Geometric Data Simplified Representation Method in the Field of Digital Twin City

Abstract

The present application discloses a method for simplified expression of geometric data in the field of digital twin cities, which relates to the field of digital twin technology and is applied to a digital twin city platform. The method includes: determining the type of geometric model in the digital twin city platform; in the case where the type of the geometric model is a regular model, converting the geometric model into a point-line structure, where the point-line structure includes key points and key edges for describing the geometric model; in the case where the type of the geometric model is an irregular model, calculating the boundary range and convex hull cells of the geometric model, and hierarchically simplifying and representing the geometric model according to the boundary range and convex hull cells, where the boundary range represents the smallest boundary containing the geometric model, and the convex hull cells are obtained by decomposing the geometric model. The present application solves the technical problem of complex representation of geometric data.

Description

Technical Field

[0001] The present application relates to the field of digital twin technology, and particularly to a method, device, electronic device, storage medium, and computer program product for simplified representation of geometric data in the field of digital twin cities. Background Art

[0002] In a digital twin city platform, spatial analysis and calculation of geometric data are often required. When the traditional digital twin city platform performs spatial analysis and operation, it usually loads all the required geometric data to the request side for processing. When the representation of geometric data is too complex, it will face the problems of low spatial analysis efficiency and long time-consuming for spatial operation, greatly increasing the difficulty and time of calculation, thus reducing the calculation efficiency. Therefore, there is a problem of complex representation of geometric data in the current digital twin city field. Summary of the Invention

[0003] The main purpose of the present application is to provide a method, device, electronic device, storage medium, and computer program product for simplified representation of geometric data in the field of digital twin cities, aiming to solve the technical problem of complex representation of geometric data.

[0004] To achieve the above object, the present application proposes a method for simplified representation of geometric data in the field of digital twin cities, which is applied to a digital twin city platform. The method for simplified representation of geometric data in the field of digital twin cities includes:

[0005] Judge the type of the geometric model in the digital twin city platform;

[0006] When the type of the geometric model is a regular model, convert the geometric model into a point-line structure, and the point-line structure includes key points and key edges for describing the geometric model;

[0007] When the type of the geometric model is an irregular model, calculate the boundary range and convex hull unit of the geometric model, and hierarchically simplify and represent the geometric model according to the boundary range and the convex hull unit. The boundary range represents the smallest boundary containing the geometric model, and the convex hull unit is obtained by decomposing the geometric model.

[0008] In one embodiment, the step of converting the geometric model into a point-line structure when the type of the geometric model is a regular model includes:

[0009] Identify the geometric type of the regular model corresponding to the geometric model, and match the corresponding simplification method in the model simplification rules according to the geometric type of the regular model;

[0010] Based on the simplification method, extract the key points and key edges for describing the geometric model to obtain the point-line structure corresponding to the geometric model.

[0011] In one embodiment, the step of determining the type of the geometric model in the digital twin city platform includes:

[0012] Extract the geometric features of the geometric model, and perform similarity matching based on the geometric features with each regular geometric shape and each regular geometric body in the preset geometric template library;

[0013] When the similarity reaches the preset similarity threshold, determine the geometric model as a regular model, and record the geometric type of the regular model corresponding to the geometric model;

[0014] When the similarity does not reach the preset similarity threshold, determine the geometric model as an irregular model.

[0015] In one embodiment, before the step of matching the corresponding simplification method in the model simplification rules according to the type of the regular model, it further includes:

[0016] Collect the calculation formulas of each preset regular geometric model, and determine each key point and each key edge describing each regular geometric model according to the calculation formulas;

[0017] Classify each key point and each key edge according to the type of the rule set model, and determine the simplification method of each regular geometric model.

[0018] In one embodiment, the step of calculating the boundary range and convex hull unit of the geometric model includes:

[0019] When the geometric model is a two-dimensional model, calculate the circumscribed rectangle of the geometric model based on the boundary coordinates of the geometric model. The circumscribed rectangle is the smallest matrix containing the geometric model, representing the boundary range of the geometric model;

[0020] Receive the point set list of the geometric model, traverse the point set list through the monotonic connection algorithm, construct the upper convex hull and lower convex hull of the geometric model, and return the point sets on the upper convex hull and the lower convex hull. The point sets constitute the convex hull unit of the geometric model.

[0021] In one embodiment, the boundary range includes a first boundary range and a second boundary range. The step of calculating the boundary range and convex hull unit of the geometric model further includes:

[0022] When the geometric model is a three-dimensional model, perform a collapse process on the geometric model to obtain a simplified model, calculate the oriented bounding box and convex hull object of the simplified model. The oriented bounding box is the first boundary range of the geometric model, and the convex hull object is the second boundary range of the geometric model;

[0023] Voxelize the simplified model to obtain each voxel. For any pair of voxels among the voxels, calculate the concavity of the voxel pair, merge the voxel pair with the minimum concavity to obtain a new voxel, form new voxel pairs with the new voxel and other voxels among the voxels, and execute the steps of calculating the concavity of the voxel pair and merging the voxel pair with the minimum concavity based on the new voxel pairs until a preset stop condition is reached;

[0024] Solve the convex hull for each voxel to obtain each convex hull unit corresponding to the voxels.

[0025] In one embodiment, the step of hierarchically simplifying and representing the geometric model according to the boundary range and the convex hull unit includes:

[0026] After the geometric model is decomposed into each convex hull unit, when the geometric model is a two-dimensional model, use the circumscribed rectangle of the geometric model as the root node of the geometric model, and use the convex hull unit of the geometric model as the first child node of the root node to obtain a hierarchical simplification index; or

[0027] After the geometric model is decomposed into each convex hull unit, when the geometric model is a three-dimensional model, use the oriented bounding box of the simplified model corresponding to the geometric model as the root node of the geometric model, use the convex hull object as the second child node of the root node, and use each convex hull unit as the third child node of the second child node. Based on the root node, the second child node, and the third child node, obtain a hierarchical simplification index; or

[0028] After the geometric model is transformed into a point-line structure, based on a preset spatial indexing algorithm, insert the point-line structure of the geometric model into an indexing structure to obtain a hierarchical simplification index.

[0029] In one embodiment, after the step of obtaining the spatial index, the following steps are further included:

[0030] When performing spatial analysis calculations, locate the geometric model based on the spatial index;

[0031] Judge the target convex hull units that intersect with the area to be analyzed among the convex hull units corresponding to the geometric model, and perform spatial analysis calculations on the target convex hull units.

