A planning height control evaluation method and system involving historical and cultural heritage resources in urban renewal projects

Abstract

The present invention discloses a method for evaluating the planning height control of historical and cultural heritage resources in an urban renewal project, comprising obtaining project data and map data of the urban renewal project, wherein the map data includes three-dimensional building model data of the urban renewal project; dividing the map data into grids using a software tool to generate a plurality of target analysis units; processing the unit data of the target analysis units to obtain index data of evaluation indicators of each target analysis unit; obtaining a planning height control evaluation index system, wherein the planning height control evaluation index system includes a plurality of evaluation indicators and weight values ​​of the evaluation indicators; calculating a comprehensive score of each target analysis unit based on the index data of each target analysis unit and the weight values ​​of each evaluation indicator; and classifying and evaluating each target analysis unit based on K-means classification and the comprehensive score of each target analysis unit.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a planning height control evaluation method and system for historical and cultural heritage resources in an urban renewal project. Background Art

[0002] Building height plays a crucial role in urban form, determining both the overall appearance of a city and the spatial scale within it. With the rapid development of modern cities and the continuous increase in building height, urban development and construction are impacting and damaging a city's historical and cultural heritage, a common concern for its preservation.

[0003] Urban height control has always been a key planning element in urban renewal projects. However, contemporary architecture within these projects has had a significant impact on the city's historical and cultural heritage resources, disrupting distinctive historical landscapes, important sightlines, and the spatial organization and layout of these resources.

[0004] Although current urban renewal plans include height controls for areas containing historical and cultural heritage resources, existing technologies lack the necessary assessment techniques for this type of control, failing to provide planners with adequate technical support and reference. Therefore, there is a need for assessment techniques for height controls in urban renewal projects. Summary of the Invention

[0005] In order to overcome the above technical defects, the present invention provides a planning height control evaluation method and system for historical and cultural heritage resources in urban renewal projects.

[0006] In order to solve the above problems, the present invention is implemented according to the following technical solutions:

[0007] In a first aspect, the present invention provides a method for evaluating the planning height control of historical and cultural heritage resources in an urban renewal project, comprising the following steps:

[0008] Acquiring project data and map data of an urban renewal project, wherein the map data includes three-dimensional building model data of the urban renewal project;

[0009] Use software tools to divide map data into grids to generate multiple target analysis units;

[0010] Assigning project data to a number of target analysis units to obtain unit data of the target analysis units;

[0011] Acquire a planning height control evaluation index system, wherein the planning height control evaluation index system includes a plurality of evaluation indicators and weight values ​​of the evaluation indicators;

[0012] Processing the unit data of the target analysis unit to obtain indicator data of the evaluation indicator of each target analysis unit;

[0013] Calculate the comprehensive score of each target analysis unit based on the indicator data of each target analysis unit and the weight value of each evaluation indicator;

[0014] Based on K-means classification and the comprehensive score of each target analysis unit, each target analysis unit is classified and evaluated.

[0015] In conjunction with the first aspect, the present invention further provides a first preferred implementation of the first aspect, specifically, using a software tool to divide the map data into grids to generate a plurality of target analysis units, specifically including:

[0016] Obtain AOI data of historical and cultural heritage architectural resources;

[0017] Inserting the AOI data into the map data to generate a historical building resource area in the map data;

[0018] Use software tools to divide the map data into 50M*50M grids to obtain multiple grid units;

[0019] Using the historical building resource area, the part of each grid unit that overlaps with the historical building resource area is clipped to obtain the target analysis unit.

[0020] In combination with the first aspect, the present invention further provides a second preferred implementation of the first aspect, specifically, assigning project data to a plurality of target analysis units, specifically including:

[0021] Process the project data in a unified coordinate system to generate surface data;

[0022] Using the surface data, the surface attribute data is assigned to the target analysis unit to obtain the unit data of the target analysis unit.

[0023] In combination with the first aspect, the present invention further provides a third preferred implementation of the first aspect, specifically, based on the indicator data of each target analysis unit and the weight value of each evaluation indicator, calculating the comprehensive score of each target analysis unit, specifically including:

[0024] Obtain indicator data of the target analysis unit;

[0025] Standardizing the indicator data to obtain an indicator value for each evaluation indicator;

[0026] Based on the index value and weight value of the evaluation index, the comprehensive score of each target analysis unit is calculated.

