Urban building model construction method and system based on climate environment simulation

Abstract

The invention provides an urban building model construction method and system based on climatic environment simulation, and relates to the technical field of information processing, and the method comprises the steps: determining a building image distribution domain of an urban building model in climatic environment simulation according to building interest point data; determining corrected characteristic attributes of the urban buildings under different climatic environment categories, and further determining building form indexes during construction of the urban building model through the corrected characteristic attributes; adding the building attribute tag image of the target city building into the building category model to obtain gradient influence coefficients of the target city building image in different building areas, and further determining building figure interest points of the city building model; and performing classification construction on the urban building models according to the building form indexes and the building figure interest points, and performing batch automatic conversion. According to the method, building model construction can be carried out under the influence of diversified urban climate environments, so that the precision of urban building model construction based on climate simulation is improved.

Description

Technical Field

[0001] The present application relates to the field of information processing technology, and more specifically, to a method and system for constructing an urban building model based on climate environment simulation. Background Art

[0002] In the process of urban development, the spatial form of urban materials has an important impact on the transfer, storage and release of materials and energy in the environment. In particular, urban buildings not only directly affect the wind field and radiation field, but also significantly change the distribution and changes of climate and environmental parameters. For example, the height, density, form and material combination of the building facade will affect the city's wind speed, thermal radiation, temperature distribution and other climate factors, thus having a profound impact on the urban climate. Specifically, high-rise buildings may block wind flow and produce a heat island effect, while low-density residential areas may form a more uniform temperature distribution. However, with the acceleration of urbanization, the spatial form, functional zoning and material combination of urban buildings have become increasingly complex, which has brought great challenges to urban climate and environmental modeling. Traditional urban climate simulation methods are usually difficult to accurately reflect the impact of buildings on local climate, especially the comprehensive effects of complex factors such as building form, functional zoning and material selection. Due to the wide variety of buildings, complex scales and intertwining, how to effectively integrate these architectural features into climate and environmental simulation has become a key issue in accurately simulating and predicting urban climate change.

[0003] The existing methods for constructing urban building models based on climate and environmental simulation are mainly divided into two types: mesoscale simulation and microscale simulation. Mesoscale simulation usually relies on land use and land cover data, and uses a canopy model (LCZ) to analyze regional environmental parameters, with a resolution usually not higher than 1 km. The LCZ method simplifies the description of the building area by classifying the ground environment. Although it refines the building characteristics to a certain extent, it is difficult to fully reflect the complexity of the urban built environment due to the use of fixed type classification, and there are problems of abstraction and lack of generalization. In contrast, microscale simulation uses a higher spatial resolution and can reveal in detail the impact of complex urban underlying surfaces such as buildings, roads, and vegetation on the local climate environment. Microscale simulation relies on high-precision urban building geometric models that can accurately describe the geometric form, function, and location of buildings, and directly apply these building characteristics as input parameters to numerical calculations, thereby providing more accurate climate simulation results. However, the existing methods for constructing urban building models based on climate and environmental simulation have problems such as inaccurate acquisition of building attribute data and over-simplification of building forms, which makes it difficult for the model to fully capture the complex spatial forms and functional differences of buildings. As a result, under diverse urban climate environments, the impact of buildings on climate change cannot be accurately reflected, thereby reducing the accuracy of building model construction in climate simulation. Therefore, how to construct building models under the influence of diverse urban climate environments to improve the accuracy of urban building model construction based on climate simulation is a problem faced by the industry. Summary of the invention

[0004] The present application provides a method and system for constructing an urban building model based on climate environment simulation, which can construct a building model under the influence of diverse urban climate environments to improve the accuracy of urban building model construction based on climate simulation.

[0005] In a first aspect, the present application provides a method for constructing an urban building model based on climate environment simulation, the model construction method comprising the following steps: Obtain a set of input images of buildings in the target city from a climate environment simulation platform; Performing hierarchical analysis on the input image set to obtain building interest point data of target urban buildings in different building areas, and determining the building image distribution domain of the urban building model in the climate environment simulation according to the building interest point data; Acquire the building geometric feature image of the target urban building complex in the climate environment simulation, perform missing filling on the building geometric feature image, obtain the corrected feature attributes of the urban buildings under different climate environment categories, and then determine the building form index when constructing the urban building model through the corrected feature attributes; Determine the building category model corresponding to the target city building complex in different climatic environments, add the building attribute label image of the target city building to the building category model, obtain the gradient influence coefficient of the target city building image in different building areas, and then determine the building shape interest point of the urban building model according to the gradient influence coefficient and the building image distribution domain; The urban building models are classified and constructed according to the building form indicators and the building shape interest points, and are automatically converted in batches.

[0006] In this embodiment, the input image set is subjected to hierarchical analysis to obtain the building interest point data of the target city building in different building areas, specifically including: Extracting features from a plurality of images in the input image set to obtain a plurality of corresponding feature images; Classify all feature images into categories and obtain a category mapping table corresponding to each category of feature images; The category mapping table is projected onto the input image set for screening to obtain building interest point data of target city buildings in different building areas.

[0007] In this embodiment, determining the building image distribution domain of the urban building model in the climate environment simulation according to the building interest point data specifically includes: Converting the building interest point data into building element points of the urban building model in the climate environment simulation; Determine the building attribute elements of the urban building model in the climate environment simulation according to the building element points; The building image distribution domain of the urban building model in the climate environment simulation is determined according to the building attribute elements.

[0008] In this embodiment, determining the architectural form index when constructing the urban architectural model through the modified characteristic attributes specifically includes: Determine the building category frequency density corresponding to the point of interest data in each building according to the modified characteristic attributes; Determine the proportion of building types corresponding to the point of interest data in each building; The building form index when constructing the urban building model is determined according to the building category frequency density and the building type ratio.

