can be adapted to specific application scenarios. This ensures that the lightweight 3D model can run normally on the target platform and that the final 3D model achieves optimal performance and visual effects, thus achieving a balance between lightweighting and performance maintenance in 3D model design.

[0062] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate for the embodiments of this application described herein.

[0063] It is understood that the methods described in the embodiments of this specification can be applied to electronic devices, which can refer to electronic devices with data computing, processing, and storage capabilities. These electronic devices can be terminals such as PCs (Personal Computers), tablets, smartphones, wearable devices, and intelligent robots; they can also be servers. A server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0064] The following section, in conjunction with the accompanying drawings, describes a method for lightweighting a three-dimensional model according to an embodiment of this specification.

[0065] Figure 1This is a flowchart illustrating a lightweight 3D model processing method provided in the embodiments of this specification. Although this specification provides method operation steps or apparatus structures as shown in the following embodiments or figures, based on conventional or non-inventive methods, the method or apparatus may include more or fewer operation steps or module units after partial merging. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the apparatus is not limited to the execution order or module structure shown in the embodiments or figures of this specification. When the method or module structure is applied in actual devices, servers, or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or figures (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed processing or server cluster implementation environment). For specific implementation, please refer to... Figure 1 As shown, the method may include the following:

[0066] S1: Obtain original 3D models in various formats;

[0067] S2: Perform multi-step lightweighting processing on the original 3D model to obtain a standard 3D model;

[0068] S3: Retrieve the target parameters and optimize the standard 3D model based on the target parameters to obtain the target 3D model. The target parameters include the target platform hardware configuration parameters and the model application scenario parameters.

[0069] Based on the above embodiments, by performing multi-step lightweighting processing on the original 3D model, not only is the real-time rendering efficiency of the model improved, but it is also more adaptable to the needs of resource-constrained environments, providing a more efficient and sustainable solution for projects in the field of ground engineering. The standard 3D model obtained after multi-step lightweighting processing can be used as the underlying model. When it is necessary to output a 3D model file in a specific format to adapt to the target platform, this underlying model can be specifically optimized. By retrieving target parameters to optimize the standard 3D model, the optimized target 3D model can adapt to specific application scenarios, ensuring that the lightweighted 3D model can run normally on the target platform and that the final 3D model achieves optimal performance and visual effects.

[0070] In some embodiments, the original 3D model in S1 above can also be referred to as the original 3D model file, which can be created using 3D modeling software (such as AutoCAD, Revit, etc.) and then saved in a standard format (such as OBJ, FBX). The obtained original 3D model or original 3D model file has integrity and correctness, thereby ensuring that the accuracy and consistency of the model can still be maintained after multi-step lightweight processing.

[0071] In some embodiments, before proceeding to the multi-step lightweighting process, the original 3D model can be inspected. This can be done by using 3D modeling software or a viewer to carefully examine each element of the model. The model should be checked for unnecessary geometric details, such as tiny bumps, small holes, or overly dense triangular meshes. Simultaneously, the overall structure and complexity of the model should be evaluated to prepare it for subsequent multi-step lightweighting processes.

[0072] In some embodiments, the multi-step lightweighting process in S2 above, which involves processing the original 3D model to obtain a standard 3D model, may include, in specific implementations:

[0073] S21: Parse the original 3D model to obtain metadata, and construct a first-level 3D model based on the metadata. The metadata includes geometric data, material data, and texture data. The geometric data includes mesh vertices. The metadata of the original 3D model and the metadata of the first-level 3D model are the same.

[0074] S22: Read the first mesh vertex of the first-level three-dimensional model, locate the surface defect through the first mesh vertex, and reconstruct the range around the surface defect to obtain the second-level three-dimensional model;

[0075] S23: Read the second mesh vertices of the second-level 3D model, delete duplicate second mesh vertices, identify geometric surfaces based on the remaining second mesh vertices after deletion, merge adjacent geometric surfaces, and obtain the third-level 3D model;

[0076] S24: Read the third mesh vertex and texture data of the third-level 3D model, identify the target region through the third mesh vertex, remove the texture data in the target region, and obtain the fourth-level 3D model. The target region includes invisible regions and regions with a distance greater than a preset threshold.