[0027] In combination with the first aspect, the present invention further provides a fourth preferred implementation of the first aspect. Specifically, the calculation formula for the comprehensive score of the target analysis unit is:

[0028]

[0029] In the formula, S i is the comprehensive score of the i-th target analysis unit; P ij is the index value of the jth target analysis unit in the i-th target analysis unit; W j is the weight value of the jth evaluation indicator, and n represents the total number of indicators in the planning height control evaluation indicator system.

[0030] In combination with the first aspect, the present invention also provides a fifth preferred implementation scheme of the first aspect. Specifically, the evaluation indicators of the planning height control evaluation index system include the highest building height, average building height, current building height, distance from the center, building coverage rate, building development intensity, street view openness rate and sky openness.

[0031] In a second aspect, the present invention further provides a planning height control and assessment system for historical and cultural heritage resources in urban renewal projects, comprising:

[0032] an acquisition module, configured to acquire project data and map data of an urban renewal project, wherein the map data includes three-dimensional building model data of the urban renewal project;

[0033] A generation module, which is used to divide the map data into grids using software tools to generate multiple target analysis units;

[0034] An assignment module, which is used to assign project data to a number of target analysis units to obtain unit data of the target analysis units;

[0035] An indicator system module is used to obtain a planning height control evaluation indicator system, wherein the planning height control evaluation indicator system includes a plurality of evaluation indicators and weight values ​​of the evaluation indicators;

[0036] a processing module, configured to process the unit data of the target analysis unit to obtain indicator data of the evaluation indicator of each target analysis unit;

[0037] A calculation module is used to calculate the comprehensive score of each target analysis unit based on the indicator data of each target analysis unit and the weight value of each evaluation indicator;

[0038] The classification and evaluation module is used to classify and evaluate each target analysis unit based on K-means classification and the comprehensive score of each target analysis unit.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention provides a planning height control evaluation method for historical and cultural heritage resources in an urban renewal project, comprising the following steps: obtaining project data and map data of the urban renewal project, wherein the map data includes three-dimensional building model data of the urban renewal project; dividing the map data into grids using a software tool to generate a plurality of target analysis units; assigning values ​​to a plurality of target analysis units using the project data to obtain unit data of the target analysis units; processing the unit data of the target analysis units to obtain index data of evaluation indicators of each target analysis unit; obtaining a planning height control evaluation index system, wherein the planning height control evaluation index system includes a plurality of evaluation indicators and weight values ​​of the evaluation indicators; calculating a comprehensive score of each target analysis unit based on the index data of each target analysis unit and the weight values ​​of each evaluation indicator; and classifying and evaluating each target analysis unit based on K-means classification and the comprehensive score of each target analysis unit.

[0041] The present invention can effectively and objectively evaluate the planning height control of historical and cultural heritage resources involved in urban renewal projects, guide the planning height control in urban renewal projects, ensure that historical and cultural heritage are properly protected, and also meet the needs of urban development.

[0042] The technology provided by this invention can provide the government with decision-making support and analysis tools for urban renewal planning, realize the effective protection of historical and cultural heritage resources in urban renewal projects, and achieve a balance between protection and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein:

[0044] Figure 1 It is a flow chart of a method for planning height control and evaluation involving historical and cultural heritage resources in an urban renewal project of the present invention;

[0045] Figure 2 Schematic diagram of the network structure of the encoding stage of the present invention;

[0046] Figure 3 Schematic diagram of the network structure of the decoding stage of the present invention;

[0047] Figure 4 It is a schematic diagram of the network structure in the fusion stage of the present invention. DETAILED DESCRIPTION

[0048] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and are not to be construed as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of protection of the present invention. Many changes may be made to the present invention without departing from the spirit and scope of the present invention.

[0049] Regarding historical and cultural heritage resources, in the present invention, historical and cultural heritage resources specifically refer to historical buildings, historical building complexes, historical building sites, etc. in the city. These historical and cultural heritage resources are located in the city.

