Methods, systems, computer equipment and storage media for optimizing the form of urban buildings

Abstract

This invention discloses a method, system, computer equipment, and storage medium for optimizing the form of buildings in urban space. The method includes: acquiring geographic information data within a target area, performing parametric modeling to construct a first sample dataset and a first sample index set; constructing a sample control set based on the number of buildings in the first sample dataset; further processing the first sample dataset using the sample control set to construct a second sample dataset; calculating on the second sample dataset and calling the first sample index set to obtain relevant indicators for evaluating the building space environment, generating a second sample index set; performing genetic algorithm optimization on the sample control set, the second sample dataset, and the second sample index set to form a final optimization result dataset; and visually displaying the genetic optimization process and the final optimization result dataset. This invention can effectively provide targeted overall and local building space environment optimization suggestions for different plots of land.

Description

Technical Field

[0001] This invention relates to a method, system, computer equipment, and storage medium for optimizing the form of urban spatial buildings, belonging to the field of urban renewal. Background Technology

[0002] In recent years, China's economy has maintained rapid and sustained growth, and the urbanization process has accelerated. While the pace of new city construction has slowed, buildings and infrastructure in older urban areas generally require renewal and improvement to meet the needs of modern cities. Therefore, in the urban stock renewal phase, updating and iterating the spatial environment of existing buildings can improve the environmental quality and quality of life in older urban areas, renew the urban landscape, and thus promote sustainable urban development.

[0003] Due to the complexity of urban built-up spaces, chaotic building ownership, and inadequate living conditions, the speed of urban renewal is determined by the scientific efficiency of the initial design phase. Furthermore, the building layout environment affects residents' quality of life, and the governance challenges encountered during major public health events have exposed loopholes in the current built-up environment. Currently, urban building renewal is mainly driven by local governments setting renewal red lines within a certain range, focusing more on the size of the coverage area and the location of the plots, neglecting to some extent the different attributes of buildings within the renewal area and the needs of the original users. In addition, current urban renewal strategies only have detailed building control plans within historical and cultural preservation areas, failing to flexibly address the differences in building space needs between different building plots under large-scale renewal, and unable to make targeted adjustments to the green space layout and building spatial form indicators within the renewal area. In construction decision-making, existing research mostly uses the Analytic Hierarchy Process (AHP) as a multi-objective decision-making method, lacking exploration of applications of multi-objective optimization algorithms based on different scenarios. Summary of the Invention

[0004] In view of this, the present invention provides a method, system, computer equipment and storage medium for optimizing the form of urban space buildings. It can effectively provide targeted overall and local building space environment optimization suggestions for different plots of building space, thereby enabling more scientific and efficient urban renewal work, improving the level of refinement in urban renewal, and providing a faster and more accurate construction basis for gradual urban renewal.

[0005] The first objective of this invention is to provide a method for optimizing the form of urban spatial buildings.

[0006] The second objective of this invention is to provide an urban spatial building form optimization system.

[0007] A third objective of this invention is to provide a computer device.

[0008] A fourth objective of this invention is to provide a storage medium.

[0009] The first objective of this invention can be achieved by adopting the following technical solution:

[0010] A method for optimizing the form of urban spatial buildings, the method comprising:

[0011] Geographic information data within the target area is acquired, and after parametric modeling, a first sample dataset and a first sample index set are constructed. The geographic information data includes road edge lines, road center lines, building heights, building baselines, and building land boundary lines.

[0012] Based on the number of buildings in the first sample dataset, construct a sample control set;

[0013] The first sample dataset is further processed using the sample control set to construct the second sample dataset;

[0014] The second sample dataset is calculated, and the first sample index set is called to obtain the relevant indicators for the evaluation of the building space environment, and then the second sample index set is generated.

[0015] The genetic algorithm is used to optimize the joint sample control set, the second sample dataset, and the second sample index set to form the final optimization result dataset.

[0016] The genetic optimization process and the final optimization result dataset are visualized.

[0017] Furthermore, the step of acquiring geographic information data within the target area, performing parametric modeling, and constructing a first sample dataset and a first sample index set specifically includes:

[0018] Based on urban basic geographic information data, the road edge lines, road center lines, building heights, building baselines, and building land boundary lines within the corresponding area are exported and labeled in different layers.

[0019] The exported building height and building baseline are used as the first building height and the first building baseline, respectively. Based on the first building height information, the first building baseline is extruded to obtain the building parametric model. The first sample dataset is constructed using this parametric model.

[0020] Based on the first building baseline and the first building height, the total building area within the region is calculated according to the input floor height, and the building volume ratio within the region is calculated. The first sample index set is constructed using the building volume ratio.

[0021] Furthermore, the construction of the sample control set based on the number of buildings in the first sample dataset specifically includes:

[0022] Based on the number of buildings in the first sample dataset, construct a first sequence with a length equal to the number of buildings and a range of 0 or 1.