[0009] In this embodiment, determining the building category model corresponding to the target city building complex in different climate environments specifically includes: Determine the frequency density of building attributes corresponding to the target urban building complex in different climate environments; Determine characteristic graphic descriptions of target urban building complexes in different climate environments according to the frequency density of the building attributes; A building category model corresponding to the target city building complex in different climate environments is constructed according to the characteristic graphic description.

[0010] In this embodiment, determining the building shape interest points of the urban building model by the gradient influence coefficient and the building image distribution domain specifically includes: Determine the building climate zone of the building according to the building image distribution domain; The building shape interest points of the urban building model are determined by the building climate zoning and the gradient influence coefficient.

[0011] In this embodiment, the gradient influence coefficient represents the degree of influence of the building on the climate environment in different building areas.

[0012] In this embodiment, the building geometric feature image refers to an image showing the geometric form and structural features of the building.

[0013] In this embodiment, the input image set refers to a high-resolution image data set of target city buildings in climate environment simulation.

[0014] In a second aspect, the present application provides a system for constructing an urban building model based on climate environment simulation, which is used to execute a method for constructing an urban building model based on climate environment simulation, and the model construction system includes: An image set acquisition module is used to acquire an input image set of target city buildings from a climate environment simulation platform; A building hierarchy analysis module is used to perform a hierarchy analysis on the input image set to obtain building interest point data of target city buildings in different building areas, and determine the building image distribution domain of the urban building model in the climate environment simulation according to the building interest point data; The feature image filling module is used to obtain the building geometric feature image of the target urban building complex in the climate environment simulation, fill in the missing parts of the building geometric feature image, obtain the corrected feature attributes of the urban buildings under different climate environment categories, and then determine the building form index when constructing the urban building model through the corrected feature attributes; A building shape determination module is used to determine the building category model corresponding to the target city building complex in different climatic environments, add the building attribute label image of the target city building to the building category model, obtain the gradient influence coefficient of the target city building image in different building areas, and then determine the building shape interest point of the urban building model based on the gradient influence coefficient and the building image distribution domain; The building model construction module is used to classify and construct urban building models according to the building form indicators and the building shape interest points, and automatically convert them in batches.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: A set of input images of target city buildings is obtained from a climate environment simulation platform; hierarchical analysis is performed on the input image set to obtain data of building interest points of target city buildings in different building areas, and a building image distribution domain of the urban building model in the climate environment simulation is determined according to the building interest point data; an architectural geometric feature image of a target city building complex in the climate environment simulation is obtained, missing information is filled in the architectural geometric feature image, and corrected feature attributes of urban buildings under different climate environment categories are obtained, and then the architectural form index when constructing the urban building model is determined by the corrected feature attributes; a building category model corresponding to the target city building complex in different climate environments is determined, and the building attribute label image of the target city building is added to the building category model to obtain the gradient influence coefficient of the target city building image in different building areas, and then the building shape interest points of the urban building model are determined according to the gradient influence coefficient and the building image distribution domain; the urban building model is classified and constructed according to the building form index and the building shape interest points, and batch automatic conversion is performed.

[0016] It can be seen that in this application, the missing and incomplete attributes of the model data caused by the excessive simplification of the building form can be effectively compensated; among them, by obtaining high-quality building image data from the climate and environment simulation platform, the accuracy and integrity of the building model can be ensured, and a consistent and accurate data basis can be provided for subsequent building analysis and climate simulation, thereby improving the reliability of the simulation results. Hierarchical analysis can effectively extract the key features of the building, accurately identify the functional differences of different building areas, and provide structured data for subsequent building attribute modeling and climate and environment simulation, thereby improving the refinement of the model and the accuracy of the simulation. By completing the missing building geometric feature images and correcting the building feature data, the integrity and accuracy of the building model can be ensured, thereby improving the adaptability of the building to different climate environments and improving the accuracy and practicality of climate simulation. By combining the building category model and the gradient influence coefficient, the impact of the building on the surrounding environment (such as wind field, thermal radiation, etc.) can be accurately described, so that the building performance in the climate simulation is more in line with the actual situation, thereby improving the environmental adaptability and accuracy of the simulation. Through automated classification, construction and batch conversion of building models, the efficiency of building data processing can be greatly improved, human intervention can be reduced, and it can be ensured that a large amount of building data can be efficiently and consistently adapted to the climate environment simulation system, significantly improving the intelligence and accuracy of urban building analysis.

[0017] In summary, the technical solution adopted in this application can construct building models under the influence of diverse urban climate environments, so as to improve the accuracy of urban building model construction based on climate simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 is a flow chart of a method for constructing an urban building model based on climate environment simulation provided by the present application; Figure 2 is a schematic diagram of a process for determining and correcting characteristic attributes according to the present application; Figure 3 It is a schematic diagram of a process for determining a gradient influence coefficient according to the present application; Figure 4 This is a module structure diagram of the urban building model construction system based on climate environment simulation provided by this application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The embodiment of the present application provides a method and system for constructing an urban building model based on climate environment simulation, the core of which is to obtain an input image set of a target urban building from a climate environment simulation platform; perform hierarchical analysis on the input image set to obtain the building interest point data of the target urban building in different building areas, and determine the building image distribution domain of the urban building model in the climate environment simulation according to the building interest point data; obtain the building geometric feature image of the target urban building group in the climate environment simulation, perform missing filling on the building geometric feature image, obtain the corrected feature attributes of the urban building under different climate environment categories, and then determine the building form index when constructing the urban building model through the corrected feature attributes; determine the building category model corresponding to the target urban building group in different climate environments, add the building attribute label image of the target urban building to the building category model, obtain the gradient influence coefficient of the target urban building image in different building areas, and then determine the building shape interest points of the urban building model according to the gradient influence coefficient and the building image distribution domain; classify and construct the urban building model according to the building form index and the building shape interest points, and automatically convert them in batches. The above scheme can be used to construct a building model under the influence of a diverse urban climate environment to improve the accuracy of the construction of the urban building model based on climate simulation.

