Three-dimensional modeling method and device, electronic equipment and storage medium

Abstract

The invention provides a three-dimensional modeling method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining multi-source data of a to-be-constructed model; dividing the initial grid based on the geometric feature density of the to-be-constructed model to obtain an initial grid frame of the to-be-constructed model; optimizing the grid density of the initial grid frame based on the curvatures of different positions of the to-be-constructed model; fusing the multi-source data to the optimized grid vertexes to obtain a grid model after each vertex is fused with the multi-source data; carrying out iterative optimization on a grid topological structure of the grid model; and outputting the three-dimensional model obtained by final iteration through a preset format. According to the invention, by dynamically adjusting the grid density and fusing the multi-source data, the precision and efficiency of three-dimensional modeling are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-dimensional modeling, and specifically relates to a three-dimensional modeling method, device, electronic equipment and storage medium. Background Art

[0002] 3D modeling technology is widely used in many fields, such as architecture, mechanical design, geographic information systems, etc. As various industries continue to increase their requirements for model accuracy and efficiency, 3D modeling technology is also continuously developing and improving.

[0003] When faced with the task of modeling complex geometric structures, existing 3D modeling methods have exposed several problems. Traditional modeling methods face many challenges when dealing with objects with complex shapes and rich details. On the one hand, traditional static meshing methods are difficult to adapt to changes in local details of complex structures, resulting in low meshing efficiency and affecting the overall modeling speed. On the other hand, modeling with a single data source is prone to model distortion and lacks a real-time fusion and optimization mechanism for multi-source heterogeneous data such as laser point clouds, image textures, and sensor data, resulting in insufficient data fusion accuracy, which ultimately limits the efficiency and accuracy of modeling. Summary of the Invention

[0004] In view of this, the present invention aims to provide a three-dimensional modeling method, device, electronic device and storage medium to solve at least one of the problems mentioned in the above background technology.

[0005] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0006] In a first aspect, the present invention provides a three-dimensional modeling method, comprising the following steps:

[0007] Obtain multi-source data for the model to be built;

[0008] Dividing the initial grid based on the geometric feature density of the model to be constructed to obtain the initial grid framework of the model to be constructed;

[0009] Optimize the mesh density of the initial mesh framework based on the curvature of different locations of the model to be built;

[0010] Fusing multi-source data to optimized mesh vertices to obtain a mesh model in which each vertex is fused with multi-source data;

[0011] Iteratively optimize the grid topology of the grid model;

[0012] The final iterative 3D model is output in a preset format.

[0013] Furthermore, the initial grid is divided based on the geometric feature density of the model to be constructed. Specifically, the initial grid is dynamically divided based on the inverse proportional function of the point cloud density of the model to be constructed. The size of the initial grid satisfies the following relationship:

[0014]

[0015] Where, is the element side length of the initial grid; is the adjustment factor; is the local point cloud density.

[0016] Furthermore, the mesh density of the initial mesh framework is optimized, including:

[0017] Calculate the local curvature of the initial mesh frame;

[0018] Determine the high curvature area according to the local curvature; the local curvature radius of the high curvature area meets the set conditions;

[0019] The high curvature area is meshed, and the subdivision direction is consistent with the curvature gradient direction.

[0020] Furthermore, multi-source data includes:

[0021] Laser point cloud data, image texture data and sensor parameters.

[0022] Furthermore, the multi-source data is fused into the optimized mesh vertices, including:

[0023] Determine the data source confidence of multi-source data;

[0024] Based on the confidence level of the data source, weights are assigned to multi-source data. If the confidence level of the data source is lower than the preset threshold, the weight of the corresponding data source is set to 0. If the confidence level of the data source is not lower than the preset threshold, the weight of the corresponding data source is set according to the preset weight rules.

[0025] According to the data weights of different data sources, a weighted interpolation algorithm is used to fuse multi-source data to grid vertices.

[0026] Furthermore, in the preset weight rule, the data weight of the laser point cloud data is greater than the data weight of the image texture data, and the data weight of the image texture data is greater than the data weight of the sensor parameters.

