Urban design scheme generation method, device and equipment and storage medium

Abstract

The application relates to a city design scheme generation method, device and equipment and a storage medium. The method comprises the following steps: obtaining multi-dimensional index data from a data source, performing a preprocessing operation to obtain a sample data set, constructing a data warehouse, obtaining related data of a city design scheme to be generated input by a user, dividing target plots according to the related data, calculating initial attribute indexes of each target plot, optimizing the initial attribute indexes by using predicted attribute indexes output by an index prediction model, obtaining target attribute indexes of each target plot, calculating building shape parameters of the city according to the target attribute indexes, generating an initial three-dimensional model of the city design scheme, and identifying the initial three-dimensional model by using different colors to obtain a target three-dimensional model of the city design scheme. The application can generate a city design scheme which is compliant, reasonable, has distinct city characteristics and designer will, and has high practicability.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device and storage medium for generating urban design schemes. Background Technology

[0002] Currently, urban design schemes are mainly generated based on rules and algorithms developed by designers. These schemes are based on parameters such as building density and floor area ratio, and urban areas are divided using geometric rules to obtain relevant design schemes.

[0003] Urban design schemes based on human-defined rules, while meeting certain requirements due to their reliance on rigid design principles, often exhibit overly rigid and homogeneous urban morphological characteristics. They lack a diverse response to urban layout features such as green corridors, locational landscapes, transportation hubs, and planning axes. Furthermore, limited by their inability to handle complex and organic site conditions, such as non-orthogonal structures, they cannot achieve organically intelligent and site-aligned automated layouts in terms of transportation networks and overall building design. Therefore, the application scenarios for urban design schemes generated by this approach are limited in planning practice, and their practicality is not strong. Summary of the Invention

[0004] In view of the above, this application provides a method, apparatus, device and storage medium for generating urban design schemes, the purpose of which is to solve the technical problem that the urban design schemes generated in the prior art have poor practicality.

[0005] Firstly, this application provides a method for generating urban design schemes, the method comprising:

[0006] Multi-dimensional indicator data of sample cities are obtained from multiple predetermined data sources. Preprocessing operations are performed on the multi-dimensional indicator data of the sample cities to obtain a sample dataset. A data warehouse is built based on the sample dataset.

[0007] The system obtains relevant data of the urban design scheme to be generated input by the user, performs a division operation on the urban land plots according to the control plan drawings in the relevant data to obtain multiple target plots, calculates the initial attribute indicators of each target plot according to the economic indicators in the relevant data, trains an indicator prediction model based on the sample dataset of the data warehouse, and optimizes the initial attribute indicators with the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators of each target plot.

[0008] Calculate the building form parameters of the city based on the target attribute indicators, generate an initial three-dimensional model of the city design scheme based on the building form parameters, and mark each element in the initial three-dimensional model with different colors to obtain the target three-dimensional model of the city design scheme.

[0009] Output the target attribute index, the initial 3D model, and / or the target 3D model.

[0010] Secondly, this application provides an apparatus for generating urban design schemes, the apparatus comprising:

[0011] The building module is used to obtain multi-dimensional indicator data of sample cities from multiple predetermined data sources, perform preprocessing operations on the multi-dimensional indicator data of the sample cities to obtain a sample dataset, and build a data warehouse based on the sample dataset.

[0012] The calculation module is used to acquire relevant data of the urban design scheme to be generated input by the user, divide the city's land into multiple target plots according to the control plan drawings in the relevant data, calculate the initial attribute indicators of each target plot according to the economic indicators in the relevant data, train an indicator prediction model based on the sample dataset of the data warehouse, and optimize the initial attribute indicators by the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators of each target plot.

[0013] The generation module is used to calculate the building form parameters of the city based on the target attribute indicators, generate an initial three-dimensional model of the city design scheme based on the building form parameters, and mark each element in the initial three-dimensional model with different colors to obtain the target three-dimensional model of the city design scheme.

[0014] Output module: used to output the target attribute index, the initial 3D model and / or the target 3D model.

[0015] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0016] Memory, used to store computer programs;

[0017] When a processor executes a program stored in memory, it implements the steps of the method for generating an urban design scheme as described in any embodiment of the first aspect.

[0018] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for generating an urban design scheme as described in any embodiment of the first aspect.

[0019] The technical solutions provided in this application have the following advantages compared with the prior art:

[0020] The data warehouse constructed in this application leverages the powerful potential of data mining to enhance the efficiency of traditional rule-based algorithms, providing real-time and reliable empirical predictions, verifications, and optimizations for the generation of urban design schemes. The generated urban design schemes will strictly meet the economic and technical constraints input by users and generate reasonable schemes that comply with relevant laws and regulations. Based on the effective identification and extraction of massive urban sample data using big data and artificial intelligence technologies, the morphological layout characteristics and paradigms of urban plots, buildings, and road networks at different scales are abstracted and precipitated. This realizes urban design schemes driven by multi-dimensional data prediction, generative algorithms, and economic and technical indicators. It can generate multiple compliant, reasonable urban design schemes that combine distinct urban characteristics and the designer's vision, with strict constraints from higher-level planning conditions and user input. Moreover, the generated urban design schemes are highly practical. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a preferred embodiment of the method for generating the urban design scheme of this application;

[0024] Figure 2 A schematic diagram of the city skyline generated for this application;

[0025] Figure 3 Another schematic diagram illustrating the city skyline generated for this application;

[0026] Figure 4 A bird's-eye view of the urban design scheme generated for this application;

[0027] Figure 5 This is a schematic diagram illustrating the division of the land parcels in this application.