[0022] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG. 1 , this figure is an exemplary flow chart of the method for constructing an urban building model based on climate environment simulation according to the embodiment of the present application. The transaction protection method includes the following steps: In step S1, an input image set of target city buildings is obtained from a climate environment simulation platform.

[0023] In the specific implementation, first, clarify the scope of the target city, define the study area in the GIS platform, and screen the POI (point of interest) and AOI (area of ​​interest) data of the corresponding area, as well as high-resolution satellite images or drone aerial images. These data usually cover core information such as building location, boundaries, and functional categories. Next, the data collected initially is cleaned to remove records that are beyond the scope of the study or do not belong to buildings. For example, the geographic boundary intersection algorithm is used to delete non-building data, and the POI and AOI categories are remapped based on classification standards (such as the General Code for Civil Buildings). For POI and AOI data formats, they are converted into GIS point features and polygon features respectively to achieve consistency with the image collection. Subsequently, the image data is preprocessed. It mainly includes resolution unification, image cropping, denoising (such as Gaussian filtering) and color enhancement to ensure that the image has sufficient analysis quality. Blurred or noisy images are replaced to ensure coverage and data quality of the study area. Finally, the preprocessed data is stored in a unified format (such as GeoTIFF or NetCDF), which will not be repeated here.

[0024] It should be noted that in this application, the input image set refers to a high-resolution image data set of target city buildings in climate environment simulation. These images may include satellite images, drone aerial images, two-dimensional floor plans or three-dimensional models of buildings, etc.

[0025] In step S2, the input image set is subjected to hierarchical analysis to obtain the building interest point data of the target city building in different building areas, and the building image distribution domain of the urban building model in the climate environment simulation is determined according to the building interest point data.

[0026] In this embodiment, the hierarchical analysis of the input image set to obtain the building interest point data of the target city building in different building areas can be achieved by the following steps: Extracting features from a plurality of images in the input image set to obtain a plurality of corresponding feature images; Classify all feature images into categories and obtain a category mapping table corresponding to each category of feature images; The category mapping table is projected onto the input image set for screening to obtain building interest point data of target city buildings in different building areas.

[0027] In the specific implementation, first, the input image set is preprocessed to ensure data consistency. Common preprocessing techniques include image size normalization (uniform resolution) and denoising (such as Gaussian filtering). For example, for satellite images and drone images from different sources, the image resolution is uniformly adjusted to 0.5 meters to remove blurry or noisy areas. Then, computer vision algorithms (such as SIFT, SURF, ORB, etc.) are used to extract significant feature points in the image. Feature points can be the edges or corners of important elements such as the outline of a building, windows, doors, and roofs. The extracted feature points are combined with the corresponding area information to generate a feature image. The feature image is not a simple original image, but an image containing key information of the building structure annotated by a computer vision algorithm. Then, a machine learning algorithm (such as K-means clustering, support vector machine (SVM) or deep learning model) is used to classify the feature images. Each category represents a building type or building area. For example, one type of image may represent a high-rise residential building, and another type may represent a commercial building. A category mapping table is generated based on the classification results, which lists the specific category of each type of feature image and its corresponding building area. For example, the category mapping table may contain "category 1: high-rise residential buildings", "category 2: commercial buildings", "category 3: public facilities", etc., and indicate the feature image set of each category. Finally, the generated category mapping table is applied to the input image set for image screening. By matching the features of each image with the predefined categories in the category mapping table, the images of points of interest in each building area are screened out. For example, in the image of the residential area, the system automatically identifies the feature images belonging to the "residential building" category and extracts the building points of interest (such as windows, balconies, etc.) in the area. The building point of interest data is extracted from the screened images. These points of interest can be structural elements such as the edges, windows, doors, roofs, etc. of the building, representing the geometric form and functional layout of the building.

[0028] It should be noted that in this application, the feature image refers to an image containing key structural information of a building; the category mapping table refers to a table or data structure that establishes a relationship between image features or data points and predefined categories; and the building interest point data refers to key positions or feature points extracted from a building image or a building geometric model.

[0029] In this embodiment, determining the building image distribution domain of the urban building model in the climate environment simulation according to the building interest point data can be achieved by using the following steps: Converting the building interest point data into building element points of the urban building model in the climate environment simulation; Determine the building attribute elements of the urban building model in the climate environment simulation according to the building element points; The building image distribution domain of the urban building model in the climate environment simulation is determined according to the building attribute elements.

[0030] In the specific implementation, first, the building interest point data (such as windows, doors, roofs, walls, etc.) extracted from the building image are standardized to make them compatible with the model format required for climate and environmental simulation. These interest points usually include information such as the spatial coordinates, size, type, etc. of the building, and then the building interest point data is converted into specific building element points. Building element points usually include the vertices, boundaries and key structural points of the building, representing the geometric characteristics of the building. For example, the corner points of the building, the center points of the windows, etc. can be regarded as building element points, and the building interest point data are used to construct detailed geometric element points for the building in the climate simulation model through geometric deduction (such as calculating the boundaries, facades, roof outlines, etc. of the building). For example, the volume, plane structure and facade form of the building are derived from the windows, doors and roof features. Then, based on the geometric information of the building element points (such as the height, width, surface area, etc. of the building), the basic attributes of the building are calculated. The building attribute elements may include the type, form, material, facade characteristics, window-to-wall ratio, etc. of the building. By analyzing the building element points and combining the functional division of the building, the functional category of the building (such as residential, commercial, public facilities, etc.) is inferred. These categories can be further associated with the physical attributes of the building (such as window area, roof type, etc.) to determine the building attribute elements. The building attribute elements are spatially associated with the building element points, that is, the physical attributes represented by each building element point in the climate simulation are determined. For example, residential buildings may have a larger window-to-wall ratio, while commercial buildings may have more glass curtain walls. The attribute elements will vary with the building category. Finally, using the building attribute elements, the specific distribution of each building in the simulation is determined through spatial mapping techniques (such as rasterization or regional division). According to the geometric form and functional division of the building, the corresponding climate environment simulation area is determined. For example, the building image distribution domain of a high-rise building may contain a larger spatial area, while the distribution domain of a low-rise residential building is smaller. Based on the geometric characteristics, attribute elements and spatial information of the building category, the image distribution domain of the building is generated.