[0023] Based on the number of buildings in the first sample dataset, construct a second sequence with a length equal to the number of buildings and a range equal to the input height range;

[0024] Based on the number of buildings in the first sample dataset, construct a third sequence with a length equal to the number of buildings and a range equal to the offset range of the input.

[0025] The first, second, and third sequences are combined to form the sample control set.

[0026] Furthermore, the step of further processing the first sample dataset with the sample control set to construct the second sample dataset specifically includes:

[0027] The buildings in the first sample dataset are filtered using the first sequence in the sample control set, with 0 indicating exclusion and 1 indicating retention, to form an updated building baseline dataset, which serves as the second building baseline dataset.

[0028] The second column in the sample control set is filtered using the first column in the sample control set, with 0 indicating exclusion and 1 indicating retention, to form an updated building height dataset, which serves as the second building height dataset.

[0029] The third column in the sample control set is filtered using the first column of the sample control set, with 0 indicating exclusion and 1 indicating retention, to form the updated building offset distance dataset;

[0030] The updated building offset distance dataset is used to offset the second building baseline dataset by the corresponding distance to form the third building baseline dataset;

[0031] Using the third building baseline dataset and the corresponding second building height dataset, the blocks are extruded to form the second sample dataset.

[0032] Furthermore, the calculation of the second sample dataset to obtain relevant indicators for the evaluation of the building space environment, combined with the first sample indicator set, generates the second sample indicator set, specifically including:

[0033] Subtract the building footprint and road area from the total building area in the region to obtain the open space area. Calculate the ratio between the open space area and the total building area in the region to obtain the building open space ratio.

[0034] Calculate the standard deviation of building height based on the building height and average building height within the region;

[0035] The intersection of roads within the area is selected as the observation origin. A cross-sectional plane perpendicular to the road is established using multiple origins. The ratio of the average height to the width of the cross-section on both sides intersecting the cross-sectional plane is calculated, and the average value is taken as the street height-to-width ratio.

[0036] Based on the observation origin, calculate the ratio of the unobstructed part of the sky at each point to the total area of ​​the sky hemisphere, and take the average value as the sky visibility.

[0037] The number of floors in each building within the area is obtained by dividing the building height by the set floor height. The total building area is obtained by multiplying the number of floors by the corresponding ground area. The total building area is summed and divided by the building land area to obtain the building floor area ratio. The absolute value of the difference between the building floor area ratio and the building floor area ratio of the first sample index set is used as the floor area ratio deviation index.

[0038] Calculate the building space that can be directly blown by the wind, and take the average proportion of the wind shadow area at different height sections as the area ratio of the wind shadow area in summer.

[0039] The second set of indicators is formed by combining the building open space ratio, building height standard deviation, street height-to-width ratio, sky visibility, plot ratio deviation, and summer monsoon shadow area area ratio.

[0040] Furthermore, the joint sample control set, the second sample dataset, and the second sample index set are optimized using a genetic algorithm to form the final optimized result dataset, specifically including:

[0041] The sample control set is connected to the Wallacei X processor. Before reaching the set number of generations, different second sample datasets are generated based on the second sample dataset. The corresponding second sample index set is calculated using the updated second sample dataset. Genetic evolution is carried out with the second sample index set as the optimization target. During the calculation process, the sample number, sample control set, second sample dataset, and second sample index set are recorded to form the final optimization result dataset.

[0042] Furthermore, the genetic number is 30 generations, with each generation containing 50 gene combinations, for a total of 1500 sample spaces.

[0043] The second objective of this invention can be achieved by adopting the following technical solution:

[0044] An urban spatial building form optimization system, the system comprising:

[0045] The acquisition module is used to acquire geographic information data within the target area, and construct a first sample dataset and a first sample index set after parametric modeling. The geographic information data includes road edge lines, road center lines, building heights, building baselines, and building land boundary lines.

[0046] The first building module is used to construct a sample control set based on the number of buildings in the first sample dataset.

[0047] The second construction module further processes the first sample dataset using the sample control set to construct the second sample dataset;

[0048] The generation module is used to calculate the second sample dataset and call the first sample index set to obtain relevant indicators for building space environment evaluation and generate the second sample index set.

[0049] The optimization module is used to perform genetic algorithm optimization on the joint sample control set, the second sample dataset, and the second sample index set to form the final optimization result dataset.

[0050] The presentation module visualizes the genetic optimization process and the final optimization result dataset.

[0051] The third objective of this invention can be achieved by adopting the following technical solution:

[0052] A computer device includes a processor and a memory for storing a processor-executable program, characterized in that when the processor executes the program stored in the memory, it implements the above-described method for optimizing the form of urban spatial buildings.

[0053] The fourth objective of this invention can be achieved by adopting the following technical solution:

[0054] A storage medium storing a program that, when executed by a processor, implements the above-described method for optimizing the form of urban spatial buildings.