[0032] In addition, to achieve the above object, the present application also proposes a geometric data simplification and expression device in the field of digital twin cities, which is applied to a digital twin city platform. The geometric data simplification and expression device in the field of digital twin cities includes:

[0033] A type judgment module for judging the type of the geometric model in the digital twin city platform;

[0034] A regular model processing module, configured to convert the geometric model into a point-line structure when the type of the geometric model is a regular model, where the point-line structure includes key points and key edges for describing the geometric model;

[0035] An irregular model processing module, configured to calculate a boundary range and convex hull cells of the geometric model when the type of the geometric model is an irregular model, and hierarchically simplify and represent the geometric model according to the boundary range and the convex hull cells, where the boundary range represents the smallest boundary containing the geometric model, and the convex hull cells are obtained by decomposing the geometric model.

[0036] In addition, to achieve the above object, the present application further provides an electronic device, where the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the geometric data simplification and representation method in the digital twin city field as described above.

[0037] In addition, to achieve the above object, the present application further provides a storage medium, where the storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, it implements the steps of the geometric data simplification and representation method in the digital twin city field as described above.

[0038] In addition, to achieve the above object, the present application further provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the geometric data simplification and representation method in the digital twin city field as described above.

[0039] The present application provides a geometric data simplification and representation method in the digital twin city field, which is applied to a digital twin city platform. The geometric data simplification and representation method in the digital twin city field includes: determining the type of a geometric model in the digital twin city platform; when the type of the geometric model is a regular model, converting the geometric model into a point-line structure, where the point-line structure includes key points and key edges for describing the geometric model; when the type of the geometric model is an irregular model, calculating a boundary range and convex hull cells of the geometric model, and hierarchically simplifying and representing the geometric model according to the boundary range and the convex hull cells, where the boundary range represents the smallest boundary containing the geometric model, and the convex hull cells are obtained by decomposing the geometric model.

[0040] By converting the geometric model into a point-line structure or decomposing it into convex hull units, this application effectively simplifies the geometric data in the digital twin city platform, reduces the overhead of data storage and processing, improves the computing efficiency. At the same time, it can handle different types of geometric models, enabling the digital twin city platform to be more widely applied to different types of urban scenarios, enhancing the adaptability and flexibility of the platform. Compared with the related solutions, when the geometric data representation is too complex, problems such as low spatial analysis efficiency and long-time-consuming spatial operations will be faced. This application makes targeted simplifications according to the type of geometric model, significantly improving the efficiency of spatial analysis and calculation. The simplified geometric data makes the calculation process more intuitive and easy to understand, reduces the calculation difficulty, and is beneficial to subsequent analysis and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0042] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the method for simplified expression of geometric data in the digital twin city field of this application;

[0044] Figure 2 It is a schematic flowchart provided for Embodiment 2 of the method for simplified expression of geometric data in the digital twin city field of this application;

[0045] Figure 3 It is a flowchart for implementing the method for simplified expression of geometric data in the digital twin city field provided by this application;

[0046] Figure 4 It is a schematic module structure diagram of the device for simplified expression of geometric data in the digital twin city field in the embodiments of this application;

[0047] Figure 5 It is a schematic device structure diagram of the hardware operating environment involved in the method for simplified expression of geometric data in the digital twin city field in the embodiments of this application.

[0048] The realization of the purpose, functional features and advantages of this application will be further described with reference to the embodiments and the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0050] To better understand the technical solution of this application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0051] The embodiments of this application are applied to a digital twin city platform. The main solution is as follows: Determine the type of geometric model in the digital twin city platform; when the type of the geometric model is a regular model, convert the geometric model into a point-line structure, where the point-line structure includes key points and key edges describing the geometric model; when the type of the geometric model is an irregular model, calculate the boundary range and convex hull cells of the geometric model, and hierarchically simplify and represent the geometric model according to the boundary range and convex hull cells. The boundary range represents the smallest boundary containing the geometric model, and the convex hull cells are obtained by decomposing the geometric model.

[0052] In this embodiment, for the convenience of description, the digital twin city system will be used as the execution subject for elaboration below.

[0053] Since in the operation of the digital twin city platform, spatial analysis and computational geometry data are a core task. However, traditional processing methods tend to load all the required geometric data into the request end for operation at one time. When facing complex geometric data representations, this approach will significantly slow down the speed of spatial analysis, extend the time of spatial operations, thereby increasing the complexity and time consumption of calculations and weakening the overall calculation efficiency.

[0054] This application provides a solution. By converting the geometric model into a point-line structure or decomposing it into convex hull cells, the geometric data in the digital twin city platform is effectively simplified, reducing the overhead of data storage and processing, improving the calculation efficiency. At the same time, it can handle different types of geometric models, enabling the digital twin city platform to be more widely applied to different types of urban scenarios, enhancing the adaptability and flexibility of the platform.

[0055] It should be noted that the execution subject of this embodiment can 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, a digital twin city system, etc. that can implement the above functions. Taking the digital twin city system as an example, this embodiment and the following embodiments will be described below.

[0056] Based on this, the embodiments of this application provide a method for simplifying the expression of geometric data in the field of digital twin cities, referring to Figure 1 , Figure 1 is a schematic flowchart of the first embodiment of the method for simplifying the expression of geometric data in the field of digital twin cities of this application.

[0057] In this embodiment, applied to the digital twin city platform, the method for simplified expression of geometric data in the field of digital twin cities includes steps S01 to S03:

[0058] Step S01, determine the type of geometric model in the digital twin city platform;

[0059] It should be noted that the digital twin city platform is a platform that uses digital technology to simulate the real urban environment, capable of realizing real-time monitoring, prediction, and optimization of the urban operation state. In the digital twin city platform, there are many two-dimensional or three-dimensional models (i.e., geometric models) used to represent urban elements (such as buildings, roads, terrain, etc.). The system classifies them into regular models or irregular models according to the shape characteristics of the geometric models. Regular models usually refer to those with simple shapes and regular structures, which are easy to describe by geometric formulas, such as basic geometric shapes: points, lines, circles, triangles, quadrilaterals (such as squares, rectangles, rhombuses), polygons (plane figures with a fixed number of sides, such as pentagons, hexagons, etc.), and regular polyhedrons: cubes, regular tetrahedrons (tetrahedrons, each face is an equilateral triangle), octahedrons, dodecahedrons, and icosahedrons, etc.; while irregular models refer to those with complex shapes and irregular boundaries, which are difficult to express with simple geometric formulas.

[0060] It can be understood that since existing solutions usually lack a clear distinction between the types of geometric models, resulting in a lack of pertinence when dealing with complex geometric data, so step S01 is carried out to perform targeted processing according to the type of geometric model, avoiding the "one-size-fits-all" processing method in traditional solutions. Different conversion strategies are adopted for regular models and irregular models, thus achieving more efficient and accurate spatial analysis and calculation.