[0028] Figure 6 This is another schematic diagram illustrating the division of the land parcels in this application.

[0029] Figure 7 This is a schematic diagram of the original site of the area to be upgraded and renovated in this application;

[0030] Figure 8 This is an aerial view of the area to be upgraded and renovated in this application;

[0031] Figure 9 This is a schematic diagram of the original site model uploaded based on the control plan drawings of the area to be updated and renovated in this application;

[0032] Figure 10 This is a schematic diagram of the parameter setting interface for the area to be updated and renovated in this application;

[0033] Figure 11 This application generates a viewable and analyzable urban design scheme for the area to be renovated and upgraded.

[0034] Figure 12 These are the nine urban design schemes generated for the area to be renovated in this application;

[0035] Figure 13 A schematic diagram of a preferred embodiment of the device for generating urban design schemes according to this application;

[0036] Figure 14 This is a schematic diagram of a preferred embodiment of the electronic device of this application;

[0037] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0039] This application provides a method for generating urban design schemes. (Refer to...) Figure 1 The diagram shown is a flowchart illustrating an embodiment of the method for generating urban design schemes according to this application. This method can be executed by an electronic device, which can be implemented using software and / or hardware. The method for generating urban design schemes includes:

[0040] Step S1: Obtain multi-dimensional indicator data of sample cities from multiple predetermined data sources, perform preprocessing operations on the multi-dimensional indicator data of the sample cities to obtain a sample dataset, and build a data warehouse based on the sample dataset.

[0041] In this embodiment, the pre-determined data source can be urban planning websites, urban and rural design websites, and GIS information related to the sample city. Multi-dimensional indicator data includes road network data, land parcel data, building data, natural resource data, and commercial data. Data crawling and other technologies can be used to collect and crawl massive amounts of urban sample data to obtain multi-dimensional indicator data. Preprocessing operations (e.g., data cleaning) are performed on the multi-dimensional indicator data to obtain a sample dataset. Based on this sample dataset, a data warehouse can be constructed. This data warehouse contains low-dimensional abstract expressions of multi-dimensional urban data, urban layout paradigms, and urban morphological characteristics, and can be continuously expanded and iterated based on the enrichment and optimization of the sample data.

[0042] Specifically, the step of performing preprocessing operations on the multi-dimensional indicator data of the sample cities to obtain a sample dataset, and constructing a data warehouse based on the sample dataset, includes:

[0043] The sample dataset is obtained by performing data cleaning, data segmentation, and data labeling on the multi-dimensional indicator data of the sample cities.

[0044] The sample dataset is stored in a Hive table, the data in the Hive table is divided into data themes, and the data warehouse construction type is determined according to the divided data themes;

[0045] The data warehouse is constructed according to the determined data warehouse construction type.

[0046] The massive amounts of multi-dimensional indicator data collected from cities are processed using data mining techniques such as data cleaning, slicing, processing, and automated labeling to obtain sample datasets. After storing the sample datasets in Hive tables, the data in the Hive tables are divided into data themes, with each data theme corresponding to multiple data dimensions. The data warehouse construction type is determined based on the divided data themes. The data warehouse construction type can include snowflake model, star model, and constellation model. The data warehouse is constructed according to the determined data warehouse construction type, and the crawled data can be precipitated into a multi-dimensional city data warehouse.

[0047] Step S2: Obtain relevant data of the urban design scheme to be generated input by the user, divide the city's land into multiple target plots according to the control plan drawings in the relevant data, calculate the initial attribute indicators of each target plot according to the economic indicators in the relevant data, train an indicator prediction model based on the sample dataset of the data warehouse, and optimize the initial attribute indicators by the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators of each target plot.

[0048] In this embodiment, the relevant data for the urban design scheme to be generated can be uploaded by the user through a human-computer interaction platform. The relevant data includes control plan drawings, generation parameters, planning axes, zoning modes, and facade modes. Among them, after the user uploads the control plan drawings that meet the layer naming requirements, the system can automatically read the site outline, water area outlines that affect the urban layout, landscape green corridors, centerlines of roads at all levels, and existing building outlines of the urban design scheme to be generated.

[0049] The generated parameters are the global economic and technical indicators of the scheme based on user requirements. These include global plot ratio, building density, greening rate, tower height limit, maximum number of floors in the podium, podium floor height, tower floor height, maximum plot ratio, minimum plot ratio, maximum building density, minimum building density, minimum distance between podium and tower, and other indicators for all plots involved in the design scheme generation.

