Data modeling method and device, storage medium and computer program product

Abstract

The invention discloses a data modeling method and device, a storage medium and a computer program product, and relates to the technical field of data processing, and the method comprises the steps: obtaining initial model data, and determining a model type of the initial model data; generating a corresponding basic white pattern based on the model type, and performing model optimization based on the basic white pattern to obtain an optimized model; a data interface is generated based on the optimization model, the data interface is stored in a preset storage space, and a model rendering engine reads the data interface to obtain the optimization model and performs view cone picture rendering on the optimization model. According to the invention, efficient transmission and management of the model data are realized, and loss and errors in a data transmission process can be reduced, so that the integrity and accuracy of the data are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to data modeling methods, devices, storage media and computer program products. Background Art

[0002] With the continuous advancement of computer technology, high-precision 3D modeling technology plays an increasingly important role in many industries. This technology is not only widely used in the design of complex buildings and the construction of geographic information systems, but also penetrates into many fields such as industrial manufacturing and digital entertainment, becoming an important force in promoting the development of related industries. However, in the actual operation of high-precision 3D modeling, it faces many challenges. In the process of processing large-scale and highly complex geometric models, the data quality of 3D modeling data is affected due to errors in the data acquisition process, equipment accuracy limitations, or losses in the data transmission process.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a data modeling method, device, storage medium and computer program product, aiming to solve the technical problem of low data quality of three-dimensional modeling data.

[0005] To achieve the above objectives, the present application proposes a data modeling method, which includes:

[0006] Acquire initial model data, and determine the model type of the initial model data;

[0007] Generate a corresponding basic white model based on the model type, and optimize the model based on the basic white model to obtain an optimized model;

[0008] A data interface is generated based on the optimization model, and the data interface is stored in a preset storage space, wherein a model rendering engine reads the data interface to obtain the optimization model, and performs a cone image rendering on the optimization model.

[0009] In one embodiment, the step of determining the model type of the initial model data includes:

[0010] A preset feature set is obtained, and feature rule matching is performed on the initial model data based on the preset feature set to obtain a model type corresponding to the initial model data.

[0011] In one embodiment, the step of generating a corresponding basic white model based on the model type includes:

[0012] Performing hierarchical processing on the initial model data to obtain hierarchical model data of multiple levels;

[0013] Drawing a vector graphics set based on the model type and the hierarchical model data of each level, wherein the vector graphics set is used to represent the geometric shapes and layouts of the initial model data at different levels;

[0014] The vector graphics set is geometrically optimized to obtain a basic white model.

[0015] In one embodiment, the step of optimizing the model based on the basic white model to obtain an optimized model includes:

[0016] Simplifying the basic white model geometrically to obtain simplified model data;

[0017] Performing model texture compression on texture data in the simplified model data to obtain first compressed data;

[0018] Compressing the vertex data and index data in the simplified model data to obtain second compressed data;

[0019] The first compressed data and the second compressed data are integrated into the simplified model data to obtain an optimized model.

[0020] In one embodiment, the step of compressing the vertex data and index data in the simplified model data to obtain second compressed data includes:

[0021] Optimizing the rendering pipeline in the simplified model data using rendering technology to obtain rendered model data;

[0022] The vertex data and index data in the rendered model data are compressed to obtain second compressed data.

[0023] In one embodiment, the preset storage space includes a main storage space and a backup storage space, and the step of storing the data interface in the preset storage space includes:

[0024] The data interface is copied to obtain a backup interface, the data interface is stored in the primary storage space, and the backup interface is stored in the backup storage space.

[0025] In one embodiment, the data modeling method further includes:

[0026] Upon receiving an access request from the model rendering engine, determining a first data interface indicated by the access request from the primary storage space, and determining a second data interface indicated by the access request from the backup storage space;

[0027] A target data interface with the least number of connections is determined from the first data interface and the second data interface, and the access request is forwarded to the target data interface.

[0028] In addition, to achieve the above-mentioned purpose, the present application also proposes a data modeling device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the data modeling method described above.

[0029] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data modeling method described above are implemented.

[0030] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the data modeling method described above.

[0031] In the present application, initial model data is obtained, and the model type of the initial model data is determined; a corresponding basic white model is generated based on the model type, and a model optimization is performed based on the basic white model to obtain an optimized model; a data interface is generated based on the optimized model, and the data interface is stored in a preset storage space, wherein a model rendering engine reads the data interface to obtain the optimized model, and performs a cone screen rendering on the optimized model.

