Method for constructing and analyzing regional urban and rural built-up environment by fusing multi-source data

Abstract

The invention belongs to the technical field of digital urban and rural and built-up environment simulation, and provides a method for constructing and analyzing a regional urban and rural built-up environment by fusing multi-source data, and the method comprises the steps: obtaining a plane grid base map based on a selected region, and vectorizing the plane grid base map to obtain a two-dimensional building vector diagram; calculating and obtaining a building height base map based on DEM and DSM data, and establishing a three-dimensional building model by using the two-dimensional building vector diagram and the building height base map; a three-dimensional terrain model is obtained based on DEM data, and the three-dimensional building model and the three-dimensional terrain model are combined to form a built environment digital twinborn model of the selected area; and carrying out multi-physics field simulation analysis on the built environment digital twinborn model, realizing multi-physics field coupling simulation by combining a turbulence model, a radiation model and the like, analyzing a simulation result, and displaying analysis data in a visual form. According to the method, multi-source data are efficiently fused, the spatial scale of regional large-scale simulation research is expanded, and multi-physics field coupling analysis and quantitative analysis of a built environment are realized.

Description

Technical Field

[0001] This invention belongs to the field of digital urban and rural areas and built environment simulation technology, and provides a method for constructing and analyzing regional urban and rural built environment by integrating multi-source data. Background Technology

[0002] As the primary venues for human activities, the urban and rural built environment functions in areas such as housing and transportation, providing basic production and living services and serving as a crucial material foundation for socio-economic development. The quality of the built environment not only profoundly impacts people's quality of life and health but also directly relates to the realization of sustainable environmental development. Furthermore, the built environment is both a hotspot for the impacts and risks of climate change and a key component in building resilient urban systems.

[0003] Studying the built environment in urban and rural areas is crucial for improving residents' comfort and livability. This field encompasses multiple aspects, primarily including: optimization of regional urban and rural wind and thermal environments, light environment planning, building and road network structural design, and optimized layout of underlying land use types. It not only relates to the sustainable development of the built environment but also has significant implications for residents' health and addressing climate change.

[0004] However, traditional research on the analysis and prediction of urban and rural built environments mainly focuses on indoor, architectural, and micro-scale studies, particularly on indoor environment optimization, individual building simulation, and micro-scale environmental simulation. For larger-scale studies at the mesoscale, these approaches fail to adequately address the coupling issues of multiphysics and face challenges such as insufficient model accuracy and limited simulation scale, significantly impacting the accuracy and practicality of the results. Therefore, there is an urgent need to propose a construction and analysis method adapted to urban and rural built environments to overcome the limitations of traditional research and achieve global optimization and analysis of regional urban and rural environments.

[0005] Therefore, in response to the above problems, how to combine regional environment, efficiently integrate multi-source data, construct refined models, expand the research scale, reduce the computational pressure of large-scale simulation modeling, and at the same time improve the accuracy of simulated regional meteorological data has become a key technical problem that urgently needs to be solved in the research on the built environment of urban and rural areas, which is related to the comfort and health of urban and rural residents in the region. Summary of the Invention

[0006] To address the technical problems of insufficient model accuracy and limited simulation scale affecting accuracy in existing methods, this invention provides a method for constructing and analyzing regional urban and rural built environments by integrating multi-source data. This method can expand the research scale, quickly construct refined models, and conduct multi-physics simulation analysis, simulating and studying urban and rural built environments from the perspectives of multiple seasons, multiple scales, and multi-physics coupling.