[0050] The planning axis can be a planned axis that users draw on the site plane by drawing polylines. Users can select the axis influence mode and also customize urban morphological characteristics such as building volume ratio, building density, greening rate, and urban skyline of the plots surrounding the planning axis.

[0051] Users can choose different zoning modes to divide the plots generated from the urban design scheme, such as random division, linear division, grid division, and composite division, for a total of four zoning modes.

[0052] Users can select different facade modes to generate facade details for the generated 3D city model, such as facade mode, vertical louver mode, horizontal louver mode, and custom mode.

[0053] Since the control plan drawings contain plot outlines and elements affecting urban layout, the city's plots can be divided into multiple reasonable target plots based on these outlines and elements. Then, initial attribute indicators for each target plot are calculated using relevant economic and technical indicators. These initial attribute indicators are those not yet optimized by the model and include the plot ratio, building density, functional allocation, and greening rate allocated to each target plot.

[0054] Since the data warehouse contains relevant sample datasets, inputting the labeled massive urban sample datasets into a machine learning model for training can yield an indicator prediction model. Based on the trained model and user input parameters, economic and technical indicators such as traffic layout, functional distribution, plot ratio, and building density within the plot to be generated can be predicted. In other words, the economic and technical indicators of the urban design scheme to be generated are predicted to obtain predicted attribute indicators. The predicted attribute indicators output by the indicator prediction model are then used to optimize the initial attribute indicators to obtain the target attribute indicators for each target plot, thereby providing timely and reliable prediction, verification, and optimization for the generation of urban design schemes.

[0055] Specifically, the process of dividing urban land parcels based on the relevant planning drawings to obtain multiple target parcels includes:

[0056] Identify the outline of the site to be generated based on the control plan drawings;

[0057] Based on the control plan drawings, the influencing factors of urban layout are identified, including water area outlines, landscape green corridor outlines, centerlines of roads at all levels, existing building outlines, planned axes, and custom axes.

[0058] Based on the outline of the site to be generated, the influencing factors, and the area and shape of the plots in the planning control drawings, the city's plots are divided into multiple target plots;

[0059] Sub-plots of each target plot are generated based on the internal road width and building setback lines of the target plot.

[0060] Based on the outline of the site to be generated, influencing factors, and the area and shape of the plot in the control plan, the city's plot is divided into multiple target plots. Since the internal road widths and building setbacks of the target plots are different, it is necessary to adjust the target plots according to the internal road widths and building setbacks to obtain multiple sub-plots in the target plots that meet the actual design requirements.

[0061] Specifically, the calculation of initial attribute indicators for each target plot based on economic indicators from relevant data includes:

[0062] Based on the control plan drawings, the influencing factors of the urban layout are identified, and the influence coefficient of each target plot is calculated based on the influencing factors and the area and shape of the multiple target plots.

[0063] Based on the economic indicators and the influence coefficients in the relevant data, the initial attribute indicators of the target plot are calculated, wherein the initial attribute indicators include the plot ratio, building density, functional allocation and greening rate of the target plot.

[0064] By traversing each target plot, the influencing factors of urban layout are identified based on the control plan drawings. Based on the influencing factors and the area and shape of each target plot, the influence coefficient of each target plot can be calculated. With strict mathematical constraints such as plot ratio, building density, maximum plot ratio, minimum plot ratio, maximum building density, minimum building density, and greening rate, the initial attribute indicators such as plot ratio, building density, functional allocation, and greening rate allocated to each target plot are calculated based on the influence coefficient of each target plot.

[0065] Specifically, the step of optimizing the initial attribute indicators with the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators for each target plot includes:

[0066] The influencing factors and economic indicators corresponding to each target plot are input into the indicator prediction model to obtain the predicted attribute indicators for each target plot.

[0067] The initial attribute indicators of each target plot are optimized using the predicted attribute indicators of each target plot, so as to obtain the target attribute indicators of each target plot.

[0068] The indicator prediction model can be trained using a massive sample dataset of cities in a data warehouse. By using the influencing factors and economic indicators corresponding to each target plot as inputs to the indicator prediction model, the predicted attribute indicators for each target plot can be obtained. Since the indicator prediction model is trained on a massive sample dataset of cities, the model output can accurately indicate the attribute indicators of the target plots. Therefore, the predicted attribute indicators output by the indicator prediction model can be used to optimize the initial attribute indicators of each target plot, and the optimized indicators can be used as the target attribute indicators for each target plot. The optimization operation can involve assigning preset weights to the initial attribute indicators and the predicted attribute indicators, and then performing a weighted sum to obtain the target attribute indicator. For example, assigning a weight of 0.6 to the predicted attribute indicator and a weight of 0.4 to the initial attribute indicator. The specific weight allocation can be set according to actual needs and is not limited here.

[0069] Step S3: Calculate the building shape parameters of the city based on the target attribute indicators, generate an initial three-dimensional model of the city design scheme based on the building shape parameters, and mark each element in the initial three-dimensional model with different colors to obtain the target three-dimensional model of the city design scheme.

