Simplified expression method for geometric data in field of digital twinborn cities

Abstract

The invention discloses a simplified expression method for geometric data in the field of digital twin cities, relates to the technical field of digital twin, is applied to a digital twin city platform, and comprises the following steps: judging the type of a geometric model in the digital twin city platform; under the condition that the type of the geometric model is a regular model, the geometric model is converted into a point-line structure, and the point-line structure comprises key points and key edges which describe the geometric model; and under the condition that the type of the geometric model is an irregular model, calculating a boundary range and a convex hull unit of the geometric model, performing hierarchical simplified representation on the geometric model according to the boundary range and the convex hull unit, the boundary range representing the minimum boundary of the geometric model, and the convex hull unit being obtained by decomposing the geometric model. The technical problem that geometric data representation is complex is solved.

Description

Technical Field

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

[0002] In the digital twin city platform, it is often necessary to perform spatial analysis and calculation on geometric data. When performing spatial analysis and calculation, traditional digital twin city platforms usually load all the required geometric data to the request end for processing. When the representation of geometric data is too complex, it will face the problems of low spatial analysis efficiency and time-consuming spatial calculation, which greatly increases the difficulty and time of calculation, thereby reducing calculation efficiency. Therefore, there is a problem of complex geometric data representation in the current digital twin city field. Summary of the invention

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

[0004] To achieve the above objectives, the present application proposes a simplified expression method for geometric data in the field of digital twin cities, which is applied to the digital twin city platform. The simplified expression method for geometric data in the field of digital twin cities 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, the boundary range and convex hull units of the geometric model are calculated, and the geometric model is hierarchically simplified according to the boundary range and the convex hull units. The boundary range represents the minimum boundary containing the geometric model, and the convex hull units are obtained by decomposing the geometric model.

[0005] In one embodiment, when the type of the geometric model is a regular model, the step of converting the geometric model into a point-line structure includes: 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.

[0006] In one embodiment, 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.

[0007] In one embodiment, before the step of matching the corresponding simplification method in the model simplification rule according to the type of the rule model, the step further includes: 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.

[0008] In one embodiment, the step of calculating the boundary range and convex hull units of the geometric model includes: 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.

[0009] In one embodiment, 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.

[0010] In one embodiment, 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.

[0011] In one embodiment, the step of obtaining the spatial 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.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a simplified expression device for geometric data in the field of digital twin cities, which is applied to the digital twin city platform. The simplified expression device for geometric data in the field of digital twin cities includes: A type judgment module, used to judge the type of the geometric model in the digital twin city platform; A regular model processing module, used for converting the geometric model into a point-line structure when the type of the geometric model is a regular model, wherein the point-line structure includes key points and key edges describing the geometric model; An irregular model processing module is used to calculate the boundary range and convex hull units of the geometric model when the type of the geometric model is an irregular model, and to hierarchically simplify the geometric model according to the boundary range and the convex hull units, wherein the boundary range represents the minimum boundary containing the geometric model, and the convex hull units are obtained by decomposing the geometric model.

[0013] In addition, to achieve the above-mentioned objectives, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for simplifying the expression of geometric data in the field of digital twin cities as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the simplified expression method of geometric data in the digital twin city field as described above are implemented.

[0015] In addition, to achieve the above-mentioned objectives, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for simplifying the expression of geometric data in the field of digital twin cities as described above.

[0016] The present application provides a method for simplifying and expressing geometric data in the field of digital twin cities, which is applied to a digital twin city platform. The method for simplifying and expressing geometric data in the field of digital twin cities includes: determining the type of 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, wherein the point-line structure includes key points and key edges that describe the geometric model; when the type of the geometric model is an irregular model, calculating the boundary range and convex hull units of the geometric model, and hierarchically simplifying the geometric model according to the boundary range and the convex hull units, wherein the boundary range represents the minimum boundary containing the geometric model, and the convex hull units are obtained by decomposing the geometric model.

[0017] This application effectively simplifies the geometric data in the digital twin city platform by converting the geometric model into a point-line structure or decomposing it into convex hull units, reducing the overhead of data storage and processing, and improving computing efficiency. At the same time, it can handle different types of geometric models, so that the digital twin city platform can be more widely used in different types of urban scenes, enhancing the adaptability and flexibility of the platform. Compared with related solutions, when the geometric data representation is too complex, it will face the problems of low spatial analysis efficiency and long spatial calculation time. This application performs targeted simplification 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 difficulty of calculation, and is conducive to subsequent analysis and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flow chart of the first embodiment of the method for simplifying the expression of geometric data in the field of digital twin cities of this application; Figure 2 A flow chart of the second embodiment of the method for simplifying the expression of geometric data in the field of digital twin cities of this application; Figure 3 A flowchart for implementing the simplified expression method of geometric data in the digital twin city field provided in this application; Figure 4 This is a schematic diagram of the module structure of the device for simplifying and expressing geometric data in the field of digital twin cities according to an embodiment of the present application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the method for simplifying the expression of geometric data in the digital twin city field in the embodiment of the present application.