[0077] S25: Create different levels of detail for the four-level three-dimensional model to obtain a five-level three-dimensional model;

[0078] S26: Read the fifth mesh vertex and material data of the fifth-level 3D model, identify small meshes through the fifth mesh vertex, merge adjacent small meshes into large meshes, identify similar material data of each large mesh, merge the similar material data, and read the fifth mesh vertex of the fifth-level 3D model again. Based on the reread fifth mesh vertex, identify and delete hidden faces to obtain the sixth-level 3D model.

[0079] S27: Perform model compression processing on the level 6 three-dimensional model to obtain a standard three-dimensional model.

[0080] In some embodiments, the multi-step lightweight processing in S2 above may include at least one of the following: preprocessing, simplification processing, texturing processing, hierarchical processing, merging and deletion processing, and model compression processing; wherein, preprocessing may include new model construction and surface defect reconstruction.

[0081] Specifically, preprocessing (corresponding to S21 and S22 above) can be used to obtain the original and complete first-level 3D model or first-level 3D model file. The first-level 3D model file is the processing object for multi-step lightweight processing. The metadata of the original 3D model is used to construct the first-level 3D model, ensuring the original structure of the 3D model. The geometric data in the metadata can include mesh vertices and geometric surfaces. After construction, the positions and numbers of mesh vertices in the original 3D model and the first-level 3D model are the same. At this time, the mesh vertices of the original 3D model (i.e., equivalent to the first mesh vertices) are directly retrieved for surface defect range reconstruction. This step reduces the time of scanning the first-level 3D model to obtain the first mesh vertices and ensures the quality of the first-level 3D model.

[0082] The simplification process (corresponding to S23 above) can be used to reduce the number of geometric objects in the secondary 3D model, so as to maintain the visual quality of the 3D model while reducing geometric complexity.

[0083] Texture processing (corresponding to S24 above) can be used to optimize and compress textures in a level 3D model, removing invisible or distant areas without requiring high-resolution textures, thus reducing file size while maintaining the visual quality of the 3D model. Distant areas refer to areas with a distance greater than a preset threshold, which can be set according to actual needs; this manual does not specify a particular threshold.

[0084] Hierarchical processing (corresponding to S25 above) can be used to create different levels of detail. The level of detail is related to the distance between the observer and the model; that is, when the distance between the observer and the model increases, the level of detail decreases, and the 3D model becomes a simplified model. When the distance between the observer and the model decreases, the level of detail increases, and the 3D model becomes a detailed model. This achieves a balance between performance and visual appeal in the 3D model.

[0085] The merging and deletion process (corresponding to S26 above) can be used to merge adjacent small meshes into a large mesh, merge similar material data, locate and delete hidden faces after merging, thereby reducing the size of the 3D model file and reducing unnecessary rendering workload. The mesh is located using its vertices.

[0086] Model compression processing (corresponding to S27 above) can be used to appropriately compress 3D models, reducing file size while retaining necessary 3D model information.

[0087] In some embodiments, the range reconstruction around the surface defect in S22 described above may, in specific implementation, include:

[0088] S221: Subdivide the mesh around the surface defect to obtain the target mesh;

[0089] S222: Extract the feature information of the target mesh, and use the feature mesh constructed with the feature information to fill the surface defects.

[0090] Specifically, surface defects can include tiny bumps, small holes, and overly dense triangular meshes. The target mesh can be smaller mesh cells obtained after subdivision. Feature meshes can be constructed using the characteristic information of small mesh cells to fill the surface defects of the first-level 3D model, resulting in a second-level 3D model.

[0091] In some embodiments, after S22 above, in specific implementation, it may further include:

[0092] S223: Back up the secondary 3D model to obtain a backup 3D model;

[0093] S224: When an anomaly occurs during the multi-step lightweighting process of the original 3D model, delete the abnormal model and call the backup 3D model.

[0094] Backups can prevent errors or unexpected situations from occurring during multi-step lightweight processing. Backup files can be created by simply copying and renaming them, ensuring that you can always revert to the original, unprocessed state.