[0061] Step S02, when the type of the geometric model is a regular model, convert the geometric model into a point-line structure, and the point-line structure includes key points and key edges for describing the geometric model;

[0062] It should be noted that for regular models, converting them into a point-line structure includes identifying and extracting key points (such as the center point of a circular building, the four corner points of a rectangular building) that describe the shape of the geometric model and key edges (such as the radius edges of a circular building, the four sides of a rectangular building) that connect these key points. The shape and boundary of the model are described by key points and key edges. The point-line structure is a structure composed of key points and key edges, used to simply represent the shape and boundary of the geometric model. Key points refer to points of great significance in the geometric model, such as the center point of the geometric model, the corner points of a building, the intersection points of roads, etc. Key edges refer to line segments connecting key points, used to describe the boundary of the geometric model.

[0063] It can be understood that since the existing solution still adopts a complex geometric data representation method when processing the rule model, resulting in unnecessary computational overhead, step S02 is performed. By converting the rule model into a point-line structure, the data representation is greatly simplified, the computational complexity is reduced, and thus the efficiency of spatial analysis and calculation is improved.

[0064] Step S03, in the case where the type of the geometric model is an irregular model, calculate the boundary range and convex hull cells of the geometric model, and hierarchically simplify the representation of the geometric model according to the boundary range and convex hull cells. The boundary range represents the smallest boundary containing the geometric model, and the convex hull cells are obtained by decomposing the geometric model.

[0065] It should be noted that for an irregular model, an irregular model refers to a model with a complex shape that cannot be described by simple geometric shapes (such as rectangles, circles, etc.). For example, a complex building model or terrain model. Calculate the boundary range and convex hull cells of the irregular model. The boundary range represents the smallest boundary containing the geometric model, and the convex hull cells are the smallest convex polygons or convex polyhedrons obtained by decomposing the geometric model and capable of covering all points of the model. According to the boundary range and convex hull cells, hierarchically simplify the representation of the geometric model to reduce the complexity of the model. Hierarchical simplification means processing the geometric model according to certain rules to reduce the complexity of the model. For example, for a complex terrain model, its boundary range and convex hull cells can be calculated first, and then hierarchical simplification representation can be performed according to this information, such as dividing the model into grids or simplifying the model structure at different levels. At the same time, the main features of the model are retained, which helps to improve the efficiency of spatial analysis and calculation.

[0066] It can be understood that since for an irregular model, the existing solution has not found an effective simplification representation method, step S03 is performed. By calculating the boundary range and convex hull cells, the main features of the model are retained, ensuring the accuracy of spatial analysis and calculation. By decomposing the geometric model into each convex hull cell, complex geometric data can be transformed into smaller blocks that are easier to process, thereby improving the calculation efficiency. Not only the main features of the model are retained, but also the complexity of the data is reduced, making spatial analysis and calculation more efficient and accurate.

[0067] In a feasible implementation manner, in step S02, in the case where the type of the geometric model is a regular model, the steps of converting the geometric model into a point-line structure include steps A01~A02:

[0068] Step A01, identify the geometric type of the geometric model corresponding to the regular model, and match the corresponding simplification method in the model simplification rules;

[0069] It should be noted that first, the geometric type of the geometric model is identified to determine which regular model it belongs to, such as a cube, cuboid, cylinder, sphere, etc. Subsequently, according to the identified geometric type, the corresponding simplification method is searched for and matched in the preset model simplification rules. The model simplification rules refer to a set of predefined simplification method sets, which guide the selection of simplification methods based on the geometric characteristics (such as symmetry, planarity, curvature, etc.) of the regular model to ensure that the simplified model can maintain the main geometric features of the original model while reducing the complexity of the data. These simplification methods are designed according to the geometric characteristics of different regular models and aim to accurately describe the shape of the model with the fewest points and edges.

[0070] Step A02, based on the simplification method, extract the key points and key edges describing the geometric model to obtain the point-line structure corresponding to the geometric model.

[0071] It should be noted that after determining the simplification method, the key points and key edges required to describe the geometric model are extracted according to this method. These key points and key edges together constitute the point-line structure of the geometric model, representing the geometric form of the original model in a simplified form.

[0072] Exemplarily, the model simplification rules are shown in Table 1, which lists the simplification expression methods for some regular models. For example, when the regular model is a three-dimensional sphere, it is represented by the simplification expression method of "center point + radius", where the key point is the center point and the key edge is the radius:

[0073] Table 1

[0074]

[0075] In this embodiment, by identifying the type of the regular model and matching the simplification method, the complex geometric model can be transformed into a point-line structure composed of key points and key edges. The simplified point-line structure reduces the amount of data and complexity in the calculation process, thereby improving the efficiency of spatial analysis and calculation. It not only simplifies the representation of geometric data but also enhances the readability of the model, making the urban model easier to understand and analyze, which helps to improve the usage experience of the digital twin city platform. At the same time, by reducing the complexity and calculation difficulty of geometric data and optimizing the utilization of computing resources, it helps to reduce the calculation time and resource consumption and improve the overall performance of the digital twin city platform.

[0076] In a feasible embodiment, in step S01, the steps of judging the type of the geometric model in the digital twin city platform include steps A11~A13:

[0077] Step A11, extract the geometric features of the geometric model and perform similarity matching based on the geometric features with each regular geometric shape and each regular geometric body in the preset geometric template library;

[0078] It should be noted that the system deeply analyzes the input geometric model and extracts its key geometric features, including but not limited to the vertex coordinates, edge lengths, face areas, volumes, curvatures, etc. of the model, which together constitute the basic data set for describing the shape of the geometric model.

[0079] In addition, it should be noted that the system compares the extracted geometric features one by one with each regular geometric shape in the preset geometric template library and the regular geometric bodies composed of these shapes. The preset geometric template library is a pre-defined database that stores a variety of common regular geometric shapes (geometric shapes with clear geometric features and rules, such as cubes, cuboids, cylinders, spheres, etc.) and the regular geometric bodies formed by rotating, scaling, translating, etc. these shapes. The process of similarity matching is achieved by calculating the similarity scores between the geometric features and the geometric shapes in the template library. The higher the score, the closer the geometric model is to a certain regular geometric shape in the template library.

[0080] In addition, it should be noted that when performing similarity matching, first, the geometric model is imaged to obtain a digital image, and preprocessing operations such as denoising, enlarging, and shrinking the image are performed to improve the accuracy of subsequent feature extraction. The edge detection operator (such as the Canny operator) is used to extract the edge information of the image. The edge is the area where the gray value changes violently in the image, usually corresponding to the contour of the geometric shape. The feature point detection algorithm (such as Harris corner points, FAST corner points, etc.) is used to detect the significant points or corner points in the image. These points are usually the positions where the image changes most violently and are of great significance for describing the geometric shape. For the extracted edges or feature points, shape descriptors (such as area, perimeter, circularity, rectangularity, etc.) are further calculated to quantify the features of the geometric shape. The extracted geometric features are matched with the template features in the preset geometric template library. The matching process can be based on feature points, edges, or shape descriptors, and matching algorithms (such as nearest neighbor search, K-D tree, etc.) are used to accelerate the matching process. For each matching pair, a similarity metric value is calculated. The similarity metric value can be determined according to the specific matching algorithm and feature type, such as Euclidean distance, Manhattan distance, cosine similarity, etc. At the same time, normalization processing can be used to improve the stability and accuracy of the similarity metric. Finally, the matching degrees of the geometric features with each regular geometric shape and each regular geometric body in the preset geometric template library are obtained.