[0021] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0023] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0024] The embodiment of the present application is applied to a digital twin city platform, and the main solution is: determining the type of 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, which includes key points and key edges that describe the geometric model; when the type of the geometric model is an irregular model, calculating the boundary range and convex hull unit of the geometric model, and hierarchically simplifying the geometric model according to the boundary range and the convex hull unit, wherein the boundary range represents the minimum boundary containing the geometric model, and the convex hull unit is obtained by decomposing the geometric model.

[0025] In this embodiment, for the convenience of description, the digital twin city system is used as the execution entity for explanation below.

[0026] Spatial analysis and computation of geometric data are core tasks in the operation of the digital twin city platform. However, the traditional processing method tends to load all the required geometric data to the requesting end at one time for calculation. This approach will significantly slow down the speed of spatial analysis and prolong the time of spatial calculation when faced with complex geometric data representation, thereby increasing the complexity and time consumption of calculations and weakening the overall computing efficiency.

[0027] The present application provides a solution, which effectively simplifies the geometric data in the digital twin city platform by converting the geometric model into a point-line structure or decomposing it into convex hull units, reduces the overhead of data storage and processing, and improves computing efficiency. At the same time, it can process different types of geometric models, so that the digital twin city platform can be more widely used in different types of urban scenarios, enhancing the adaptability and flexibility of the platform.

[0028] 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 capable of realizing the above functions, a digital twin city system, etc. The following takes the digital twin city system as an example to illustrate this embodiment and the following embodiments.

[0029] Based on this, the embodiment of the present application provides a simplified expression method of geometric data in the field of digital twin cities, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for simplifying the expression of geometric data in the field of digital twin cities of the present application.

[0030] In this embodiment, applied to the digital twin city platform, the simplified expression method of geometric data in the digital twin city field includes steps S01 to S03: Step S01, determining the type of geometric model in the digital twin city platform; It should be noted that the digital twin city platform is a platform that uses digital technology to simulate the real urban environment. It can realize real-time monitoring, prediction and optimization of the city's operating status. The digital twin city platform includes many two-dimensional or three-dimensional models used to represent urban elements (such as buildings, roads, terrain, etc.), namely geometric models. The system classifies geometric models into regular models or irregular models according to their shape characteristics. Regular models usually refer to those with simple shapes, regular structures, and 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 of which is an equilateral triangle), regular octahedrons, regular dodecahedrons and regular icosahedrons; while irregular models refer to models with complex shapes and irregular boundaries that are difficult to express with simple geometric formulas.

[0031] It is understandable that, since existing solutions usually lack a clear distinction between geometric model types, resulting in a lack of specificity when processing complex geometric data, step S01 is performed to perform targeted processing according to the type of geometric model, avoiding the "one-size-fits-all" approach in traditional solutions. Different conversion strategies are adopted for regular models and irregular models, respectively, thereby achieving more efficient and accurate spatial analysis and calculation.

[0032] Step S02, when 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; It should be noted that for regular models, converting them into point-line structures includes identifying and extracting key points that describe the shape of the geometric model (such as the center point of a circular building and the four corner points of a rectangular building) and key edges connecting these key points (such as the radius edge of a circular building and the four sides of a rectangular building). 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, which is used to concisely represent the shape and boundary of the geometric model. Key points refer to points that are of great significance in the geometric model, such as the center point of the geometric model, the corner points of the building, the intersection of the roads, etc. Key edges refer to line segments connecting key points, which are used to describe the boundary of the geometric model.

[0033] It is understandable that since the existing solutions still use complex geometric data representation methods when processing rule models, resulting in unnecessary computing overhead, step S02 is performed to greatly simplify the data representation and reduce the complexity of calculation by converting the rule model into a point-line structure, thereby improving the efficiency of spatial analysis and calculation.

[0034] Step S03, when the type of the geometric model is an irregular model, the boundary range and convex hull units of the geometric model are calculated, and the geometric model is hierarchically simplified according to the boundary range and convex hull units. The boundary range represents the minimum boundary containing the geometric model, and the convex hull units are obtained by decomposing the geometric model.