[0055] The present invention has the following advantages over the prior art:

[0056] This invention provides a refined regional architectural space renewal optimization strategy for different plots of land during urban renewal, making targeted adjustments to the layout of green spaces and architectural spatial morphology indicators within the renewal area. During the optimization phase, thanks to genetic algorithms, this invention can foster a superior living environment while maintaining similar development intensities. Furthermore, based on real-time calculation of morphological indicators, the computation time is significantly reduced compared to traditional environmental simulation, effectively improving planning efficiency. In the scheme generation phase, this invention offers the possibility of multiple scheme optimizations, further considering the complexity and feasibility of the urban renewal development environment. For urban managers, the architectural space renewal optimization suggestions provided by this invention can offer practical and effective advice, thereby providing favorable policy guidance for urban renewal. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0058] Figure 1 This is a simplified flowchart of the urban spatial building form optimization method of Embodiment 1 of the present invention.

[0059] Figure 2 This is a detailed flowchart of the urban spatial building form optimization method of Embodiment 1 of the present invention.

[0060] Figure 3 This is a schematic diagram of the parametric reconstruction process in Embodiment 1 of the present invention.

[0061] Figure 4 This is a schematic diagram illustrating the calculation of building open space ratio in Embodiment 1 of the present invention.

[0062] Figure 5 This is a schematic diagram illustrating the calculation of the standard deviation of building height in Embodiment 1 of the present invention.

[0063] Figure 6 This is a schematic diagram of the street height-to-width ratio calculation in Embodiment 1 of the present invention.

[0064] Figure 7 This is a schematic diagram of sky visibility calculation in Embodiment 1 of the present invention.

[0065] Figure 8 This is a schematic diagram of the plot ratio deviation calculation in Embodiment 1 of the present invention.

[0066] Figure 9 This is a schematic diagram illustrating the calculation of the summer monsoon shadow area ratio in Embodiment 1 of the present invention.

[0067] Figures 10a-10c This is a schematic diagram illustrating the optimization process of Embodiment 1 of the present invention.

[0068] Figure 11 This is a schematic diagram of the optimization results and indicator display interface of Embodiment 1 of the present invention.

[0069] Figures 12a-12c This is a schematic diagram illustrating the optimized comprehensive suggestions for Embodiment 1 of the present invention.

[0070] Figure 13 This is a structural block diagram of the urban spatial building form optimization system of Embodiment 2 of the present invention.

[0071] Figure 14 This is a structural block diagram of the computer device according to Embodiment 3 of the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] Example 1:

[0074] This embodiment provides a method for optimizing the form of urban spatial buildings. The method uses a city map with multi-dimensional features, selects the target plot for optimization, collects the spatial environmental features of the original plot, performs parametric reconstruction, analyzes the spatial environmental features of the reconstructed target plot and optimizes it using a genetic algorithm; uses the Rhinoceros 3D modeling platform as the operating terminal and the Grasshopper graphical programming language as the platform for index calculation and Wallacei X genetic optimization.

[0075] like Figure 1 and Figure 2 As shown, the urban spatial building form optimization method of this embodiment includes the following steps:

[0076] S201. Obtain geographic information data within the target area, perform parametric modeling, and construct the first sample dataset and the first sample index set.

[0077] This embodiment targets a local area within Yuancun, Tianhe District, Guangzhou City. The geographic information data includes road edges, road centerlines, building heights, building baselines, and building land boundaries.

[0078] Furthermore, step S201 specifically includes:

[0079] S2011. Based on urban basic geographic information data, export the road edge lines, road center lines, building heights, building baselines, and building land boundary lines within the corresponding area using ArcGIS software, and label them separately using different layers.

[0080] Specifically, the process involves acquiring basic urban geographic information data: obtaining water surface feature data and road line feature data from the OSM open-source wiki map; extracting building surface and building height data from the architectural CAD data of Guangzhou's central urban area, importing them into ArcGIS software, selecting the corresponding layers in ArcGIS software, and exporting them as DWG format files layer by layer.

[0081] S2012. The exported building height and building baseline are respectively used as the first building height H1 and the first building baseline L1. Based on the first building height information H1, the first building baseline L1 is extruded in the Grasshopper platform to obtain the building parametric model. The first sample dataset is constructed using this parametric model.

[0082] Specifically, the obtained dwg format file is imported into the Rhinoceros software and picked up into the Grasshopper parametric design platform; after picking up the height field of the building baseline on the Grasshopper platform, the Project operator is called to project it onto the same horizontal plane; based on the first building height information, the Excrude operator is called in the Grasshopper platform to extrude the first building baseline L1 to the corresponding first building height H1, thereby obtaining the building parametric model, and the first sample dataset is constructed using this parametric model.