[0027] Furthermore, the grid topology of the grid model is iteratively optimized, including:

[0028] In each round of iteration, the mesh topology of the mesh model is tested for conflicts and the conflicting area mesh is determined;

[0029] Reconstruct the conflict area mesh using the Delaunay triangulation algorithm and delete isolated vertices;

[0030] The mesh model is continuously optimized iteratively until the change in the model vertex coordinates is less than the set value.

[0031] In a second aspect, the present invention provides a three-dimensional modeling device, comprising:

[0032] Data acquisition module, used to obtain multi-source data for the model to be built;

[0033] A meshing module is used to divide the initial mesh based on the geometric feature density of the model to be constructed to obtain the initial mesh framework of the model to be constructed;

[0034] Density analysis module, used to optimize the mesh density of the initial mesh framework based on the curvature of different locations of the model to be built;

[0035] The data fusion module is used to fuse multi-source data into the optimized mesh vertices to obtain a mesh model in which each vertex is fused with multi-source data;

[0036] A grid optimization module is used to iteratively optimize the grid topology of the grid model;

[0037] The model output module is used to output the final iterative three-dimensional model in a preset format.

[0038] In a third aspect, the present invention provides a computer device, comprising a processor and a memory:

[0039] The memory is used to store computer programs and send instructions of the computer programs to the processor;

[0040] The processor executes the three-dimensional modeling method of the first aspect according to instructions of the computer program.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a three-dimensional modeling method as in the first aspect.

[0042] In summary, the present application provides a three-dimensional modeling method, device, electronic equipment and storage medium. The method comprises: acquiring multi-source data of a model to be constructed; dividing an initial grid based on the geometric feature density of the model to be constructed, to obtain an initial grid framework of the model to be constructed; optimizing the grid density of the initial grid framework based on the curvature of different positions of the model to be constructed; fusing the multi-source data to the grid vertices after optimization, to obtain a grid model with each vertex fused with multi-source data; iteratively optimizing the grid topology of the grid model; and outputting the three-dimensional model obtained after final iteration in a preset format. The present application improves the accuracy and efficiency of three-dimensional modeling by dynamically adjusting the grid density and fusing multi-source data. Specifically, by considering the geometric feature density and curvature of the model to be constructed, the present application can adaptively divide and optimize the grid, thereby better capturing the detailed features of the model. Meanwhile, by fusing multi-source data, the present application further improves the accuracy and authenticity of the model. In addition, by iteratively optimizing the grid topology of the grid model, the present application ensures that the three-dimensional model output finally has high-quality geometric and texture features. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0044] Figure 1 A flow chart of a three-dimensional modeling method provided by an embodiment of the present application;

[0045] Figure 2 A composition diagram of a three-dimensional modeling device provided by an embodiment of the present application;

[0046] Figure 3 A composition diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the objectives, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the following described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0048] The embodiments of the present invention provide a three-dimensional modeling method, device, electronic device and storage medium, which relate to the field of three-dimensional modeling technology. The three-dimensional modeling method, device, electronic device and storage medium provided by the embodiments of the present invention can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements a three-dimensional modeling method, etc., but is not limited to the above forms.

[0049] The present invention can be used in a wide variety of general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0050] See also Figure 1 , an embodiment of the present invention provides a three-dimensional modeling method, comprising the following steps:

[0051] S11: Obtain multi-source data for the model to be constructed.

[0052] It should be noted that multi-source data refers to data from different channels and of different types. For example, it may include laser scanning data, satellite imagery, drone imagery, CAD drawings, etc. This data describes the information of the model to be constructed from different perspectives and methods, providing rich foundational information for subsequent modeling.

[0053] S12: Divide the initial grid based on the geometric feature density of the model to be constructed to obtain the initial grid framework of the model to be constructed.

[0054] It's important to note that geometric feature density reflects the complexity and density of geometric features in different regions of the model. For example, regions with large variations in surface curvature and rich detail have high geometric feature density, while relatively flat, simple regions have low geometric feature density. The initial mesh is a preliminary mesh structure for dividing the model, serving as the foundation for further processing and refinement. It divides the spatial region of the model into a series of interconnected small units (such as triangles and quadrilaterals).

[0055] S13: Optimize the mesh density of the initial mesh framework based on the curvature of different positions of the model to be constructed.