[0035] It should be noted that for irregular models, irregular models refer to models with complex shapes that cannot be described by simple geometric shapes (such as rectangles, circles, etc.). For example, a complex building model or terrain model, the boundary range and convex hull unit of the irregular model are calculated. The boundary range represents the minimum boundary containing the geometric model, and the convex hull unit is the minimum convex polygon or convex polyhedron obtained by decomposing the geometric model and covering all points of the model. According to the boundary range and convex hull unit, the geometric model is hierarchically simplified to reduce the complexity of the model. Hierarchical simplification refers to hierarchical processing of 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 unit can be calculated first, and then hierarchical simplification can be performed based on this information, such as dividing the model into grids of different levels or simplifying the model structure. At the same time, the main features of the model are retained, which helps to improve the efficiency of spatial analysis and calculation.

[0036] It is understandable that, since the existing solutions have not found an effective simplified representation method for irregular models, step S03 is performed. By calculating the boundary range and convex hull units, the main features of the model are retained, ensuring the accuracy of spatial analysis and calculation. By decomposing the geometric model into convex hull units, complex geometric data can be converted into small blocks that are easier to process, thereby improving 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.

[0037] In a feasible implementation manner, in step S02, when the type of the geometric model is a regular model, the step of converting the geometric model into a point-line structure includes steps A01 to A02: Step A01, identifying the geometric type of the geometric model corresponding to the rule model, and matching the corresponding simplification method in the model simplification rule according to the geometric type of the rule model; It should be noted that the geometric model is first identified for its geometric type to determine which regular model it belongs to, such as a cube, a cuboid, a cylinder, a sphere, etc. Then, based on the identified geometric type, the corresponding simplification method is searched and matched in the preset model simplification rules. Model simplification rules refer to a set of predefined simplification methods that guide the selection of simplification methods based on the geometric properties of the regular model (such as symmetry, planarity, curvature, etc.) 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 based on the geometric features of different regular models, aiming to accurately describe the shape of the model with the least points and edges.

[0038] Step A02: based on a simplification method, extract key points and key edges describing the geometric model to obtain the corresponding point-line structure of the geometric model.

[0039] It should be noted that after the simplification method is determined, the key points and key edges required to describe the geometric model are extracted according to the method. These key points and key edges together constitute the point-line structure of the geometric model, representing the geometric shape of the original model in a simplified form.

[0040] Exemplarily, the model simplification rules are shown in Table 1, which lists simplified expression methods of some rule models. For example, when the rule model is a three-dimensional sphere, it is expressed by a simplified expression method of "center point + radius", where the key point is the center point and the key edge is the radius: Table 1

[0041] In this implementation, by identifying the type of regular model and matching the simplification method, a complex geometric model can be converted into a point-line structure composed of key points and key edges. The simplified point-line structure reduces the amount and complexity of data 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 user experience of the digital twin city platform. At the same time, by reducing the complexity of geometric data and the difficulty of calculation, it optimizes the utilization of computing resources, helps to reduce computing time and resource consumption, and improves the overall performance of the digital twin city platform.

[0042] In a feasible implementation, in step S01, the step of determining the type of the geometric model in the digital twin city platform includes steps A11 to A13: Step A11, extracting geometric features of the geometric model, and performing similarity matching with various regular geometric shapes and various regular geometric bodies in a preset geometric template library based on the geometric features; It should be noted that the system conducts an in-depth analysis of the input geometric model and extracts its key geometric features, including but not limited to the model's vertex coordinates, edge length, surface area, volume, curvature, etc., which together constitute the basic data set that describes the morphology of the geometric model.

[0043] In addition, it should be noted that the system compares the extracted geometric features with each regular geometric shape in the preset geometric template library and the regular geometric bodies composed of these shapes one by one. The preset geometric template library is a predefined database, which stores a variety of common regular geometric shapes (geometric shapes with clear geometric features and rules, such as cubes, cuboids, cylinders, spheres, etc.) and regular geometric bodies formed by these shapes through rotation, scaling, translation and other operations. The similarity matching process is achieved by calculating the similarity score between the geometric features and the geometric shapes in the template library. The higher the score, the closer the geometric model is to a regular geometric shape in the template library.

[0044] In addition, it should be noted that when performing similarity matching, firstly, the geometric model is imaged to obtain a digital image, and the image is preprocessed such as denoising, enlarging, and reducing 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 in the image where the gray value changes dramatically, usually corresponding to the outline of a geometric shape. The feature point detection algorithm (such as Harris corner points, FAST corner points, etc.) is used to detect significant points or corner points in the image. These points are usually the locations where the changes are most dramatic in the image, and are of great significance for describing the geometric shape. For the extracted edges or feature points, the shape descriptor is further calculated. The features of geometric shapes are quantified by using features such as area, perimeter, circularity, rectangularity, etc. 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, KD tree, etc.) are used to accelerate the matching process. For each matching pair, a similarity measure is calculated. The similarity measure 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 measure, and finally the matching degree between the geometric features and each regular geometric shape and each regular geometric body in the preset geometric template library is obtained.