[0007] The purpose of this invention is to provide a method for constructing and analyzing regional urban and rural built environment by fusing multi-source data, thereby solving the problems existing in current methods. To achieve the above objective, the technical solution adopted by this invention includes the following steps: Step 1: Based on the latitude and longitude coordinates of the selected area, obtain a planar raster base map containing the building outlines of the selected area, and preprocess the planar raster base map; Step 2: Use a geographic information system to vectorize the preprocessed planar raster base map to obtain a two-dimensional building vector map; Step 3: Based on DEM (bare ground elevation) data and DSM (top elevation of ground objects) data, use a geographic information system to calculate the building height base map, and use the two-dimensional building vector map and the building height base map to build a three-dimensional building model of the selected area; Step 4: Based on DEM data, obtain a 3D terrain model of the selected area through data processing and transformation, and combine the 3D building model and 3D terrain model using the unified coordinate system of the geographic information system, and integrate multi-source data to construct a digital twin model of the built environment of the selected area. Step 5: Combining geographic information systems, 3D modeling, and computational fluid dynamics theory, conduct multiphysics simulation analysis on the digital twin model of the built environment; Step Six: Based on the results of the multiphysics simulation analysis, extract the simulation analysis results using post-processing tools to perform in-depth analysis of the built environment of the selected area.

[0008] As a preferred technical solution, in step one, satellite maps of the selected area are crawled using Python through the Baidu Developer Platform to obtain a planar raster base map of the selected area. The planar raster base map includes building outlines, road networks, area polygons, and labels. The planar raster base map is preprocessed using a Geographic Information System (GIS), including format conversion, coordinate system transformation, and spatial clipping and resolution resampling. To ensure the acquisition of the latest and most accurate geographic data, content in the planar raster base map can be retained as needed. GIS preprocesses the data for quality control and unified spatial reference. Format conversion converts the data to a more compatible format to ensure smooth operation in different software environments. Coordinate system transformation unifies the spatial reference system, ensuring accurate and consistent geographic positioning across different datasets. Spatial clipping and resolution resampling optimize the spatial utilization and display accuracy of the data. Clipping techniques are used to obtain relevant data for the target area, and the resolution is adjusted to meet specific analytical needs.

[0009] As a preferred technical solution, in step two, the planar raster base map is georeferenced using a geographic information system (GIS), and binarization enhances the distinction between buildings and the background. A raster-to-polygon tool is used to batch generate building outline vector surfaces, pseudo-polygons are deleted, and boundary nodes are simplified to smooth jagged outlines. Attribute information is then added to the building outline vector surfaces, and finally, a standard vector format 2D building vector map is exported. Georeferenced mapping ensures accurate spatial location, and binarization significantly improves the accuracy of building detection, ensuring the reliability of building detection results in complex urban environments. To achieve accurate vectorization of building outlines, a raster-to-polygon tool is used to batch generate preliminary building vector surfaces. Deleting pseudo-polygons effectively eliminates irrelevant or misidentified polygons, facilitating subsequent data cleaning; simplifying boundary nodes to smooth jagged outlines ensures higher accuracy of the final output data in both visual and spatial analysis.

[0010] As a preferred technical solution, in step three, it is ensured that the coverage, spatial resolution (cell size), and projected coordinate system of the DEM and DSM data are completely consistent. If necessary, processing is performed using tools such as clipping, mosaicking, resampling, and coordinate system transformation. Outliers or invalid areas in the data are checked and repaired. A subtraction operation between the DSM and DEM data is performed in the GIS raster calculator to generate a new height difference raster. Each cell value in the height difference raster represents the building height above the ground at that point. Corresponding to the building height base map and the 2D building vector map, the height difference raster is associated with the attribute table of the 2D building vector map. Based on the building outline vector surface features, raster statistical values ​​within the corresponding range are extracted and written into the attribute table as the building height field. Finally, the building outline vector file contains the calculated building height information, and a 3D building model of the urban and rural areas of the region is established. Step three proposes an innovative method for accurately calculating urban building heights, fully combining data difference analysis between DEM and DSM data to calculate building heights. This method achieves accurate measurement of building heights through GIS tools, providing a reliable foundation for 3D urban modeling and ensuring data integrity and ease of use. Ultimately, the building outline vector file not only contains accurately calculated building height information, but also lays a solid foundation for 3D building models of regional urban and rural areas. This method is suitable for detailed 3D modeling in complex urban and rural built environments.