[0083] S2013. Based on the first building baseline and the first building height, after the user inputs the floor height, calculate the total building area S0 within the area, calculate the building volume ratio (Plot Radio) within the area, and construct the first sample index set using the building volume ratio. The formula for calculating the building volume ratio is as follows:

[0084]

[0085] Among them, H i Where d is the building height, d is the set floor height, and S is the floor height. i S0 represents the building footprint area, and S0 represents the total building area, i.e., the total area of ​​the designated land area.

[0086] S202. Based on the number of buildings in the first sample dataset, construct a sample control set.

[0087] Furthermore, step S202 specifically includes:

[0088] S2021. Based on the number of buildings in the first sample dataset, call the GenePool operator in the Grasshopper platform to construct a first sequence N1 with a length equal to the number of buildings and a range of 0 or 1.

[0089] S2022. Based on the number of buildings in the first sample dataset, call the GenePool operator in the Grasshopper platform to construct a second sequence N2 with a length equal to the number of buildings and a range equal to the height range input by the user.

[0090] S2023. Based on the number of buildings in the first sample dataset, call the GenePool operator in the Grasshopper platform to construct a third sequence N3 with a length equal to the number of buildings and a range equal to the offset range input by the user.

[0091] S2024, together with the first sequence N1, the second sequence N2, and the third sequence N3, constitutes the sample control set.

[0092] S203. The first sample dataset is further processed using the sample control set to construct the second sample dataset.

[0093] Furthermore, step S203 specifically includes:

[0094] S2031. Call the Dispatch operator in the Grasshopper platform to filter the buildings in the first sample dataset using the first sequence N1 in the sample control set. Use 0 to indicate exclusion and 1 to indicate retention to form an updated building baseline dataset, which serves as the second building baseline dataset L2.

[0095] S2032. Call the Dispatch operator in the Grasshopper platform to filter the second column of the sample control set using the first column of the sample control set, with 0 indicating exclusion and 1 indicating retention, to form an updated building height dataset, which serves as the second building height dataset H2.

[0096] S2033. Call the Dispatch operator in the Grasshopper platform to filter the third column N3 in the sample control set using the first column N1 in the sample control set. Use 0 to indicate exclusion and 1 to indicate retention to form the updated building offset distance dataset O2.

[0097] S2034. Call the Offset calculator in the Grasshopper platform to offset the second building baseline dataset L2 by the corresponding distance using the updated building offset distance dataset, forming the third building baseline dataset L3.

[0098] S2035. Call the Extrude operator in the Grasshopper platform to extrude the blocks using the third building baseline dataset L3 and the corresponding second building height dataset H2 to form the second sample dataset.

[0099] The specific implementation of the above steps S202 and S203 is as follows: Figure 3 As shown.

[0100] S204. Calculate the second sample dataset and call the first sample index set to obtain the relevant indicators for building space environment evaluation, and generate the second sample index set.

[0101] The relevant indicators for the evaluation of the building space environment in this embodiment include the building blank ratio, the standard deviation of building height (Hstd), the street height-to-width ratio (Hvsl), the sky visibility (SVF), the plot ratio deviation, and the summer wind shadow area ratio.

[0102] Furthermore, step S204 specifically includes:

[0103] S2041, such as Figure 4 As shown, using the second sample dataset, the building vacancy rate is calculated as follows: Subtract the building footprint and road area from the total building area within the region to obtain the vacancy area. Then, calculate the ratio between the vacancy area and the total building area within the region to obtain the building vacancy rate. The calculation formula is as follows:

[0104]

[0105] Where S0 is the total building area, S i S is the area of ​​the building's base. R This refers to the road area.

[0106] S2042, such as Figure 5 As shown, using the second sample dataset, the standard deviation of building height Hstd is calculated: based on the building height and average building height within the region, the standard deviation of building height is calculated using the following formula:

[0107]

[0108] Among them, H i For building height, This represents the average building height.

[0109] S2043, such as Figure 6 As shown, using the second sample dataset, the street height-to-width ratio Hvsl is calculated: the intersection point of roads within the area is selected as the observation origin, and a cross-sectional plane perpendicular to the road is established with many origins. The ratio of the average height on both sides intersecting the cross-sectional plane to the cross-sectional width is calculated, and the average value is taken as the street height-to-width ratio.

[0110] Furthermore, step S2043 specifically includes:

[0111] S20431. Call the Intercept operator in the Grasshopper platform. At the intersection of roads within the region, call the Shatter operator to divide the internal roads using the intersection point. Use the center point of the divided internal roads as the observation origin and establish a cross-sectional plane perpendicular to the roads using multiple origins.

[0112] S20432. Call the Isovisit calculator in the Grasshopper platform to calculate the ratio of the average height to the width of the cross-section of the buildings on both sides intersecting the cutting plane. Finally, take the average value as the area's height-to-width ratio. The calculation formula is as follows:

[0113]

[0114] in, The height is the higher side of the cross-section. L is the height of the lower side of the cross-section. i This represents the width of the cross-section.