[0032] In this application, by generating a basic white model, an optimizable starting point is provided for subsequent data processing. Through model optimization, the complexity of the model is reduced while the visual effect and accuracy of the model are maintained. By generating a standardized data interface and storing it in a preset storage space, efficient transmission and management of model data is achieved, which can reduce losses and errors in the data transmission process, thereby improving data integrity and accuracy, and solving the data quality problems faced by high-precision three-dimensional modeling technology in practical applications, thereby improving the accuracy of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings used in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A flowchart of the first embodiment of the data modeling method of the present application is provided;

[0036] Figure 2 A schematic diagram of an architecture involved in a data modeling method provided in an embodiment of the present application;

[0037] Figure 3 A brief flowchart of a data modeling method provided in one embodiment of the present application;

[0038] Figure 4 This is a schematic diagram of the module structure of the data modeling device according to an embodiment of the present application;

[0039] Figure 5 A schematic diagram of the device structure of the hardware operating environment involved in the data modeling method in the embodiment of the present application.

[0040] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0041] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0042] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0043] The main solution of the embodiment of the present application is: obtaining initial model data and determining the model type of the initial model data; generating a corresponding basic white model based on the model type, and optimizing the model based on the basic white model to obtain an optimized model; generating a data interface based on the optimized model, and storing the data interface in a preset storage space, wherein a model rendering engine reads the data interface to obtain the optimized model, and performs a cone screen rendering on the optimized model.

[0044] In this embodiment, for ease of description, the following description is made with the data modeling device as the execution subject.

[0045] Although high-precision 3D modeling technology has broad application prospects, it faces many challenges in actual operation. First, there is a problem of low data quality in the processing of large-scale and highly complex geometric models. This may be caused by errors in the data acquisition process, equipment accuracy limitations, or losses in the data transmission process. Low data quality will directly affect the accuracy and efficiency of subsequent modeling. Secondly, the existing modeling data processing technology has a relatively complex processing flow and limited model accuracy when processing complex models. Traditional modeling methods perform a lot of manual adjustments and optimizations on the model, which is not only time-consuming and labor-intensive, but also difficult to ensure the final accuracy of the model. At the same time, due to technical limitations, the existing modeling methods may not achieve the expected modeling effect when processing certain specific shapes or structures. In addition, with the continuous expansion of application scenarios, the demand for high-precision modeling technology is becoming increasingly diversified. Different fields and different application scenarios have different requirements for the accuracy, processing speed, flexibility, etc. of modeling data. However, the existing modeling technology is difficult to meet these diverse needs, which limits the application of high-precision modeling technology in a wider range of fields.

[0046] The present application provides a solution, which provides an optimizable starting point for subsequent data processing by generating a basic white model, reduces the complexity of the model through model optimization while maintaining the visual effect and accuracy of the model, and achieves efficient transmission and management of model data by generating a standardized data interface and storing it in a preset storage space, which can reduce losses and errors in the data transmission process, thereby improving data integrity and accuracy, and solving data quality problems faced by high-precision three-dimensional modeling technology in practical applications, thereby improving the accuracy of the model.

[0047] In addition, this application achieves efficient and accurate processing of modeling data by optimizing data processing procedures, introducing advanced modeling algorithms and enhancing the degree of system automation to meet the needs of high-precision modeling in complex scenarios.

[0048] At the same time, this application also proposes innovative technologies for automatic multi-level detail division and virtualized geometry. The automatic multi-level detail division technology can automatically select different detail levels according to the distance of the lens, thereby reducing the rendering overhead of the model, while the virtualized geometry technology only loads and renders the geometric data in the lens field of view. Even if there are hundreds of millions of polygons in the scene, only those geometric data that contribute to the rendering of the current frame are processed, thereby improving processing efficiency.

[0049] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a data modeling device, etc. The following takes a data modeling device as an example to illustrate this embodiment and the following embodiments.

[0050] Based on this, the present application embodiment provides a data modeling method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the data modeling method of the present application.

[0051] In this embodiment, the data modeling method includes steps S10 to S30:

[0052] Step S10, obtaining initial model data, and determining the model type of the initial model data;

[0053] It should be noted that the initial model data refers to the basic data for building the model that is collected, sorted or generated before the modeling process or in the early stage of modeling. It may include basic information such as geometry, size, material, texture, etc. The initial model data is the basis for model construction, and its quality and accuracy directly affect the accuracy and effect of the final model. Model type refers to the classification of models into different categories or types based on factors such as modeling purpose, application scenario and required functions. Model type is used to characterize the type of model, for example, plant model, architectural model, animal model, etc.