[0011] As a preferred technical solution, in step four, a three-dimensional terrain model is constructed based on high-precision DEM data. First, data preprocessing is performed to obtain DEM data fully covering the selected area. Data holes and outliers are checked and corrected. If a geographic coordinate system is encountered, it is converted to a projected coordinate system. Then, the three-dimensional building model and the three-dimensional terrain model are combined using a unified coordinate system within the geographic information system. Finally, multi-source data are integrated to construct a digital twin model of the regional urban and rural built environment. The entire process requires maintaining the consistency of the projected coordinate system. Checking and correcting data holes and outliers improves data quality and avoids the adverse effects of inaccuracies on the construction of the three-dimensional terrain model. This process accurately depicts the terrain features of the urban and rural areas in the region.

[0012] As a preferred technical solution, in step five, a digital twin model of the built environment is exported from the geographic information system. The exported file is a built environment mesh model in a projected coordinate system. Geometric optimization processing is performed in 3D modeling software to convert the built environment mesh model into a surface model. At the same time, the problems of holes and edge overlaps in the surface model are repaired. The model is exported in a format that can be recognized by computational fluid dynamics preprocessing software. The computational fluid dynamics preprocessing software is used to divide the mesh into polyhedrals. For the near-wall surface of the building, boundary layer meshing technology is used to ensure the accuracy of the simulation of fluid dynamic characteristics in the wall area. For the regional terrain, an adaptive densification method is introduced to adjust the mesh density according to the terrain complexity, thereby improving the accuracy and efficiency of flow field calculation.

[0013] As a preferred technical solution, in computational fluid dynamics software, a preferred turbulence model (such as the k-ε or RNG model in Fluent software) is selected to adapt to the wind field characteristics of the built environment, while supporting dynamic parameter adjustment according to research needs; when configuring the radiation model, multi-seasonal characteristics (such as solar radiation heat transfer) are considered, and a discrete trajectory model (DO model) is selected to solve the radiation field (efficiently solving the heat transfer behavior of solar radiation under different seasonal conditions); inlet conditions are defined, including wind profile settings based on measured data (wind speed distribution and turbulence intensity), and thermal boundary conditions optimized through the scenario (such as surface temperature, heat transfer on building exterior surfaces, etc.) to ensure the accuracy and representativeness of the simulation conditions; a multi-core parallel computing solution method is adopted, and the residuals are dynamically monitored and adjusted during the computational fluid dynamics software simulation process, realizing an efficient and stable numerical solution process to ensure the stability and accuracy of the simulation results; by solving until the residuals converge, multi-seasonal, multi-scale, and refined simulations are achieved, and computational fluid dynamics simulation research on the built environment in urban and rural areas is carried out from the perspective of multi-physics coupling.

[0014] As a preferred technical solution, in step six, based on the results of the multiphysics simulation analysis, in order to analyze the urban and rural built environment, cross-sectional cloud maps at different heights of the simulation results are extracted. Surface transformation is used to create surfaces at different heights, or internal surfaces at different heights are established above the three-dimensional terrain model when meshing. This internal surface is essentially an "imaginary internal wall" with zero thickness, allowing any medium to pass freely. Furthermore, the internal surface has only one mesh type, meaning the mesh nodes of the connected computational domain are shared, thus forming a consistent single mesh type. Cross-sectional cloud maps at different heights are extracted using the internal surface, providing a multi-dimensional perspective for urban and rural built environment analysis. Post-processing tools are used to deeply analyze the simulation data, intuitively revealing the flow field information (velocity field, pressure field, turbulence), temperature field (temperature distribution, heat flux), and pollutant diffusion phenomena in the urban and rural built environment. Through the extraction of multiphysics parameters and visualization processing of scalar and vector fields, the monotonous and complex numerical solution results can be represented visually and intuitively, and the simulation results can be visualized in the form of dynamic graphs, cloud maps, etc. Further analysis of the characteristics of the urban and rural built environment in the region, and support for multi-dimensional output of urban and rural built environment simulation, to conduct in-depth analysis of the urban and rural built environment in the region from a multi-dimensional, multi-scale, quantitative and qualitative perspective.