[0045] In addition, it should be noted that for each regular geometric shape and each regular geometric body in the preset geometric template library, the system can automatically generate the required geometric figures based on user needs. For regular geometric shapes, the system receives the lower limit and upper limit of the number of sides set by the user, generates regular polygons with sides ranging from the lower limit to the upper limit, and stores them in the preset geometric template library. For regular geometric bodies, the system receives the lower limit and upper limit of the number of faces set by the user, and on the premise of satisfying the Euler formula, generates a regular polyhedron with faces ranging from the lower limit to the upper limit, and stores it in the preset geometric template library.

[0046] In addition, 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 record its corresponding simplified expression method for subsequent use.

[0047] Step A12, when the similarity reaches a preset similarity threshold, the geometric model is determined to be a regular model, and the geometric type of the geometric model corresponding to the regular model is recorded; It should be noted that if the similarity score between the geometric model and a 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.

[0048] Step A13: when the similarity does not reach a preset similarity threshold, the geometric model is determined to be an irregular model.

[0049] It should be noted that if the similarity score between the geometric model and all regular geometric shapes in the template library does not reach the preset similarity threshold, the system will judge the geometric model as an irregular model and identify those complex geometric models that cannot be accurately described by preset regular geometric shapes, providing a basis for subsequent flexible processing of irregular models.

[0050] In this embodiment, by extracting the geometric features of the geometric model and performing similarity matching with regular geometric shapes and geometric bodies in a preset geometric template library, the geometric model can be quickly classified as a regular model or an irregular model, thereby avoiding the tedious and time-consuming manual classification and significantly improving processing efficiency. The preset geometric template library contains various common regular geometric shapes and geometric bodies. 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 classification.

[0051] In a feasible implementation manner, in step A01, before the step of matching the corresponding simplification method in the model simplification rule according to the type of the rule model, steps A21-A22 are also included: Step A21, 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; It should be noted that the system collects the corresponding calculation formula for each preset regular geometric model. These calculation formulas are the basis for mathematically accurately describing the shape, size and position of the geometric model. For example, for a circle, its calculation formula may be the equation of the circle ( ); For a rectangle, it may be an expression of its length, width, and vertex coordinates.

[0052] In addition, it should be noted that after collecting the calculation formulas, the system standardizes the collected calculation formulas, such as unifying units, adjusting formula forms, etc., and further analyzes these formulas to determine the key points and key edges that describe each regular geometric model. Key points are usually points on the geometric model that have special meanings or determine the shape of the model, such as the center of a circle, the vertices of a rectangle, etc. The key edges are the line segments connecting these key points, which together constitute the basic framework of the geometric model.

[0053] 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 regular geometric model.

[0054] It should be noted that the system classifies the previously determined key points and key edges according to the type of regular geometric model (such as circle, rectangle, triangle, etc.) so that appropriate simplification methods can be used for different types of geometric models in the future. After the classification is completed, the system determines the corresponding simplification method for each type of geometric model, including reducing the number of key points, merging key edges, using approximate shapes and other strategies, aiming to reduce its data complexity and processing cost while ensuring the basic characteristics of the geometric model.

[0055] 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 to first simplify the overall shape and then refine the local features. At the same time, in order to meet the needs of different users and application scenarios, the system may provide users with the function of customizing simplification rules. Users can adjust the classification method of key points and key edges according to their own needs, and select or define simplification methods.

[0056] In this embodiment, by collecting calculation formulas, the system can describe various regular geometric models in a unified and accurate manner, which not only simplifies the representation of geometric data, but also reduces data redundancy. The determination of 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. Classification and determination of simplification methods enable the system to use the most appropriate processing algorithm for different types of geometric models, thereby improving processing efficiency. The application of simplification methods also reduces the amount of calculation, allowing the system to complete the processing and analysis of geometric data in a shorter time.

[0057] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 In step S03, the step of calculating the boundary range and convex hull unit of the geometric model includes steps S11 to S12: Step S11, when the geometric model is a two-dimensional model, the circumscribed rectangle of the geometric model is calculated based on the boundary coordinates of the geometric model, where the circumscribed rectangle is the minimum matrix containing the geometric model and represents the boundary range of the geometric model; 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, circle, polygon, etc. These models are composed of basic elements such as points and lines and are located in the same plane. 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 minimum rectangle that can completely contain the geometric model. It is calculated based on the boundary coordinates of the geometric model and is used to simplify the boundary range representation of the geometric model. The circumscribed rectangle provides an intuitive and accurate way to define the spatial position of a geometric model.