[0070] In this embodiment, each land parcel is traversed, and the building form parameters of the city can be calculated based on the allocated plot ratio, building density, greening rate, and functional allocation of each target land parcel. These building form parameters include the footprint and number of floors of the tower and podium. Based on the shape of the target land parcel, the footprint and number of floors of the tower and podium, the layout relationship of the tower and podium and an initial 3D model are generated within the target land parcel. Furthermore, based on the initial 3D model, core tube blocks are generated inside the tower, and corresponding floor lines are generated according to the number of floors and floor height of each building. Rooftop landscapes are generated on the top roofs of the podium and tower. Additionally, based on the target land parcel outline, greening rate, and buffer distance of the building form, an internal landscape area meeting the area requirements is generated within the target land parcel. Within this area, a landscape tree library is invoked to generate randomly arranged landscape tree clusters. When the user selects a certain facade customization mode, the corresponding facade division parameter combination is invoked from the facade parameter library according to that mode, and the facade details of the building form facades in each target land parcel are generated using this parameter combination.

[0071] Each element in the initial 3D model is identified using a different color. For example, the outlines of the influence range of water areas on the plot, the outlines of the influence range of landscape green corridors on the plot, the analysis outlines with arrow indicators corresponding to the planning axis and road centerline, and the building functions corresponding to building forms (towers, podiums, core tubes) are identified using different colors. Furthermore, the plot's floor area ratio, building density, and greening rate can be converted into their proportions relative to the global floor area ratio, building density, and greening rate, and corresponding clusters are generated based on these proportions. Figure 3 By analyzing the shape, a target three-dimensional model of the urban design scheme can be obtained.

[0072] Specifically, the step of calculating the building form parameters of the city based on the target attribute index includes:

[0073] The area between each target plot and the building setback line at a preset distance is designated as the area where buildings will be generated for the target plot.

[0074] Based on the target plot's building area to be generated and the target plot's target attribute indicators, the city's building shape parameters are calculated, including the floor area and number of floors of the tower and podium.

[0075] By traversing each target plot, the area of ​​each target plot at a preset distance from the building setback line is taken as the building area to be generated for the target plot. This allows the building area to move away from the building setback line. Then, based on the target attribute indicators such as the plot ratio, building density, greening rate, and functional allocation allocated to the current target plot, the respective land area and number of floors of the tower and podium in the target plot can be calculated.

[0076] Specifically, the step of identifying each element in the initial 3D model using different colors includes:

[0077] Based on the influence distance of the water area outline in the initial 3D model on the target plot, construct the influence range outline of the water area on the target plots around the water area, and use different colors to mark the different influence range outlines;

[0078] Based on the influence distance of the landscape green corridor outline in the initial 3D model on the target plot, construct the influence range outline of the landscape green corridor on the target plots around the landscape green corridor, and use different colors to mark the different influence range outlines.

[0079] Based on the influence width of the planning axis on the target plot in the initial three-dimensional model, an analysis contour line with arrows at both ends is constructed, and different analysis contour lines are marked with different colors according to the importance level of the planning axis;

[0080] Based on the level of the road centerline in the initial 3D model, an analysis contour line with arrows at both ends is constructed using the road width. Different analysis contour lines are identified using different colors according to the importance level of the road centerline.

[0081] Based on the area of ​​each water body, select the set of water bodies with high importance in the site, traverse the outline of each water body in the set, construct the outline of the influence range of the current water body on the surrounding plots according to three levels of influence distance, and assign different shades of color for identification.

[0082] Based on the building type and corresponding building function in the initial 3D model, each building is identified using different colors.

[0083] Traverse the landscape green corridor outline, construct the outline of the current landscape green corridor's influence range on the surrounding plots based on the current green corridor outline and the green corridor's influence distance, and assign different shades of color to mark it;

[0084] Traverse the set of planning axes, construct an analysis outline with arrows at both ends according to the importance level and polyline corresponding to the current planning axis, and assign different shades of color to mark it according to the importance level;

[0085] Traverse the set of road centerlines, construct analysis outlines with arrows at both ends according to the corresponding width based on the current road centerline level, and assign different shades of color to them according to their importance level.

[0086] Traverse the internal building shapes of each target plot and assign different colored materials to identify each tower, podium, and core according to their corresponding building functions;

[0087] Iterate through each target plot, converting the current plot's plot ratio, building density, and greening rate into their proportions relative to the global plot ratio, building density, and greening rate, and generate corresponding clusters based on these proportions. Figure 3 Dimensional body.

[0088] Step S4: Output the target attribute index, the initial 3D model, and / or the target 3D model.

[0089] In this embodiment, target attribute indicators, initial 3D model and / or target 3D model can be output according to user needs. Users (designers) can export the 3D model to their local machine for further editing, or compare and evaluate the output results.