[0081] Additionally, it should be noted that for each regular geometric shape and each regular geometric solid in the preset geometric template library, the system can automatically generate the required geometric figures in combination with the user's needs. For regular geometric shapes, it receives the lower limit and upper limit of the number of sides set by the user, generates regular polygons with the number of sides ranging from the lower limit to the upper limit, and stores them in the preset geometric template library. For regular geometric solids, it receives the lower limit and upper limit of the number of faces set by the user, and on the premise of satisfying Euler's formula, generates regular polyhedra with the number of faces ranging from the lower limit to the upper limit, and stores them in the preset geometric template library.

[0082] Additionally, it should be noted that when a geometric figure that does not exist in the preset geometric template library is detected, it is pushed to the user, and the user decides whether to include it in the preset geometric template library and records its corresponding simplified expression method for subsequent use.

[0083] Step A12, in the case where the similarity reaches the preset similarity threshold, determine the geometric model as a regular model and record the geometric type of the geometric model corresponding to the regular model;

[0084] It should be noted that if the similarity score of a geometric model with a certain regular geometric shape in the template library reaches the preset similarity threshold (this threshold is set according to actual needs and is used to determine whether the similarity between the geometric model and the regular geometric shape is high enough), then the system will determine the geometric model as a regular model and record its corresponding regular geometric shape, that is, the geometric type.

[0085] Step A13, in the case where the similarity does not reach the preset similarity threshold, determine the geometric model as an irregular model.

[0086] It should be noted that if the similarity scores of the geometric model with all regular geometric shapes in the template library do not reach the preset similarity threshold, then the system will determine the geometric model as an irregular model, identifying those complex geometric models that cannot be accurately described by the preset regular geometric shapes, providing a basis for subsequent flexible processing of irregular models.

[0087] In this embodiment, by extracting the geometric features of the geometric model and performing similarity matching with the regular geometric shapes and geometric solids in the preset geometric template library, the geometric model can be quickly classified as a regular model or an irregular model, avoiding the cumbersome and time-consuming manual classification, significantly improving the processing efficiency. The preset geometric template library contains various common regular geometric shapes and geometric solids. Through similarity matching, geometric models similar to the geometric shapes in the template library can be accurately identified. At the same time, the setting of the preset similarity threshold also ensures the accuracy of the classification.

[0088] In a feasible implementation manner, before the step of matching the corresponding simplification method in the model simplification rules according to the type of the rule model in step A01, steps A21 to A22 are further included:

[0089] Step A21, collect the calculation formulas of each preset rule geometric model, and determine each key point and each key edge describing each rule geometric model according to the calculation formulas;

[0090] It should be noted that for each preset rule geometric model, the system collects its corresponding calculation formula, and these calculation formulas are the basis for accurately describing the shape, size and position of the geometric model mathematically. For example, for a circle, its calculation formula may be the equation of the circle ( ); for a rectangle, it may be the expressions of its length, width and vertex coordinates.

[0091] In addition, it should be noted that after collecting the calculation formulas, the system performs standardization processing on the collected calculation formulas, such as unifying units, adjusting the formula form, etc., and further analyzes these formulas to determine the key points and key edges describing each rule geometric model. Key points are usually points on the geometric model with special significance or determining the shape of the model, such as the center of the circle, the vertices of the rectangle, etc. Key edges are the line segments connecting these key points, and they together constitute the basic framework of the geometric model.

[0092] Step A22, classify each key point and each key edge according to the type of the rule set model, and determine the simplification method of each rule geometric model.

[0093] It should be noted that the system classifies the previously determined key points and key edges according to the type of the rule geometric model (such as circle, rectangle, triangle, etc.) so that appropriate simplification methods can be adopted for different types of geometric models in the follow-up. After the classification is completed, the system determines the corresponding simplification method for each type of geometric model, including strategies such as reducing the number of key points, merging key edges, using approximate shapes, etc., aiming to reduce its data complexity and processing cost on the premise of ensuring the basic characteristics of the geometric model.

[0094] In addition, it should be noted that when determining the simplification method, the system may consider the combination and optimization of multiple strategies to achieve the best simplification effect. For example, for complex geometric models, the system may adopt a hierarchical simplification method, first simplifying the overall shape and then refining the local features. At the same time, to meet the needs of different users and application scenarios, the system may provide the function of user-defined simplification rules, and users can adjust the classification method of key points and key edges according to their own needs, as well as select or define simplification methods.

[0095] In this embodiment, by collecting calculation formulas, the system can describe various regular geometric models in a unified and precise manner, which not only simplifies the representation form of geometric data but also reduces data redundancy. Determining key points and key edges further simplifies the description of the geometric model, retaining only the basic features of the model, thereby reducing the complexity of data processing. Classifying and determining the simplification method enables the system to adopt the most suitable processing algorithm for different types of geometric models, thus improving the processing efficiency. The application of the simplification method also reduces the amount of calculation, enabling the system to complete the processing and analysis of geometric data in a shorter time.

[0096] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as that in the above-mentioned first embodiment can be referred to the above introduction and will not be elaborated hereinafter. On this basis, please refer to Figure 2 , in step S03, the steps of calculating the boundary range of the geometric model and the convex hull unit include steps S11 to S12:

[0097] Step S11, when the geometric model is a two-dimensional model, calculate the circumscribed rectangle of the geometric model based on the boundary coordinates of the geometric model. The circumscribed rectangle is the smallest matrix that contains the geometric model and represents the boundary range of the geometric model;

[0098] It should be noted that for a two-dimensional geometric model, its boundary coordinates are first determined. A two-dimensional model refers to a geometric figure on a plane, such as a rectangle, a circle, a polygon, etc. These models are composed of basic elements such as points and lines and are located in the same plane. The boundary coordinates refer to the coordinate information of the key points that describe the outer contour of the geometric model. For a two-dimensional model, these coordinate points define the shape and size of the model. Based on these boundary coordinates, a circumscribed rectangle is calculated. The circumscribed rectangle is the smallest rectangle that can exactly contain the geometric model. It is calculated based on the boundary coordinates of the geometric model and is used to simplify the representation of the boundary range of the geometric model. The circumscribed rectangle provides an intuitive and accurate way to define the spatial position of the geometric model.