[0056] It's important to note that curvature describes the degree of curvature of a model's surface at a specific point. Larger curvatures indicate a sharply curved surface, while smaller curvatures indicate a relatively flat surface. By adjusting mesh density based on curvature, we can increase the number of meshes in areas of large curvature to better capture model details, while reducing the number of meshes in areas of small curvature to improve computational efficiency.

[0057] S14: Fusing the multi-source data to the optimized mesh vertices to obtain a mesh model in which each vertex is fused with the multi-source data.

[0058] It's important to note that data fusion involves integrating and merging data from different sources, allowing each mesh vertex to incorporate information from multiple sources. This allows for the combined advantages of multiple data sources to improve model accuracy and realism. Mesh vertices are the fundamental points that form the mesh structure, connecting the mesh's edges and cells.

[0059] S15: Iteratively optimize the mesh topology structure of the mesh model.

[0060] It's important to note that mesh topology describes the connections between vertices, edges, and faces within a mesh. Iteratively optimizing the topology structure aims to make the mesh connections more rational, such as by reducing narrow cells and optimizing triangle angles, thereby improving model quality and stability.

[0061] S16: Output the final iterative three-dimensional model in a preset format.

[0062] It should be noted that the preset format is a pre-set file format for storing and transmitting 3D models. Different formats have different characteristics and applicable scenarios.

[0063] This embodiment provides a three-dimensional modeling method. First, multi-source data is acquired. This data contains multiple aspects of the model to be constructed, providing rich basic data for modeling. Second, an initial mesh is divided according to the density of the model's geometric features to preliminarily determine the model's framework structure. The mesh is denser in areas with complex geometric features and sparser in simple areas to balance model accuracy and computational complexity. The mesh density is then further optimized based on curvature. Meshes are added in areas with large curvature to ensure accurate representation of the model's complex shapes, while meshes are reduced in areas with small curvature to improve computational efficiency. The multi-source data is then fused into the optimized mesh vertices, allowing each vertex of the model to integrate information from multiple sources, improving the model's accuracy and realism. The mesh topology is then iteratively optimized to improve mesh connectivity and enhance the model's quality and stability. Finally, the final three-dimensional model is output in a preset format for easy subsequent use and processing. This method addresses the problem that traditional modeling may rely solely on a single data source. By combining multi-source data for modeling, this method leverages the advantages of different data sources and improves the comprehensiveness and accuracy of the model. To address the problem that traditional static meshing methods are difficult to adapt to changes in local details of complex structures, this method uses meshing and optimization based on geometric feature density and curvature. This method can dynamically adjust the mesh distribution according to the actual shape characteristics of the model, ensuring accurate expression of model details while improving computational efficiency and avoiding over-subdivision of the mesh in unnecessary areas.

[0064] In one embodiment of the present invention, the initial grid is divided based on the density of geometric features of the model to be constructed. Specifically, the initial grid is dynamically divided based on an inverse proportional function of the point cloud density of the model to be constructed. The size of the initial grid satisfies the following relationship:

[0065]

[0066] Where, is the element side length of the initial grid; is the adjustment factor; is the local point cloud density.

[0067] It should be noted that the point cloud is a set composed of many points in a three-dimensional space, used to describe the surface morphology and other information of an object. The point cloud density refers to the density of points in the point cloud in a certain local area of the model, that is, the number of points in a unit volume (or area, depending on the specific circumstances). A large point cloud density indicates that the point cloud data in this area is rich, and the geometric features may be more complex; a small point cloud density indicates that the point cloud data in this area is sparse, and the geometric features are relatively simple. The inverse proportional function is a mathematical function relationship, that is, the product of two variables is a constant. In this embodiment, the division of the initial grid is inversely proportional to the point cloud density, which means that the higher the point cloud density, the smaller a certain parameter (such as the unit edge length) of the initial grid related to it will be; on the contrary, the lower the point cloud density, the larger the parameter will be. The unit edge length of the initial grid is the length of the edge of the basic unit (such as triangle, quadrilateral, etc.) that constitutes the initial grid. It is an important indicator to measure the fineness of the initial grid. The smaller the unit edge length, the finer the grid, and the more details of the model it can describe; the larger the unit edge length, the coarser the grid, and the weaker the expression ability of the model details.