[0095] In some embodiments, the creation of different levels of detail in S25 above may, in specific implementation, include:

[0096] Create different levels of detail using the following formula:

[0097]

[0098] Among them, LOD adjusted To create different levels of detail; LOD original The original level of detail is represented by: DistanceFactor, a constant between 0 and 1; DetailFactor, a positive integer; Distance, the distance between the observer and the model; and QualityFactor, a constant between 0 and 1.

[0099] This allows us to derive five-level 3D models with different levels. When a change in the distance between the observer and the model is detected, the level of detail can be calculated using the above formula, and the five-level 3D model belonging to the current level can be located based on the level of detail.

[0100] In some embodiments, merging adjacent small grids into a large grid in S26 above may, in specific implementation, include:

[0101] S261: Adjust the matrix information of the small mesh. The adjustment method can include translation, rotation, and scaling.

[0102] S262: Merge the vertex, edge, and face data of the adjusted small mesh into a new mesh object to form the initial large mesh;

[0103] S263: Delete duplicate mesh vertices in the initial large mesh, identify geometric surfaces based on the remaining mesh vertices after deletion, merge adjacent geometric surfaces to obtain a large mesh.

[0104] In some embodiments, the similar material data for identifying each large mesh in S26 above may, in specific implementation, include:

[0105] S264: Extract texture features from each large mesh, the texture features including color information, texture information, and reflectance information;

[0106] S265: Determine the color information difference, texture information difference, and reflectance difference in each large mesh;

[0107] S266: When the difference in color information, texture information, and reflectance of any two large meshes are all within the corresponding preset difference threshold range, the material data of the two large meshes are identified as similar material data.

[0108] Specifically, texture features can be extracted from each large mesh first, then the difference results of the extracted texture features from each large mesh can be determined, and then pairwise difference comparisons can be performed. If the difference in color information, texture information, and reflectivity between two meshes are all within the corresponding preset difference threshold range, then the material data of the two meshes are similar material data. The preset difference threshold can be set according to actual needs, and this manual does not impose specific limitations on it.

[0109] In some embodiments, optimizing the standard 3D model according to the target parameters in S3 above may, in specific implementation, include:

[0110] S31: Read the target platform hardware configuration parameters and model application scenario parameters, and obtain optimization parameters based on the target platform hardware configuration parameters and application scenario parameters. The target platform hardware configuration parameters include working efficiency, memory capacity, and processing speed. The model application scenario parameters include lighting requirements, material requirements, and the amount of data in the standard 3D model.

[0111] S32: Adjust the standard 3D model according to the optimization parameters to obtain the 3D model to be tested. The adjustment of the standard 3D model includes texture data adjustment, geometry data adjustment, and material property adjustment.

[0112] S33: Build a simulation platform according to the hardware configuration parameters of the target platform. The three-dimensional model to be tested is tested in the simulation platform. Adjust the optimization parameters according to the test results until the optimal optimization parameters are obtained.

[0113] S34: Adjust the standard 3D model according to the optimal optimization parameters to obtain the target 3D model.

[0114] It should be noted that the purpose of the test is to verify the integrity of the model and to ensure that the processed model is not distorted.

[0115] It should also be noted that different target platforms and application scenarios will result in different lightweight parameters for 3D models. Optimizing standard 3D models according to target parameters ensures that the 3D models can be loaded and rendered normally on the target platform to meet performance requirements, while also adapting to actual application scenarios to ensure the best performance and visual effects.

[0116] Specifically, the target platform's hardware configuration parameters and the model's application scenario parameters can be read to obtain optimization parameters. Then, the standard 3D model is adjusted based on these optimization parameters to obtain the 3D model to be tested. A simulation platform is then used to test the 3D model, and the optimization parameters are adjusted based on the test results. When the test results are optimal, the optimal optimization parameters (i.e., the target optimization parameters) are obtained. Finally, the standard 3D model can be adjusted based on the target optimization parameters to obtain the optimal target 3D model. By continuously adjusting the optimization parameters, the optimized model can adapt to specific application scenarios and run normally on the target platform, ensuring that the final optimized model achieves optimal performance and visual effects. The efficiency mentioned above refers to the processor's efficiency.

[0117] In some embodiments, the document also includes a detailed record of each lightweight processing operation and the parameter settings used. This document not only helps with internal team collaboration and communication, but also provides a reliable reference for future 3D model maintenance and modification, so as to better manage the model.

