Non-structural adaptive grid construction system for urban refined numerical simulation

Abstract

The invention discloses a non-structural adaptive grid construction system for urban refined numerical simulation, which relates to the technical field of urban meteorological simulation and is technically characterized by comprising an urban element acquisition module, an urban geometric shape construction module and an adaptive grid construction module. According to the system, by collecting three-dimensional building, green land, water body, asphalt road and tree data, an urban geometric figure is constructed, and a mode grid for numerical simulation is generated. The system can set the grid resolution according to the characteristics of different city elements, and fine simulation is realized. The invention also provides a method for performing urban refined numerical simulation by using the system. The method comprises the steps of acquiring urban element data, constructing urban geometric figures, generating pattern grids and adjusting the density of the grids according to urban element characteristics. According to the method, the simulation accuracy and reliability are effectively improved, and meanwhile, the computing resource consumption is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of urban meteorological simulation, and in particular to a non-structured adaptive grid construction system for urban refined numerical simulation. Background Art

[0002] With the acceleration of urbanization, the impact of urban meteorological environment on residents' lives is becoming increasingly significant, and the demand for accurate forecasting and rapid warning of extreme weather events is increasing. Urban refined numerical simulation is one of the key technologies to solve these problems. The resolution of urban meteorological simulation directly affects the accuracy of the simulation. Currently, there are three main resolutions: global / regional models, urban models, and block-scale models, which correspond to different degrees of urban solvability and building simulation methods. In addition, the impact of the height and distribution density of buildings in the urban canopy on atmospheric turbulence is an important factor that needs to be considered in urban refined simulation. Improving the model's solvability scale is the key to solving this problem.

[0003] At present, numerical simulation mainly includes three modes: global / regional mode, urban mode and block-scale mode. The global / regional mode has a low resolution and cannot consider the impact of buildings in the urban canopy; the urban mode can distribute cities to a certain extent, but it is still quite different from the real city; although the block-scale mode can accurately simulate the city, the calculation amount is huge and the computing resource requirements are high. In addition, the existing technology fails to fully consider the environmental factors such as green spaces and trees in the city that have an important impact on the meteorological environment during the simulation. These problems have resulted in the limited accuracy of the existing technology in simulating small-scale elements in the city, and it is impossible to achieve refined simulation, and the computing resources are consumed a lot.

[0004] There are two main problems with existing urban simulation grid construction methods: First, the demand for computing resources is high. In order to depict the shape of urban buildings, a very dense grid is required, which leads to an exponential increase in the number of computing grids and computing resource requirements, limiting the area of ​​the simulation area; second, there is a lack of consideration of environmental factors. The existing grid construction method does not specifically consider the elements in the city that have an important impact on the atmosphere and environment, such as green spaces, trees, rivers, oceans, and urban traffic roads, which may lead to huge errors in urban simulation. In addition, the grid construction method in the prior art fails to adjust the density according to the characteristics of urban elements and the distribution of physical quantities, resulting in an excessive number of grids, a waste of computing resources, and limited simulation accuracy.

[0005] Therefore, the present invention provides a non-structured adaptive grid construction system for urban refined numerical simulation to improve simulation accuracy and save computing resources. Summary of the invention

[0006] The purpose of the present invention is to solve the above problems and to provide a non-structured adaptive grid construction system for urban refined numerical simulation to solve the problems of the prior art, such as large consumption of computing resources, lack of comprehensive consideration of environmental factors and single grid resolution.

[0007] In order to achieve the above object, the technical solution of the present invention is as follows: a non-structured adaptive grid construction system for urban refined numerical simulation, including the following modules:

[0008] Urban element acquisition module, used to obtain 3D building, green space, water, asphalt road and tree data in the simulation area;

[0009] The urban geometry construction module is used to construct urban geometry based on the collected urban basic data, simplify each element according to the numerical simulation resolution requirements, and generate shapefile files;

[0010] The adaptive grid construction module is used to generate a model grid that can be directly used for numerical simulation based on the urban geometry file. This module adopts unstructured grid technology and adjusts the density according to the scale characteristics of urban elements and the distribution of physical quantities.