[0099] Step S12, receive the point set list of the geometric model, traverse the point set list through the monotonic connection algorithm, construct the upper convex hull and the lower convex hull of the geometric model, and return the point sets on the upper convex hull and the lower convex hull. The point sets constitute the convex hull unit of the geometric model.

[0100] It should be noted that the system receives a list of point sets of the geometric model. The list of point sets contains the coordinate information of all the points that make up the geometric model. These points define the shape and contour of the model. To construct the convex hull of the geometric model, the monotone chain algorithm is used to traverse these points. The monotone chain algorithm is an algorithm for constructing a convex hull. It traverses the list of point sets and constructs the convex hull of the geometric model based on the positional relationship of the points. The core idea of the monotone chain algorithm is to find a set of points, and the polygon formed by these points is the smallest convex polygon that contains all the input points. During the traversal process, the algorithm constructs the upper convex hull and the lower convex hull of the geometric model respectively. The upper convex hull is composed of all the convex points located above the geometric model, while the lower convex hull is composed of all the convex points located below the geometric model. Finally, the algorithm returns the point sets on the upper convex hull and the lower convex hull, and these point sets together constitute the convex hull unit of the geometric model.

[0101] In this embodiment, by calculating the circumscribed rectangle of the geometric model, the complex boundary coordinates are simplified into a rectangular area. This rectangular area not only contains the entire geometric model but also is the smallest, representing the boundary range of the geometric model in a simple and accurate manner. The calculation of the circumscribed rectangle is relatively simple and does not require a detailed analysis of each point or edge of the geometric model, greatly improving the efficiency of geometric data processing and providing convenience for subsequent data analysis and applications. By using the monotone chain algorithm to traverse the list of point sets of the geometric model and construct the upper convex hull and the lower convex hull, these two convex hulls accurately represent the outermost contour of the geometric model in the vertical direction, providing an accurate basis for subsequent geometric analysis and calculation. The convex hull unit only contains the point set that makes up the convex hull. Therefore, compared with the original geometric model, the data storage amount is greatly reduced, not only reducing the complexity of data processing but also saving storage space and improving the efficiency of data processing.

[0102] In a feasible embodiment, the boundary range includes a first boundary range and a second boundary range. In step S03, the steps of calculating the boundary range of the geometric model and the convex hull unit further include steps B01 to B03:

[0103] Step B01, when the geometric model is a three-dimensional model, perform a collapse process on the geometric model to obtain a simplified model, and calculate the oriented bounding box and the convex hull object of the simplified model. The oriented bounding box is the first boundary range of the geometric model, and the convex hull object is the second boundary range of the geometric model;

[0104] It should be noted that in the case where the geometric model is a three-dimensional model, a collapse process is first performed. A three-dimensional model refers to a geometric entity with three-dimensional spatial coordinates, such as buildings, vehicles, trees, etc. The collapse process is a simplification technique aimed at reducing the complexity of the model and the number of polygons while trying to maintain the original shape characteristics of the model. Through this process, a simplified model with lower complexity is obtained. Calculate the oriented bounding box and convex hull object of the simplified model. The oriented bounding box is a minimum bounding volume with directionality that tightly wraps the simplified model and serves as the first boundary range of the geometric model. The convex hull object is the smallest convex polyhedron that contains all the points of the simplified model. It serves as the second boundary range of the geometric model and is contained by the oriented bounding box. These two boundary ranges together provide an accurate and efficient representation of the geometric model.

[0105] Exemplarily, the system reads an irregular geometric model, sets a larger weight for the boundary line in the QEM algorithm for boundary protection, sets a larger weight when calculating the face normal difference for feature protection of the model surface, sets the overall simplification rate to 50%, and uses the QEM simplification algorithm to thin out half of the original model's face count. The model at this time is the simplified model A. Calculate the OBB for the simplified model A to obtain the oriented bounding box R of the simplified model A, which serves as the root node of the simplified model A. Calculate the convex hull for the simplified model A to obtain the convex hull object P, and use the convex hull object as the child node of the above root node R.

[0106] Step B02: Perform voxelization on the simplified model to obtain each voxel. For any pair of voxels in each voxel, calculate the concavity of the voxel pair, merge the voxel pair with the minimum concavity to obtain a new voxel, form new voxel pairs by combining the new voxel with other voxels in each voxel, and execute the steps of calculating the concavity of the voxel pair and merging the voxel pair with the minimum concavity until a preset stop condition is reached.

[0107] It should be noted that performing voxelization on the simplified model means dividing the model into a series of small cubes (voxels). For each pair of voxels, calculate their concavity. Concavity is an index that measures the shape difference between voxel pairs and is usually related to the angle, area, or volume change between voxel pairs. Merge the voxel pair with the minimum concavity to form a new voxel. Then, recombine this new voxel with the remaining voxels to form voxel pairs and repeat the steps of calculating concavity and merging until a preset stop condition is reached, such as reaching the required number of voxels, the merge count reaching the upper limit, the model simplification degree meeting the requirements, or the model quality loss being within an acceptable range.

[0108] Exemplarily, the concavity of the simplified model A is checked, and the convex hull decomposition of the model is performed using the V-HACD algorithm. First, the input simplified model A is voxelized and converted into a volume representation. Subsequently, the concavity between each pair of adjacent voxels is calculated. The concavity measures the non-convexity of the shape formed after merging two voxels. The voxel pair with the minimum concavity is selected for merging, and this process is repeated until the required number of voxels is reached or the stopping condition (such as the maximum concavity threshold) is satisfied.

[0109] Step B03: Solve the convex hull for each voxel to obtain the convex hull units corresponding to each voxel.

[0110] It should be noted that for each voxel obtained after voxelization and merging, its convex hull is solved. The convex hull is the smallest convex polyhedron that contains all the vertices of the voxel, and it precisely represents the boundary range of the voxel. By solving the convex hull of each voxel, a series of convex hull units are obtained. These convex hull units not only simplify the representation of the voxel but also provide a basis for subsequent geometric analysis and calculations.

[0111] In this embodiment, through collapse processing and voxelization processing, the complexity of the three-dimensional geometric model is reduced, and the difficulty and cost of data processing are decreased. The calculation of the oriented bounding box and the convex hull object provides an accurate description of the boundary range of the model, ensuring the accuracy of subsequent analysis. Voxel merging and convex hull unit solving further simplify the representation of the model, improve the efficiency of data processing, and provide a basis for subsequent geometric analysis and calculations.