[0050] Urban height control has always been a key planning element in urban renewal projects. However, contemporary architecture within these projects can impact the perception of a city's historical and cultural heritage resources, disrupting distinctive historical landscapes, sightlines to key historical landmarks, and the spatial organization and layout of these resources. While current urban renewal plans implement height control measures for areas containing historical and cultural heritage resources, existing technologies lack the necessary expertise to assess planned height control, failing to provide planners with sound technical support and reference. Therefore, a technology for assessing planned height control in urban renewal projects remains to be developed.

[0051] For this purpose, refer to Figure 1 An embodiment of the present invention provides a flow chart of a method for evaluating the planning height control of historical and cultural heritage resources in urban renewal projects. This method can effectively and objectively evaluate the planning height control of historical and cultural heritage resources in urban renewal projects, guiding planning height control in urban renewal projects and ensuring the proper protection of historical and cultural heritage while also meeting the needs of urban development.

[0052] The method for evaluating the planning height control of historical and cultural heritage resources in urban renewal projects of the present invention can be implemented by a system for evaluating the planning height control of historical and cultural heritage resources in urban renewal projects. The system can be implemented in the form of hardware and / or software, and the system can be configured in a computer. Figure 1 As shown, the method includes:

[0053] S100: Acquire project data and map data of an urban renewal project, wherein the map data includes three-dimensional building model data of the urban renewal project;

[0054] S200: Using software tools to divide the map data into grids to generate multiple target analysis units;

[0055] S300: Assigning project data to a number of target analysis units to obtain unit data of the target analysis units;

[0056] S400: Acquire a planning height control evaluation index system, wherein the planning height control evaluation index system includes a plurality of evaluation indicators and weight values ​​of the evaluation indicators;

[0057] S500: Processing the unit data of the target analysis unit to obtain indicator data of the evaluation indicator of each target analysis unit;

[0058] S600: Calculating a comprehensive score for each target analysis unit based on the indicator data of each target analysis unit and the weight value of each evaluation indicator;

[0059] S700: Classify and evaluate each target analysis unit based on K-means classification and the comprehensive score of each target analysis unit.

[0060] The technology provided by this invention can provide decision support and analysis tools for the government in urban renewal planning, effectively protect historical and cultural heritage resources in urban renewal projects, and achieve a balance between protection and development. Specifically, this embodiment provides a detailed description of each step.

[0061] S100: Acquire project data and map data of an urban renewal project, wherein the map data includes three-dimensional building model data of the urban renewal project.

[0062] In specific implementations, the three-dimensional building model data for urban renewal projects is obtained by the urban planning department or the urban renewal project, or can be generated based on relevant design drawings of the urban renewal project. Map data comes from the Geographic Information System (GIS), which is the basis for subsequent analysis. The map data is the map data of the city where the urban renewal project involving historical and cultural heritage resources is located. In one example, the map data can include existing building information, such as building height, building site area, building name, etc.

[0063] Specifically, it can be the administrative area where the urban renewal project of historical and cultural heritage resources is located, or the street area, or the area within a 2-kilometer radius centered on historical and cultural heritage resources.

[0064] Specifically, the 3D building model data can be derived from BIM models, such as models generated by software such as Autodesk Revit and ArchiCAD. The 3D building model data is input into ArcGIS and generated in the map data.

[0065] Specifically, project data may include basic project information (project name, project location, etc.), land use information (including land ownership, land use right type, land use nature, land area, volume ratio, building density, green space ratio, etc.), and building information (including building conditions, building area, building height, building structure, building function, etc.).

[0066] S200: Using software tools to divide the map data into grids to generate multiple target analysis units.

[0067] In practice, ArcGIS software is used to divide map data into multiple grids, or analysis units. Each grid represents an independent analysis area, providing a spatial foundation for subsequent project data assignment. The grid size (e.g., 200m x 200m per unit) is determined based on the project's scale and detailed requirements. Specifically, ArcGIS generates the target analysis units by converting the map data into raster data.

[0068] In a specific implementation, S200: using a software tool to divide the map data into grids to generate multiple target analysis units, specifically includes the following steps:

[0069] S210: Acquire AOI data of historical and cultural heritage architectural resources.