[0058] Step S12, receiving the point set list of the geometric model, traversing the point set list through the monotone connection algorithm, constructing the upper convex hull and lower convex hull of the geometric model, and returning the point sets on the upper convex hull and the lower convex hull, which constitute the convex hull unit of the geometric model.

[0059] It should be noted that the system receives a point set list of a geometric model. The point set list contains the coordinate information of all points that constitute the geometric model. These points define the shape and outline of the model. In order to construct the convex hull of the geometric model, the monotone connection algorithm is used to traverse these points. The monotone connection algorithm is an algorithm for constructing a convex hull. It traverses the point set list and constructs the convex hull of the geometric model according to the positional relationship of the points. The core idea of ​​the monotone connection algorithm is to find a set of points. The polygon formed by these points is the minimum convex polygon containing all the input points. During the traversal process, the algorithm constructs the upper convex hull and lower convex hull of the geometric model respectively. The upper convex hull is composed of all convex points located above the geometric model, and the lower convex hull is composed of all convex points located below the geometric model. Finally, the algorithm returns the point sets on the upper convex hull and the lower convex hull. These point sets together constitute the convex hull unit of the geometric model.

[0060] In this embodiment, the complex boundary coordinates are simplified into a rectangular area by calculating the circumscribed rectangle of the geometric model. This rectangular area not only includes the entire geometric model but is also the smallest, and represents the boundary range of the geometric model in a concise and accurate manner. The calculation of the circumscribed rectangle is relatively simple, and there is no need to perform detailed analysis on each point or edge of the geometric model, which greatly improves the efficiency of geometric data processing and provides convenience for subsequent data analysis and application. The point set list of the geometric model is traversed by the monotone connection algorithm to 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, and provide an accurate basis for subsequent geometric analysis and calculation. The convex hull unit only contains the point set that constitutes the convex hull. Therefore, compared with the original geometric model, the amount of data storage is greatly reduced, which not only reduces the complexity of data processing, but also saves storage space and improves the efficiency of data processing.

[0061] In a feasible implementation manner, the boundary range includes a first boundary range and a second boundary range. In step S03, the step of calculating the boundary range and the convex hull unit of the geometric model further includes steps B01 to B03: Step B01, when 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; It should be noted that, when the geometric model is a three-dimensional model, a collapse process is performed first. A three-dimensional model refers to a geometric entity with three-dimensional space coordinates, such as buildings, vehicles, trees, etc. The collapse process is a simplification technique that aims to reduce the complexity and number of polygons of the model while trying to maintain the original shape characteristics of the model. Through this process, a simplified model with lower complexity is obtained, and the directed bounding box and convex hull object of the simplified model are calculated. The directed bounding box is a directional minimum bounding volume that tightly wraps the simplified model and serves as the first boundary range of the geometric model. The convex hull object is the minimum convex polyhedron that contains all points of the simplified model. It serves as the second boundary range of the geometric model and is contained in the directed bounding box. These two boundary ranges together provide an accurate and efficient representation for the geometric model.

[0062] Exemplarily, the system reads an irregular geometric model, sets a larger weight for the boundary line in the QEM algorithm to perform boundary protection, sets a larger weight when calculating the surface normal difference to perform feature protection on the model surface, sets the overall simplification rate to 50%, and uses the QEM simplification algorithm to reduce the number of faces of the original model by half. The model at this time is the simplified model A, calculates the OBB for the simplified model A, and obtains the directed bounding box R of the simplified model A, which is used as the root node of the simplified model A. The convex hull of the simplified model A is calculated to obtain the convex hull object P, and the convex hull object is used as a child node of the above-mentioned root node R.

[0063] Step B02, voxelizing the simplified model to obtain each voxel, calculating the concavity of the voxel pair for any pair of voxels in each voxel, merging the voxel pair with the smallest concavity to obtain a new voxel, combining the new voxel with other voxels in each voxel to form a new voxel pair, and calculating the concavity of the voxel pair based on the new voxel pair, merging the voxel pair with the smallest concavity, until a preset stop condition is reached; It should be noted that the simplified model is voxelized, that is, the model is divided into a series of small cubes (voxels). For each pair of voxels, its concavity is calculated. Concavity is an indicator to measure the shape difference between voxel pairs, which is usually related to the angle, area or volume change between voxel pairs. The voxel pair with the smallest concavity is merged to form a new voxel. Then, this new voxel is recombined with the remaining voxels to form a voxel pair, and the steps of calculating concavity and merging are repeated until the preset stopping conditions are reached, such as reaching the required number of voxels, the number of merges reaches the upper limit, the degree of model simplification meets the requirements, or the model quality loss is within an acceptable range.