[0054] The initial model data is obtained through data acquisition equipment or systems. These data may come from various channels, such as laser scanning, photogrammetry, CAD design, etc. After obtaining the initial data, the initial data is preprocessed. The specific preprocessing process is not limited and may include operations such as noise removal, hole filling, and error correction to improve data quality. The specific settings can be made according to actual needs. Subsequently, feature recognition and analysis technology is used to determine the model type of the initial model data, such as buildings, terrain, industrial equipment, etc. By accurately acquiring and preprocessing the initial model data and accurately identifying the model type, a solid foundation is laid for the smooth progress of subsequent steps, which can improve the accuracy and efficiency of modeling and reduce errors and redundancy in subsequent processing.

[0055] Step S20, generating a corresponding basic white model based on the model type, and performing model optimization based on the basic white model to obtain an optimized model;

[0056] After the model type is determined, the corresponding basic white model is generated by the algorithm according to the characteristics of different types of models. It should be noted that the basic white model refers to a simple geometric shape or structure without material, texture, color and other attributes created during the modeling process. The basic white model serves as the basis for subsequent model optimization, texture mapping and rendering.

[0057] After generating the basic white model, a series of model optimization operations are performed to obtain the optimized model. Specific optimization operations may include geometry simplification, texture compression, rendering pipeline optimization, etc., which are not limited here. The optimization operation can reduce the complexity of the model and improve rendering efficiency while maintaining the visual effect and accuracy of the model. By generating a basic white model and performing model optimization, the complexity and rendering overhead of the model can be significantly reduced, and the rendering speed and accuracy can be improved. At the same time, the optimized model has better scalability and flexibility, which is convenient for subsequent application and modification.

[0058] Step S30, generating a data interface based on the optimization model, and storing the data interface in a preset storage space, wherein a model rendering engine reads the data interface to obtain the optimization model, and performs a cone image rendering on the optimization model.

[0059] In this embodiment, after the optimization model is obtained, it is converted into a format that is convenient for storage and transmission, namely, a data interface. The data interface is a standardized data structure that contains all necessary information of the optimization model, such as vertex data, texture data, rendering parameters, etc. By generating the data interface, the optimization model can be conveniently stored in a preset storage space for subsequent use.

[0060] When using an optimized model, the model rendering engine will read the data interface stored in the server, obtain all the information of the optimized model, and then use the rendering engine's cone rendering technology to render the optimized model. It should be noted that the model rendering engine refers to a computer program or software component used to convert a three-dimensional model into a two-dimensional image or animation. The rendering engine converts the three-dimensional model into a realistic two-dimensional image or animation by calculating effects such as light, shadow, and texture. Cone rendering refers to the process of cropping and rendering a three-dimensional model based on the observer's perspective and the range of the cone. The cone is a geometric body determined by the observer's position, line of sight direction, and field of view. During the rendering process, only the part of the model located within the cone will be rendered, thereby improving rendering efficiency and visual effects.

[0061] By generating a data interface and storing it in a preset storage space, the optimized model can be easily stored, transmitted and used. At the same time, the cone rendering technology of the model rendering engine can further improve the rendering efficiency and speed to meet the high performance requirements in practical applications.

[0062] In a feasible implementation manner, the preset storage space includes a main storage space and a backup storage space, and the step S30: storing the data interface in the preset storage space includes:

[0063] Step S301, the step is to copy the data interface to obtain a backup interface, store the data interface in the primary storage space and store the backup interface in the backup storage space.

[0064] In order to ensure data security and reliability and avoid data loss or damage caused by a single server failure, it is a common practice to use a primary-backup storage space architecture. After the data interface is stored on the primary storage space, a copy is immediately copied to the backup storage space. In this way, even if there is a problem with the primary storage space, the backup interface on the backup storage space can still be used by the model rendering engine. The storage method of primary and backup storage spaces ensures redundant backup of data interfaces and reduces the risk of data loss. When the primary storage space fails, the backup storage space can quickly take over to ensure normal access to the model rendering engine.

[0065] In a feasible implementation manner, the data modeling method further includes steps S40 to S50:

[0066] Step S40, upon receiving an access request from the model rendering engine, determining a first data interface indicated by the access request from the primary storage space, and determining a second data interface indicated by the access request from the backup storage space;

[0067] Since the data interface is stored in both the primary and backup storage spaces, when the model rendering engine requests access to a data interface, the system queries the primary and backup storage spaces at the same time, and determines the data interface indicated by the access request from the primary and backup storage spaces, that is, the first data interface and the second data interface. Specifically, the data interface can be found by parsing and matching the identifier in the request (such as the unique ID of the data interface), and the specific details are not limited here. By querying the primary and backup storage spaces at the same time, it can be ensured that the data interface obtained is the latest, avoiding inconsistencies caused by data synchronization delays.