[0090] This application leverages big data and artificial intelligence technologies to effectively identify and extract massive amounts of urban sample data. It abstracts and precipitates the morphological layout characteristics and paradigms of urban plots, buildings, and road networks at different scales. This enables the rapid, massive generation of urban design schemes, driven by multi-dimensional data prediction, generative algorithms, and economic and technical indicators, as well as the comparison and evaluation of multiple schemes. The urban feature data warehouse constructed in this application leverages the powerful potential of data mining to enhance the efficiency of technical paths relying on traditional rule-based algorithms, providing immediate and reliable empirical predictions, verification, and optimization for the generation of urban design schemes. The generated urban design schemes will strictly meet the constraints of user-input economic and technical indicators and generate reasonable schemes that comply with relevant laws and regulations.

[0091] This application supports users in drawing and inputting planning axes. Based on the built-in intelligent indicator calculation module, it can automatically generate a city skyline with significant landmark features according to the layout and influence range of the planning axes and global natural landscape resources. Figure 2 , Figure 3 and Figure 4 As shown in the case, this application can automatically identify the unique waterfront resources of the port and, in combination with the planning axis and global indicators, generate the commercial and office high-rise buildings with the highest building density and the most prominent landmark characteristics in the best area of ​​the port landscape, which greatly enhances the land premium and urban landscape features.

[0092] The intelligent land parcel division technology provides users with four land parcel division modes: random division, linear division, grid division, and composite division. Based on the intelligent facade generation technology, users can choose to generate different facade modes, such as vertical louver mode and horizontal louver mode. Furthermore, it supports users to upload custom parameter ratios to achieve personalized facade generation.

[0093] This application, based on intelligent land parcel division technology, can effectively adapt to the flexible structure of urban fabric, road networks, water systems, and landscape layout, achieving the organic division of arbitrarily complex site boundary forms, such as... Figure 5 and Figure 6 The diagram shows the effect of land parcel division. However, the traditional land parcel division methods used in the prior art can only handle relatively orthogonal and regular site boundary shapes. As a result, the road network shape is also limited to a regular shape with horizontal and vertical lines, and cannot present the organic effect of land parcel division in this application.

[0094] Compared to traditional urban planning and design workflows, this application significantly reduces labor time costs. Designers only need to spend about ten minutes organizing higher-level planning drawings, inputting parameters, drawing planning axes, selecting zoning and facade patterns, etc., to quickly generate multiple urban design schemes that meet the higher-level planning conditions and all economic and technical indicators, and export the 3D model to their local machine for further editing. Statistical indicators for each design scheme can be automatically generated and downloaded locally, providing reliable quantification for the comparison and evaluation of multiple schemes. This reduces the traditional workflow, which requires 0.5 to 1 full working day, to within half an hour, greatly improving productivity while expanding the diversity, selectivity, and economy of urban design schemes.

[0095] This application utilizes big data and machine learning technologies, leveraging the high computing power and automation of computers while incorporating an understanding of the designer's intent and algorithmic implementation. It comprehensively quantifies, evaluates, and utilizes the advantages of topography, landscape resources, and transportation layout at the meso-level of the planning conditions. The design scheme fully reflects the diversity of urban characteristics and optimal value orientation, such as the strong waterfront accessibility, rich skyline, and high-density urban CBD coordinated with the planning axis. This effectively avoids the pitfalls of traditional technologies that rely too heavily on rule-based algorithms to generate overly similar, homogeneous urban design schemes lacking distinct locational characteristics.

[0096] The effectiveness of this application will be further illustrated by an example of an urban design scheme generated for a redevelopment area in a certain city. Figure 7 The original site of a city's redevelopment area. Figure 8 This is an aerial view of the area to be renovated. Figure 9 This is a schematic diagram showing the upload of the original site model to this design platform based on the control plan drawings of the area to be updated and renovated. Figure 10 This is the parameter setting interface for the area to be updated and renovated. Figure 11 Urban design schemes generated for areas to be upgraded and renovated, which can be viewed and analyzed. Figure 12 Nine urban design schemes were generated for the area to be upgraded and renovated.

[0097] Reference Figure 14 The diagram shown is a functional module schematic of the urban design scheme generation device 100 of this application.

[0098] The urban design scheme generation device 100 described in this application can be installed in an electronic device. Depending on the functions implemented, the urban design scheme generation device 100 may include a construction module 110, a calculation module 120, a generation module 130, and an output module 140. The module described in this application can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0099] In this embodiment, the functions of each module / unit are as follows:

[0100] Construction module 110: used to obtain multi-dimensional indicator data of sample cities from multiple predetermined data sources, perform preprocessing operations on the multi-dimensional indicator data of the sample cities to obtain a sample dataset, and build a data warehouse based on the sample dataset;

[0101] Calculation module 120: used to acquire relevant data of the urban design scheme to be generated input by the user, perform a division operation on the urban land plots according to the control plan drawings in the relevant data to obtain multiple target plots, calculate the initial attribute indicators of each target plot according to the economic indicators in the relevant data, train an indicator prediction model based on the sample dataset of the data warehouse, and optimize the initial attribute indicators by the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators of each target plot.

[0102] Generation module 130: is used to calculate the building shape parameters of the city based on the target attribute indicators, generate an initial three-dimensional model of the city design scheme based on the building shape parameters, and mark each element in the initial three-dimensional model with different colors to obtain the target three-dimensional model of the city design scheme.