[0064] Exemplarily, the simplified model A is checked for concavity 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 the two voxels are merged. The voxel pair with the smallest 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 met.

[0065] Step B03, solving the convex hull for each voxel to obtain each convex hull unit corresponding to each voxel.

[0066] 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 containing all the vertices of the voxel. It accurately 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 voxels, but also provide a basis for subsequent geometric analysis and calculations.

[0067] In this implementation, the complexity of the three-dimensional geometric model is reduced through collapse and voxelization processing, and the difficulty and cost of data processing are reduced. The calculation of directed bounding boxes and convex hull objects provides an accurate description of the boundary range of the model, ensuring the accuracy of subsequent analysis. Voxel merging and convex hull unit solutions further simplify the representation of the model, improve the efficiency of data processing, and provide a basis for subsequent geometric analysis and calculations.

[0068] In a feasible implementation manner, in step S13, the step of hierarchically simplifying the geometric model according to the boundary range and the convex hull unit includes steps B11 to B13: Step B11, 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 It should be noted that for a two-dimensional geometric model, it is decomposed into several convex hull units. The convex hull unit is the smallest convex polygon containing all the points of the model, which can accurately represent the shape characteristics of the model. The circumscribed rectangle of the entire geometric model is calculated. 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 unit is used as the first child node of the root node. In this way, each convex hull unit is accurately located in the index structure, which is convenient for subsequent query and search operations. Through the above steps, a simple tree-like spatial index structure is constructed, that is, a hierarchical simplified index. This index structure uses the circumscribed rectangle as the root node and the convex hull unit as the leaf node, which can efficiently support query and search operations of two-dimensional geometric data.

[0069] Step B12, after the geometric model is decomposed into each convex hull unit, if the geometric model is a three-dimensional model, the directed bounding box of the geometric model corresponding to the simplified model is used as the root node of the geometric model, the convex hull object is used as the second child node of the root node, and each convex hull unit is used as the 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 It should be noted that for a three-dimensional geometric model, it is decomposed into several convex hull units. These convex hull units are the smallest convex polyhedrons containing all the points of the model, which can accurately represent the three-dimensional shape of the model. The directed bounding box (OBB) of the simplified model is calculated. The directed bounding box is a minimum bounding volume that tightly wraps the simplified model and has directionality. It is used as the root node of the spatial index and represents the position and range of the model in space. The convex hull object of the entire geometric model (that is, the smallest convex polyhedron containing all points) is used as the second child node of the root node. The convex hull object provides an accurate description of the shape of the model, which is helpful for subsequent query and search operations, and each convex hull unit is used as the third child node of the second child node. In this way, each convex hull unit is accurately located in the index structure, which is convenient for subsequent query and search operations. Through the above steps, a hierarchical three-dimensional spatial index structure is constructed, namely, a hierarchical simplified index. This index structure uses the directed bounding box as the root node, the convex hull object as the intermediate node, and the convex hull unit as the leaf node, which can efficiently support the query and search operations of three-dimensional geometric data.

[0070] Step B13, after the geometric model is converted into a point-line structure, based on a preset spatial index algorithm, the point-line structure of the geometric model is inserted into the index structure to obtain a hierarchical simplified index.

[0071] It should be noted that a preset spatial index algorithm is used to insert the point-line structure into the index structure. The preset spatial index 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 index algorithm, a spatial index suitable for the point-line structure is constructed, that is, a hierarchical simplified index. This index structure can efficiently support query and search operations on point-line data, and provide strong support for subsequent geometric data processing and analysis.

[0072] In this embodiment, for two-dimensional geometric models, a concise and efficient spatial index is constructed by using the circumscribed rectangle as the root node and the convex hull unit as the first child node. For three-dimensional geometric models, a more complex hierarchical structure is adopted, and the directed bounding box of the simplified model is used as the root node, the convex hull object as the second child node, and the convex hull unit as the third child node to construct a more sophisticated spatial index. The point and line structure of the geometric model is inserted into the index structure to construct a spatial index, which supports query and search operations of point and line data, and provides strong support for subsequent geometric data processing and analysis. These spatial indexes can quickly locate specific areas or elements in the geometric data, significantly improving the efficiency of data query and processing.