[0068] Step S50: determining a target data interface with the least number of connections from the first data interface and the second data interface, and forwarding the access request to the target data interface.

[0069] The number of connections refers to the number of clients currently connected to the server and transmitting data. Selecting the data interface with the least number of connections for access can reduce the load pressure on the server and improve the response speed and system performance. By selecting the data interface with the least number of connections between the first data interface and the second data interface for access, the load of the server can be balanced to avoid a server crash due to overload.

[0070] By introducing the master-slave storage space architecture, receiving access requests and determining the data interface, and determining the target data interface and forwarding the access requests, this implementation not only improves the security and reliability of the data, but also optimizes the server load and system performance, making the high-precision three-dimensional modeling technology more stable, efficient and easy to maintain in practical applications.

[0071] In this embodiment, an optimizable starting point is provided by generating a basic white model, and the complexity of the model is reduced through model optimization while maintaining the visual effect and accuracy of the model. By generating a standardized data interface and storing it in a preset storage space, efficient transmission and management of model data is achieved, which can reduce losses and errors in the data transmission process, thereby improving data integrity and accuracy, and solving data quality problems faced by high-precision three-dimensional modeling technology in practical applications, thereby improving the accuracy of the model.

[0072] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 Step S10: The step of determining the model type of the initial model data comprises:

[0073] Step S101: Obtain a preset feature set, and perform feature rule matching on the initial model data based on the preset feature set to obtain a model type corresponding to the initial model data.

[0074] A preset feature set refers to a set of predefined data or rules used to identify and process model features during model optimization or rendering. The preset feature set may include geometric features (such as edges and corners), texture features (such as color and texture patterns), etc. Feature rule matching refers to the process of finding and matching specific features in model data based on the preset feature set. By matching features, key parts or details in the model can be identified and processed or optimized accordingly.

[0075] A preset feature set is obtained, which contains key features or rules of various model types. Then, these features or rules are used to match the initial model data to find the most suitable feature set, thereby determining the model type of the initial model data. Specifically, the feature rule matching process may involve a variety of methods such as comparison of geometric shapes, similarity calculation of texture features, and matching of material properties, which are not limited here.

[0076] By matching the preset feature set and feature rules, the model type of the initial model data can be automatically determined without manual intervention, thereby improving the automation of the entire data modeling process. The feature set and rules are defined based on a deep understanding and analysis of various model types, so the matching results have a high degree of accuracy, allowing the subsequent basic white model generation and model optimization steps to be performed for the correct model type. Through automated and accurate model type determination, subsequent processing steps can be entered more quickly, thereby improving the processing efficiency of the entire data modeling process.

[0077] In a feasible implementation, the step S20: the step of generating a corresponding basic white model based on the model type, comprises:

[0078] Step S201, hierarchical processing is performed on the initial model data to obtain hierarchical model data of multiple levels;

[0079] Layered processing refers to the process of dividing model data into multiple layers or levels, and processing or optimizing each layer independently. Layered processing helps to simplify the complexity of model optimization and rendering, and improve processing efficiency and flexibility. For example, in a three-dimensional model, the model can be divided into a geometry layer, a texture layer, a lighting layer, etc., and each layer can be optimized and rendered separately. Layered model data refers to model data obtained after layered processing, which contains multiple layers or levels. Layered model data allows designers to have more precise control and adjustment of the model. For example, in the field of architectural design, the architectural model can be divided into a structural layer, a decorative layer, an equipment layer, etc.

[0080] The specific process of hierarchical processing of the initial model data can be: parsing the initial model data to identify the various components of the model, such as walls, roofs, doors and windows, etc.; dividing the model into multiple levels according to the components of the model and their interrelationships. For example, the entire building can be divided into different levels such as floors, rooms, and furniture; reorganizing the parsed data according to the hierarchical relationship to generate multiple levels of hierarchical model data.

[0081] Step S202, drawing a vector graphics set based on the model type and the hierarchical model data of each level, wherein the vector graphics set is used to represent the geometric shapes and layouts of the initial model data at different levels;

[0082] A vector image collection is a collection of vector images. A vector image is mathematically defined as a series of points connected by lines. These points are described by mathematical formulas or algorithms and can accurately represent the image's shape, size, color and other attributes. Vector images take up less memory space and can be freely and unlimitedly recombined and edited. Because they are based on mathematical formulas, they can be easily scaled, rotated and deformed without losing image quality.