[0103] Output module 140: used to output the target attribute index, the initial three-dimensional model and / or the target three-dimensional model.

[0104] In one embodiment, the preprocessing of the multi-dimensional indicator data of the sample cities to obtain a sample dataset, and the construction of a data warehouse based on the sample dataset, includes:

[0105] The sample dataset is obtained by performing data cleaning, data segmentation, and data labeling on the multi-dimensional indicator data of the sample cities.

[0106] The sample dataset is stored in a Hive table, the data in the Hive table is divided into data themes, and the data warehouse construction type is determined according to the divided data themes;

[0107] The data warehouse is constructed according to the determined data warehouse construction type.

[0108] In one embodiment, the step of dividing urban land parcels according to the control plan drawings in the relevant data to obtain multiple target land parcels includes:

[0109] Identify the outline of the site to be generated based on the control plan drawings;

[0110] Based on the control plan drawings, the influencing factors of urban layout are identified, including water area outlines, landscape green corridor outlines, centerlines of roads at all levels, existing building outlines, planned axes, and custom axes.

[0111] Based on the outline of the site to be generated, the influencing factors, and the area and shape of the plots in the planning control drawings, the city's plots are divided into multiple target plots;

[0112] Sub-plots of each target plot are generated based on the internal road width and building setback lines of the target plot.

[0113] In one embodiment, calculating the initial attribute indicators for each target plot based on economic indicators from relevant data includes:

[0114] Based on the control plan drawings, the influencing factors of the urban layout are identified, and the influence coefficient of each target plot is calculated based on the influencing factors and the area and shape of the multiple target plots.

[0115] Based on the economic indicators and the influence coefficients in the relevant data, the initial attribute indicators of the target plot are calculated, wherein the initial attribute indicators include the plot ratio, building density, functional allocation and greening rate of the target plot.

[0116] In one embodiment, optimizing the initial attribute indicators with the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators for each target plot includes:

[0117] The influencing factors and economic indicators corresponding to each target plot are input into the indicator prediction model to obtain the predicted attribute indicators for each target plot.

[0118] The initial attribute indicators of each target plot are optimized using the predicted attribute indicators of each target plot, so as to obtain the target attribute indicators of each target plot.

[0119] In one embodiment, calculating the building form parameters of the city based on the target attribute index includes:

[0120] The area between each target plot and the building setback line at a preset distance is designated as the area where buildings will be generated for the target plot.

[0121] Based on the target plot's building area to be generated and the target plot's target attribute indicators, the city's building shape parameters are calculated, including the floor area and number of floors of the tower and podium.

[0122] In one embodiment, the step of identifying each element in the initial 3D model using different colors includes:

[0123] Based on the influence distance of the water area outline in the initial 3D model on the target plot, construct the influence range outline of the water area on the target plots around the water area, and use different colors to mark the different influence range outlines;

[0124] Based on the influence distance of the landscape green corridor outline in the initial 3D model on the target plot, construct the influence range outline of the landscape green corridor on the target plots around the landscape green corridor, and use different colors to mark the different influence range outlines.

[0125] Based on the influence width of the planning axis on the target plot in the initial three-dimensional model, an analysis contour line with arrows at both ends is constructed, and different analysis contour lines are marked with different colors according to the importance level of the planning axis;

[0126] Based on the level of the road centerline in the initial 3D model, an analysis contour line with arrows at both ends is constructed using the road width. Different analysis contour lines are identified using different colors according to the importance level of the road centerline.

[0127] Based on the building type and corresponding building function in the initial 3D model, each building is identified using different colors.

[0128] Reference Figure 14 The diagram shown is a schematic diagram of a preferred embodiment of the electronic device 1 of this application.

[0129] The electronic device 1 includes, but is not limited to, a memory 11, a processor 12, a display 13, and a communication interface 14. The electronic device 1 is connected to a network via the communication interface 14. The network can be an intranet, the Internet, a Global System for Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA) network, a 4G network, a 5G network, Bluetooth, Wi-Fi, a voice communication network, or other wireless or wired networks.

[0130] The memory 11 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as the hard disk or memory of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. of the electronic device 1. Of course, the memory 11 may include both the internal storage unit and the external storage device of the electronic device 1. In this embodiment, the memory 11 is typically used to store the operating system and various application software installed on the electronic device 1, such as the program code of the urban design scheme generation program 10. In addition, the memory 11 can also be used to temporarily store various types of data that have been output or will be output.

[0131] In some embodiments, processor 12 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. Processor 12 is typically used to control the overall operation of the electronic device 1, such as performing data interaction or communication-related control and processing. In this embodiment, processor 12 is used to run program code stored in memory 11 or process data, such as running the program code of the urban design scheme generation program 10.

[0132] The display 13 may be referred to as a display screen or display unit. In some embodiments, the display 13 may be an LED display, a liquid crystal display, a touch liquid crystal display, or an organic light-emitting diode (OLED) touch screen, etc. The display 13 is used to display information processed in the electronic device 1 and to display a visual working interface.

[0133] The communication interface 14 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface), which is typically used to establish a communication connection between the electronic device 1 and other electronic devices.