[0073] In a feasible implementation manner, in step B13, after the step of obtaining the spatial index, steps B21 to B22 are also included: Step B21, when performing spatial analysis calculations, locating the geometric model based on the spatial index; It should be noted that before performing spatial analysis calculations, the system first uses the previously constructed spatial index (which may be based on quadtree, octree, R-tree and other data structures) to quickly locate the geometric model related to the task to be analyzed. Spatial analysis calculations refer to a series of spatial operations and calculations on geometric models, such as distance calculations, area calculations, volume calculations, intersection detection, inclusion relationship judgment, etc. These calculations are the basis for understanding the position, shape and mutual relationship of geometric models in space.

[0074] Step B22, determining the target convex hull unit intersecting with the area to be analyzed among the convex hull units corresponding to the geometric model, and performing spatial analysis calculation on the target convex hull unit.

[0075] It should be noted that once the geometric models are located, the system will then determine whether the convex hull units of these models (as the basic components of geometric shapes) intersect with the area to be analyzed. The area to be analyzed refers to a specific spatial range specified by the user or application that requires spatial analysis calculations. This area can be a simple geometric shape (such as a rectangle or circle) or a more complex area (such as a polygon or irregular shape). The part that intersects with the area to be analyzed is defined as the target convex hull unit, which will be further used for spatial analysis calculations. For convex hull units that do not intersect with the area to be analyzed, the system will selectively eliminate them to avoid unnecessary computational overhead.

[0076] Exemplarily, when performing spatial analysis calculations on an irregular three-dimensional geometric model, the directed bounding box of the irregular three-dimensional geometric model is first determined based on the root node of the hierarchical simplified index, and the convex hull object under the root node is searched to determine the approximate range of the irregular three-dimensional geometric model. The convex hull units that intersect with the area to be analyzed are searched in the child nodes under the convex hull object, that is, the actual content that needs to be analyzed, to reduce the calculation of irrelevant content.

[0077] In this embodiment, by using spatial indexes to quickly locate the geometric model and further screening the target convex hull units for spatial analysis calculations, the system can significantly reduce unnecessary computing overhead and improve analysis efficiency. This targeted calculation method enables the system to more effectively utilize computing resources and avoid waste on irrelevant data. Since only the target convex hull units are calculated, the system can more accurately reflect the geometric features of the area to be analyzed, thereby improving the accuracy of the analysis results.

[0078] For example, to help understand the technical concept or technical principle of this application, please refer to Figure 3 , Figure 3 A flowchart for the implementation of a simplified expression method for geometric data in the field of digital twin cities is provided. The input geometric models are divided into two types: two-dimensional models and three-dimensional models. For the two-dimensional model, if it is a regular model, it is represented by a point-line structure. If it is an irregular geometric model, its circumscribed rectangle and convex hull are calculated for hierarchical simplified expression. Similarly, for the three-dimensional model, if it is a regular model, it is represented by a point-line structure. If it is an irregular geometric model, its bounding box and convex hull are calculated for hierarchical simplified expression.

[0079] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the simplified expression method of geometric data in the field of digital twin cities in the present application. More forms of simple transformations based on this technical concept are all within the scope of protection of the present application.

[0080] This application also provides a simplified expression device for geometric data in the field of digital twin cities, please refer to Figure 4 , the simplified expression device of geometric data in the field of digital twin city includes: A type determination module 10 is used to determine the type of the geometric model in the digital twin city platform; A regular model processing module 20, for converting the geometric model into a point-line structure when the type of the geometric model is a regular model, wherein the point-line structure includes key points and key edges describing the geometric model; The irregular model processing module 30 is used to calculate the boundary range and convex hull unit of the geometric model when the type of the geometric model is an irregular model, and to hierarchically simplify the geometric model according to the boundary range and convex hull unit. The boundary range represents the minimum boundary containing the geometric model, and the convex hull unit is obtained by decomposing the geometric model.

[0081] Optionally, the rule model processing module 20 is further used for: Identify the geometric type of the geometric model corresponding to the rule model, and match the corresponding simplification method in the model simplification rule according to the geometric type of the rule model; Based on the simplification method, the key points and key edges describing the geometric model are extracted to obtain the corresponding point and line structure of the geometric model.

[0082] Optionally, the type determination module 10 is further used for: Extracting geometric features of the geometric model, and performing similarity matching with various regular geometric shapes and various 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 geometric model corresponding to the regular model is recorded; When the similarity does not reach a preset similarity threshold, the geometric model is determined to be an irregular model.

[0083] Optionally, the rule model processing module 20 is further used for: 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 key edges are classified according to the type of the rule set model, and the simplification method of each rule geometric model is determined.