[0068] This embodiment uses the point cloud density information of the model to be constructed to guide the division of the initial grid. Since the point cloud density reflects the complexity of the geometric features of the model to some extent (the geometric features of the area with high point cloud density are usually more complex), by establishing an inverse proportional function relationship between the unit edge length of the initial grid and the point cloud density, the unit edge length of the initial grid in the area with high point cloud density (complex geometric features) will automatically become smaller, so that a denser and finer grid is generated to better capture and express the detailed features of the area. In the area with low point cloud density (relatively simple geometric features), the unit edge length of the initial grid will become larger, and the grid will become relatively sparse, which can avoid generating too many fine grids in unnecessary places, thereby reducing the amount of calculation and improving the modeling efficiency. The introduction of the adjustment coefficient provides a certain flexibility for the grid division based on the point cloud density. According to the specific model characteristics, modeling accuracy requirements, and computing resources, etc., the coefficient can be adjusted to achieve the best grid division effect.

[0069] In an embodiment of the present application, the grid density of the initial grid framework is optimized, comprising:

[0070] S21: Calculate the local curvature of the initial grid framework.

[0071] The local curvature is a quantity that describes the bending degree of a local area on the initial grid framework. For a three-dimensional grid model, the local curvature can be calculated at each grid element (such as triangle, quadrilateral, etc.) or vertex. The larger the value of the local curvature, the more severe the bending degree of the area; the smaller the value of the local curvature, the relatively flat the area. It is an important indicator to measure the surface geometric features of the model.

[0072] S22: determining a high curvature area according to the local curvature; the local curvature radius of the high curvature area meets a set condition.

[0073] The radius of curvature and the curvature are inversely proportional to each other. The larger the radius of curvature, the smaller the curvature and the flatter the surface. The smaller the radius of curvature, the larger the curvature and the more curved the surface. In this embodiment, the high curvature area is determined by setting the condition of the radius of curvature.

[0074] High curvature regions are areas of the initial mesh frame with large local curvature (for example, areas where the local curvature radius R < 5mm). These areas typically correspond to parts of the model with complex geometric features, such as sharp edges, corners, protrusions, and depressions. Special processing of high curvature regions (such as mesh subdivision) can better capture and express these complex geometric details.

[0075] S23: Subdivide the high curvature area into grids, with the subdivision direction consistent with the curvature gradient direction.

[0076] Mesh subdivision is an operation that increases mesh density by further dividing existing mesh units (such as triangles and quadrilaterals) into smaller mesh units. Mesh subdivision can improve the accuracy of the model, enabling it to more accurately represent the shape of the object. The curvature gradient direction is the direction in which the curvature changes fastest. When performing mesh subdivision in high curvature areas, subdividing along the curvature gradient direction can more effectively capture the change in curvature, thereby better adapting to the geometric form of the model surface and improving the subdivision effect. Specifically, the size of the subdivided mesh unit can be 1 / 4 to 1 / 8 of the original size, and a non-uniform subdivision strategy is used, with the subdivision direction consistent with the curvature gradient direction.

[0077] This embodiment calculates the local curvature of the initial grid frame to understand the degree of curvature of each area, and determines the high curvature areas according to the set local curvature radius conditions. These areas are parts of the model with complex geometric features and require fine representation. Then, the high curvature area is meshed along the curvature gradient direction, so that the mesh after meshing can better fit the curvature changes of the model surface, while ensuring the accuracy of the key areas of the model, avoiding over-meshing in flat areas, and improving modeling efficiency and quality. This embodiment proposes a non-uniform grid density optimization method based on local curvature, which is different from the traditional uniform subdivision method. This method can accurately identify and process the high curvature areas of the model, and at the same time, subdivide according to the curvature gradient direction to make the mesh distribution more reasonable, effectively balance the modeling accuracy and the utilization of computing resources, and enhance the model's ability to express complex geometric shapes.

[0078] In one embodiment of the present invention, the multi-source data includes:

[0079] Laser point cloud data, image texture data and sensor parameters.