[0115] S2044, such as Figure 7 As shown, using the second sample dataset, the sky visibility SVF is calculated: Based on the observation origin of S20431, using the Ladybug plugin with the second sample dataset as the obstacle input plugin, the ratio of the unobstructed portion of the sky at each point to the total area of ​​the sky hemisphere is calculated, and the average value is taken as the sky visibility. The calculation formula is as follows:

[0116]

[0117] Where, γ i Let be the terrain elevation angle of azimuth angle i, and n be the number of azimuth angles involved in the calculation.

[0118] S2045, such as Figure 8 As shown, using the second sample dataset, the plot ratio deviation is calculated as follows: the number of building floors in the area is obtained by dividing the building height by the set floor height, the total building area is obtained by multiplying the number of building floors by the corresponding ground area, the total building area is summed and divided by the building land area to obtain the building plot ratio, and the absolute value of the difference between the building plot ratio and the building plot ratio of the first sample index set is used as the plot ratio deviation index.

[0119] Furthermore, step S2045 specifically includes:

[0120] S20451. Divide the building height in the second sample dataset by the set floor height to obtain the number of floors in each building within the range. Multiply the number of floors by the corresponding ground area to obtain the total building area. Sum the total building areas and divide them by the building land area to obtain the updated floor area ratio. The calculation formula is as follows:

[0121]

[0122] Among them, H i Where d is the building height, d is the set floor height, and S is the floor height. i S0 represents the building's base area, and S0 represents the total building area.

[0123] S20452. The building floor area ratio from the first sample dataset is retrieved, and the absolute value of the difference between the building floor area ratio before and after the update is used as the floor area ratio deviation index. The calculation formula is as follows:

[0124] PRD = P1 - P0

[0125] Where P1 is the updated building volume ratio, and P0 is the building volume ratio in the first sample dataset, i.e., the original building volume ratio.

[0126] S2046, such as Figure 9 As shown, using the second sample dataset, the proportion of the summer monsoon wind shadow area is calculated: the building space that can be directly blown by the wind is calculated, and the average proportion of the wind shadow area at different height sections is taken as the proportion of the summer monsoon wind shadow area.

[0127] Furthermore, step S2046 specifically includes:

[0128] S20461. Call the Isovisit calculator in the Grasshopper platform, with the default wind direction being southeast, to calculate the building space that can be directly blown by the wind at different heights.

[0129] S20462. Calculate the average percentage of the wind shadow area after summing the areas at different height sections. The calculation formula is as follows:

[0130]

[0131] Among them, S i S0 represents the area directly exposed to wind at various altitudes, and S0 represents the original area of ​​the wind field.

[0132] S2047, combined with building open space ratio, building height standard deviation, street height-to-width ratio, sky visibility, plot ratio deviation, and summer monsoon shadow area ratio, forms the second sample index set.

[0133] S205, the joint sample control set, the second sample dataset, and the second sample index set are optimized using a genetic algorithm to form the final optimized result dataset.

[0134] Furthermore, step S2051 specifically includes:

[0135] S2051. Wallacei X is selected as the multi-objective automatic optimization operator, and non-dominated hierarchical genetic algorithm II (NSGA-II) is used for genetic algorithm optimization. The space of 1500 samples with 30 generations of genetic data, each generation containing 50 gene combinations, is selected for automatic optimization of the indicators.

[0136] In this embodiment, the Wallacei X operator uses the Non-Dominated Hierarchical Genetic Algorithm II (NSGA-II). By manually setting the initial population size N in the NSGA-II algorithm, the algorithm automatically generates an initial population solution P1. Then, through genetic mutation and independent assortment of genes in the initial population, it continues to generate a progeny population Q1 of size N. Q1 and P1 are combined and subjected to Pareto non-dominated sorting to achieve hierarchical division, resulting in a new progeny population Q2 of size N. Before reaching the iteration limit, the above steps are repeated. After reaching the required number of iterations S, the entire population of size N*S is output, from which a suitable solution can be selected, completing the automatic optimization process.

[0137] S2052. Connect the sample control set to the Wallacei X processor. Before reaching the set number of generations, generate different second sample datasets based on the second sample dataset. Calculate the corresponding second sample index set using the updated second sample dataset. Perform genetic evolution with the second sample index set as the optimization target.

[0138] S2053. During the calculation process, record the sample number, sample control set, second sample dataset, and second sample index set to form the final optimization result dataset.

[0139] Specifically, in the Wallacei X calculator, the final result is selected for export, along with the corresponding sample control set, second sample indicator set, and sample control set, forming a final optimization result dataset of 50 samples. Based on the final optimization result dataset, the 50 second sample indicator sets and second sample datasets are arranged into a tree structure. The ExcelWrite calculator (the calculator within the LunchBox plugin on the Grasshopper platform) is then called to export the data to the specified Excel file according to the sample number.

[0140] S206. Visualize the genetic optimization process and the final optimization result dataset.