[0015] Compared with existing technologies, the method for constructing and analyzing regional urban and rural built environment by fusing multi-source data proposed in this invention has the following advantages: (1) Efficient data sources and processing. Compared with field measurements, it makes efficient use of spatial information technologies such as geographic information systems and combines multi-source data such as remote sensing to achieve efficient data acquisition and processing, significantly reducing the input cost and time of data acquisition.

[0016] (2) Efficient modeling method. By integrating multi-source data, a flexible and adjustable digital twin model is constructed from two-dimensional to three-dimensional, which significantly reduces the complexity of model construction while ensuring or even improving accuracy.

[0017] (3) High efficiency in solving problems. Compared with on-site measurement, simulation analysis is more economical, efficient and comprehensive. By using high-performance computers for simulation, multi-parameter analysis of the built environment can be achieved. The influence of each parameter on the built environment can be clarified by controlling a single variable, and low-cost three-dimensional visualization simulation can be realized.

[0018] (4) Diverse application scenarios. The built environment digital twin model constructed using this method has good scalability, breaks through the limitations of traditional research areas, and realizes simulation research on urban and rural built environments from multiple seasonal and multi-scale perspectives.

[0019] (5) Realize three-dimensional dynamic visualization of multi-physics fields. Simultaneously conduct research on wind environment, thermal environment, light environment, pollutant diffusion, etc. of urban and rural built environment, combine different data to carry out coupled analysis, more accurately simulate the microclimate characteristics of urban and rural areas, and realize three-dimensional dynamic visualization.

[0020] (6) Achieve quantitative spatiotemporal analysis of the built environment over the years. Through efficient simulation, based on annual spatiotemporal data, quantitatively analyze and describe the microclimate characteristics and patterns of urban and rural built environments over a year. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the basic process of the method provided in Embodiment 1 of the present invention; Figure 2 The study area provided in Embodiment 1 of this invention includes a planar grid base map containing building outlines and a two-dimensional building vector map. Figure 2 (a) is a planar raster base map. Figure 2 (b) is a two-dimensional architectural vector diagram; Figure 3 The data provided in Embodiment 1 of this invention are the DEM and DSM data of the research area. Figure 3 (a) is DEM data. Figure 3 (b) is DSM data; Figure 4 This is a base map of the building heights in the research area 1 provided in Embodiment 1 of the present invention; Figure 5 This is a three-dimensional architectural model of the research area provided in Embodiment 1 of the present invention; Figure 6 This refers to the three-dimensional terrain model of the research area provided in Embodiment 1 of the present invention; Figure 7 This is a digital twin model of the built environment of research area one provided in Embodiment 1 of the present invention; Figure 8 The results of the multiphysics simulation analysis of the built environment of study area one provided in Embodiment 1 of the present invention are as follows. Figure 8 (a) is a cloud map showing wind speed in the urban area. Figure 8 (b) is a temperature cloud map of the urban area; Figure 9 This is the architectural plan of research area two provided in Embodiment 2 of the present invention; Figure 10 This is a three-dimensional architectural model of research area two provided in Embodiment 2 of the present invention; Figure 11 The results of the multiphysics simulation analysis of the rural built environment in study area two provided in Embodiment 2 of the present invention are as follows. Figure 11 (a) is a rural wind speed cloud map. Figure 11(b) is a temperature cloud map of the rural area. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described clearly and in detail below with reference to embodiments. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. Example 1