[0080] Specifically, the acquisition frequency of laser point cloud data is not less than 100 Hz, and the point cloud density is ≥500 points per square meter; the image texture data is obtained and input using a multispectral camera, and the resolution of the image texture data is not less than 4096×2160 pixels; the sensor parameters include the acceleration data of the inertial measurement unit, the ambient temperature and humidity sensor data, and the GPS positioning coordinates.

[0081] In one embodiment of the present invention, fusing multi-source data into optimized mesh vertices includes:

[0082] S31: Determine the data source confidence of the multi-source data.

[0083] Data source confidence is an indicator used to measure the reliability or trustworthiness of the data provided by each data source. It reflects factors such as the accuracy, completeness, and consistency of the data with actual conditions. A higher confidence level indicates a more reliable data source; a lower confidence level indicates a less reliable data source.

[0084] S32: Based on the data source confidence, weights are assigned to multi-source data; if the data source confidence is lower than a preset threshold, the corresponding data source data weight is set to 0; if the data source confidence is not lower than the preset threshold, the corresponding data source data weight is set according to the preset weight rule.

[0085] Weight assignment involves assigning a weight to each data source. The weight reflects the importance of that data source in the multi-source data fusion process. The larger the weight, the greater the impact of that data source on the final fusion result; the smaller the weight, the smaller its impact.

[0086] S33: Based on the data weights of different data sources, a weighted interpolation algorithm is used to fuse multi-source data to grid vertices.

[0087] The weighted interpolation algorithm is a mathematical algorithm that, in multi-source data fusion, performs a comprehensive calculation based on the weight of each data source and fuses them onto the mesh vertices. This weighted approach gives data sources with higher confidence a larger share in the fusion result, resulting in more accurate and reliable fused data.

[0088] This example assigns weights based on data source confidence and employs a weighted interpolation algorithm for fusion. This approach fully considers the importance of different data sources, resulting in more reasonable fusion results. Compared to methods such as simply averaging and fusing multi-source data, this method better leverages the advantages of high-quality data sources, improving the accuracy and authenticity of the model.

[0089] In one embodiment of the present invention, in the preset weighting rule, the data weight of the laser point cloud data is greater than the data weight of the image texture data, and the data weight of the image texture data is greater than the data weight of the sensor parameters. For example, the laser point cloud coordinate weight w 点云 =0.6 0.8, image texture weight w 纹理 =0.2 0.4, sensor parameter weight w 传感器 =0.05 0.1, and each weight is dynamically adjusted according to the calibration accuracy of the data acquisition equipment.

[0090] In one embodiment of the present invention, iteratively optimizing the grid topology of the grid model includes:

[0091] S41: In each round of iteration, a conflict detection is performed on the grid topology of the grid model to determine the conflict area grid.

[0092] Clash detection is an operation used to locate and identify areas within a mesh model that have topological issues or violate certain rules. These problem areas may include intersecting edges, overlapping faces, and irregular connections, and are referred to as conflict regions. Clash detection is the foundation for subsequent mesh optimization. Only by identifying the problem areas can targeted treatment be carried out. The conflict region mesh refers to the portion of the mesh identified through clash detection as having topological issues. The mesh topology in these areas requires correction and optimization to improve the quality of the entire mesh model.

[0093] S42: Reconstruct the conflict area mesh using the Delaunay triangulation algorithm and delete isolated vertices.

[0094] The Delaunay triangulation algorithm is a classic geometric algorithm used to triangulate a set of points in a plane or space so that the resulting triangles satisfy the Delaunay condition (i.e., the circumcircle of any triangle contains no other points). In this embodiment, the algorithm is used to reconstruct the mesh in the conflicting area. By rebuilding the triangular mesh, the topology of the conflicting area is improved, making the mesh more regular and reasonable. Isolated vertices are vertices in the mesh model that are not connected to other vertices. The presence of isolated vertices disrupts the mesh's topology, affecting the model's integrity and subsequent processing, and therefore need to be removed during the optimization process.

[0095] S43: Continuously iterate and optimize the mesh model until the change in the model vertex coordinates is less than the set value.

[0096] During each iterative optimization process, the coordinates of the model's vertices change. The change in vertex coordinates refers to the difference between the two iterations. It measures the convergence of the iterative optimization. When the change is less than a set value, the model has reached a stable state and the iterative optimization can be terminated. In practice, the number of iterations can be set to 3-5, and the optimization termination condition can be set to a change in vertex coordinates of less than 0.01 mm.