[0083] According to the model type and hierarchical model data, a vector diagram set representing the geometric shape and layout of the model at different levels is drawn. The drawing process of the vector diagram set can be: according to the model type and hierarchical model data, the vector elements drawn at each level, such as lines, polygons, etc., are determined; using drawing tools or software, according to the determined vector elements, the corresponding vector diagrams are drawn at each level; and the vector diagrams of each level are integrated into a vector diagram set.

[0084] Step S203, geometrically optimizing the vector image set to obtain a basic white model.

[0085] Perform geometric optimization on the vector map set to generate a basic white model. Geometric simplification refers to reducing the complexity and computational cost of the model by reducing the number of geometric elements in the model while maintaining the basic shape and features of the model. The main purpose of geometric simplification is to improve the rendering speed of the model and reduce the computational cost while maintaining the visual effect and accuracy of the model. The methods of geometric simplification can include vertex clustering, edge reduction, triangle folding, etc. By merging or deleting some vertices and polygons in the model, the geometric complexity of the model can be reduced.

[0086] It is understandable that geometry optimization can reduce the complexity of the model, reduce the amount of calculation during rendering, and improve rendering efficiency. By optimizing the direction of lines and adjusting the layout, the visual effect of the model can be improved.

[0087] In a feasible implementation manner, the step S20: the step of optimizing the model based on the basic white model to obtain the optimized model, comprises:

[0088] Step S204, geometrically simplifying the basic white model to obtain simplified model data;

[0089] By optimizing the basic white model, the complexity of the model can be significantly reduced, rendering efficiency can be improved, and the visual effect and accuracy of the model can be maintained. Geometry simplification is the first step in model optimization, which aims to remove unnecessary geometric details in the basic white model, reduce the number of vertices and faces of the model, and thus reduce the complexity of the model.

[0090] The specific process of geometric simplification can be: conduct a detailed analysis of the basic white model to identify low-precision details with accuracy below the preset threshold; select a suitable geometric simplification algorithm, such as edge collapse, vertex clustering, etc., according to the type and characteristics of the model; apply the selected simplification algorithm to geometrically simplify the basic white model to obtain simplified model data. Geometric simplification can reduce the number of vertices and faces of the model, thereby reducing the complexity of the model. The simplified model requires less calculation when rendering, which improves rendering efficiency. Although some geometric details are removed, the simplified model still maintains good visual effects and accuracy.

[0091] Step S205, performing model texture compression on the texture data in the simplified model data to obtain first compressed data;

[0092] Texture data refers to image data used to describe the surface details of an object. The image data comes from field photography photos and material image libraries, and is mapped to the surface of a three-dimensional model after processing. Texture data can simulate the material, color, gloss and other properties of an object. Texture data determines the appearance and texture of the model.

[0093] The specific process of model texture compression can be: extracting texture data from simplified model data; selecting a suitable compression algorithm, such as JPEG, PNG, etc., according to the type and characteristics of texture data; performing compression operation: applying the selected compression algorithm to compress the texture data to obtain first compressed data. It can be understood that model texture compression significantly reduces the size of texture data, reduces storage and transmission costs, and reduces the amount of calculation required for loading and rendering of compressed texture data, thereby improving rendering efficiency. In addition, although the texture data is compressed, the compressed texture still maintains good quality and visual effects.

[0094] Step S206, compressing the vertex data and index data in the simplified model data to obtain second compressed data;

[0095] Vertex data refers to the data that represents the key points of a specific thing or event in a graph or network. Vertex data usually contains information such as location and attributes. In 3D modeling, vertex data is one of the basic elements that make up the model. Each vertex has its unique 3D coordinates and possible attributes (such as color, normal, etc.). Index data is a data structure used to accelerate data retrieval and sorting. It is usually a pointer or reference to a data row in a data table. By creating an index for the data table, you can quickly locate the required data row without traversing the entire data table. The types of data include clustered indexes and non-clustered indexes. Vertex data and index data are the core components of the model. They determine the geometry and structure of the model. Compressing these data can further reduce the size of the model and improve rendering efficiency.

[0096] The process of compressing vertex data and index data can be: extracting vertex data and index data from simplified model data; selecting a suitable compression method, such as quantization, compression algorithm, etc., according to the characteristics of vertex data and index data; applying the selected compression method to compress vertex data and index data to obtain second compressed data. It can be understood that the compression of vertex data and index data further reduces the size of the model, reduces storage and transmission costs, and reduces the amount of calculation required for loading and rendering the compressed vertex data and index data, thereby improving rendering efficiency.

[0097] Step S207: Integrate the first compressed data and the second compressed data into the simplified model data to obtain an optimized model.