[0023] like Figure 1 As shown in the basic flowchart, this embodiment 1 provides a method for constructing and analyzing the built environment of urban and rural areas by integrating multi-source data. This method can effectively expand the research scale, efficiently construct refined models, and simulate and analyze the built environment of urban and rural areas from the perspectives of multi-seasonal, multi-scale, and multi-physics coupling. Specific steps include: In this embodiment 1, a city's urban area is selected as the research area. The method provided by this invention, which integrates multi-source data to construct and analyze the built environment of the urban and rural areas, is used to build a digital twin model of the built environment of this research area. Specific steps include: Step 1: Based on the latitude and longitude coordinates of study area 1, use Python to obtain a planar raster base map containing building outlines for study area 1, and preprocess the planar raster base map to obtain a preprocessed planar raster base map. Specifically: The latitude and longitude coordinates of study area 1 were obtained. A planar raster base map containing building outlines of the selected area 1 was acquired through the Baidu Developer Platform. The planar raster base map was crawled using Python software. Data quality and unified spatial reference were preprocessed using a Geographic Information System (GIS). Preprocessing mainly included: format conversion (converting to a format such as GeoTIFF to ensure compatibility), coordinate system transformation (calibrating or reprojecting to the target coordinate system to ensure spatial accuracy), and spatial clipping and resolution resampling (extracting study area 1 as needed and adjusting cell size to meet analytical requirements or improve processing efficiency). The preprocessed planar raster base map (e.g., ...) was then obtained. Figure 2 (a) is shown.

[0024] Step Two: Based on the preprocessed raster base map of study area one, the preprocessed raster base map is vectorized using a geographic information system to obtain a two-dimensional building vector map. Specifically: For the preprocessed planar raster base map, relying on a geographic information system, a high-precision georegistration operation is first performed to unify the coordinate system of the raster image, ensuring the authenticity and accuracy of its spatial location. To improve the accuracy of subsequent vectorization, the raster is binarized, significantly enhancing the distinction between building areas and backgrounds (such as roads, vegetation, and open spaces). Subsequently, a raster-to-vector tool is used to batch extract and generate initial building outline vector surface data. During this process, necessary manual checks and data cleaning are required on the automatically generated initial building outline vector surface data, focusing on removing meaningless pseudo-polygons caused by classification errors. Node simplification or boundary smoothing algorithms are applied to the boundaries of the filtered vector surfaces to effectively eliminate jagged edges caused by raster resolution limitations and optimize the geometry of the vector outline. Finally, key attribute information (such as building type, number of floors, etc.) is added to each of the cleaned and optimized building outline vector surfaces. Through the above data processing flow, a two-dimensional building vector map (such as...) is successfully extracted. Figure 2 (b) is shown.

[0025] Step 3: Based on DEM and DSM data, a building height base map is calculated using a geographic information system. A 3D building model of the study area is then constructed using the 2D building vector map and the building height base map. Specifically: Based on the study area 1, download the required DEM and DSM data (such as...). Figure 3 As shown, building height is calculated based on DEM and DSM data. The core idea is to use the difference between the two (DSM data - DEM data) to calculate the building height. First, it is essential to ensure that the coverage, spatial resolution (cell size), and projected coordinate system of the DEM and DSM data are completely consistent. If necessary, this is done through cropping, mosaicking, resampling, and coordinate system transformation tools, and outliers or invalid areas are checked and repaired. Then, the core subtraction operation is performed using the GIS raster calculator to generate a new height difference raster. The raster is a regular array where each cell value represents the height of the object above the ground at that point (e.g., DEM data). Figure 4 (As shown). Corresponding to the 2D building vector map and the building height base map, the spatial information of the height difference raster is further associated with the 2D building vector map. Based on the building outline vector surface features, the raster statistical values ​​within the corresponding range are extracted and written into the attribute table as the building height field (the 2D building vector map contains an attribute table containing vector data information, such as building number, floors, area, etc., which is similar to an Excel spreadsheet when opened). Finally, the building outline vector file contains the calculated building height information, and based on the 2D building vector map and the building height base map, a 3D building model of study area one is generated through 3D extrusion and other modeling processes (e.g., Figure 5 (As shown).

[0026] DEM and DSM data are publicly available online, but they vary in resolution; higher resolution results in more accurate data. Both DEM and DSM data are raster data and can be opened and viewed using geographic information system (GIS) software. As raster datasets, they contain information such as band numbers, cell size, and extent, and processing ensures data consistency.