[0070] In the present invention, AOI (Area of ​​Interest) data relates to land use types, functional zoning, etc. in a specific area. AOI (Area of ​​Interest) is the area of ​​interest in an electronic map.

[0071] In a specific implementation, the AOI data of historical and cultural heritage resources are usually provided by cultural heritage protection departments, urban planning departments or historical building databases.

[0072] S220: Inserting the AOI data into the map data to generate a historical building resource area in the map data.

[0073] In one embodiment, the AOI data is loaded into ArcGIS software and overlaid on existing map data to generate identifiable and distinguishable historical building resource areas.

[0074] S230: Using a software tool, the map data is divided into 50M*50M grids to obtain a plurality of grid units.

[0075] In the specific implementation, the grid generation tool of ArcGIS software was used to set the grid size to 300M*300M according to the project requirements and the size of the study area. The software will automatically generate evenly distributed grid cells on the map data.

[0076] S240: Using the historical building resource area, clipping the portion of each grid unit that overlaps with the historical building resource area to obtain a target analysis unit.

[0077] In one embodiment, the ArcGIS software clipping tool is used to clip the overlapping grid cells using the historical building resource area as a clipping mask. The clipping operation deletes the portion of the grid cell that overlaps with the historical building resource area, thereby eliminating the historical building resource area of ​​the grid cell.

[0078] In practice, ArcGIS software was used to load different data layers into the GIS project. Data overlay: AOI data was combined with map data through overlay analysis. Grid generation: ArcGIS tools were used to create a regular grid. Spatial clipping: Spatial analysis tools were used to clip the grid, using the historic building resource area as a clipping mask to exclude the historic building resource area from the analysis area.

[0079] In another embodiment, the present invention also provides a specific method for generating AOI data of historical and cultural heritage resources. The AOI data can be generated by identifying remote sensing data of the administrative area where the urban renewal project is located. In the remote sensing image, the spectral characteristics are used to identify specific building classifications as AOIs. Specifically, a high-resolution satellite land cover user graphical interface (GID) dataset of the administrative area where the urban renewal project is located is obtained, and a building classification network model (MSNet) is used for encoding, decoding, and image fusion to obtain the final classification result, and a building type distribution map is output. The building type distribution map includes different AOIs such as roads, different buildings (for example, hospitals, schools, historical buildings, and residences).

[0080] Specifically, six channels are selected and calculated as the input data of the building classification network model (MSNet), namely 3 visible light bands of RGB, near infrared (NIR), normalized vegetation index (NDVI) and normalized water index (NDWI), and the classification results are output.

[0081] Among them, the near-infrared band NIR obtains effective information when the visible light band is affected by different lighting conditions and causes information loss; NDVI can detect vegetation coverage and play a role in eliminating some radiation errors in building classification.

[0082] The calculation formula of vegetation normalization index is: in, is the normalized vegetation index, Near-infrared band, It is the red band.

[0083] Among them, the NDWI index can highlight the water body information in the image and can play a role in distinguishing water bodies in building classification.

[0084] The calculation formula of NDWI index is: in, is the normalized water index, is the near-infrared band, It is the green band.

[0085] The MSNet model for building classification consists of three stages: encoding, decoding, and fusion. The encoding stage is used to capture deep features of the image; the decoding stage restores the details and spatial dimensions of the image; and the fusion stage combines different features.

[0086] Specifically, the ResNet-50 network is used as the backbone network to encode image information. ResNet-50 is a residual network widely used in image processing. Experiments have shown that using a 7×7 convolution kernel with 64 kernels, a stride of 2, and a padding of 3 in the convolutional layer yields better results and higher extraction efficiency. Batch normalization (BN) accelerates convergence, while the Relu activation function helps the network learn nonlinear relationships. Max pooling layers also reduce the dimensionality of feature maps.

[0087] like Figure 2 、 Figure 3 and Figure 4 As shown in Figure 1, the convolutional layers Conv2_x, Conv3_x, Conv4_x, and Conv5_x are the backbone of ResNet-50. Each layer retains the encoding result, resulting in e1, e2, e3, and e4.