[0097] This embodiment uses an iterative optimization method to process the mesh topology structure, and gradually improves the quality of the mesh model by continuously detecting and repairing conflicting areas. This method can handle various problems existing in the mesh topology more comprehensively and meticulously. Compared with one-time processing, it has better results and can obtain a higher-quality mesh model. The Delaunay triangulation algorithm is also used to reconstruct the mesh of the conflicting area. This algorithm has good geometric properties and can generate high-quality triangular meshes, effectively improving the topological structure of the conflicting area. At the same time, combined with the operation of deleting isolated vertices, the integrity of the mesh and the rationality of the topology are guaranteed, and the stability of the model is improved.

[0098] In one embodiment of the present invention, the final iteratively obtained three-dimensional model is outputted in a preset format, wherein the preset format includes: OBJ (Object File), STL (Stereolithography), PLY (Polygon File Format), and includes vertex color and texture map binding;

[0099] Point cloud formats: LAS (Lidar Data Exchange Format), PCD (Point Cloud Data), and retaining the mapping relationship between the original collected data and the fused coordinates;

[0100] Engineering software compatible formats: DWG (Drawing, engineering drawing), RVT (Building Information Model file), and lossless format conversion can be achieved through plug-ins.

[0101] Next, the solution of this application will be described with specific implementation methods.

[0102] Specific implementation method 1: Digital reconstruction of substation equipment based on three-dimensional modeling.

[0103] Application scenario: Refined modeling and condition monitoring of transformers, circuit breakers, and busbars in 500kV substations.

[0104] Implementation steps:

[0105] 1. Multi-source data input:

[0106] Laser point cloud: Use a ground three-dimensional laser scanner (scanning accuracy ±1mm, point cloud density ≥800 points / m²) to obtain the geometric data of the transformer shell and bus support;

[0107] Image texture: Use a multi-spectral camera (resolution 8192×4320 pixels) mounted on a drone to take high-definition images of the surface rust and oil distribution of the equipment;

[0108] Sensor parameters: Integrate temperature sensor (accuracy ±0.5℃) and current transformer (accuracy 0.2 level) data to monitor the equipment operating state in real time.

[0109] 2. Dynamic grid division:

[0110] The initial grid cell size is set according to the complexity of the transformer surface. For flat areas (such as the side of the box), a 10cm×10cm grid is used, and for high-curvature areas such as bolt connections (curvature radius R<3mm), the initial grid is automatically reduced to 2cm×2cm.

[0111] Use curvature gradient analysis to perform non-uniform subdivision on the bus connection terminal area. The subdivided grid size is 0.5cm×0.5cm, ensuring the geometric accuracy of the electrical contact surface.

[0112] 3. Multi-source data fusion:

[0113] Weight distribution: Laser point cloud coordinate weight (w 点云 =0.7), image texture weight (w 纹理 =0.25);

[0114] For the surface contamination area of the insulator (image recognition gray value>200), automatically increase the texture weight to 0.4 to enhance the visualization of corrosion features.

[0115] 4. Iterative optimization:

[0116] Detect grid topology conflicts of bus support (difference in length of adjacent cells>35%), trigger Delaunay triangulation reconstruction;

[0117] After 4 iterations, the model vertex coordinate change is stable within 0.008mm, and the output error of the substation equipment model is ≤0.15mm.

[0118] Advantages:

[0119] Modeling efficiency improvement: The modeling time of a single transformer is reduced from 4 hours to 1.5 hours;

[0120] State monitoring integration: After integrating temperature data, hot areas (>85℃) can be automatically labeled to assist in fault warning;

[0121] Compatibility: Export RVT format models and directly import them into building information modeling systems for collision detection.

[0122] Specific implementation method 2: Three-dimensional modeling of transmission line corridors and safety distance analysis.

[0123] Application scenario: Automatic measurement of conductor sag and tree barrier distance for 220kV high-voltage transmission lines.

[0124] Implementation steps:

[0125] 1. Multi-source data input:

[0126] Laser point cloud: Use airborne LiDAR (Light Detection and Ranging) (flight altitude 100m, point density ≥ 300 points / m²) to obtain point clouds of conductors, towers, and surrounding vegetation.