[0027] Step 4: Based on the DEM data, a 3D terrain model of study area 1 is obtained through data processing and transformation. Then, using the unified coordinate system of the Geographic Information System (GIS), the 3D building model and the 3D terrain model are combined (spatial fusion), and multi-source data are integrated to construct a digital twin model of the built environment of study area 1. Specifically: Data Acquisition and Preprocessing: First, for the acquired DEM data that fully covers the study area, data quality checks and repairs are performed, focusing on addressing data holes (areas with no values) and outliers. Simultaneously, it must be ensured that all DEM data uses a unified projected coordinate system; if the original data is in a geographic coordinate system, it must be converted to a projected coordinate system first. Using the DEM data, data processing and transformation (raster data to TIN data; the Triangular Irregular Network (TIN) layer is typically an elevation surface representing height values ​​within a certain range) are employed to obtain a three-dimensional terrain model of the study area (e.g., Figure 6 (As shown).

[0028] Model integration: Under the framework of a unified projection coordinate system, the three-dimensional data manipulation capabilities of the geographic information system are used to spatially register and merge the three-dimensional terrain model of study area 1 with the existing three-dimensional building model.

[0029] Model Output: Through the above steps, a digital twin model of the built environment in study area 1 is finally generated (e.g., ...). Figure 7 (As shown). The core requirement of the entire construction process is to maintain a high degree of consistency in the projected coordinate system.

[0030] Step 5: Based on the software interface between the Geographic Information System (GIS), 3D modeling software (Rhino), and computational fluid dynamics (CFD) software (Fluent), perform multiphysics simulation analysis on the constructed digital twin model of the built environment of study area 1. Specifically: A digital twin model of the built environment is exported from a Geographic Information System (GIS). The exported file is a built environment mesh model in a projected coordinate system, which requires data format conversion before use. Based on the exported built environment mesh model, a high-quality surface model is obtained through surface reconstruction and geometric repair in Rhino. This model is then exported to recognizable formats such as ICEM (a mesh generation software) and Fluent Meshing (another mesh generation software), and a polyhedral computational mesh is created, including a near-wall boundary layer and terrain-adaptive densification features. To analyze the built environment of study area one and extract the results (the spatially distributed data field), CFD simulation can calculate detailed numerical predictions of cross-sectional cloud maps for fluid flow (liquid or gas), heat transfer, mass transfer, and related physical phenomena (such as chemical reactions and phase transitions). An internal surface, i.e., an "imaginary internal wall," is established based on the 3D terrain model. This internal wall has a thickness of 0, allowing any medium to pass through. Using the pre-set internal surface, cross-sectional cloud maps at different heights can be extracted for in-depth analysis of the built environment. In Fluent, by configuring turbulence models (such as k-ε) and radiation models (such as solar radiation models), setting inlet wind profiles and various types of thermal boundary conditions, and through steady-state / transient solutions until residual convergence, a multi-seasonal, multi-scale, and refined simulation of the wind and heat environment of the study area is finally achieved (e.g., Figure 8 As shown in the figure, the simulation analysis couples multiple physical fields such as flow heat transfer and radiation, providing a high-precision numerical solution for environmental research in the study area.

[0031] Step Six: Based on the multiphysics simulation analysis, the analysis results are extracted using post-processing tools (software specifically designed for extracting, transforming, analyzing, visualizing, and interpreting the massive raw numerical data generated by CFD simulations) to conduct an in-depth analysis of the built environment in study area one.

[0032] Based on multiphysics simulation analysis, a cross-sectional cloud map extraction method is proposed to deeply analyze the built environment of study area one. Specifically, during mesh generation, an "imaginary internal surface" is constructed above the 3D terrain model. This imaginary internal surface is designed as a zero-thickness "inner wall," allowing any medium to freely pass through it, while ensuring that its mesh nodes are fully shared with the computational domain mesh nodes, thus forming a unified mesh structure. Using this predefined internal surface, cross-sectional cloud maps of different height regions can be quickly extracted, providing multi-dimensional perspective support for built environment research and overcoming post-processing analysis challenges.