[0098] The first compressed data and the second compressed data are integrated into the simplified model data to obtain a final optimized model. The process of integrating the first compressed data and the second compressed data may be: integrating the first compressed data and the second compressed data into the simplified model data according to the original data structure and format; verifying and optimizing: verifying and optimizing the integrated optimized model to ensure the integrity, correctness and performance of the model. By integrating the first compressed data and the second compressed data, the final optimized model is obtained, and the optimized model has higher performance in storage, transmission and rendering.

[0099] Through steps such as geometry simplification, model texture compression, vertex data and index data compression, and data integration, the basic white model is effectively optimized, and a high-quality optimized model is obtained, which reduces the complexity and size of the model, improves rendering efficiency and model performance, and maintains the visual effect and accuracy of the model.

[0100] In one feasible implementation, the step S206: the step of compressing the vertex data and index data in the simplified model data to obtain second compressed data, comprises:

[0101] Step S2061, using rendering technology to optimize the rendering pipeline in the simplified model data to obtain rendered model data;

[0102] Rendering technology refers to the process of converting a three-dimensional model or scene into a two-dimensional image. The rendering process may include modeling, texture mapping, lighting calculation, projection transformation, viewpoint transformation and other steps. These steps work together on the three-dimensional model or scene to ultimately generate a realistic two-dimensional image.

[0103] Before compressing vertex data and index data, the rendering pipeline is optimized through rendering technology to improve the rendering efficiency and visual effect of the model. The optimization of the rendering pipeline may include reducing unnecessary rendering operations, optimizing shader code, using more efficient data structures, etc.

[0104] The process of rendering technology optimizing the rendering pipeline can be: conducting a detailed analysis of the current rendering pipeline to identify which links have performance bottlenecks or room for optimization, that is, identifying the pipeline to be optimized; based on the analysis results, selecting the appropriate rendering technology optimization strategy; applying the selected optimization strategy to optimize the rendering pipeline to obtain the rendered model data. By optimizing the rendering pipeline, unnecessary rendering operations can be reduced and the rendering speed can be increased. The optimized rendering pipeline may use more efficient shader code and data processing methods, thereby improving the visual effect of the model.

[0105] Step S2062: compress the vertex data and index data in the rendered model data to obtain second compressed data.

[0106] After the rendering pipeline is optimized, the vertex data and index data in the rendered model data are compressed. The purpose of compression is to further reduce the size of the model data and improve storage and transmission efficiency. The specific compression process is not described here.

[0107] It can be understood that through rendering pipeline optimization and vertex / index data compression, the simplified model data is effectively optimized and compressed, the rendering efficiency and visual effect of the model are improved, the size of the model data is significantly reduced, and the storage and transmission efficiency is improved. At the same time, by maintaining the accuracy and geometry of the model, it is ensured that the optimized model still has good visual effects and performance.

[0108] In this embodiment, an optimizable starting point is provided by generating a basic white model, and the complexity of the model is reduced through model optimization while maintaining the visual effect and accuracy of the model. By generating a standardized data interface and storing it in a preset storage space, efficient transmission and management of model data is achieved, which can reduce losses and errors in the data transmission process, thereby improving data integrity and accuracy, and solving data quality problems faced by high-precision three-dimensional modeling technology in practical applications, thereby improving the accuracy of the model.

[0109] For example, to help understand the implementation process of the data modeling method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 2 , Figure 2The system architecture involved in a data modeling method is provided, which specifically includes: data import, designing and implementing a SHP file import system, parsing the legality and validity of the SHP file; model analysis, extracting data information model data in the file for feature rule matching and stratification, thereby obtaining an initial classification; a strategy selection module, drawing a corresponding vector map set according to the classification information, and generating a basic white model by using an appropriate amount of calculation; a lightweight operation module, performing dynamic threshold calculation through algorithms such as NTC and Draco to improve the accuracy and effectiveness of the fine division model, and structuring the model by assigning material IDs through automatic texture mapping; storage and output, making the model into a data interface after optimization, and storing the file locally or in a server; user interface, after optimization, reading the model data interface through the UnrealEngine rendering engine to obtain the optimized model, and rendering the cone screen; a maintenance and update module, designing a system to support regular updates of algorithms and libraries, and implementing a logging and monitoring system to track performance and user behavior; deployment and testing, deploying the application to a local server or a cloud server, such as AWS or Azure, setting automatic expansion and load balancing to cope with high load conditions, and performing unit testing and integration testing in a development environment. Use sample SHP files to test all functions and ensure that the output interface files can be displayed normally and the performance meets expectations.