[0088] Specifically, to facilitate data decoding, a decoding module is defined with input channels C1 and output channels C2. Batch normalization, ReLU activation, and upsampling are performed sequentially to restore image resolution. The decoding stage consists of five layers. In the first layer, e5 is passed through the decoding module to generate a new feature map d5. In the next four layers, the decoded feature map is concatenated with the feature map retained in the encoding stage along dimension 1, resulting in decoding results d1, d2, d3, and d4. This design and optimization of the encoding stage minimizes information loss.

[0089] Specifically, a list of feature maps d1, d2, d3, and d4 is input, and convolution operations are performed on each to obtain f2, f3, and f4. The feature maps are upsampled using bilinear interpolation to maintain the same number of channels. D1 and f2 are added to obtain P1, f2 and f3 are added to obtain P2, f3 and f4 are added to obtain P3, and f4 is used alone as P4. A total of four feature maps are input into the image fusion module to obtain the final result. Multi-layer feature fusion through the fusion layer can preserve and integrate multi-level semantic information, improving the quality and effectiveness of image fusion.

[0090] In a preferred implementation, seven channels are selected and calculated as input data for the building classification network model (MSNet), namely, three visible light bands of RGB, near infrared (NIR), normalized vegetation index (NDVI), normalized water index (NDWI) and POI kernel density map, and the classification results are output.

[0091] POI (Point of Interest) kernel density maps can reflect the distribution of points of interest within a specific area, which is valuable for the division of urban functional areas and the identification of building types. The addition of POI kernel density maps can improve the refined identification of buildings: POI data can assist in more refined identification of building types.

[0092] Combining remote sensing imagery and POI data can improve the accuracy and reliability of building classification. Using the MSNet building classification network model to extract building information, coupled with kernel density analysis of POI data, allows for a more detailed analysis of urban information and achieves high-precision classification of building functions.

[0093] Specifically, the POI kernel density map is obtained by calculating the POI kernel density of POI data within the administrative area. Specifically, it includes data classification: reclassification according to the functional category of POI, such as hospitals, schools, historical buildings, residential buildings, industrial buildings, etc. Data cleaning: including cleaning duplicate data, deleting invalid data, pre-classifying data, etc., to improve the validity of the data. Kernel density calculation: Use kernel density analysis tools, such as the "Kernel Density" tool in ArcGIS. Set the search radius (bandwidth) and output grid cell size. Select a suitable kernel function, commonly used is the Gaussian kernel function. Perform calculations to generate a kernel density map.

[0094] S300: Assign project data to a number of target analysis units to obtain unit data of the target analysis units.

[0095] In a specific implementation, step S300 assigns project data to a plurality of target analysis units, specifically including the following steps:

[0096] S310: Process the project data in a unified coordinate system to generate surface data.

[0097] In a specific implementation, the unified coordinate system processing can be parameterized and adjusted to the coordinates of the map data of the urban renewal project by using ArcGIS's SpatialAdajustment, with the accuracy meeting the research needs.

[0098] S320: Using the planar data, assign planar attribute data to the target analysis unit to obtain unit data of the target analysis unit.

[0099] In practice, the identify tool is used to overlay the planar data onto the target analysis unit, thus assigning planar attribute data. Project data contains a wide range of data, which needs to be converted into planar data and then associated with the target analysis unit, becoming its unit data.

[0100] S400: Acquire a planning height control evaluation index system, wherein the planning height control evaluation index system includes a plurality of evaluation indicators and weight values ​​of the evaluation indicators.

[0101] In one specific implementation, the evaluation indicators of the planned height control evaluation index system include maximum building height, average building height, current building height, distance from the center, building coverage ratio, building development intensity, streetscape openness ratio, and sky openness. The planned height control evaluation index system is shown in Table 1.

[0102] Table 1 Planning height control evaluation index system

[0103]

[0104] Specifically, the center (centroid) of the target analysis unit can be obtained by generating the AOI. For example, the AOI data is input into a geographic information system (GIS), the AOI boundary is obtained through the GIS, and the centroid of the AOI is directly calculated by the GIS.