[0141] Step S205 allows users to easily determine whether the optimization is effective and to select the better solution from the final results.

[0142] like Figures 10a-10c As shown, based on the second sample index set in the genetic optimization process, the average value and standard deviation of each index in each generation are calculated, and a line graph and trend line are plotted with the generation as the horizontal axis.

[0143] Figures 10a-10cThe description specifically shows the distribution of various optimization metrics across different generations, plotted sequentially from the first to the last generation. The standard deviation trend line represents the degree of difference between each generation during the optimization process, while the average trend line represents the changes in the optimization metrics during the optimization process.

[0144] like Figure 11 As shown, based on the final optimization result dataset, the 50 second sample datasets within it are arranged in a 5x10 matrix, and the corresponding second sample index set is displayed on its left.

[0145] like Figures 12a-12c As shown, based on the final optimization result dataset, the 50 sample control sets within it are marked with different colors on the graph to draw a distribution map, forming the final comprehensive optimization recommendations.

[0146] From the perspective of building height optimization trends, the overall trend shows lower buildings in the southeast and higher buildings in the northwest. Due to the relatively small original site area, the average building height is relatively high. This indicates that in urban renewal strategies, when the site area is significantly limited, local high-rise or super high-rise buildings can be constructed to increase the open space ratio within the area, with the overall building layout conforming to the characteristics of lower buildings in the south and higher buildings in the north, and lower buildings in the east and higher buildings in the west.

[0147] In terms of average offset, strip-shaped buildings are more likely to shift outwards than square ones. Simultaneously, due to the need to reduce building height while maintaining floor area ratio, buildings on the southeast side tend to expand outwards more than those on the northwest side. Reflecting on urban renewal strategies, southeast-side buildings should ideally be low-rise, large-area public buildings, while northwest-side buildings should be taller, more imposing residential buildings. High-rise slab-shaped buildings, which are not well-ventilated, require a more optimized form during new town development.

[0148] Overall, this embodiment achieves rapid iteration of the genetic algorithm by parametrically reconstructing land parcels and calculating spatial evaluation indicators in real time for parametric modeling. From a research perspective, this invention has broad applicability. In the process of old city renovation, to achieve urban renewal planning goals, calculations can be performed on different land parcels to obtain recommended demolition buildings, recommended renovation buildings, and buildings that can be retained; while in new city construction, it can be applied to compare and select the optimal building layout scheme.

[0149] It should be noted that although the method operations of the above embodiments are described in a specific order, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the described steps may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0150] Example 2:

[0151] like Figure 13 As shown, this embodiment provides an urban spatial building form optimization system. The system includes an acquisition module 1301, a first construction module 1302, a second construction module 1303, a generation module 1304, an optimization module 1305, and a display module 1306. The specific functions of each module are as follows:

[0152] The acquisition module 1301 is used to acquire geographic information data within the target area, and construct a first sample dataset and a first sample index set after parametric modeling. The geographic information data includes road edge lines, road center lines, building heights, building baselines, and building land boundary lines.

[0153] The first building module 1302 is used to build a sample control set based on the number of buildings in the first sample dataset.

[0154] The second construction module 1303 further processes the first sample dataset using the sample control set to construct the second sample dataset.

[0155] The generation module 1304 is used to calculate the second sample dataset and call the first sample index set to obtain the relevant indicators for building space environment evaluation and generate the second sample index set.

[0156] The optimization module 1305 is used to perform genetic algorithm optimization on the joint sample control set, the second sample dataset, and the second sample index set to form the final optimization result dataset.

[0157] Display module 1306 provides a visual representation of the genetic optimization process and the final optimization result dataset.

[0158] It should be noted that the system provided in this embodiment is only an example of the above-described division of functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0159] It is understood that the terms "first," "second," etc., used in the above system can be used to describe various modules, but these modules are not limited by these terms. These terms are only used to distinguish the first module from another module. For example, without departing from the scope of the invention, the first building module can be referred to as the second building module, and similarly, the second building module can be referred to as the first building module. Both the first and second building modules are building modules, but they are not the same building module.

[0160] Example 3:

[0161] This embodiment provides a computer device, such as... Figure 14 As shown, it includes a processor 1402, a memory, an input device 1403, a display 1404, and a network interface 1405 connected via a system bus 1401. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 1406 and internal memory 1407. The non-volatile storage medium 1406 stores an operating system, computer programs, and a database. The internal memory 1407 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. When the processor 1402 executes the computer programs stored in the memory, it implements the urban spatial building form optimization method of Embodiment 1 described above, as follows:

[0162] Geographic information data within the target area is acquired, and after parametric modeling, a first sample dataset and a first sample index set are constructed. The geographic information data includes road edge lines, road center lines, building heights, building baselines, and building land boundary lines.