[0011] Furthermore, the city element acquisition module obtains city element data through public map websites and Google Earth software.

[0012] Furthermore, the city geometry building module uses QGIS software to perform location matching and vector simplification of city elements, and calls Python program to build city geometry.

[0013] Furthermore, the adaptive grid construction module uses GMSH software to construct an unstructured grid, and can further use Fluidity adaptive grid power module to perform grid adaptive adjustment.

[0014] Furthermore, the system can specifically set the grid resolution corresponding to the grid according to the characteristics of different urban elements.

[0015] Another object of the present invention is to provide a method for performing urban fine numerical simulation using an unstructured adaptive grid construction system for urban fine numerical simulation, the method comprising the following steps:

[0016] Step 1: Obtain urban element data in the simulation area through the urban element acquisition module;

[0017] Step 2: Construct city geometry using the city geometry building module;

[0018] Step 3: Generate the model grid for numerical simulation through the adaptive grid building module;

[0019] Step 4: Adjust the density of the generated model grid according to the scale characteristics of urban elements and the distribution of physical quantities.

[0020] Furthermore, the specific method of step one is to obtain city element data from public map websites and Google Earth software.

[0021] Furthermore, the specific method of step two is to use QGIS software and Python program to construct the city geometry.

[0022] Furthermore, the specific method of step three is to use GMSH software and Fluidity adaptive grid dynamics module to build and adjust the unstructured grid.

[0023] Compared with the prior art, the beneficial effects of this solution are as follows: the unstructured adaptive grid construction system for urban refined numerical simulation of the present invention can comprehensively obtain key urban element data in the simulation area, including three-dimensional buildings, green spaces, water bodies, asphalt roads and trees, etc., through the urban element acquisition module. The acquisition of these data not only relies on public map websites and Google Earth software to ensure the extensiveness and accuracy of the data, but also realizes the precise construction of urban geometric shapes through the processing of QGIS software and Python programs. This process not only improves the resolution of the simulation, but also provides a solid foundation for subsequent numerical simulation by generating shapefile files.

[0024] The adaptive grid construction module of the present invention uses GMSH software and Fluidity adaptive grid power module to dynamically adjust the density of the grid according to the scale characteristics and physical quantity distribution of urban elements. The application of this unstructured adaptive grid technology makes the grid denser in key areas such as building corners and near trees, while the grid is sparse on flat surfaces and on top of large buildings, thereby significantly reducing the consumption of computing resources while improving the simulation accuracy. This method not only solves the problem of single grid resolution in the prior art, but also realizes the refined simulation of small and medium-scale elements in the city by setting the grid resolution in a targeted manner, thereby improving the accuracy and reliability of the simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of a method for constructing an unstructured adaptive grid for urban refined numerical simulation in an embodiment of the present invention;

[0026] Figure 2 is a schematic diagram of the simulation results of an adaptive unstructured grid in an embodiment of the present invention;

[0027] Figure 3 is a schematic diagram of grid density distribution in a traditional grid construction method in an urban simulation according to an embodiment of the present invention;

[0028] in, Figure 3 a is a bird's-eye view of the urban simulation area and surrounding buildings; Figure 3 b, 3c are the calculation grid distribution maps of the building surface in the simulation area. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will be described in detail below in conjunction with the embodiments.

[0031] Example 1: Figure 1 As shown, a non-structured adaptive grid construction system for urban refined numerical simulation includes three modules: urban element acquisition, urban geometric shape construction, and adaptive grid construction;

[0032] Specific implementation steps of the urban element collection module:

[0033] 1. Data source determination: Determine data requirements, including data on three-dimensional buildings, green spaces (parks, grasslands, forests, etc.), water bodies (lakes, rivers, oceans, etc.), asphalt roads, and trees (location, height, crown radius) within the simulation area.

[0034] 2.OpenStreetMap data acquisition:

[0035] 2.1 Visit the public map website OpenStreetMap, which is free and open to everyone.