[0134] Figure 14Only an electronic device 1 with components 11-14 and a city design scheme generation program 10 is shown. However, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0135] Optionally, the electronic device 1 may further include a user interface, which may include a display, an input unit such as a keyboard, and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0136] The electronic device 1 may also include radio frequency (RF) circuits, sensors, and audio circuits, etc., which will not be described in detail here.

[0137] In the above embodiment, when the processor 12 executes the urban design scheme generation program 10 stored in the memory 11, it can perform the following steps:

[0138] Multi-dimensional indicator data of sample cities are obtained from multiple predetermined data sources. Preprocessing operations are performed on the multi-dimensional indicator data of the sample cities to obtain a sample dataset. A data warehouse is built based on the sample dataset.

[0139] The system obtains relevant data of the urban design scheme to be generated input by the user, performs a division operation on the urban land plots according to the control plan drawings in the relevant data to obtain multiple target plots, calculates the initial attribute indicators of each target plot according to the economic indicators in the relevant data, trains an indicator prediction model based on the sample dataset of the data warehouse, and optimizes the initial attribute indicators with the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators of each target plot.

[0140] Calculate the building form parameters of the city based on the target attribute indicators, generate an initial three-dimensional model of the city design scheme based on the building form parameters, and mark each element in the initial three-dimensional model with different colors to obtain the target three-dimensional model of the city design scheme.

[0141] Output the target attribute index, the initial 3D model, and / or the target 3D model.

[0142] The storage device can be the memory 11 of the electronic device 1, or it can be other storage devices that are communicatively connected to the electronic device 1.

[0143] For a detailed explanation of the above steps, please refer to the above. Figure 13 Functional block diagram of embodiment 100 of urban design scheme generation device and Figure 1 A flowchart illustrating an example of a method for generating urban design schemes.

[0144] Furthermore, this application embodiment also proposes a computer-readable storage medium, which can be non-volatile or volatile. This computer-readable storage medium can be any one or any combination of several of the following: hard disk, multimedia card, SD card, flash memory card, SMC, read-only memory (ROM), erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), USB memory, etc. The computer-readable storage medium includes a data storage area and a program storage area. The program storage area stores a city design scheme generation program 10, which, when executed by a processor, performs the following operations:

[0145] Multi-dimensional indicator data of sample cities are obtained from multiple predetermined data sources. Preprocessing operations are performed on the multi-dimensional indicator data of the sample cities to obtain a sample dataset. A data warehouse is built based on the sample dataset.

[0146] The system obtains relevant data of the urban design scheme to be generated input by the user, performs a division operation on the urban land plots according to the control plan drawings in the relevant data to obtain multiple target plots, calculates the initial attribute indicators of each target plot according to the economic indicators in the relevant data, trains an indicator prediction model based on the sample dataset of the data warehouse, and optimizes the initial attribute indicators with the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators of each target plot.

[0147] Calculate the building form parameters of the city based on the target attribute indicators, generate an initial three-dimensional model of the city design scheme based on the building form parameters, and mark each element in the initial three-dimensional model with different colors to obtain the target three-dimensional model of the city design scheme.

[0148] Output the target attribute index, the initial 3D model, and / or the target 3D model.

[0149] The specific implementation of the computer-readable storage medium in this application is largely the same as the specific implementation of the method for generating the urban design scheme described above, and will not be repeated here.

[0150] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0151] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, electronic device, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0152] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for generating urban design schemes, characterized in that, The method includes: Multi-dimensional indicator data of sample cities are obtained from multiple predetermined data sources. Preprocessing operations are performed on the multi-dimensional indicator data of the sample cities to obtain a sample dataset. A data warehouse is built based on the sample dataset. The system obtains relevant data of the urban design scheme to be generated input by the user, performs a division operation on the urban land plots according to the control plan drawings in the relevant data to obtain multiple target plots, calculates the initial attribute indicators of each target plot according to the economic indicators in the relevant data, trains an indicator prediction model based on the sample dataset of the data warehouse, and optimizes the initial attribute indicators with the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators of each target plot. Calculate the building form parameters of the city based on the target attribute indicators, generate an initial three-dimensional model of the city design scheme based on the building form parameters, and mark each element in the initial three-dimensional model with different colors to obtain the target three-dimensional model of the city design scheme. Output the target attribute index, the initial 3D model, and / or the target 3D model; The step of dividing urban land parcels based on the relevant planning drawings to obtain multiple target land parcels includes: Identify the outline of the site to be generated based on the control plan drawings; Based on the control plan drawings, the influencing factors of urban layout are identified, including water area outlines, landscape green corridor outlines, centerlines of roads at all levels, existing building outlines, planned axes, and custom axes. Based on the outline of the site to be generated, the influencing factors, and the area and shape of the plots in the planning control drawings, the city's plots are divided into multiple target plots; Sub-plots of each target plot are generated based on the internal road width and building setback lines of the target plot; The calculation of initial attribute indicators for each target plot based on economic indicators from relevant data includes: Based on the control plan drawings, the influencing factors of the urban layout are identified, and the influence coefficient of each target plot is calculated based on the influencing factors and the area and shape of the multiple target plots. Based on the economic indicators and the influence coefficients in the relevant data, the initial attribute indicators of the target plot are calculated, wherein the initial attribute indicators include the plot ratio, building density, functional allocation and greening rate of the target plot. The step of optimizing the initial attribute indicators with the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators for each target plot includes: The influencing factors and economic indicators corresponding to each target plot are input into the indicator prediction model to obtain the predicted attribute indicators for each target plot. The initial attribute indicators of each target plot are optimized using the predicted attribute indicators of each target plot, so as to obtain the target attribute indicators of each target plot.