[0163] Based on the number of buildings in the first sample dataset, construct a sample control set;

[0164] The first sample dataset is further processed using the sample control set to construct the second sample dataset;

[0165] The second sample dataset is calculated, and the first sample index set is called to obtain the relevant indicators for the evaluation of the building space environment, and then the second sample index set is generated.

[0166] The genetic algorithm is used to optimize the joint sample control set, the second sample dataset, and the second sample index set to form the final optimization result dataset.

[0167] The genetic optimization process and the final optimization result dataset are visualized.

[0168] Example 4:

[0169] This embodiment provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, it implements the urban spatial building form optimization method of Embodiment 1 above, as follows:

[0170] Geographic information data within the target area is acquired, and after parametric modeling, a first sample dataset and a first sample index set are constructed. The geographic information data includes road edge lines, road center lines, building heights, building baselines, and building land boundary lines.

[0171] Based on the number of buildings in the first sample dataset, construct a sample control set;

[0172] The first sample dataset is further processed using the sample control set to construct the second sample dataset;

[0173] The second sample dataset is calculated, and the first sample index set is called to obtain the relevant indicators for the evaluation of the building space environment, and then the second sample index set is generated.

[0174] The genetic algorithm is used to optimize the joint sample control set, the second sample dataset, and the second sample index set to form the final optimization result dataset.

[0175] The genetic optimization process and the final optimization result dataset are visualized.

[0176] It should be noted that the computer-readable storage medium in this embodiment can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0177] In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this embodiment, the computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0178] The computer-readable storage medium described above can be used to write computer programs for executing this embodiment in one or more programming languages ​​or combinations thereof. These programming languages ​​include object-oriented programming languages—such as Java, Python, and C++—and conventional procedural programming languages—such as C or similar programming languages. The program can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0179] In summary, this invention provides a refined regional architectural space renewal optimization strategy for different plots during urban renewal, allowing for targeted adjustments to green space layout and architectural spatial morphology indicators within the renewal area. During the optimization phase, thanks to genetic algorithms, this invention can foster a superior living environment while maintaining similar development intensities. Furthermore, the real-time calculation of morphological indicators significantly reduces computation time compared to traditional environmental simulation, effectively improving planning efficiency. In the scheme generation phase, this invention offers the possibility of multiple scheme optimizations, further considering the complexity and feasibility of the urban renewal development environment. For urban managers, the architectural space renewal optimization suggestions provided by this invention offer practical and effective advice, thereby providing favorable policy guidance for urban renewal.

[0180] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope disclosed by the present invention, based on the technical solution and inventive concept of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the form of urban spatial buildings, characterized in that, The method includes: Geographic information data within the target area is acquired, and after parametric modeling, a first sample dataset and a first sample index set are constructed. The geographic information data includes road edge lines, road center lines, building heights, building baselines, and building land boundary lines. Based on the number of buildings in the first sample dataset, construct a sample control set; The first sample dataset is further processed using the sample control set to construct the second sample dataset; The second sample dataset is calculated, and the first sample index set is called to obtain the relevant indicators for the evaluation of the building space environment, and then the second sample index set is generated. The genetic algorithm is used to optimize the joint sample control set, the second sample dataset, and the second sample index set to form the final optimization result dataset. Visualize the genetic optimization process and the final optimization result dataset; The construction of the sample control set based on the number of buildings in the first sample dataset specifically includes: constructing a first sequence with a length equal to the number of buildings and a range of 0 or 1 based on the number of buildings in the first sample dataset; constructing a second sequence with a length equal to the number of buildings and a range equal to the input height range based on the number of buildings in the first sample dataset; constructing a third sequence with a length equal to the number of buildings and a range equal to the input offset range based on the number of buildings in the first sample dataset; and combining the first, second, and third sequences to form the sample control set. The step of further processing the first sample dataset with the sample control set to construct the second sample dataset specifically includes: filtering the buildings in the first sample dataset using the first column of the sample control set, with 0 indicating exclusion and 1 indicating retention, to form an updated building baseline dataset, which serves as the second building baseline dataset; filtering the second column of the sample control set using the first column of the sample control set, with 0 indicating exclusion and 1 indicating retention, to form an updated building height dataset, which serves as the second building height dataset; filtering the third column of the sample control set using the first column of the sample control set, with 0 indicating exclusion and 1 indicating retention, to form an updated building offset distance dataset; offsetting the second building baseline dataset by a corresponding distance using the updated building offset distance dataset, to form a third building baseline dataset; and extruding the blocks using the third building baseline dataset and the corresponding second building height dataset to form the second sample dataset.

2. The method for optimizing the form of urban spatial buildings according to claim 1, characterized in that, The process of acquiring geographic information data within the target area, performing parametric modeling, and constructing a first sample dataset and a first sample index set specifically includes: Based on urban basic geographic information data, the road edge lines, road center lines, building heights, building baselines, and building land boundary lines within the corresponding area are exported and labeled in different layers. The exported building height and building baseline are used as the first building height and the first building baseline, respectively. Based on the first building height information, the first building baseline is extruded to obtain the building parametric model. The first sample dataset is constructed using this parametric model. Based on the first building baseline and the first building height, the total building area within the region is calculated according to the input floor height, and the building volume ratio within the region is calculated. The first sample index set is constructed using the building volume ratio.