[0118] The present invention has the following technical effects:

[0119] Preprocessing ensures that the original and complete 3D model file is available before simplification, texturing, hierarchical processing, merging and deletion, and model compression. This provides a good foundation for subsequent processing. At the same time, backing up the original file ensures that it can be rolled back to the initial state in case of any unexpected situation during the processing.

[0120] In terms of simplifying geometry, the number of triangles in the model has been effectively reduced. This not only visually reduces the complexity of the model and improves rendering efficiency, but also makes it possible to load and interact more easily in performance-constrained environments;

[0121] Texture optimization and hierarchical LOD further emphasize the balance between visual quality and performance. By optimizing textures and removing unnecessary texture information, file size is reduced while maintaining visual quality. Hierarchical LOD technology allows for the use of different levels of detail at different viewing distances, effectively reducing model complexity at long distances and further improving real-time rendering performance.

[0122] Merging meshes and materials helps reduce the number of render calls, thereby improving rendering efficiency. Removing hidden faces also optimizes the rendering process, reduces unnecessary rendering calculations, and improves the model's performance in real-time rendering environments.

[0123] Model compression, as a key step, significantly reduces the size of model files, which helps with fast loading and transmission;

[0124] During the testing and optimization phase, system testing ensured that the lightweight model could run normally on the target platform. By continuously adjusting the lightweight parameters, the model was optimized to adapt to specific application scenarios, ensuring that the final model could achieve the best performance and visual effects.

[0125] The creation of documentation enabled the entire lightweighting process to be traceable and managed, providing clear records that facilitated team collaboration and future maintenance. The final lightweight model not only improved performance but also met design and engineering requirements while retaining key details.

[0126] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. For details, please refer to the foregoing descriptions of the relevant processing embodiments; they will not be repeated here.

[0127] The foregoing description of this method is for illustrative purposes only and describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0128] In a specific implementation scenario, see Figure 2 As shown, the lightweight processing method for 3D models provided in this manual can be applied to improve the real-time rendering efficiency of 3D models and reduce their complexity. The specific implementation process may include the following:

[0129] Preprocessing steps: The first processing tool can be used to analyze the original 3D model, construct a primary 3D model, and reconstruct surface defects of the primary 3D model to obtain a secondary 3D model. Afterwards, the model can be inspected using professional 3D modeling software or viewers to examine various aspects of the model, check for any flaws or details, and assess the overall structure and complexity of the model to prepare for subsequent simplification and other processing. The original file can also be backed up to prevent errors or unexpected situations during lightweight processing. Backup files can be created through simple copying and renaming, ensuring that the process can be reverted to its original, unprocessed state at any time.

[0130] Simplified processing steps: Secondary processing tools (such as model simplification tools) can be used to simplify the secondary 3D model, reduce the number of triangles in the model, and reduce geometric complexity while maintaining the visual quality of the model;

[0131] Texture optimization steps: Third-party processing tools (such as image editing software) can be used to texture the level 3D model to obtain a level 4D model. Specifically, the resolution of each texture can be checked to ensure that it still meets the project requirements visually. Textures that do not require high resolution in invisible areas or at a distance can be removed. Image editing software can be used to optimize and compress the textures to reduce file size.

[0132] Hierarchical processing steps: A fourth processing tool (such as the LOD tool) can be used to hierarchically process the level 4 3D model to obtain a level 5 3D model. Specifically, different levels of detail can be created for the model using the LOD tool or manually, ensuring that a simplified model is used in the distance and a more detailed model is used in the foreground, thus achieving a balance between performance and visual effects.

[0133] Merging and Deletion Processing Steps: A fifth processing tool (such as the hidden face culling tool or the rendering engine's clipping algorithm) can be used to merge and delete elements from the level 5 3D model, resulting in a level 6 3D model. By merging adjacent small meshes into a large mesh, the number of rendering calls is reduced, thereby improving the model's rendering efficiency. Simultaneously, merging similar materials reduces the number of materials, further reducing the file size. The hidden face culling tool or the rendering engine's clipping algorithm removes these invisible faces from the model.