[0031] It should be noted that in this application, building element points represent basic data units in urban building models, representing geometric key positions or feature points of buildings, and are usually used to describe the spatial layout and structural form of buildings; building attribute elements represent data units that describe the physical characteristics of buildings, including the building's facade, window-to-wall ratio, building materials, functional categories, etc.; building image distribution domain represents the spatial influence area of ​​the building image in climate simulation, and is usually used to simulate the impact of buildings on the climate environment (such as thermal radiation, wind speed changes, etc.).

[0032] In step S3, the architectural geometric feature image of the target urban building complex in the climate environment simulation is obtained, and the missing parts of the architectural geometric feature image are filled to obtain the corrected characteristic attributes of the urban buildings under different climate environment categories, and then the architectural form indicators when constructing the urban building model are determined by the corrected characteristic attributes.

[0033] In specific implementation, the following methods can be used to obtain the building geometric feature images of the target urban building complex in the climate environment simulation, namely: first, use remote sensing image processing technology to extract the shape features of the building from satellite images or drone images. This process can be achieved through image segmentation algorithms (such as semantic segmentation based on convolutional neural networks), separate the building from the background, and extract its geometric information such as contours and vertices. Then, combine the laser radar data (LiDAR) for three-dimensional reconstruction to further obtain the three-dimensional information such as the height, volume, and roof shape of the building. Then, through the geometric fitting technology, the extracted building boundaries and three-dimensional data are integrated to generate the building geometric feature images of the target urban building complex. These images can accurately reflect the shape structure of the building, such as the facade, roof structure, window position, etc., and thus provide accurate building geometry input data for climate environment simulation. Finally, these building geometry feature images are standardized to ensure consistent data format, which will not be repeated here.

[0034] It should be noted that in this application, building geometric feature images refer to images that show the geometric form and structural features of buildings. These images include not only the outline of the building, but also the building's facade, roof shape, window distribution, door location and other geometric features.

[0035] Preferably, in this embodiment, the missing geometric feature image of the building is filled to obtain the corrected feature attributes of the urban building under different climate environment categories, and the reference Figure 3 The figure is a schematic diagram of a process for determining and modifying characteristic attributes in some embodiments of the present application. In this embodiment, determining and modifying characteristic attributes can be implemented by the following steps: In step S31, the architectural feature deviations of urban buildings under different climate environment categories are determined according to the architectural geometric feature graph; In step S32, the building geometry feature graph is analyzed using a trust analysis model to obtain a trust coefficient corresponding to the building geometry feature graph; In step S33, the attribute occupancy information of urban buildings under different climate environment categories is determined according to the building feature deviation and the trust coefficient; In step S34, the corrected characteristic attributes of the urban buildings under different climate environment categories are determined according to the attribute occupancy information.

[0036] In the specific implementation, first, the geometric attributes of the building, such as shape, size, roof height, window-to-wall ratio, etc., are extracted from the acquired building geometric feature images. For example, some buildings may have large windows or special roof structures, and these geometric features may cause heat loss or wind flow changes in climate environment simulation. Then, according to the climate type of the city (such as tropical, temperate or cold regions), the building is associated with a specific climate environment category. For example, buildings in tropical areas usually have larger window areas to maximize natural lighting, while cold regions may have more insulation measures. And based on the comparison of climate environment categories and building geometric features, the possible deviations of buildings in various climate environments are calculated. For example, the window-to-wall ratio may be higher in tropical areas and lower in cold regions. The feature deviation is determined by comparing the standard values ​​of the target building with similar buildings under similar climate conditions. Then, based on the quality, source and applicability of the building geometric feature map to climate simulation, a trust analysis model is established. The trust model assigns a trust coefficient to each feature image based on factors such as the source of the building data (such as satellite images, field measurements or 3D scanning), accuracy and completeness. The trust coefficient is calculated for each building geometric feature map using the model. The value of the trust coefficient is usually between 0 and 1, and the larger the value, the higher the data reliability of the feature map. The trust of each feature map is determined by analyzing the missing data, noise, and the consistency with other data sources. Then, the attribute occupancy information of buildings in different climate environments is determined. The attribute occupancy information represents the relationship between the design characteristics of the building in a specific climate environment (such as window-to-wall ratio, facade type, etc.) and its deviation. For example, for the tropical climate category, the window-to-wall ratio of the building is large, and the data with higher trust will indicate that such buildings are more adaptable to solar radiation. Low trust data may reflect that the design of the building does not meet the requirements of the tropical climate. A weighted algorithm is used to weight the influence of each building feature according to the trust coefficient, and its attribute occupancy in different climate environments is calculated. The weighted results help determine the corrected building features. Finally, the geometric features of the building can be adjusted through mathematical modeling (such as linear regression, random forest, etc.). For example, the window-to-wall ratio of the building in the tropical climate is too small, and the corrected feature attribute may be to increase the window-to-wall ratio to improve the ventilation and lighting efficiency of the building. Finally, the corrected feature attribute is calculated and output. These corrected properties will be used as input data in subsequent climate and environmental simulations to ensure high accuracy and adaptability of the simulation results.