3. The method for optimizing the form of urban spatial buildings according to claim 1, characterized in that, The calculation of the second sample dataset to obtain relevant indicators for the evaluation of the building space environment, combined with the first sample indicator set, to generate the second sample indicator set, specifically includes: Subtract the building footprint and road area from the total building area in the region to obtain the open space area. Calculate the ratio between the open space area and the total building area in the region to obtain the building open space ratio. Calculate the standard deviation of building height based on the building height and average building height within the region; The intersection of roads within the area is selected as the observation origin. A cross-sectional plane perpendicular to the road is established using multiple origins. The ratio of the average height to the width of the cross-section on both sides intersecting the cross-sectional plane is calculated, and the average value is taken as the street height-to-width ratio. Based on the observation origin, calculate the ratio of the unobstructed part of the sky at each point to the total area of ​​the sky hemisphere, and take the average value as the sky visibility. The number of floors in each building within the area is obtained by dividing the building height by the set floor height. The total building area is obtained by multiplying the number of floors by the corresponding ground area. The total building area is summed and divided by the building land area to obtain the building floor area ratio. The absolute value of the difference between the building floor area ratio and the building floor area ratio of the first sample index set is used as the floor area ratio deviation index. Calculate the building space that can be directly blown by the wind, and take the average proportion of the wind shadow area at different height sections as the area ratio of the wind shadow area in summer. The second set of indicators is formed by combining the building open space ratio, building height standard deviation, street height-to-width ratio, sky visibility, plot ratio deviation, and summer monsoon shadow area area ratio.

4. The method for optimizing the form of urban spatial buildings according to claim 1, characterized in that, The joint sample control set, the second sample dataset, and the second sample index set are optimized using a genetic algorithm to form the final optimization result dataset, which specifically includes: The sample control set is connected to the Wallacei X processor. Before reaching the set number of generations, different second sample datasets are generated based on the second sample dataset. The corresponding second sample index set is calculated using the updated second sample dataset. Genetic evolution is carried out with the second sample index set as the optimization target. During the calculation process, the sample number, sample control set, second sample dataset, and second sample index set are recorded to form the final optimization result dataset.

5. The method for optimizing the form of urban spatial buildings according to claim 4, characterized in that, The genetic sequence consists of 30 generations, with each generation containing 50 gene combinations, for a total of 1500 samples.

6. A system for optimizing the form of urban buildings, characterized in that, The system includes: The acquisition module is used to acquire geographic information data within the target area, and after parametric modeling, construct a first sample dataset and a first sample index set. The geographic information data includes road edge lines, road center lines, building heights, building baselines, and building land boundary lines. The first building module is used to construct a sample control set based on the number of buildings in the first sample dataset. The second construction module further processes the first sample dataset using the sample control set to construct the second sample dataset; The generation module is used to calculate the second sample dataset and call the first sample index set to obtain relevant indicators for building space environment evaluation and generate the second sample index set. The optimization module is used to perform genetic algorithm optimization on the joint sample control set, the second sample dataset, and the second sample index set to form the final optimization result dataset. The display module provides a visual representation of the genetic optimization process and the final optimization result dataset. The construction of the sample control set based on the number of buildings in the first sample dataset specifically includes: constructing a first sequence with a length equal to the number of buildings and a range of 0 or 1 based on the number of buildings in the first sample dataset; constructing a second sequence with a length equal to the number of buildings and a range equal to the input height range based on the number of buildings in the first sample dataset; constructing a third sequence with a length equal to the number of buildings and a range equal to the input offset range based on the number of buildings in the first sample dataset; and combining the first, second, and third sequences to form the sample control set. The step of further processing the first sample dataset with the sample control set to construct the second sample dataset specifically includes: filtering the buildings in the first sample dataset using the first column of the sample control set, with 0 indicating exclusion and 1 indicating retention, to form an updated building baseline dataset, which serves as the second building baseline dataset; filtering the second column of the sample control set using the first column of the sample control set, with 0 indicating exclusion and 1 indicating retention, to form an updated building height dataset, which serves as the second building height dataset; filtering the third column of the sample control set using the first column of the sample control set, with 0 indicating exclusion and 1 indicating retention, to form an updated building offset distance dataset; offsetting the second building baseline dataset by a corresponding distance using the updated building offset distance dataset, to form a third building baseline dataset; and extruding the blocks using the third building baseline dataset and the corresponding second building height dataset to form the second sample dataset.

7. A computer device comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the urban spatial building form optimization method according to any one of claims 1-5.

8. A storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the urban spatial building form optimization method according to any one of claims 1-5.

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