[0134] Model compression processing steps: A sixth-level processing tool (such as a model compression tool) can be used to compress the Level 6 3D model to obtain a standard 3D model. Model compression aims to reduce the size of the model file and improve loading and rendering efficiency. The model file is compressed using a model compression tool, and appropriate compression parameters are selected according to requirements to retain necessary model information while reducing file size. Afterwards, testing and optimization can be performed to ensure that the model can be loaded and rendered normally on the target platform and meets performance requirements. By continuously adjusting the lightweighting parameters, the model is optimized to adapt to real-world application scenarios, ensuring optimal performance and visual effects.

[0135] The 3D modeling software used is AutoCAD or Revit. The imperfections include minor bumps, small holes, and overly dense triangular meshes. The model simplification tools include Decimate Modifier or ProOptimizer. The model compression tool is Draco. The lightweighting parameters include the degree of simplification and the distance setting for Level of Detail (LOD).

[0136] Although this specification provides the following examples or appendices Figure 3 The method or apparatus structure shown may include more or fewer combined operation steps or module units based on conventional or non-creative labor. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the apparatus is not limited to the execution order or module structure shown in the embodiments or drawings of this specification. When the method or module structure is applied in actual devices, servers, or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or drawings (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed processing or server cluster implementation environment). Based on the above-described lightweight 3D model processing method, this specification also proposes an embodiment of a lightweight 3D model processing apparatus. Figure 3 As shown, the device may specifically include the following modules:

[0137] The acquisition module 301 can be used to acquire original 3D models in various formats;

[0138] The lightweight processing module 302 can be used to perform multi-step lightweight processing on the original three-dimensional model to obtain a standard three-dimensional model.

[0139] The optimization module 303 can be used to retrieve target parameters and optimize the standard 3D model according to the target parameters to obtain the target 3D model. The target parameters include target platform hardware configuration parameters and model application scenario parameters.

[0140] In some embodiments, the lightweight processing module 302 can be specifically used to parse the original 3D model to obtain metadata, construct a first-level 3D model based on the metadata, wherein the metadata includes geometric data, material data, and texture data, and the geometric data includes mesh vertices, and the metadata of the original 3D model is the same as the metadata of the first-level 3D model; read the first mesh vertices of the first-level 3D model, locate surface defects through the first mesh vertices, and reconstruct the area around the surface defects to obtain a second-level 3D model; read the second mesh vertices of the second-level 3D model, delete duplicate second mesh vertices, identify geometric surfaces based on the remaining second mesh vertices after deletion, merge adjacent geometric surfaces to obtain a third-level 3D model; and read the third mesh of the third-level 3D model. Vertex and texture data are used to identify target regions through the third mesh vertices. Texture data in the target regions is removed to obtain a level four 3D model. The target regions include invisible areas and areas with a distance greater than a preset threshold. Different levels of detail are created on the level four 3D model to obtain a level five 3D model. The fifth mesh vertices and material data of the level five 3D model are read. Small meshes are identified through the fifth mesh vertices, and adjacent small meshes are merged into large meshes. Similar material data of each large mesh is identified and merged. The fifth mesh vertices of the level five 3D model are read again. Hidden faces are identified and deleted based on the read fifth mesh vertices to obtain a level six 3D model. The level six 3D model is then compressed to obtain a standard 3D model.

[0141] In some embodiments, the lightweight processing module 302 described above can also be used to subdivide the mesh around the surface defect to obtain a target mesh; extract the feature information of the target mesh; and use the feature information to construct a feature mesh to fill the surface defect.

[0142] In some embodiments, the lightweight processing module 302 described above can also be used to create different levels of detail according to the following formula:

[0143]

[0144] Among them, LOD adjusted To create different levels of detail; LOD original The original level of detail is represented by: DistanceFactor, a constant between 0 and 1; DetailFactor, a positive integer; Distance, the distance between the observer and the model; and QualityFactor, a constant between 0 and 1.

[0145] In some embodiments, the lightweight processing module 302 described above can also be used to extract texture features from each large mesh, the texture features including color information, texture information, and reflectivity information; determine the color information difference, texture information difference, and reflectivity difference in each large mesh; and when the color information difference, texture information difference, and reflectivity difference between any two large meshes are all within the corresponding preset difference threshold range, identify the material data of the two large meshes as similar material data.