[0105] In a specific implementation, the street view openness rate refers to the visual openness rate of the original built environment of the target analysis unit, reflecting the building height of the original built environment in the target analysis unit. The larger the value, the higher the existing building height of the target analysis unit. The street view image data of the present invention is obtained from a third-party map service provider, such as Baidu Street View Map Open Platform. In a specific implementation, the area proportion of various environmental elements in each sample point of the target analysis unit can be obtained through operations such as sampling point generation, street view image capture, image semantic recognition, and data space matching. Among them, the calculation formula of the street view openness rate is: VGR is the streetscape openness ratio; S i_sky is the pixel area of ​​the sky in the i-th street view image of the target analysis unit; S i_SV is the total pixel area of ​​the i-th street view image of the target analysis unit; m is the number of street view images in the site area.

[0106] In a specific implementation, sky openness indicates the range of sky visible to pedestrians within the target analysis unit, reflects the height of buildings of various types of land within the grid area of ​​the target analysis unit, and also reflects the travel environment friendliness of the target analysis unit. The calculation formula of sky openness is: Ω direction=sin 2 γ(α / 360°); where SVF i is the sky openness within the walking attraction range of station i; α is the azimuth step length; Ω direction The shielding degree of each direction for the point; γ is the maximum building height angle formed by the building in each direction, which is used to calculate the shielding degree.

[0107] In practice, the weights of evaluation indicators can be obtained using the Analytic Hierarchy Process (AHP). AHP is a commonly used decision analysis method that determines the weights of evaluation indicators by constructing a judgment matrix and combining it with expert scoring. This method is suitable for solving complex decision-making problems involving multiple objectives and criteria, and can transform the decision-maker's subjective judgment into quantifiable values.

[0108] In a specific implementation, the evaluation indicator needs to calculate the CR value and pass the test to meet the analysis requirements, as follows:

[0109] in, When CI is less than 0.1, the judgment matrix is ​​acceptable for consistency, otherwise it needs to be revised. When n ≥ 3, in order to eliminate the influence of CI on the order, it is necessary to introduce the average random consistency index RI of the judgment matrix, taking CR = CI / RI, and perform consistency test on the constructed judgment matrix.

[0110] It is generally believed that when CR is less than 0.1, the judgment matrix passes the consistency test, otherwise it does not have satisfactory consistency. Using the weighted combination of hierarchical single sorting, the weight of the previous layer is calculated to obtain the weight value of each single indicator in the evaluation system.

[0111] In calculating the weight values ​​of the evaluation indicators, the AHP method is implemented through the following steps:

[0112] 1. Establish a hierarchical structure model: decompose the decision problem into the target layer, criterion layer, indicator layer, and possible sub-criteria layer to form a multi-level hierarchical structure model.

[0113] 2. Construct a pairwise comparison matrix: Compare each factor at the criterion level pairwise, construct a pairwise comparison matrix, and fill in the matrix elements. In this process, a 1-9 scale is usually used to quantify the relative importance of the factors.

[0114] 3. Calculate the weight vector and perform consistency check: Calculate the maximum eigenvalue and corresponding eigenvector of the pairwise comparison matrix and perform consistency check. If the consistency ratio CR is less than 0.1, the matrix consistency is considered acceptable. Otherwise, the judgment matrix needs to be readjusted.

[0115] 4. Hierarchical total ranking and consistency check: Calculate the combined weight vector of the lowest level for the target and perform a combination consistency check. If the check passes, a decision can be made based on it.

[0116] 5. Result analysis: Based on the calculated weight values, sort and analyze the evaluation indicators to determine their relative importance.

[0117] The advantage of the AHP method is that it can combine the decision maker's subjective judgment with objective data, making it suitable for decision-making problems where data information is incomplete or difficult to quantify. In addition, the calculation process of the AHP method is relatively simple, easy to understand and operate, allowing decision makers to quickly analyze and make decisions on complex problems.

[0118] S500: Processing the unit data of the target analysis unit to obtain indicator data of the evaluation indicator of each target analysis unit.

[0119] In a specific implementation, the unit data is composed of map data, project data, 3D building model data, etc. The unit data of the target analysis unit is processed, including clustering, statistics, recognition, calculation, etc., to obtain the index data of the evaluation index of each target analysis unit.