[0112] In a feasible embodiment, in step S13, the steps of hierarchically simplifying the representation of the geometric model according to the boundary range and convex hull units include steps B11 to B13:

[0113] Step B11: After the geometric model is decomposed into each convex hull unit, when the geometric model is a two-dimensional model, the circumscribed rectangle of the geometric model is used as the root node of the geometric model, and the convex hull units of the geometric model are used as the first child nodes of the root node to obtain a hierarchical simplification index; or

[0114] It should be noted that for a two-dimensional geometric model, it is decomposed into several convex hull cells. A convex hull cell is the smallest convex polygon that contains all the points of the model and can accurately represent the shape characteristics of the model. Calculate the circumscribed rectangle of the entire geometric model. This rectangle tightly wraps the entire model. The circumscribed rectangle is used as the root node of the spatial index, which represents the position and range of the model in space. Each convex hull cell is used as the first child node of the root node. In this way, each convex hull cell is accurately positioned in the index structure, facilitating subsequent query and search operations. Through the above steps, a simple tree-like spatial index structure, namely the hierarchical simplified index, is constructed. This index structure uses the circumscribed rectangle as the root node and the convex hull cells as the leaf nodes, and can efficiently support the query and search operations of two-dimensional geometric data.

[0115] Step B12, after the geometric model is decomposed into each convex hull cell, when the geometric model is a three-dimensional model, use the oriented bounding box of the corresponding simplified model of the geometric model as the root node of the geometric model, use the convex hull object as the second child node of the root node, and use each convex hull cell as the third child node of the second child node. Based on the root node, the second child node, and the third child node, obtain the hierarchical simplified index; or

[0116] It should be noted that for a three-dimensional geometric model, it is decomposed into several convex hull cells. These convex hull cells are the smallest convex polyhedra that contain all the points of the model and can accurately represent the three-dimensional shape of the model. Calculate the oriented bounding box (OBB) of the simplified model. The oriented bounding box is the smallest bounding volume that tightly wraps the simplified model and has directionality. It is used as the root node of the spatial index, representing the position and range of the model in space. Use the convex hull object of the entire geometric model (i.e., the smallest convex polyhedron that contains all the points) as the second child node of the root node. The convex hull object provides an accurate description of the model shape, facilitating subsequent query and search operations, and use each convex hull cell as the third child node of the second child node. In this way, each convex hull cell is accurately positioned in the index structure, facilitating subsequent query and search operations. Through the above steps, a hierarchical three-dimensional spatial index structure, namely the hierarchical simplified index, is constructed. This index structure uses the oriented bounding box as the root node, the convex hull object as the intermediate node, and the convex hull cells as the leaf nodes, and can efficiently support the query and search operations of three-dimensional geometric data.

[0117] Step B13, after the geometric model is transformed into a point-line structure, based on a preset spatial index algorithm, insert the point-line structure of the geometric model into the index structure to obtain the hierarchical simplified index.

[0118] It should be noted that the preset spatial indexing algorithm is used to insert the point-line structure into the indexing structure. The preset spatial indexing algorithm refers to a specific algorithm for constructing a spatial index, which is predefined and optimized according to the characteristics of the data and the application scenario, and can efficiently organize and store geometric data. Through the spatial indexing algorithm, a spatial index suitable for the point-line structure, that is, a hierarchical simplified index, is constructed. This indexing structure can efficiently support the query and search operations of point-line data, providing strong support for subsequent geometric data processing and analysis.

[0119] In this embodiment, for a two-dimensional geometric model, the bounding rectangle is used as the root node, and the convex hull cell is used as the first child node to construct a simple and efficient spatial index. For a three-dimensional geometric model, a more complex hierarchical structure is adopted, and the oriented bounding box of the simplified model is used as the root node, the convex hull object is used as the second child node, and the convex hull cell is used as the third child node to construct a more refined spatial index. The point-line structure of the geometric model is inserted into the indexing structure to construct a spatial index, which supports the query and search operations of point-line data, providing strong support for subsequent geometric data processing and analysis. These spatial indexes can quickly locate specific regions or elements in the geometric data, significantly improving the efficiency of data query and processing.

[0120] In a feasible embodiment, in step B13, after the step of obtaining the spatial index, steps B21 to B22 are further included:

[0121] Step B21, when performing spatial analysis calculations, locate the geometric model based on the spatial index;

[0122] It should be noted that before performing spatial analysis calculations, the system first uses the previously constructed spatial index (which may be based on data structures such as quadtrees, octrees, R-trees, etc.) to quickly locate the geometric model related to the analysis task to be performed. Spatial analysis calculations refer to a series of spatial operations and calculations performed on the geometric model, such as distance calculation, area calculation, volume calculation, intersection detection, inclusion relationship judgment, etc. These calculations are the basis for understanding the position, shape, and mutual relationship of the geometric model in space.

[0123] Step B22, determine the target convex hull cells that intersect the region to be analyzed among the convex hull cells corresponding to the geometric model, and perform spatial analysis calculations on the target convex hull cells.

[0124] It should be noted that once the geometric models are located, the system will next determine whether the convex hull cells of these models (as the basic constituent units of the geometric shape) intersect with the area to be analyzed. The area to be analyzed refers to a specific spatial range specified by the user or the application and requires spatial analysis calculations. This area can be a simple geometric shape (such as a rectangle, a circle), or a more complex area (such as a polygon, an irregular shape). The parts that intersect with the area to be analyzed are defined as target convex hull cells, and these cells will be further used for spatial analysis calculations. For the convex hull cells that do not intersect with the area to be analyzed, the system will selectively eliminate them to avoid unnecessary computational overhead.

[0125] Exemplarily, when performing spatial analysis calculations on an irregular three-dimensional geometric model, first determine the oriented bounding box of the irregular three-dimensional geometric model based on the root node of the hierarchical simplification index, and search for the convex hull object under the root node to determine the approximate range of the irregular three-dimensional geometric model. Search for the convex hull cells that intersect with the area to be analyzed among the child nodes under the convex hull object, which is the actual content to be analyzed, and reduce the calculations of irrelevant content.

[0126] In this embodiment, by using the spatial index to quickly locate the geometric models and further screening the target convex hull cells for spatial analysis calculations, the system can significantly reduce unnecessary computational overhead and improve the analysis efficiency. This targeted calculation method enables the system to more effectively utilize computational resources and avoid waste on irrelevant data. Since the calculations are only performed on the target convex hull cells, the system can more accurately reflect the geometric features within the area to be analyzed, thereby improving the accuracy of the analysis results.

[0127] Exemplarily, to facilitate understanding of the technical concept or technical principle of this application, please refer to Figure 3 , Figure 3 provides an implementation flowchart of a method for simplified expression of geometric data in the field of digital twin cities. For the input geometric models, there are two types: two-dimensional models and three-dimensional models. For two-dimensional models, if they are regular models, they are represented by a point-line structure. If they are irregular geometric models, calculate their circumscribed rectangles and convex hulls for hierarchical simplified expression. Similarly, for three-dimensional models, if they are regular models, they are represented by a point-line structure. If they are irregular geometric models, calculate their bounding boxes and convex hulls for hierarchical simplified expression.