[0036] 2.2 Input the latitude and longitude range of the simulation area and accurately locate the urban area to be simulated.

[0037] 2.3 Select the required urban elements, such as three-dimensional buildings, green spaces, water bodies and asphalt roads.

[0038] 2.4 Download and save the data of the selected features in OSM file format.

[0039] 3. Obtaining tree data: Since OpenStreetMap does not contain 3D tree files, it is necessary to obtain tree distribution information from Google Earth software. In Google Earth software, select the tree information corresponding to the simulation area. Download and save the tree data for subsequent processing.

[0040] 4. Data integration: Integrate the data obtained from OpenStreetMap and Google Earth to form a complete urban feature dataset. Check the integrity and consistency of the data to ensure that all urban feature data have been collected.

[0041] 5. Data preprocessing: Preprocess the downloaded OSM format files, which may include format conversion, data cleaning, etc., to ensure that the data can be correctly read and processed by subsequent modules.

[0042] 6. Data output: Output the processed urban element data to provide input data for the next step of urban geometry construction module.

[0043] The specific embodiment of the present invention describes in detail the workflow of the city geometry construction module, which is responsible for constructing accurate city geometry based on the collected city basic data. The following are the specific implementation steps of the module:

[0044] 1. Data preparation: Prepare basic urban data in OSM format obtained from OpenStreetMap, including 3D buildings, green spaces, water bodies, asphalt roads, etc. Prepare tree distribution data obtained from Google Earth software.

[0045] 2. Data verification and adjustment: Use QGIS software to open all city feature files, including OSM files and tree data. Verify and adjust the position matching relationship of each feature to ensure that the tree position and building position do not overlap.

[0046] 3. Data simplification: According to the resolution requirements of the numerical simulation, the vector simplification function (Simplify) of QGIS is used to simplify each element to reduce data complexity and retain necessary details.

[0047] 4. Shapefile file generation: Generate corresponding shapefile files from simplified data, including: 3D buildings (building.shp), green land (green.shp), water bodies (water.shp), traffic roads (road.shp), and trees (tree.shp).

[0048] 5. Python program call: Call the pre-written Python program, which is responsible for reading all shapefiles and creating the outline information of buildings, green spaces, water bodies, roads, and trees based on the location information in the file.

[0049] 6. Grid number and resolution setting: Use different groups of grid numbers to represent different types of urban elements to distinguish and manage different elements. Set the model grid resolution of different elements according to simulation needs to meet the needs of different simulation scenarios.

[0050] 7. Construction of city geometry: The constructed city geometry will be as shown in the attached Figure 1 As shown in , providing accurate city geometry files for the next step of adaptive mesh building module.

[0051] The specific embodiment of the present invention describes in detail the workflow of the adaptive grid construction module, which is responsible for generating a model grid that can be directly used for numerical simulation from a city geometry file. The following are the specific implementation steps of the module:

[0052] 1. Unstructured grid generation: Use GMSH software to directly read the urban geometry file generated in module 2. According to the location information, element distribution and element resolution information in the file, GMSH software generates the initial unstructured grid. The resolution of the initial grid varies in different locations, which is based on the resolution requirements of different elements set manually in module 2.

[0053] 2. Grid optimization: If the grid needs to be further optimized so that the grid density distribution is directly related to the atmospheric physical field, the Fluidity adaptive grid dynamic module is used. Fluidity reads the initial structured grid and performs 10 grid adaptive adjustments under the given initial conditions. Through this process, an adaptive unstructured grid that is closely related to the distribution of the atmospheric circulation field is obtained.

[0054] 3. Initial condition setting: In this method, a highly stable log wind profile initial condition has been set for Fluidity. This reduces the need for users to make complex settings and simplifies the operation process.

[0055] 4. Grid density adjustment effect: Figure 1 As shown in the figure, the overall density of the grid is obviously different, especially near trees, building corners and small-scale buildings. On the contrary, the grid is very sparse on flat surfaces and on top of large buildings. This design achieves the purpose of finely depicting small-scale elements in the city while saving computing resources.