[0127] Image texture: High-resolution oblique photography (5cm ground resolution) is used to capture details of conductor insulators and hardware.

[0128] Sensor parameters: Synchronous weather station wind speed (±0.1m / s) and conductor temperature (±1°C) data for dynamic sag correction.

[0129] 2. Dynamic meshing:

[0130] The initial grid is segmented according to the conductor span: the straight tower section adopts a 20m×20m grid, and the grid is intensified to 5m×5m near the corner towers;

[0131] For the wire surface (curvature radius R≈5cm), adaptive subdivision is triggered and the grid size is reduced to 1cm×1cm, accurately characterizing surface scratches and wear.

[0132] 3. Multi-source data fusion:

[0133] Dynamic weight adjustment: Under windless conditions, the laser point cloud weight w 点云 =0.75;

[0134] For vegetation areas (point clouds classified as trees), a two-layer fusion is used: the trunk is divided into point clouds (w 点云 =0.8), the crown part is superimposed with image texture (w 纹理 =0.6).

[0135] Iterative optimization:

[0136] Detect penetration conflicts between wires and tree grids (normal vector angle > 20°), and automatically mark areas with insufficient safety distance;

[0137] After 3 iterations, the conductor sag simulation error is less than 2 cm, and the tree barrier distance calculation accuracy is ± 5 cm.

[0138] Advantages:

[0139] Safety assessment automation: high-risk tree barrier areas (distance < 3 m) in the output model are automatically marked red, saving 70% of manual inspection costs;

[0140] Dynamic adaptability: after integrating meteorological data, the maximum conductor sag prediction error is reduced from ± 8 cm to ± 3 cm;

[0141] Multi-platform support: output LAS format point cloud can be directly imported into the power grid GIS (Geographic Information System) for spatial analysis.

[0142] Based on the above embodiments, the three-dimensional modeling method provided by the application has at least the following advantages:

[0143] (1) Dynamic mesh division: dynamically adjust mesh division through geometric feature density and curvature analysis, significantly reduce redundant calculation, improve modeling efficiency by more than 30%, and ensure high curvature area detail accuracy;

[0144] (2) Multi-source data fusion: use weighted interpolation algorithm to fuse heterogeneous data, reduce the influence of single data source noise, and reduce model geometric error to below 0.1 mm;

[0145] (3) Real-time optimization mechanism: real-time adjustment of mesh topology in the iteration optimization process, avoiding manual intervention, and shortening the modeling cycle by 50%.

[0146] Based on the same inventive concept, the embodiments of the present application also provide a three-dimensional modeling device for implementing the three-dimensional modeling method described above. The problem-solving implementation scheme provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in the three-dimensional modeling device embodiments provided below can be referred to the limitations of the three-dimensional modeling method in the above text, which will not be repeated here.

[0147] Please refer to Figure 2 , the embodiments of the present application also provide a three-dimensional modeling device, comprising:

[0148] The data acquisition module is configured to acquire multi-source data of a to-be-constructed model.

[0149] The mesh division module is configured to divide an initial mesh based on the geometric feature density of the to-be-constructed model, to obtain an initial mesh framework of the to-be-constructed model.

[0150] Density analysis module, used to optimize the mesh density of the initial mesh framework based on the curvature of different locations of the model to be built;

[0151] The data fusion module is used to fuse multi-source data into the optimized mesh vertices to obtain a mesh model in which each vertex is fused with multi-source data;

[0152] A grid optimization module is used to iteratively optimize the grid topology of the grid model;

[0153] The model output module is used to output the final iterative three-dimensional model in a preset format.

[0154] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0155] Reference Figure 3 An embodiment of the present invention further provides a computer device, comprising: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, the three-dimensional modeling method as described in any one of the above methods is implemented.

[0156] The computer device may be a desktop computer, notebook computer, PDA, cloud server or other computing device. The computer device may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that Figure 3 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0157] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0158] The memory can be an internal storage unit of the computer device in some embodiments, for example, a hard disk or a memory of the computer device. The memory can also be an external storage device of the computer device in other embodiments, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory can include both the internal storage unit and the external storage device of the computer device. The memory is used to store an operating system, an application program, a boot loader, data and other programs, for example, program codes of the computer program, etc. The memory can also be used to temporarily store data that has been output or will be output.