[0146] In some embodiments, the lightweight processing module 302 can also be used to back up the secondary 3D model to obtain a backup 3D model; when an anomaly occurs during the multi-step lightweight processing of the original 3D model, the abnormal model is deleted and the backup 3D model is called.

[0147] In some embodiments, the optimization module 303 can be specifically used to read the target platform hardware configuration parameters and model application scenario parameters, obtain optimization parameters based on the target platform hardware configuration parameters and application scenario parameters, wherein the target platform hardware configuration parameters include working efficiency, memory capacity, and processing speed, and the model application scenario parameters include lighting requirements, material requirements, and the amount of data in the standard 3D model; adjust the standard 3D model according to the optimization parameters to obtain the 3D model to be tested, wherein the adjustment of the standard 3D model includes texture data adjustment, geometric data adjustment, and material property adjustment; build a simulation platform according to the target platform hardware configuration parameters, test the 3D model to be tested on the simulation platform, adjust the optimization parameters according to the test results until the optimal optimization parameters are obtained; and adjust the standard 3D model according to the optimal optimization parameters to obtain the target 3D model.

[0148] In some embodiments, the lightweight processing module described above may include a first processing tool, a second processing tool, a third processing tool, a fourth processing tool, a fifth processing tool, and a sixth processing tool;

[0149] The first processing tool is used to parse the original 3D model to obtain metadata, construct a first-level 3D model based on the metadata, read the first mesh vertex of the first-level 3D model, locate surface defects through the first mesh vertex, and perform range reconstruction around the surface defects to obtain a second-level 3D model.

[0150] The second processing tool is used to read the second mesh vertices of the secondary 3D model, delete duplicate second mesh vertices, identify geometric surfaces based on the remaining second mesh vertices after deletion, merge adjacent geometric surfaces, and obtain a tertiary 3D model.

[0151] The third processing tool is used to read the third mesh vertices and texture data of the third-level 3D model, identify the target region through the third mesh vertices, remove the texture data in the target region, and obtain the fourth-level 3D model.

[0152] The fourth processing tool is used to create different levels of detail on the four-level three-dimensional model to obtain a five-level three-dimensional model.

[0153] The fifth processing tool is used to read the fifth mesh vertex and material data of the fifth-level 3D model, identify small meshes through the fifth mesh vertex, merge adjacent small meshes into large meshes, identify similar material data of each large mesh, merge the similar material data, and read the fifth mesh vertex of the fifth-level 3D model again. Based on the reread fifth mesh vertex, it identifies and deletes hidden faces to obtain a sixth-level 3D model.

[0154] The sixth processing tool is used to compress the level 6 3D model to obtain a standard 3D model.

[0155] As can be seen from the above, the lightweight 3D model processing device provided in the embodiments of this specification can achieve the following technical effects:

[0156] 1. It has broad compatibility with various formats of original 3D model files, can read original 3D model files of multiple formats, extract metadata by parsing the original 3D model, use the metadata to construct a new model and reconstruct surface defects, ensuring that an original and complete 3D model file is available before subsequent lightweight processing. Furthermore, after reconstructing the surface defects, this invention automatically saves the new model and creates a backup, ensuring that it can be rolled back to the initial state in any unexpected situation during processing.

[0157] 2. Through multi-step lightweight processing, the original 3D model is transformed into a standard model. This standard model serves as the underlying foundation. When a 3D model file in a specific format needs to be output to adapt to the target platform, this invention will perform targeted optimization on this underlying model, thereby converting it into a target model that is adapted to the specific target platform. This not only ensures the flexibility and customizability of the model, but also improves the compatibility and performance of the model in different application scenarios.

[0158] 3. When optimizing the standard model, the optimization parameters are continuously adjusted to adapt the model to specific application scenarios. This ensures that the lightweight model can run normally on the target platform and that the final model achieves the best performance and visual effect, thus achieving an effective balance between model lightweighting and performance preservation.

[0159] This specification also provides an electronic device based on the above-described lightweight 3D model processing method, including a processor and a memory for storing processor-executable programs / instructions. Specifically, the processor can execute the following steps according to the program / instructions: acquiring original 3D models in various formats; performing multi-step lightweight processing on the original 3D models to obtain a standard 3D model; retrieving target parameters and optimizing the standard 3D model based on the target parameters to obtain a target 3D model. The target parameters include target platform hardware configuration parameters and model application scenario parameters.