[0037] It should be noted that, in this application, building feature deviation refers to the difference between the actual geometric features of the building (such as height, window-to-wall ratio, etc.) and the expected features under standard or ideal conditions; the trust coefficient represents the numerical value of the reliability of the data or model output, and is usually used to evaluate the validity of the input data; attribute occupancy information is information describing the relationship between the characteristics that a building should have under a specific climatic environment and the actual characteristics, reflecting the adaptability of the building to different environments; modified feature attributes represent the attributes after adjusting the geometric features of the building (such as window-to-wall ratio, facade material, etc.).

[0038] In this embodiment, the following steps can be used to determine the architectural form index when constructing the urban architectural model by modifying the characteristic attributes: Determine the building category frequency density corresponding to the point of interest data in each building according to the modified characteristic attributes; Determine the proportion of building types corresponding to the point of interest data in each building; The building form index when constructing the urban building model is determined according to the building category frequency density and the building type ratio.

[0039] In the specific implementation, first, extract the corresponding point of interest data from each building according to the modified feature attributes (such as window-to-wall ratio, building height, building facade material, etc.). These points of interest are usually the significant features of the building (such as windows, doors, roof forms, etc.), which can help determine the distribution of building categories. Classify the point of interest data of each building to determine the frequency of occurrence of each type of point of interest in the building. For example, in a high-rise commercial building, the frequency density of windows and facade curtain walls may be high, while in a low-rise residential building, the frequency of doors and windows is more prominent. Calculate the frequency density of points of interest in each building category in the building, indicating the proportion of each type of point of interest in the building. For example, for a building, the frequency density of points of interest for windows is 30%, for roofs is 20%, and for doors is 50%. Then, by classifying the types of building points of interest (such as "windows", "doors", "roofs", etc.), determine the building type (such as residential, commercial, public buildings, etc.) to which each point of interest belongs. Based on the frequency density of points of interest, calculate the proportion of each building type (such as residential, commercial, public buildings, etc.) in the building. The calculation method is to compare the frequency density of points of interest of a certain building category with the frequency density of the total points of interest of the building to obtain the proportion of the building category. For example: in a comprehensive building, it is assumed that the points of interest of windows, doors and roofs are 30%, 50% and 20% respectively, and according to the data of each type of points of interest, windows and doors may correspond to residential functions, and roofs correspond to public functions, so it is inferred that the proportion of residential functions of the building is 60%, commercial functions is 30%, and public functions is 10%. Finally, the building form indicators include the shape coefficient (the ratio of building volume to external surface area), window-to-wall ratio (the ratio of window area to external wall area), building plane shape index, etc. For example, through the frequency density of building categories and the proportion of building types, the window-to-wall ratio of the building can be calculated, and the thermal performance of the building under different climatic environments can be further inferred. The comprehensive building form index is calculated through a weighted algorithm or a multi-objective optimization model. According to the calculated building form index, the building model is further optimized. For example, if the shape coefficient of a building is high, it may indicate that its thermal energy consumption is large, and the ratio of building volume to surface area can be reduced through optimized design.

[0040] It should be noted that, in this application, the frequency density of building categories refers to the frequency or density of different types of building interest points (such as windows, doors, roofs, etc.) in a certain building, which is usually used to describe the structural characteristics of the building and its spatial layout; the proportion of building types refers to the proportion of different building function types in a certain building, which is usually calculated based on the frequency density of various interest points (such as windows, doors, etc.) in the building; the building form index represents an indicator that quantifies the spatial layout, external structure and other characteristics of the building, usually including shape coefficient, window-to-wall ratio, etc., which is used to analyze the adaptability of the building to the environment and energy efficiency.

[0041] In step S4, the building category model corresponding to the target city building complex in different climatic environments is determined, and the building attribute label image of the target city building is added to the building category model to obtain the gradient influence coefficient of the target city building image in different building areas, and then the building shape interest points of the urban building model are determined according to the gradient influence coefficient and the building image distribution domain.

[0042] In this embodiment, determining the building category model corresponding to the target city building complex in different climate environments can be achieved by using the following steps: Determine the frequency density of building attributes corresponding to the target urban building complex in different climate environments; Determine characteristic graphic descriptions of target urban building complexes in different climate environments according to the frequency density of the building attributes; A building category model corresponding to the target city building complex in different climate environments is constructed according to the characteristic graphic description.