[0110] Based on the above system architecture, please refer to Figure 3 , Figure 3 A brief flow chart of a data modeling method is provided. Specifically, the system first matches and layers the initial model data according to the feature rules to achieve preliminary classification of the model data. Then, the corresponding vector map set is drawn according to the classification information, and the basic white model is generated by appropriate calculation. The basic white model is geometrically simplified, and the model details at different levels are automatically divided according to the set number of levels. The system then compresses the model texture through the NTC compression algorithm, thereby reducing the amount of image data while ensuring the texture details and color. In order to further optimize the model operation efficiency, the system uses the Draco compression algorithm to compress the vertex data and index data, thereby reducing the storage and transmission requirements of the model data.

[0111] In this embodiment, geometric simplification refers to reducing the geometric complexity of the model by merging or deleting some vertices and polygons in the model, balancing the complexity and visual effect of the model by using appropriate geometric simplification algorithms including vertex clustering, edge reduction, triangle folding, etc., and reducing the number of vertices and polygons as much as possible while maintaining the original shape and features of the model. Texture compression refers to reducing the data volume of the texture image through compression algorithms through neural texture compression (NTC) to reduce storage and rendering overhead. The innovation lies in that when compressing textures, a high compression ratio can be achieved, and the data volume of the image can be greatly reduced while maintaining the detailed information and color of the texture. Optimizing the rendering pipeline: The rendering pipeline is optimized by using vertex and pixel shaders reasonably, optimizing the settings of the rendering state, and using efficient rendering technologies such as batch processing, instantiation, pre-calculated lighting, etc. Automatic division of multiple levels of detail refers to automatically selecting different levels of detail from a series of pre-calculated and stored models according to the distance of the lens. Objects far away from the observer can use lower levels of detail, thereby reducing the complexity of rendering, thereby effectively reducing the rendering overhead of the model and improving the visual effect. Virtualized geometry is a geometry streaming processing method, that is, only the geometric data in the camera's field of view is loaded and rendered to virtualize the geometry. This means that even if there are hundreds of millions of polygons in the scene, this embodiment only processes those geometric data that contribute to the rendering of the current frame. In particular, different rasterization schemes are selected according to the size of the triangle. Small triangles can use soft rasterization based on Compute Shader, and large triangles can use traditional hard rasterization.

[0112] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the data modeling method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0113] This application also provides a data modeling device, please refer to Figure 4 , the data modeling device comprises:

[0114] An acquisition module 10 is used to acquire initial model data and determine a model type of the initial model data;

[0115] An optimization module 20, configured to generate a corresponding basic white model based on the model type, and perform model optimization based on the basic white model to obtain an optimized model;

[0116] The generation module 30 is used to generate a data interface based on the optimization model and store the data interface in a preset storage space, wherein the model rendering engine reads the data interface to obtain the optimization model and performs cone rendering on the optimization model.

[0117] Optionally, the acquisition module 10 is further used for:

[0118] A preset feature set is obtained, and feature rule matching is performed on the initial model data based on the preset feature set to obtain a model type corresponding to the initial model data.

[0119] Optionally, the optimization module 20 is further used for:

[0120] Performing hierarchical processing on the initial model data to obtain hierarchical model data of multiple levels;

[0121] Drawing a vector graphics set based on the model type and the hierarchical model data of each level, wherein the vector graphics set is used to represent the geometric shapes and layouts of the initial model data at different levels;

[0122] The vector graphics set is geometrically optimized to obtain a basic white model.

[0123] Optionally, the optimization module 20 is further used for:

[0124] Simplifying the basic white model geometrically to obtain simplified model data;

[0125] Performing model texture compression on texture data in the simplified model data to obtain first compressed data;

[0126] Compressing the vertex data and index data in the simplified model data to obtain second compressed data;

[0127] The first compressed data and the second compressed data are integrated into the simplified model data to obtain an optimized model.

[0128] Optionally, the optimization module 20 is further used for:

[0129] Optimizing the rendering pipeline in the simplified model data using rendering technology to obtain rendered model data;

[0130] The vertex data and index data in the rendered model data are compressed to obtain second compressed data.

[0131] Optionally, the preset storage space includes a main storage space and a backup storage space, and the generating module 30 is further used for:

[0132] The data interface is copied to obtain a backup interface, the data interface is stored in the primary storage space, and the backup interface is stored in the backup storage space.

[0133] Optionally, the generating module 30 is further used for:

[0134] Upon receiving an access request from the model rendering engine, determining a first data interface indicated by the access request from the primary storage space, and determining a second data interface indicated by the access request from the backup storage space;

[0135] A target data interface with the least number of connections is determined from the first data interface and the second data interface, and the access request is forwarded to the target data interface.