[0160] To execute the above instructions more accurately, please refer to... Figure 4 As shown in the embodiments of this specification, another specific electronic device is also provided, wherein the electronic device includes a network communication port 401, a processor 402 and a memory 403, and the above structures are connected by internal cables so that the various structures can perform specific data interaction.

[0161] Specifically, the network communication port 401 can be used to acquire original 3D models in various formats;

[0162] The processor 402 can be used to perform multi-step lightweight processing on the original 3D model to obtain a standard 3D model; retrieve target parameters, optimize the standard 3D model according to the target parameters to obtain a target 3D model, wherein the target parameters include target platform hardware configuration parameters and model application scenario parameters;

[0163] The memory 403 can be used to store the corresponding instruction program.

[0164] In this embodiment, the network communication port 401 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0165] In this embodiment, the processor 402 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0166] In this embodiment, the memory 403 may include multiple layers. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0167] This specification also provides a computer storage medium based on the above-described lightweight 3D model processing method. The computer storage medium stores a computer program / instruction, which, when executed, performs the following: acquiring original 3D models in various formats; performing multi-step lightweight processing on the original 3D models to obtain a standard 3D model; retrieving target parameters; and optimizing the standard 3D model according to the target parameters to obtain a target 3D model. The target parameters include target platform hardware configuration parameters and model application scenario parameters.

[0168] In this embodiment, the storage medium includes, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), cache, hard disk drive (HDD), or memory card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured according to standards specified in the communication protocol for network connection communication.

[0169] In this embodiment, the specific functions and effects implemented by the program instructions stored in the computer storage medium can be explained in comparison with other implementation methods, and will not be repeated here.

[0170] While this specification provides the steps of operation for the methods described in the embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible order of execution among many steps and does not represent the only possible order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. The terms "first," "second," etc., are used to denote names and do not indicate any particular order.

[0171] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0172] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0173] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments of this specification.

[0174] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. This specification can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0175] Although this specification has been described by way of examples, those skilled in the art will recognize that many variations of this specification are possible without departing from its spirit, and it is intended that the appended claims cover such variations without departing from the spirit of this specification.

Claims

1. A method for lightweighting three-dimensional models, characterized in that, include: Obtain original 3D models in multiple formats; The original 3D model is subjected to a multi-step lightweighting process to obtain a standard 3D model; The target parameters are retrieved, and the standard 3D model is optimized based on the target parameters to obtain the target 3D model. The target parameters include the target platform hardware configuration parameters and the model application scenario parameters.

2. The method according to claim 1, characterized in that, The process of performing multi-step lightweighting on the original 3D model to obtain a standard 3D model includes: Metadata is obtained by parsing the original 3D model, and a first-level 3D model is constructed based on the metadata. The metadata includes geometric data, material data, and texture data. The geometric data includes mesh vertices. The metadata of the original 3D model and the metadata of the first-level 3D model are the same. The first mesh vertex of the first-level 3D model is read, the surface defect is located through the first mesh vertex, and the area around the surface defect is reconstructed to obtain the second-level 3D model; Read the second mesh vertices of the second-level 3D model, delete duplicate second mesh vertices, identify geometric surfaces based on the remaining second mesh vertices after deletion, merge adjacent geometric surfaces, and obtain the third-level 3D model; Read the third mesh vertex and texture data of the third-level 3D model, identify the target region through the third mesh vertex, remove the texture data in the target region, and obtain the fourth-level 3D model. The target region includes invisible regions and regions with a distance greater than a preset threshold. Different levels of detail are created on the aforementioned level four 3D model to obtain a level five 3D model; Read the fifth mesh vertex and material data of the fifth-level 3D model, identify small meshes through the fifth mesh vertex, merge adjacent small meshes into large meshes, identify similar material data of each large mesh, merge the similar material data, and read the fifth mesh vertex of the fifth-level 3D model again. Based on the read fifth mesh vertex, identify and delete hidden faces to obtain the sixth-level 3D model. The six-level 3D model is compressed to obtain a standard 3D model.