[0043] In the specific implementation, first, collect the building attribute data of the target city building complex, which may include the building's window-to-wall ratio, shape coefficient, facade material, roof type, etc. Building attributes are important indicators that reflect the adaptability of building structure to climate. Then extract these building attribute data through remote sensing technology, laser radar (LiDAR) scanning or building information model (BIM) data. The attributes of each building include its geometric size, functional zoning, thermal performance, etc. The target city building complex under different climate environments is classified, and the frequency density of each building attribute in each climate environment is calculated. For example, the window-to-wall ratio of a building may be larger in a tropical climate and smaller in a cold climate. By counting the frequency of occurrence of various building attributes, their density in the building complex is obtained. For example, in a tropical climate, the frequency density of a building with a window-to-wall ratio of 0.4 is 70%, while in a temperate climate, the frequency density of a building with a window-to-wall ratio of 0.2 is 50%. Then, create a characteristic graphical description of the building complex. Characteristic graphics usually visualize the distribution of building attributes through statistical methods (such as histograms, scatter plots, or heat maps) to reflect the distribution trend of building characteristics under different climate environments. Characteristic graphic description can extract the main characteristics of the building complex through dimensionality reduction methods such as cluster analysis or principal component analysis (PCA). For example, in tropical climates, characteristic graphics may highlight large windows, sunshade facilities, etc., while in cold climates, characteristic graphics may focus on smaller windows and heavy facades. Map the distribution of building attributes to the adaptability model of different climate environments. For example, tropical climates may prefer buildings with larger windows, while cold climates are more suitable for buildings with low window-to-wall ratios. Adaptive characteristic graphics are created by analyzing the changes in the frequency density of building attributes under different climate environments. For example, in a tropical climate area, the window-to-wall ratio and facade materials of buildings may show a specific frequency density distribution, forming a characteristic graphic of the climate area. Finally, a building category model of the target city building complex is constructed. The building category model divides building types (such as residential, commercial, public buildings, etc.) and their performance in different climate environments based on information such as building attribute characteristics and climate adaptability. For example, residential buildings in tropical climates have larger window walls and may include sunshades, while commercial buildings in cold climates may have thermal insulation on their facades. Therefore, the building category model will generate a set of adaptive building types based on the needs of different climate environments, and optimize and verify the building category model to ensure that the model can accurately reflect the adaptability of building characteristics in different climate environments. The accuracy of the model is verified by comparison with actual building data, and adjustments are made based on climate data and building performance feedback. The building complex is then classified through algorithms (such as K-means clustering or decision tree classification) to determine the optimal design solution for each type of building in different climate environments and further refine the category characteristics of the building.

[0044] It should be noted that in this application, the frequency density of building attributes refers to the frequency or density of a specific building attribute (such as window-to-wall ratio, shape coefficient, etc.) in a building complex, which usually indicates the distribution of building characteristics under different climatic environments; the characteristic graphic description describes the characteristics of the distribution of building attributes of the building complex under different climatic environments; the building category model represents the characteristics of the building category under different climatic conditions.

[0045] Preferably, in this embodiment, the building attribute label image of the target city building is added to the building category model to obtain the gradient influence coefficient of the target city building image in different building areas. Figure 4 As described above, the figure is a schematic diagram of a process for determining a gradient influence coefficient in some embodiments of the present application. In this embodiment, the gradient influence coefficient can be determined by the following steps: In step S41, the building attribute label image of the target city building is determined; In step S42, the building shape index of the target city building is determined according to the building attribute label image; In step S43, a random forest model is established using building categories to predict missing building categories where point of interest data and regional interest data of buildings in the target city are missing; In step S44, the building shape index and the missing building category are added to the building category model to obtain the gradient influence coefficient of the target city building image in different building areas.

[0046] In the specific implementation, first, the building attribute label images of the target city buildings are usually derived from building information models (BIM), satellite images, drone aerial images or laser radar (LiDAR) scanning data. These images contain key attribute information of the building, such as window-to-wall ratio, facade material, building height, roof structure, etc. The building attribute data is extracted from the original image through image processing and data extraction techniques (such as image segmentation and target recognition). Based on the extracted building attribute data, a building attribute label image is generated. Each label represents a building attribute category and is marked in the corresponding area of ​​the building image. For example, the window area may be marked with a "window" label and the facade area is marked with a "facade" label. Use image processing tools (such as image annotation tools) to match the building attribute data with the image. Next, the building shape index is an indicator used to measure the geometric complexity of the building. The commonly used calculation method is to use the building's outline, area, perimeter and other data to calculate the building's shape index through a formula. For example, the building shape index can be calculated by the following formula: S i =P 2 / A, where P is the outer perimeter of the building and A is the base area of ​​the building. The lower the shape index, the more regular the plane shape of the building, and the higher the shape, the more complex the building shape. Then, according to the building attribute label image, the geometric features of the building, such as the outer contour, window layout, etc., are extracted, and the perimeter and area of ​​the building are calculated. These geometric data are extracted by image processing algorithms (such as edge detection and regional analysis). The shape index of the building is calculated to evaluate the geometric complexity of the building. Then, the random forest model is an integrated learning method that uses multiple decision trees for classification and prediction. In this step, the building category (such as residential, commercial, public buildings, etc.) is used as a label to establish a random forest model based on the building attribute data (such as window-to-wall ratio, facade material, shape index, etc.). Random forests learn patterns through training data (known building categories and feature data) and are used to predict building categories with missing data or that cannot be directly obtained. The random forest model can handle the problem of missing data. For example, when the point of interest data or regional interest data of some buildings is missing, the model predicts these missing building categories based on known building features (such as shape index, window-to-wall ratio, etc.). By training on building features and categories, the model can fill in the missing data with the appropriate prediction category. When calculating the gradient influence coefficient, the influence of the building's shape index and the missing building category on the environment (such as wind field, thermal radiation, etc.) is considered. Buildings with a lower building shape index may have less impact on the wind field, while buildings with more complex shapes may produce stronger air disturbances and heat island effects. Therefore, based on the shape index and building category, the influence coefficient of the building in different areas is calculated. Finally, the building shape index and the predicted missing building category are added to the building category model to ensure that the influence coefficient of the building in different areas can be accurately reflected. In climate simulation, the gradient influence coefficient of the building can help simulate how the building changes the local climate (such as temperature, wind speed, etc.).

[0047] It should be noted that in this application, the building attribute label image represents the building attribute data presented in image format, which usually includes key features such as the building's window-to-wall ratio, facade material, roof shape, etc. Each area is marked with a corresponding building attribute label; the building shape index refers to an indicator that describes the complexity of the building's plane form, which is usually calculated by the building's perimeter and base area; the missing building category refers to the fact that the category information of some buildings cannot be obtained due to missing or incomplete data. Through model prediction, the missing category can be supplemented; the gradient influence coefficient indicates the degree of influence of the building on the climate environment in different building areas, and is usually used in climate simulation to evaluate the physical effect of the building on the surrounding environment.