[0033] Furthermore, through in-depth analysis of the simulation data using post-processing tools, the flow field information (velocity field, pressure field, turbulence), temperature field (temperature distribution, heat flux), and pollutant diffusion phenomena in the built environment of study area 1 can be intuitively displayed (e.g., Figure 8(As shown). By combining multiphysics parameter extraction and visualization techniques for scalar and vector fields, the simulation results are vividly presented in the form of dynamic graphs, cloud maps, etc., further revealing the specific characteristics of the built environment in study area 1. Thus, the built environment of study area 1 is analyzed in depth from a multi-dimensional, multi-scale, and quantitative and qualitative perspective. Example 2

[0034] In this embodiment 2, a typical rural environment was selected as the second study area. Using the method provided by this invention for constructing and analyzing the built environment of urban and rural areas, a digital twin model of the built environment of the second study area was successfully constructed. During the multi-source data fusion process, through data preprocessing, 3D modeling, and multi-source data fusion, a refined reconstruction of the rural built environment was achieved.

[0035] In Example 2, the method described in Example 1 is also used to construct a digital twin model of the rural environmental research area using multi-source data fusion, and to analyze the corresponding research area (e.g., Figure 9-11 As shown in the figure, the universality and efficiency of this method are verified. Practical research applications demonstrate that regardless of variations in the geographical features, land cover types, or data complexity of the study area, this method can efficiently fuse multi-source data to construct and analyze the urban and rural built environment of the region. Specific steps include: Step 1: Based on the latitude and longitude coordinates of the selected area, obtain a planar raster base map containing the building outlines of the selected area, and preprocess the planar raster base map; Step 2: Use a geographic information system to vectorize the preprocessed planar raster base map to obtain a two-dimensional building vector map; Step 3: Based on DEM and DSM data, calculate the building height base map using a geographic information system, and use the two-dimensional building vector map and the building height base map to build a three-dimensional building model of the selected area; Step 4: Based on DEM data, obtain a 3D terrain model of the selected area through data processing and transformation, and combine the 3D building model and 3D terrain model using the unified coordinate system of the geographic information system, and integrate multi-source data to construct a digital twin model of the built environment of the selected area. Step 5: Combining geographic information systems, 3D modeling technology, and computational fluid dynamics theory, conduct multiphysics simulation analysis on the digital twin model of the built environment; Step Six: Based on the multiphysics simulation analysis, extract the simulation analysis results using post-processing tools to perform in-depth analysis of the built environment of the selected area.

[0036] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any equivalent substitutions or modifications made by those skilled in the art within the spirit and principle of the present invention without any creative effort should be included within the protection scope of the present invention.

Claims

1. A method for constructing and analyzing the built environment of urban and rural areas by integrating multi-source data, characterized in that, Includes the following steps: Step 1: Based on the latitude and longitude coordinates of the selected area, obtain a planar raster base map containing the building outlines of the selected area, and preprocess the planar raster base map; Step 2: Use a geographic information system to vectorize the preprocessed planar raster base map to obtain a two-dimensional building vector map; Step 3: Based on DEM and DSM data, calculate the building height base map using a geographic information system, and use the two-dimensional building vector map and the building height base map to build a three-dimensional building model of the selected area; Step 4: Based on DEM data, obtain a 3D terrain model of the selected area through data processing and transformation, and combine the 3D building model and 3D terrain model using the unified coordinate system of the geographic information system, and integrate multi-source data to construct a digital twin model of the built environment of the selected area. Step 5: Combining geographic information systems, 3D modeling technology, and computational fluid dynamics theory, conduct multiphysics simulation analysis on the digital twin model of the built environment; Step Six: Based on the results of the multiphysics simulation analysis, extract the simulation analysis results using post-processing tools to perform in-depth analysis of the built environment of the selected area.

2. The method for constructing and analyzing regional urban and rural built environment by fusing multi-source data according to claim 1, characterized in that: In step one, satellite maps of the selected area are crawled using Python through the Baidu Developer Platform to obtain a planar raster base map of the selected area. The planar raster base map includes building outlines, road networks, area surfaces, and labels. The planar raster base map is preprocessed using a geographic information system. The preprocessing includes format conversion, coordinate system conversion, spatial clipping, and resolution resampling.