3. The method according to claim 2, characterized in that, The range reconstruction around the surface defect includes: The mesh around the surface defect is subdivided to obtain the target mesh; Extract the feature information of the target mesh, and use the feature information to construct a feature mesh to fill surface defects.

4. The method according to claim 2, characterized in that, The creation of different levels of detail includes: Create different levels of detail using the following formula: Among them, LOD adjusted To create different levels of detail; LOD original The original level of detail is represented by: DistanceFactor, a constant between 0 and 1; DetailFactor, a positive integer; Distance, the distance between the observer and the model; and QualityFactor, a constant between 0 and 1.

5. The method according to claim 2, characterized in that, The identification of similar material data for each large mesh includes: Extract texture features from each large grid, the texture features including color information, texture information, and reflectance information; Determine the differences in color information, texture information, and reflectance within each large mesh; When the differences in color information, texture information, and reflectivity between any two large meshes are all within the corresponding preset difference threshold range, the material data of the two large meshes are identified as similar material data.

6. The method according to claim 2, characterized in that, The method further includes: The secondary 3D model is backed up to obtain a backup 3D model; If an anomaly occurs during the multi-step lightweighting process of the original 3D model, the abnormal model is deleted and the backup 3D model is invoked.

7. The method according to claim 1, characterized in that, The optimization of the standard 3D model based on the target parameters includes: Read the target platform hardware configuration parameters and model application scenario parameters, and obtain optimization parameters based on the target platform hardware configuration parameters and application scenario parameters. The target platform hardware configuration parameters include working efficiency, memory capacity, and processing speed. The model application scenario parameters include lighting requirements, material requirements, and the amount of data in the standard 3D model. The standard 3D model is adjusted according to the optimization parameters to obtain the 3D model to be tested. The adjustment of the standard 3D model includes texture data adjustment, geometry data adjustment, and material property adjustment. A simulation platform is built based on the hardware configuration parameters of the target platform. The 3D model to be tested is tested on the simulation platform. The optimization parameters are adjusted based on the test results until the optimal optimization parameters are obtained. The standard 3D model is adjusted based on the optimal optimization parameters to obtain the target 3D model.

8. A lightweight processing device for three-dimensional models, characterized in that, include: The acquisition module is used to acquire original 3D models in various formats; The lightweight processing module is used to perform multi-step lightweight processing on the original 3D model to obtain a standard 3D model. The optimization module is used to retrieve target parameters and optimize the standard 3D model according to the target parameters to obtain the target 3D model. The target parameters include target platform hardware configuration parameters and model application scenario parameters.

9. The apparatus according to claim 8, characterized in that, The lightweight processing module includes a first processing tool, a second processing tool, a third processing tool, a fourth processing tool, a fifth processing tool, and a sixth processing tool; The first processing tool is used to parse the original 3D model to obtain metadata, construct a first-level 3D model based on the metadata, read the first mesh vertex of the first-level 3D model, locate surface defects through the first mesh vertex, and perform range reconstruction around the surface defects to obtain a second-level 3D model. The second processing tool is used to read the second mesh vertices of the secondary 3D model, delete duplicate second mesh vertices, identify geometric surfaces based on the remaining second mesh vertices after deletion, merge adjacent geometric surfaces, and obtain a tertiary 3D model. The third processing tool is used to read the third mesh vertices and texture data of the third-level 3D model, identify the target region through the third mesh vertices, remove the texture data in the target region, and obtain the fourth-level 3D model. The fourth processing tool is used to create different levels of detail on the four-level three-dimensional model to obtain a five-level three-dimensional model. The fifth processing tool is used to read the fifth mesh vertex and material data of the fifth-level 3D model, identify small meshes through the fifth mesh vertex, merge adjacent small meshes into large meshes, identify similar material data of each large mesh, merge the similar material data, and read the fifth mesh vertex of the fifth-level 3D model again. Based on the reread fifth mesh vertex, it identifies and deletes hidden faces to obtain a sixth-level 3D model. The sixth processing tool is used to compress the level 6 3D model to obtain a standard 3D model.

10. An electronic device, characterized in that, include: A memory and a processor, the processor and the memory being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to implement the steps of the method according to any one of claims 1 to 7.

11. A computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.

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