[0048] In this embodiment, determining the building shape interest points of the urban building model based on the gradient influence coefficient and the building image distribution domain can be achieved by using the following steps: Determine the building climate zone of the building according to the building image distribution domain; The building shape interest points of the urban building model are determined by the building climate zoning and the gradient influence coefficient.

[0049] In specific implementation, building climate zones are zones divided according to the climate characteristics of the building's location. For example, different areas of a city may have different climate conditions, such as tropical climate, temperate climate, cold climate, etc. Each climate zone will affect the requirements of building design (such as window-to-wall ratio, roof form, facade materials, etc.). The building image distribution domain provides the spatial proportion and distribution of buildings in the city. Combining the building's geometry and the urban area in which it is located, the climate zone to which each building belongs is determined through a geographic information system (GIS) or spatial analysis tool. For example, a building located in the central area of ​​the city may be affected by the urban heat island effect, while a building located in the suburbs may be more affected by the natural climate. According to the climate characteristics of the area where the building is located, the building image distribution domain is mapped with the climate zone to determine the climate zone where each building is located. For example, a building located in the tropical area of ​​the city may have its climate zone marked as "tropical climate", while a building located in a high-latitude area may be marked as a "cold climate" zone. When determining the building shape point of interest, the adaptability requirements are first set according to the building's climate zone. Different climate zones have different standards for the thermal performance, ventilation requirements, and sunlight requirements of buildings. Then, each point of interest of the building is weighted by the gradient influence coefficient to determine the location and influence range of the building shape point of interest. For example, in tropical climate zones, the facade of the building may have a significant impact on local temperature and thermal radiation, while in cold climate zones, the building's shape and window-to-wall ratio may have a greater impact on daylighting and heat retention. Finally, the location and influence range of the building shape point of interest are determined by combining the climate zone and the gradient influence coefficient. These points of interest are usually the key influence areas of the building on the environment (such as air flow, heat conduction, etc.). For example, in tropical climates, the south facade of the building may become a building shape point of interest because the south facade absorbs more solar radiation; in cold areas, the north facade of the building may become a key point of interest because the north facade is usually exposed to low temperature environments, affecting the heat loss of the building.

[0050] It should be noted that in this application, building climate zoning refers to the area in which the city is divided according to the climate characteristics of the area where the building is located, and is used to describe the adaptability requirements of the building to climate factors (such as temperature, humidity, precipitation, etc.); the building shape point of interest refers to the key impact area or location of the building on the environment (such as wind field, thermal radiation, etc.) in climate simulation.

[0051] In step S5, the urban building models are classified and constructed according to the building form indicators and the building shape interest points, and are automatically converted in batches.

[0052] In the specific implementation, first, a set of structured attribute data is generated for each building by calculating the building form indicators (such as shape coefficient, window-to-wall ratio, shape index, etc.). These indicators reflect the geometric characteristics of the building and its impact on the environment (such as thermal radiation, air flow, etc.). For example, buildings with low building shape index usually have more regular shapes and can adapt to the climate environment better, while buildings with complex shapes may have a stronger impact on the surrounding environment. According to these morphological indicators, buildings can be preliminarily classified into categories such as "regular buildings", "complex buildings", and "high-efficiency buildings". Then, building shape interest points (such as key functional areas, facades, windows, etc. of the building) are used as the basis for further classification in this process. These interest points reflect the energy efficiency, ventilation, lighting and other characteristics of the building. For example, in tropical climates, buildings with larger windows usually have higher heat loads; while in cold climates, building shape interest points may include efficient window-to-wall ratios and insulation layers. Therefore, by identifying the building shape interest points, the classification of buildings can be further refined. After completing the building classification, the next step is batch automatic conversion. This process relies on rule-based or machine learning algorithms to convert building form data (such as form index, points of interest, etc.) into a suitable software input format for climate simulation. Common conversion formats include BIM, GIS format or NetCDF format. Use machine learning models (such as random forests or support vector machines) to automatically identify different building types and map them to corresponding building category models.

[0053] It can be seen that in this application, the missing and incomplete attributes of the model data caused by the excessive simplification of the building form can be effectively compensated; among them, by obtaining high-quality building image data from the climate and environment simulation platform, the accuracy and integrity of the building model can be ensured, and a consistent and accurate data basis can be provided for subsequent building analysis and climate simulation, thereby improving the reliability of the simulation results. Hierarchical analysis can effectively extract the key features of the building, accurately identify the functional differences of different building areas, and provide structured data for subsequent building attribute modeling and climate and environment simulation, thereby improving the refinement of the model and the accuracy of the simulation. By completing the missing building geometric feature images and correcting the building feature data, the integrity and accuracy of the building model can be ensured, thereby improving the adaptability of the building to different climate environments and improving the accuracy and practicality of climate simulation. By combining the building category model and the gradient influence coefficient, the impact of the building on the surrounding environment (such as wind field, thermal radiation, etc.) can be accurately described, so that the building performance in the climate simulation is more in line with the actual situation, thereby improving the environmental adaptability and accuracy of the simulation. Through automated classification, construction and batch conversion of building models, the efficiency of building data processing can be greatly improved, human intervention can be reduced, and it can be ensured that a large amount of building data can be efficiently and consistently adapted to the climate environment simulation system, significantly improving the intelligence and accuracy of urban building analysis.

[0054] In summary, the technical solution adopted in this application can construct building models under the influence of diverse urban climate environments, so as to improve the accuracy of urban building model construction based on climate simulation.