[0084] Optionally, the irregular model processing module 30 is further used for: In the case where the geometric model is a two-dimensional model, the circumscribed rectangle of the geometric model is calculated based on the boundary coordinates of the geometric model, where the circumscribed rectangle is a minimum matrix containing the geometric model and represents the boundary range of the geometric model; Receive the point set list of the geometric model, traverse the point set list through the monotone 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 lower convex hull. The point sets constitute the convex hull units of the geometric model.

[0085] Optionally, the boundary range includes a first boundary range and a second boundary range, and the irregular model processing module 30 is further used for: In the case where the geometric model is a three-dimensional model, the geometric model is collapsed to obtain a simplified model, and a directed bounding box and a convex hull object of the simplified model are calculated, 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 each voxel, calculate the concavity of the voxel pair for any pair of voxels in each voxel, merge the voxel pair with the smallest concavity to obtain a new voxel, combine the new voxel with other voxels in each voxel 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; Solve the convex hull for each voxel to obtain the convex hull units corresponding to each voxel.

[0086] Optionally, the irregular model processing module 30 is further used for: 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, the directed bounding box of the geometric model corresponding to the simplified model is used as the root node of the geometric model, the convex hull object is used as the second child node of the root node, and each convex hull unit is used as the 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.

[0087] Optionally, the irregular model processing module 30 is further used for: When performing spatial analysis calculations, the geometric model is located based on the spatial index; The target convex hull unit intersecting with the area to be analyzed among the convex hull units corresponding to the geometric model is determined, and spatial analysis calculation is performed on the target convex hull unit.

[0088] The device for simplifying and expressing geometric data in the field of digital twin cities provided by the present application adopts the method for simplifying and expressing geometric data in the field of digital twin cities in the above-mentioned embodiment, which can solve the technical problem of complex geometric data representation. Compared with the prior art, the beneficial effects of the device for simplifying and expressing geometric data in the field of digital twin cities provided by the present application are the same as the beneficial effects of the method for simplifying and expressing geometric data in the field of digital twin cities provided by the above-mentioned embodiment, and the other technical features in the device for simplifying and expressing geometric data in the field of digital twin cities are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0089] The present 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 method for simplifying geometric data in the digital twin city field in the above-mentioned embodiment one.

[0090] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing an embodiment of the present application. The electronic device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, notebook computers, PADs (Portable Application Description: tablet computers), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0091] like Figure 5 As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 to a 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 via a bus 1005. An 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 touch pad, 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 by wire to exchange data. Although the electronic device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have instead.

[0092] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0093] The electronic device provided by this application adopts the simplified expression method of geometric data in the field of digital twin cities in the above embodiment, which can solve the technical problem of complex geometric data representation. Compared with the prior art, the beneficial effects of the electronic device provided by this application are the same as the beneficial effects of the simplified expression method of geometric data in the field of digital twin cities provided by the above embodiment, and the other technical features in the electronic device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0094] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0095] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0096] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the method for simplifying and expressing geometric data in the field of digital twin cities in the above-mentioned embodiment.

[0097] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0098] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0099] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the digital twin city field geometric data simplified expression device is applied to the digital twin city platform, and can determine the type of geometric model in the digital twin city platform; when the type of the geometric model is a regular model, the geometric model is converted into a point-line structure, and 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, the boundary range and 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, the boundary range represents the minimum boundary containing the geometric model, and the convex hull unit is obtained by decomposing the geometric model.

[0100] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0103] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for simplifying the expression of geometric data 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 in this application are the same as the beneficial effects of the method for simplifying the expression of geometric data in the field of digital twin cities provided in the above-mentioned embodiments, and will not be repeated here.

[0104] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for simplifying the expression of geometric data in the field of digital twin cities.

[0105] The computer program product provided by this 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 this application are the same as the beneficial effects of the simplified expression method of geometric data in the field of digital twin cities provided by the above embodiment, which will not be repeated here.

[0106] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A 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, the boundary range and convex hull units of the geometric model are calculated, and the geometric model is hierarchically simplified according to the boundary range and the convex hull units. The boundary range represents the minimum boundary containing the geometric model, and the convex hull units are obtained by decomposing the geometric 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: The step of determining the type of the geometric model in the digital twin city platform comprises: 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.

4. 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.

5. 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.

6. 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.

7. The simplified expression method of digital twin city domain geometric data according to claim 1, 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.

8. The simplified expression method of digital twin city domain geometric data according to claim 7, characterized in that: The step of obtaining the spatial 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.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for simplifying the expression of geometric data in the digital twin city field as described in any one of claims 1 to 8 are implemented.

10. 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 8.

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