[0056] The present invention significantly improves the accuracy and practicality of urban simulation by introducing key elements such as urban green space, water surface, roads and trees. Therefore, the present method not only considers three-dimensional buildings on the basis of existing technologies, but also incorporates other elements that have a significant impact on the urban environment, such as green space and trees. The physical characteristic scales of these elements are used to optimize the corresponding grids, thereby improving the simulation performance of various physical processes of urban elements. Figure 2 As shown in the figure, the adaptive unstructured grid technology makes the simulated air humidity increase significantly in urban green spaces and tree distribution areas, which shows that this method can effectively achieve refined simulation of trees and green spaces.

[0057] In addition, the non-structural adaptive technology used in this method has shown its superiority in urban simulation grid construction. By adjusting the grid density according to the scale characteristics and physical quantity distribution of urban elements, this method densifies the grid in the corner areas of trees and buildings, and expands the grid spacing in the flat surface and roof areas, thereby reducing the number of grids. This optimization and Figure 3 This is in stark contrast to the traditional methods shown in the figure, which usually use overall consistency encryption, resulting in a significant increase in the number of grids and computing resource requirements. As shown in Table 1 below, for the same simulation area and time (5000 seconds), regardless of the numerical approximation scheme, the running time of the adaptive unstructured grid is half the time of the other two types of grids. This result shows that this method can significantly reduce the consumption of computing resources while improving the simulation accuracy, achieving dual optimization of the efficiency and effect of urban refined numerical simulation.

[0058] Table 1 Running time of three grids under three panel mean approximation schemes

[0059]

[0060] The above specific embodiments are merely explanations of the present invention and are not limitations of the present invention. After reading this specification, those skilled in the art may make modifications to the embodiments without any creative contribution as needed. However, such modifications are protected by the patent law as long as they are within the scope of the claims of the present invention.

Claims

1. An unstructured adaptive grid construction system for urban refined numerical simulation, characterized by: The system includes the following modules: Urban element acquisition module, used to obtain 3D building, green space, water, asphalt road and tree data in the simulation area; The urban geometry construction module is used to construct urban geometry based on the collected urban basic data, simplify each element according to the numerical simulation resolution requirements, and generate shapefile files; The adaptive grid construction module is used to generate a model grid that can be directly used for numerical simulation based on the urban geometry file. This module adopts unstructured grid technology and adjusts the density according to the scale characteristics of urban elements and the distribution of physical quantities.

2. The system according to claim 1, characterized in that: The city element acquisition module obtains city element data through public map websites and Google Earth software.

3. The system according to claim 1, characterized in that: The city geometry building module uses QGIS software to perform location matching and vector simplification of city elements, and calls Python program to build city geometry.

4. The system according to claim 1, characterized in that: The adaptive grid construction module uses GMSH software to construct an unstructured grid, and can further use the Fluidity adaptive grid dynamic module to perform grid adaptive adjustment.

5. The system according to any one of claims 1 to 4, characterized in that: The system can specifically set the grid resolution corresponding to the grid according to the characteristics of different urban elements.

6. A method for performing detailed urban numerical simulation using the system according to any one of claims 1 to 5, characterized in that: The method comprises the following steps: Step 1: Obtain urban element data in the simulation area through the urban element acquisition module; Step 2: Construct city geometry using the city geometry building module; Step 3: Generate the model grid for numerical simulation through the adaptive grid building module; Step 4: Adjust the density of the generated model grid according to the scale characteristics of urban elements and the distribution of physical quantities.

7. The method according to claim 7, characterized in that: The specific method of step one is to obtain urban element data from public map websites and Google Earth software.

8. The method according to claim 7, characterized in that: The specific method of step 2 is to use QGIS software and Python program to construct the city geometry.

9. The method according to claim 7, characterized in that: The specific method of step three is to use GMSH software and Fluidity adaptive grid dynamics module to build and adjust the unstructured grid.

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