[0128] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method for simplified expression of geometric data in the field of digital twin cities of this application. Based on this technical concept, more forms of simple transformations are within the protection scope of this application.

[0129] This application also provides a device for simplified expression of geometric data in the field of digital twin cities. Please refer toFigure 4 , the geometric data simplification expression device in the field of digital twin cities includes:

[0130] A type judgment module 10, configured to judge the type of the geometric model in the digital twin city platform;

[0131] A regular model processing module 20, configured to convert the geometric model into a point-line structure when the type of the geometric model is a regular model, and the point-line structure includes key points and key edges for describing the geometric model;

[0132] An irregular model processing module 30, configured to calculate the boundary range and convex hull cells of the geometric model when the type of the geometric model is an irregular model, and hierarchically simplify and represent the geometric model according to the boundary range and convex hull cells. The boundary range represents the smallest boundary containing the geometric model, and the convex hull cells are obtained by decomposing the geometric model.

[0133] Optionally, the regular model processing module 20 is further configured to:

[0134] Identify the geometric type of the regular model corresponding to the geometric model, and match the corresponding simplification method in the model simplification rules according to the geometric type of the regular model;

[0135] Based on the simplification method, extract the key points and key edges for describing the geometric model to obtain the point-line structure corresponding to the geometric model.

[0136] Optionally, the type judgment module 10 is further configured to:

[0137] Extract the geometric features of the geometric model, and perform similarity matching based on the geometric features with each regular geometric shape and each regular geometric body in the preset geometric template library;

[0138] When the similarity reaches the preset similarity threshold, determine the geometric model as a regular model and record the geometric type of the regular model corresponding to the geometric model;

[0139] When the similarity does not reach the preset similarity threshold, determine the geometric model as an irregular model.

[0140] Optionally, the regular model processing module 20 is further configured to:

[0141] Collect the calculation formulas of each preset regular geometric model, and determine each key point and each key edge for describing each regular geometric model according to the calculation formulas;

[0142] Classify each key point and each key edge according to the type of the regular set model, and determine the simplification method of each regular geometric model.

[0143] Optionally, the irregular model processing module 30 is further configured to:

[0144] In the case where the geometric model is a two-dimensional model, calculate the circumscribed rectangle of the geometric model based on the boundary coordinates of the geometric model. The circumscribed rectangle is the smallest rectangle that contains the geometric model and represents the boundary range of the geometric model.

[0145] Receive the point set list of the geometric model, traverse the point set list through the monotonic chain algorithm, construct the upper convex hull and the lower convex hull of the geometric model, and return the point sets on the upper convex hull and the lower convex hull. The point sets form the convex hull cells of the geometric model.

[0146] Optionally, the boundary range includes a first boundary range and a second boundary range. The irregular model processing module 30 is further configured to:

[0147] In the case where the geometric model is a three-dimensional model, perform a collapse process on the geometric model to obtain a simplified model, calculate the oriented bounding box and the convex hull object of the simplified model. The oriented bounding box is the first boundary range of the geometric model, and the convex hull object is the second boundary range of the geometric model.

[0148] Perform a voxelization process on the simplified model to obtain each voxel. For any pair of voxels in each voxel, calculate the concavity of the voxel pair, merge the voxel pair with the minimum concavity to obtain a new voxel, form new voxel pairs with the new voxel and the other voxels in each voxel, and execute the steps of calculating the concavity of the voxel pair and merging the voxel pair with the minimum concavity until a preset stop condition is reached.

[0149] Solve the convex hull for each voxel to obtain each convex hull cell corresponding to each voxel.

[0150] Optionally, the irregular model processing module 30 is further configured to:

[0151] After the geometric model is decomposed into each convex hull cell, in the case where the geometric model is a two-dimensional model, use the circumscribed rectangle of the geometric model as the root node of the geometric model, and use the convex hull cells of the geometric model as the first child nodes of the root node to obtain a hierarchical simplified index; or

[0152] After the geometric model is decomposed into each convex hull cell, in the case where the geometric model is a three-dimensional model, use the oriented bounding box of the simplified model corresponding to the geometric model as the root node of the geometric model, use the convex hull object as the second child node of the root node, and use each convex hull cell as the third child node of the second child node. Based on the root node, the second child node, and the third child node, obtain a hierarchical simplified index; or

[0153] After the geometric model is converted into a point-line structure, based on a preset spatial indexing algorithm, insert the point-line structure of the geometric model into the indexing structure to obtain a hierarchical simplified index.

[0154] Optionally, the irregular model processing module 30 is further configured to:

[0155] When performing spatial analysis calculations, the geometric model is located based on the spatial index;

[0156] Determine the target convex hull cells that intersect the area to be analyzed among the convex hull cells corresponding to the geometric model, and perform spatial analysis calculations on the target convex hull cells.

[0157] The geometric data simplified expression device in the digital twin city field provided by this application adopts the geometric data simplified expression method in the above embodiment, and can solve the technical problem of complex geometric data representation. Compared with the prior art, the beneficial effects of the geometric data simplified expression device in the digital twin city field provided by this application are the same as those of the geometric data simplified expression method provided by the above embodiment, and other technical features in the geometric data simplified expression device in the digital twin city field are the same as the features disclosed in the method of the above embodiment, and will not be elaborated here.

[0158] This application provides an electronic 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 geometric data simplified expression method in Embodiment 1 above.

[0159] Refer to the following Figure 5 , which shows a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, PADs (Portable Application Description: tablet computers), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0160] As shown in Figure 5As shown, the electronic device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in the read-only memory 1002 or a program loaded from the storage device 1003 into the random access memory 1004. In the random access memory 1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. The input / output interface 1006 is also connected to the bus. Generally, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a microphone, 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 may allow the electronic device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or had alternatively.

[0161] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. 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 contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0162] The electronic device provided by the present application adopts the geometric data simplified expression method in the digital twin city field in the above embodiment, and can solve the technical problem of complex geometric data representation. Compared with the prior art, the beneficial effects of the electronic device provided by the present application are the same as those of the geometric data simplified expression method in the digital twin city field provided in the above embodiment, and other technical features in the electronic device are the same as those disclosed in the method of the previous embodiment, which will not be elaborated here.

[0163] It should be understood that each part disclosed in the present application may be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0164] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claimed rights.

[0165] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the geometric data simplified expression method in the digital twin city field in the above embodiments.

[0166] The computer-readable storage medium provided by 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 the computer-readable storage medium may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0167] The above computer-readable storage medium may be included in an electronic device; or it may exist separately and not be assembled into the electronic device.