3. The method for constructing and analyzing regional urban and rural built environment by fusing multi-source data according to claim 1, characterized in that: In step two, the planar raster base map is georeferenced using a geographic information system, and the distinction between buildings and background is enhanced by binarization. Building outline vector surfaces are generated in batches using a raster-to-polygon tool, pseudo-polygons are deleted, boundary nodes are simplified to smooth jagged outlines, attribute information is added to the building outline vector surfaces, and finally, a two-dimensional building vector map in standard vector format is exported.

4. The method for constructing and analyzing regional urban and rural built environment by fusing multi-source data according to claim 1, characterized in that: In step three, ensure that the coverage, spatial resolution, and projected coordinate system of the DEM data and DSM data are completely consistent. Process the data using tools such as cropping, mosaicking, resampling, and coordinate system transformation, and check and repair outliers or invalid areas in the data. Perform a subtraction operation between the DSM data and the DEM data in the GIS raster calculator to generate a new height difference raster. Corresponding to the building height base map and the two-dimensional building vector map, the height difference raster is linked to the attribute table of the two-dimensional building vector map; Based on the building outline vector surface features, extract the raster statistical values ​​within the corresponding range, and write the raster statistical values ​​as the building height field into the attribute table.

5. The method for constructing and analyzing regional urban and rural built environment by fusing multi-source data according to claim 1, characterized in that: In step four, a three-dimensional terrain model is constructed based on DEM data. First, data preprocessing is performed to obtain DEM data that fully covers the selected area. Data holes and outliers are checked and corrected. If a geographic coordinate system is encountered, it is converted to a projected coordinate system. Then, the three-dimensional building model and the three-dimensional terrain model are combined using the unified coordinate system in the geographic information system. Finally, the multi-source data are integrated to construct a digital twin model of the urban and rural built environment of the region.

6. The method for constructing and analyzing regional urban and rural built environment by fusing multi-source data according to claim 1, characterized in that: In step five, a digital twin model of the built environment is exported from the geographic information system. The exported file is a built environment mesh model in a projected coordinate system. The built environment mesh model is converted into a surface model in the 3D modeling software. At the same time, the holes and edge overlap problems in the surface model are repaired. The output is in a format that can be recognized by computational fluid dynamics preprocessing software. The preprocessing software is used to divide the mesh into polyhedrals. For the near wall of the building, boundary layer meshing technology is used. For the regional terrain, an adaptive densification method is introduced to adjust the mesh density according to the terrain complexity.

7. The method for constructing and analyzing regional urban and rural built environment by fusing multi-source data according to claim 1, characterized in that: In computational fluid dynamics software, the preferred turbulence model is adapted to the wind field characteristics of the built-up environment, while supporting dynamic parameter adjustment according to research needs; when configuring the radiation model, multi-seasonal characteristics are considered, and the discrete trajectory model is selected to solve the radiation field; inlet conditions are defined, including wind profile settings based on measured data, and thermal boundary conditions optimized through the scenario. A multi-core parallel computing solution method is adopted to dynamically monitor and adjust the residuals during the simulation and analysis process of computational fluid dynamics software, and solve until the residuals converge.

8. The method for constructing and analyzing regional urban and rural built environment by fusing multi-source data according to claim 1, characterized in that: In step six, based on the results of the multiphysics simulation analysis, surfaces at different heights are created using surface transformation, or internal surfaces at different heights are established above the 3D terrain model during mesh generation. Cross-sectional cloud maps at different heights are extracted using these internal surfaces. Post-processing tools are used to perform in-depth analysis of the simulation data, intuitively revealing the flow field information, temperature field, and pollutant diffusion phenomena in the urban and rural built environment. Through the extraction of multiphysics parameters and the visualization of scalar and vector fields, the monotonous and complex numerical solution results are presented in a vivid and intuitive way, and the simulation results are visualized in the form of dynamic graphs and cloud maps. Through multi-dimensional and multi-scale analysis, combined with quantitative and qualitative methods, the microclimate characteristics of the regional urban and rural built environment are analyzed in depth.

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