[0136] The data modeling device provided by the present application adopts the data modeling method in the above embodiment, which can solve the technical problem of low data quality of three-dimensional modeling data. Compared with the prior art, the beneficial effects of the data modeling device provided by the present application are the same as the beneficial effects of the data modeling method provided by the above embodiment, and other technical features in the data modeling device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0137] The present application provides a data modeling device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data modeling method in the above-mentioned embodiment one.

[0138] Reference below Figure 5 , which shows a schematic diagram of the structure of a data modeling device suitable for implementing an embodiment of the present application. The data modeling device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The data modeling device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0139] like Figure 5As shown, the data modeling device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the data modeling device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. The following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the data modeling device to communicate with other devices wirelessly or by wire to exchange data. Although the data modeling device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0140] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0141] The data modeling device provided by the present application adopts the data modeling method in the above embodiment to solve the technical problem of low data quality of three-dimensional modeling data. Compared with the prior art, the beneficial effects of the data modeling device provided by the present application are the same as the beneficial effects of the data modeling method provided by the above embodiment, and other technical features in the data modeling device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0142] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0143] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0144] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the data modeling method in the above-mentioned embodiment.

[0145] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0146] The computer-readable storage medium may be included in the data modeling device; or may exist independently without being assembled into the data modeling device.

[0147] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the data modeling device, the data modeling device: obtains initial model data and determines the model type of the initial model data; generates a corresponding basic white model based on the model type, and performs model optimization based on the basic white model to obtain an optimized model; generates a data interface based on the optimized model, and stores the data interface in a preset storage space, wherein the model rendering engine reads the data interface to obtain the optimized model, and performs a cone screen rendering on the optimized model.

[0148] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0150] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0151] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned data modeling method, and can solve the technical problem of low data quality of three-dimensional modeling data. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the data modeling method provided in the above-mentioned embodiment, and will not be repeated here.

[0152] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned data modeling method when executed by a processor.

[0153] The computer program product provided by the present application can solve the technical problem of low data quality of three-dimensional modeling data. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the data modeling method provided by the above embodiment, which will not be repeated here.

[0154] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A data modeling method, characterized in that: The data modeling method comprises: Acquire initial model data, and determine the model type of the initial model data; Generate a corresponding basic white model based on the model type, and optimize the model based on the basic white model to obtain an optimized model; A data interface is generated based on the optimization model, and the data interface is stored in a preset storage space, wherein a model rendering engine reads the data interface to obtain the optimization model, and performs a cone rendering on the optimization model.

2. The data modeling method according to claim 1, characterized in that: The step of determining the model type of the initial model data comprises: A preset feature set is obtained, and feature rule matching is performed on the initial model data based on the preset feature set to obtain a model type corresponding to the initial model data.

3. The data modeling method according to claim 2, characterized in that: The step of generating a corresponding basic white model based on the model type includes: Performing hierarchical processing on the initial model data to obtain hierarchical model data of multiple levels; Drawing a vector graphics set based on the model type and the hierarchical model data of each level, wherein the vector graphics set is used to represent the geometric shapes and layouts of the initial model data at different levels; The vector graphics set is geometrically optimized to obtain a basic white model.

4. The data modeling method according to claim 1, characterized in that: The step of optimizing the model based on the basic white model to obtain an optimized model comprises: Simplifying the basic white model geometrically to obtain simplified model data; Performing model texture compression on texture data in the simplified model data to obtain first compressed data; Compressing the vertex data and index data in the simplified model data to obtain second compressed data; The first compressed data and the second compressed data are integrated into the simplified model data to obtain an optimized model.

5. The data modeling method according to claim 4, characterized in that: The step of compressing the vertex data and index data in the simplified model data to obtain second compressed data comprises: Optimizing the rendering pipeline in the simplified model data using rendering technology to obtain rendered model data; The vertex data and index data in the rendered model data are compressed to obtain second compressed data.

6. The data modeling method according to any one of claims 1 to 5, characterized in that: The preset storage space includes a main storage space and a backup storage space, and the step of storing the data interface in the preset storage space includes: The data interface is copied to obtain a backup interface, the data interface is stored in the primary storage space, and the backup interface is stored in the backup storage space.

7. The data modeling method according to claim 6, characterized in that: The data modeling method further includes: Upon receiving an access request from the model rendering engine, determining a first data interface indicated by the access request from the primary storage space, and determining a second data interface indicated by the access request from the backup storage space; A target data interface with the least number of connections is determined from the first data interface and the second data interface, and the access request is forwarded to the target data interface.

8. A data modeling device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data modeling method according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data modeling method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the data modeling method according to any one of claims 1 to 7 are implemented.

Images