[0168] The above computer-readable storage medium carries one or more programs, which, when executed by an electronic device, enable the geometric data simplification and representation device in the field of digital twin cities to be applied to the digital twin city platform, and can determine the type of geometric model in the digital twin city platform; in the case where the type of geometric model is a regular model, convert the geometric model into a point-line structure, and the point-line structure includes key points and key edges describing the geometric model; in the case where the type of geometric model is an irregular model, calculate the boundary range and convex hull cells of the geometric model, and hierarchically simplify and represent the geometric model according to the boundary range and convex hull cells. The boundary range represents the smallest boundary containing the geometric model, and the convex hull cells are obtained by decomposing the geometric model.

[0169] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent 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 can 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 can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0170] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0171] The modules involved in the embodiments of the present application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0172] The readable storage medium provided by the present application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above geometric data simplification expression method in the field of digital twin cities, and can solve the technical problem of complex geometric data representation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as those of the geometric data simplification expression method in the field of digital twin cities provided by the above embodiments, and will not be elaborated here.

[0173] The present application also provides a computer program product, including a computer program, and the steps of the geometric data simplification expression method in the field of digital twin cities as described above are implemented when the computer program is executed by a processor.

[0174] The computer program product provided by the present application can solve the technical problem of complex geometric data representation. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the geometric data simplification expression method in the field of digital twin cities provided by the above embodiments, and will not be elaborated here.

[0175] The above are only some embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural transformation made by using the specification and drawings of the present application under the technical concept of the present application, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A simplified expression method for geometric data in the field of digital twin cities, characterized in that: Applied to the digital twin city platform, the simplified expression method of geometric data in the digital twin city field includes: Determining the type of geometric model in the digital twin city platform; In the case where the type of the geometric model is a regular model, converting the geometric model into a point-line structure, wherein the point-line structure includes key points and key edges that describe the geometric model; In the case where the type of the geometric model is an irregular model, a boundary range and a convex hull unit of the geometric model are calculated, and the geometric model is hierarchically simplified according to the boundary range and the convex hull unit, wherein the boundary range represents a minimum boundary containing the geometric model, and the convex hull unit is obtained by decomposing the geometric model; Wherein, the step of determining the type of the geometric model in the digital twin city platform includes: Extracting geometric features of the geometric model, and performing similarity matching with regular geometric shapes and regular geometric bodies in a preset geometric template library based on the geometric features; When the similarity reaches a preset similarity threshold, the geometric model is determined to be a regular model, and the geometric type of the regular model corresponding to the geometric model is recorded; When the similarity does not reach a preset similarity threshold, the geometric model is determined to be an irregular model.

2. The method for simplifying and expressing geometric data in the digital twin city domain according to claim 1, characterized in that: When the type of the geometric model is a regular model, the step of converting the geometric model into a point-line structure comprises: Identify the geometric type of the regular model corresponding to the geometric model, and match the corresponding simplification method in the model simplification rule according to the geometric type of the regular model; Based on the simplification method, key points and key edges describing the geometric model are extracted to obtain the corresponding point-line structure of the geometric model.

3. The simplified expression method of digital twin city domain geometric data according to claim 2, characterized in that: Before the step of matching the corresponding simplification method in the model simplification rule according to the type of the rule model, the following step is further included: Collecting calculation formulas of each preset regular geometric model, and determining key points and key edges describing each regular geometric model according to the calculation formula; The key points and the key edges are classified according to the type of the rule set model, and the simplification method of the rule geometric models is determined.

4. The simplified expression method of digital twin city domain geometric data according to claim 1, characterized in that: The step of calculating the boundary range and convex hull unit of the geometric model comprises: In the case where the geometric model is a two-dimensional model, a bounding rectangle of the geometric model is calculated based on the boundary coordinates of the geometric model, wherein the bounding rectangle is a minimum matrix containing the geometric model and represents the boundary range of the geometric model; Receive a point set list of the geometric model, traverse the point set list through a monotone connection algorithm, construct an upper convex hull and a lower convex hull of the geometric model, and return point sets on the upper convex hull and the lower convex hull, wherein the point sets constitute convex hull units of the geometric model.

5. The simplified expression method of digital twin city domain geometric data according to claim 1, characterized in that: The boundary range includes a first boundary range and a second boundary range, and the step of calculating the boundary range and the convex hull unit of the geometric model further includes: In the case where the geometric model is a three-dimensional model, collapse the geometric model to obtain a simplified model, and calculate a directed bounding box and a convex hull object of the simplified model, wherein the directed bounding box is a first boundary range of the geometric model, and the convex hull object is a second boundary range of the geometric model; voxelize the simplified model to obtain voxels, calculate the concavity of any voxel pair among the voxels, merge the voxel pair with the smallest concavity to obtain a new voxel, combine the new voxel with other voxels among the voxels to form a new voxel pair, and perform the steps of calculating the concavity of the voxel pair and merging the voxel pair with the smallest concavity based on the new voxel pair until a preset stop condition is reached; The convex hull is solved for each voxel to obtain each convex hull unit corresponding to the voxel.

6. The simplified expression method of digital twin city domain geometric data according to claim 5 is characterized in that: The step of hierarchically simplifying the geometric model according to the boundary range and the convex hull unit comprises: After the geometric model is decomposed into convex hull units, if the geometric model is a two-dimensional model, the circumscribed rectangle of the geometric model is used as the root node of the geometric model, and the convex hull unit of the geometric model is used as the first child node of the root node to obtain a hierarchical simplified index; or After the geometric model is decomposed into convex hull units, if the geometric model is a three-dimensional model, a directed bounding box of a simplified model corresponding to the geometric model is used as a root node of the geometric model, the convex hull object is used as a second child node of the root node, and each convex hull unit is used as a third child node of the second child node, and a hierarchical simplification index is obtained based on the root node, the second child node and the third child node; or After the geometric model is converted into a point-line structure, the point-line structure of the geometric model is inserted into the index structure based on a preset spatial index algorithm to obtain a hierarchical simplified index.

7. The simplified expression method of digital twin city domain geometric data according to claim 6, characterized in that: The step of obtaining the hierarchical simplified index further includes: When performing spatial analysis calculations, locating the geometric model based on the spatial index; A target convex hull unit intersecting with the area to be analyzed among the convex hull units corresponding to the geometric model is determined, and a spatial analysis calculation is performed on the target convex hull unit.

8. 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 method for simplifying the expression of geometric data in the digital twin city field as described in any one of claims 1 to 7 are implemented.

9. A computer program product, characterized in that The computer program product includes a computer program, which, when executed by a processor, implements the steps of the method for simplifying the expression of geometric data in the digital twin city domain as described in any one of claims 1 to 7.

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