[0159] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. When the computer program is run by a processor, the three-dimensional modeling method in any one of the above methods is implemented.

[0160] In this embodiment, the integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the computer program for instructing the relevant hardware to complete all or part of the processes in the above-described embodiment methods can be stored in a computer readable storage medium. The computer program can be executed by a processor to implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium can not be an electrical carrier signal and a telecommunication signal.

[0161] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0162] Those of ordinary skill in the art can understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0163] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal equipment and methods can be implemented in other ways. For example, the apparatus / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0164] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A three-dimensional modeling method, characterized in that: The steps include: Obtain multi-source data for the model to be built; Dividing an initial grid based on the geometric feature density of the model to be constructed to obtain an initial grid framework of the model to be constructed; Optimizing the mesh density of the initial mesh framework based on the curvature of different positions of the model to be constructed; Fusing the multi-source data to the optimized mesh vertices to obtain a mesh model in which each vertex is fused with the multi-source data; Iteratively optimizing a grid topology structure of the grid model; The final iterative 3D model is output in a preset format.

2. The three-dimensional modeling method according to claim 1, characterized in that: The initial grid is divided based on the geometric feature density of the model to be constructed, specifically, the initial grid is dynamically divided based on the inverse proportional function of the point cloud density of the model to be constructed, and the size of the initial grid satisfies the following relationship: Where, is the unit side length of the initial grid; is the adjustment factor; is the local point cloud density.

3. The three-dimensional modeling method according to claim 1, characterized in that: Optimizing the mesh density of the initial mesh framework includes: calculating a local curvature for the initial grid frame; Determining a high curvature area according to the local curvature; wherein the local curvature radius of the high curvature area meets a set condition; The high curvature region is meshed, and the subdivision direction is consistent with the curvature gradient direction.

4. The three-dimensional modeling method according to claim 1, characterized in that: The multi-source data includes: Laser point cloud data, image texture data and sensor parameters.

5. The three-dimensional modeling method according to claim 4, characterized in that: Fusing the multi-source data into optimized mesh vertices, including: Determining the data source confidence of the multi-source data; Based on the data source confidence, weights are assigned to the multi-source data; if the data source confidence is lower than a preset threshold, the corresponding data source data weight is set to 0; if the data source confidence is not lower than the preset threshold, the corresponding data source data weight is set according to a preset weight rule; According to the data weights of different data sources, a weighted interpolation algorithm is used to fuse multi-source data to grid vertices.

6. The three-dimensional modeling method according to claim 1, characterized in that: In the preset weight rule, the data weight of the laser point cloud data is greater than the data weight of the image texture data, and the data weight of the image texture data is greater than the data weight of the sensor parameter.

7. The three-dimensional modeling method according to claim 1, characterized in that: Iteratively optimizing the grid topology of the grid model includes: During each round of iteration, a conflict detection is performed on the grid topology of the grid model to determine the conflict area grid; Reconstructing the conflict area mesh by using a Delaunay triangulation algorithm and deleting isolated vertices; The mesh model is continuously optimized iteratively until the change in the model vertex coordinates is less than a set value.

8. A three-dimensional modeling device, characterized in that: include: Data acquisition module, used to obtain multi-source data for the model to be built; A mesh division module, configured to divide an initial mesh based on the geometric feature density of the model to be constructed, so as to obtain an initial mesh framework of the model to be constructed; A density analysis module, configured to optimize the mesh density of the initial mesh framework based on the curvature of different positions of the model to be constructed; A data fusion module is used to fuse the multi-source data into the optimized mesh vertices to obtain a mesh model in which each vertex is fused with the multi-source data; A grid optimization module, configured to iteratively optimize the grid topology structure of the grid model; The model output module is used to output the final iterative three-dimensional model in a preset format.

9. A computer device, characterized in that: The device includes a processor and a memory: The memory is used to store the computer program and send instructions of the computer program to the processor; The processor executes a three-dimensional modeling method according to any one of claims 1 to 7 according to instructions of the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a three-dimensional modeling method according to any one of claims 1 to 7.

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