[0120] For example, using statistical tools in ArcGIS software, data within each grid cell can be aggregated to calculate average building height, distance from the center, building density, building coverage, etc. The statistical results will be used for subsequent evaluations.

[0121] S600: Calculate the comprehensive score of each target analysis unit based on the indicator data of each target analysis unit and the weight value of each evaluation indicator.

[0122] In a specific implementation, based on the indicator data of each target analysis unit and the weight value of each evaluation indicator, the comprehensive score of each target analysis unit is calculated, which specifically includes:

[0123] S610: Obtain indicator data of the target analysis unit.

[0124] S620: Standardize the indicator data to obtain an indicator value for each evaluation indicator.

[0125] In a specific implementation, the normalization method includes minimum-maximum normalization, Z-score normalization, etc. In the present invention, minimum-maximum normalization is used to convert the indicator data into a value between 0 and 1, and the formula is:

[0126] Among them, x is the indicator data of the evaluation indicator, xmin is the minimum value of the evaluation indicator, and xmax is the maximum value of the evaluation indicator.

[0127] S670: Calculate the comprehensive score of each target analysis unit based on the indicator value and weight value of the evaluation indicator.

[0128] In a specific implementation, the calculation formula for the comprehensive score of the target analysis unit is:

[0129]

[0130] In the formula, S i is the comprehensive score of the i-th target analysis unit; P ij is the index value of the jth target analysis unit in the i-th target analysis unit; W j is the weight value of the jth evaluation indicator, and n represents the total number of indicators in the planning height control evaluation indicator system.

[0131] S700: Classify and evaluate each target analysis unit based on K-means classification and the comprehensive score of each target analysis unit.

[0132] The grid cells are classified using the K-means clustering algorithm. The number of clusters (k value) is determined, and then the algorithm groups target analysis cells with similar composite scores into one category. The clustering results are used for evaluation and planning decisions.

[0133] In one example, the steps include:

[0134] S710: Obtain a comprehensive score for each target analysis unit.

[0135] S720: Determine the number of clusters (k value): Select an appropriate number of clusters k, which is usually determined based on domain knowledge and actual needs. For example, if the planned height control is divided into extremely unreasonable planned height control, unreasonable planned height control, reasonable planned height control, and relatively good planned height control, then the k value should be 4.

[0136] S730: Execute K-means Algorithm: Use the K-means clustering algorithm to classify the target analysis units. Each analysis unit will be assigned to the nearest cluster center based on its comprehensive score, forming k clusters. In GIS software, you can use the Cluster Analysis tool in the Spatial Analysis Toolbox to perform this step.

[0137] S740: Evaluate clustering results: Check the clustering results to ensure that the analysis units within each cluster are geographically continuous and that there are clear boundaries between clusters. Use the visualization tools of GIS software to assist in this evaluation.

[0138] Develop height control strategies: Based on the clustering results, we can evaluate and develop a corresponding building height control strategy for each cluster. For example, for clusters with higher overall scores, we can develop stricter height restrictions to protect historical and cultural heritage or landscape corridors.

[0139] The present invention also provides a system for evaluating the planning height control of historical and cultural heritage resources in urban renewal projects, which is used to implement the above-mentioned method for evaluating the planning height control of historical and cultural heritage resources in urban renewal projects. Specifically, the system includes:

[0140] an acquisition module, configured to acquire project data and map data of an urban renewal project, wherein the map data includes three-dimensional building model data of the urban renewal project;

[0141] A generation module, which is used to divide the map data into grids using software tools to generate multiple target analysis units;

[0142] An assignment module, which is used to assign project data to a number of target analysis units to obtain unit data of the target analysis units;

[0143] An indicator system module is used to obtain a planning height control evaluation indicator system, wherein the planning height control evaluation indicator system includes a plurality of evaluation indicators and weight values ​​of the evaluation indicators;

[0144] a processing module, configured to process the unit data of the target analysis unit to obtain indicator data of the evaluation indicator of each target analysis unit;

[0145] A calculation module is used to calculate the comprehensive score of each target analysis unit based on the indicator data of each target analysis unit and the weight value of each evaluation indicator;

[0146] The classification and evaluation module is used to classify and evaluate each target analysis unit based on K-means classification and the comprehensive score of each target analysis unit.