[0055] This application provides a system for constructing an urban building model based on climate environment simulation. Figure 2 As shown, this figure is a schematic diagram of a system for constructing an urban building model based on climate environment simulation according to this embodiment of the present application, and the model construction system includes: An image set acquisition module 100 is used to acquire an input image set of target city buildings from a climate environment simulation platform; The building hierarchy analysis module 200 is used to perform a hierarchy analysis on the input image set to obtain the building interest point data of the target city building in different building areas, and determine the building image distribution domain of the urban building model in the climate environment simulation according to the building interest point data; The feature image filling module 300 is used to obtain the building geometric feature image of the target urban building complex in the climate environment simulation, fill in the missing parts of the building geometric feature image, obtain the corrected feature attributes of the urban buildings under different climate environment categories, and then determine the building form index when constructing the urban building model through the corrected feature attributes; The building shape determination module 400 is used to determine the building category model corresponding to the target city building complex in different climate environments, add the building attribute label image of the target city building to the building category model, obtain the gradient influence coefficient of the target city building image in different building areas, and then determine the building shape interest point of the urban building model based on the gradient influence coefficient and the building image distribution domain; The building model construction module 500 is used to classify and construct the urban building model according to the building form indicators and the building shape interest points, and automatically convert them in batches.

[0056] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0058] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A method for constructing an urban building model based on climate environment simulation, characterized in that: The model building method comprises the following steps: Obtain a set of input images of buildings in the target city from a climate environment simulation platform; Performing hierarchical analysis on the input image set to obtain building interest point data of target urban buildings in different building areas, and determining the building image distribution domain of the urban building model in the climate environment simulation according to the building interest point data; Acquire the building geometric feature image of the target urban building complex in the climate environment simulation, perform missing filling on the building geometric feature image, obtain the corrected feature attributes of the urban buildings under different climate environment categories, and then determine the building form index when constructing the urban building model through the corrected feature attributes; Determine the building category model corresponding to the target city building complex in different climatic environments, add the building attribute label image of the target city building to the building category model, obtain the gradient influence coefficient of the target city building image in different building areas, and then determine the building shape interest point of the urban building model according to the gradient influence coefficient and the building image distribution domain; The urban building models are classified and constructed according to the building form indicators and the building shape interest points, and are automatically converted in batches.

2. A method for constructing an urban building model based on climate environment simulation as claimed in claim 1, characterized in that: The input image set is subjected to hierarchical analysis to obtain the building interest point data of the target city building in different building areas, specifically including: Extracting features from a plurality of images in the input image set to obtain a plurality of corresponding feature images; Classify all feature images into categories and obtain a category mapping table corresponding to each category of feature images; The category mapping table is projected onto the input image set for screening to obtain building interest point data of target city buildings in different building areas.

3. The method for constructing an urban building model based on climate environment simulation according to claim 1, characterized in that: Determining the building image distribution domain of the urban building model in the climate environment simulation according to the building interest point data specifically includes: Converting the building interest point data into building element points of the urban building model in the climate environment simulation; Determine the building attribute elements of the urban building model in the climate environment simulation according to the building element points; The building image distribution domain of the urban building model in the climate environment simulation is determined according to the building attribute elements.

4. The method for constructing an urban building model based on climate environment simulation according to claim 1, characterized in that: Determining the architectural form index when constructing the urban architectural model by modifying the characteristic attributes specifically includes: Determine the building category frequency density corresponding to the point of interest data in each building according to the modified characteristic attributes; Determine the proportion of building types corresponding to the point of interest data in each building; The building form index when constructing the urban building model is determined according to the building category frequency density and the building type ratio.

5. The method for constructing an urban building model based on climate environment simulation according to claim 1, characterized in that: The building category models corresponding to the target urban building complexes in different climate environments specifically include: Determine the frequency density of building attributes corresponding to the target urban building complex in different climate environments; Determine characteristic graphic descriptions of target urban building complexes in different climate environments according to the frequency density of the building attributes; A building category model corresponding to the target city building complex in different climate environments is constructed according to the characteristic graphic description.

6. The method for constructing an urban building model based on climate environment simulation according to claim 1, characterized in that: Determining the building shape interest points of the urban building model by the gradient influence coefficient and the building image distribution domain specifically includes: Determine the building climate zone of the building according to the building image distribution domain; The building shape interest points of the urban building model are determined by the building climate zoning and the gradient influence coefficient.

7. The method for constructing an urban building model based on climate environment simulation according to claim 1, characterized in that: The gradient influence coefficient indicates the influence of a building on the climate environment in different building areas.

8. The method for constructing an urban building model based on climate environment simulation according to claim 1, characterized in that: The architectural geometric feature image refers to an image showing the geometric form and structural features of a building.

9. The method for constructing an urban building model based on climate environment simulation according to claim 1, characterized in that: The input image set refers to a high-resolution image data set of target city buildings in climate environment simulation.

10. A system for constructing a city building model based on climate environment simulation, used to execute a method for constructing a city building model based on climate environment simulation as claimed in any one of claims 1 to 9, characterized in that: The model building system comprises: An image set acquisition module is used to acquire an input image set of target city buildings from a climate environment simulation platform; A building hierarchy analysis module is used to perform a hierarchy analysis on the input image set to obtain building interest point data of target city buildings in different building areas, and determine the building image distribution domain of the urban building model in the climate environment simulation according to the building interest point data; The feature image filling module is used to obtain the building geometric feature image of the target urban building complex in the climate environment simulation, fill in the missing parts of the building geometric feature image, obtain the corrected feature attributes of the urban buildings under different climate environment categories, and then determine the building form index when constructing the urban building model through the corrected feature attributes; A building shape determination module is used to determine the building category model corresponding to the target city building complex in different climatic environments, add the building attribute label image of the target city building to the building category model, obtain the gradient influence coefficient of the target city building image in different building areas, and then determine the building shape interest point of the urban building model based on the gradient influence coefficient and the building image distribution domain; The building model construction module is used to classify and construct urban building models according to the building form indicators and the building shape interest points, and automatically convert them in batches.

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