2. The method for generating urban design schemes as described in claim 1, characterized in that, The process of preprocessing the multi-dimensional indicator data of the sample cities to obtain a sample dataset, and then constructing a data warehouse based on the sample dataset, includes: The sample dataset is obtained by performing data cleaning, data segmentation, and data labeling on the multi-dimensional indicator data of the sample cities. The sample dataset is stored in a Hive table, the data in the Hive table is divided into data themes, and the data warehouse construction type is determined according to the divided data themes; The data warehouse is constructed according to the determined data warehouse construction type.

3. The method for generating urban design schemes as described in claim 1, characterized in that, The calculation of the city's building form parameters based on the target attribute indicators includes: The area between each target plot and the building setback line at a preset distance is designated as the area where buildings will be generated for the target plot. Based on the target plot's building area to be generated and the target plot's target attribute indicators, the city's building shape parameters are calculated, including the floor area and number of floors of the tower and podium.

4. The method for generating urban design schemes as described in claim 1, characterized in that, The step of identifying each element in the initial 3D model using different colors includes: Based on the influence distance of the water area outline in the initial 3D model on the target plot, construct the influence range outline of the water area on the target plots around the water area, and use different colors to mark the different influence range outlines; Based on the influence distance of the landscape green corridor outline in the initial 3D model on the target plot, construct the influence range outline of the landscape green corridor on the target plots around the landscape green corridor, and use different colors to mark the different influence range outlines. Based on the influence width of the planning axis on the target plot in the initial three-dimensional model, an analysis contour line with arrows at both ends is constructed, and different analysis contour lines are marked with different colors according to the importance level of the planning axis; Based on the level of the road centerline in the initial 3D model, an analysis contour line with arrows at both ends is constructed using the road width. Different analysis contour lines are identified using different colors according to the importance level of the road centerline. Based on the building type and corresponding building function in the initial 3D model, each building is identified using different colors.

5. A device for generating urban design schemes, characterized in that, The device includes: The building module is used to obtain multi-dimensional indicator data of sample cities from multiple predetermined data sources, perform preprocessing operations on the multi-dimensional indicator data of the sample cities to obtain a sample dataset, and build a data warehouse based on the sample dataset. The calculation module is used to acquire relevant data of the urban design scheme to be generated input by the user, divide the city's land into multiple target plots according to the control plan drawings in the relevant data, calculate the initial attribute indicators of each target plot according to the economic indicators in the relevant data, train an indicator prediction model based on the sample dataset of the data warehouse, and optimize the initial attribute indicators by the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators of each target plot. The generation module is used to calculate the building form parameters of the city based on the target attribute indicators, generate an initial three-dimensional model of the city design scheme based on the building form parameters, and mark each element in the initial three-dimensional model with different colors to obtain the target three-dimensional model of the city design scheme. Output module: used to output the target attribute index, the initial 3D model and / or the target 3D model; The process of dividing urban land parcels based on relevant planning drawings yields multiple target parcels, including: Identify the outline of the site to be generated based on the control plan drawings; Based on the control plan drawings, the influencing factors of urban layout are identified, including water area outlines, landscape green corridor outlines, centerlines of roads at all levels, existing building outlines, planned axes, and custom axes. Based on the outline of the site to be generated, the influencing factors, and the area and shape of the plots in the planning control drawings, the city's plots are divided into multiple target plots; Sub-plots of each target plot are generated based on the internal road width and building setback lines of the target plot; The calculation of initial attribute indicators for each target plot based on economic indicators from relevant data includes: Based on the control plan drawings, the influencing factors of the urban layout are identified, and the influence coefficient of each target plot is calculated based on the influencing factors and the area and shape of the multiple target plots. Based on the economic indicators and the influence coefficients in the relevant data, the initial attribute indicators of the target plot are calculated, wherein the initial attribute indicators include the plot ratio, building density, functional allocation and greening rate of the target plot. The step of optimizing the initial attribute indicators with the predicted attribute indicators output by the indicator prediction model to obtain the target attribute indicators for each target plot includes: The influencing factors and economic indicators corresponding to each target plot are input into the indicator prediction model to obtain the predicted attribute indicators for each target plot. The initial attribute indicators of each target plot are optimized using the predicted attribute indicators of each target plot, so as to obtain the target attribute indicators of each target plot.

6. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method for generating an urban design scheme as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method for generating urban design schemes as described in any one of claims 1 to 4.

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