[0147] For other structures of the planning height control evaluation method and system involving historical and cultural heritage resources in an urban renewal project described in this embodiment, refer to the prior art.

[0148] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Therefore, any modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A planning height control and assessment method for historical and cultural heritage resources in urban renewal projects, characterized by: The following steps are involved: Acquiring project data and map data of an urban renewal project, wherein the map data includes three-dimensional building model data of the urban renewal project; Use software tools to divide the map data into grids to generate multiple target analysis units, including: Obtain AOI data of historical and cultural heritage architectural resources; Inserting the AOI data into the map data to generate a historical building resource area in the map data; Use software tools to divide the map data into 50M*50M grids to obtain multiple grid units; Using the historical building resource area, the part of each grid unit that overlaps with the historical building resource area is clipped to obtain the target analysis unit; Assigning project data to a number of target analysis units to obtain unit data of the target analysis units; Acquire a planning height control evaluation index system, wherein the planning height control evaluation index system includes a plurality of evaluation indicators and weight values ​​of the evaluation indicators; The evaluation indicators of the planning height control evaluation index system include the highest building height, average building height, current building height, distance from the center, building coverage ratio, building development intensity, streetscape openness ratio and sky openness; The street view openness ratio refers to the visual openness ratio of the original built environment of the target analysis unit, reflecting the building height of the original built environment in the target analysis unit. The larger the value, the higher the existing building height of the target analysis unit. Processing the unit data of the target analysis unit to obtain indicator data of the evaluation indicator of each target analysis unit; Calculate the comprehensive score of each target analysis unit based on the indicator data of each target analysis unit and the weight value of each evaluation indicator; Based on K-means classification and the comprehensive score of each target analysis unit, each target analysis unit is classified and evaluated; The calculation formula for the comprehensive score of the target analysis unit is: ； In the formula, It is The comprehensive score of each target analysis unit; For the The first target analysis unit The index value of each; For the The weight value of each evaluation indicator, and n represents the total number of indicators in the planning height control evaluation indicator system.

2. The method for planning height control and evaluation of historical and cultural heritage resources in urban renewal projects according to claim 1 is characterized in that: Assign project data to several target analysis units, including: Process the project data in a unified coordinate system to generate surface data; Using the surface data, the surface attribute data is assigned to the target analysis unit to obtain the unit data of the target analysis unit.

3. The method for evaluating the planning height control of historical and cultural heritage resources in urban renewal projects according to claim 1, wherein the comprehensive score of each target analysis unit is calculated based on the indicator data of each target analysis unit and the weight value of each evaluation indicator, specifically comprising: Obtain indicator data of the target analysis unit; Standardizing the indicator data to obtain an indicator value for each evaluation indicator; Based on the index value and weight value of the evaluation index, the comprehensive score of each target analysis unit is calculated.

4. A system for evaluating the planning height control of historical and cultural heritage resources in urban renewal projects, applied to the method for evaluating the planning height control of historical and cultural heritage resources in urban renewal projects according to any one of claims 1 to 3, characterized in that: include: an acquisition module, configured to acquire project data and map data of an urban renewal project, wherein the map data includes three-dimensional building model data of the urban renewal project; A generation module, which is used to divide the map data into grids using software tools to generate multiple target analysis units; An assignment module, which is used to assign project data to a number of target analysis units to obtain unit data of the target analysis units; An indicator system module is used to obtain a planning height control evaluation indicator system, wherein the planning height control evaluation indicator system includes a plurality of evaluation indicators and weight values ​​of the evaluation indicators; a processing module, configured to process the unit data of the target analysis unit to obtain indicator data of the evaluation indicator of each target analysis unit; A calculation module is used to calculate the comprehensive score of each target analysis unit based on the indicator data of each target analysis unit and the weight value of each evaluation indicator; The classification and evaluation module is used to classify and evaluate each target analysis unit based on K-means classification and the comprehensive score of each target analysis unit.

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