Urban block green land planning method and device based on thermal environment big data

Abstract

The invention discloses an urban block green land planning method and device based on thermal environment big data, and the method comprises the steps: obtaining the thermal environment data, vegetation coverage data and a vegetation database of a target block; constructing a thermal environment map of the target block based on the thermal environment data, and obtaining a thermal environment feature vector of each sub-block according to the thermal environment map; acquiring a microclimate influence value of each vegetation type; performing correlation analysis on the thermal environment feature vector and the microclimate influence value of each sub-block to obtain the microclimate adjustment contribution degree of each vegetation type to each sub-block; and according to the microclimate adjustment contribution degree, determining a target vegetation type to be planted in each sub-block and a planting proportion of each target vegetation type, and obtaining a vegetation configuration scheme of the target block. The microclimate adjusting capability of the urban green land can be improved.

Description

Technical Field

[0001] This application relates to the fields of urban planning and environmental protection, and particularly relates to a method and device for urban block green space planning based on thermal environment big data. Background Art

[0002] The greening construction of urban blocks is one of the important means to improve the quality of the urban ecological environment. With the acceleration of global climate change and urbanization, the urban heat island effect has become increasingly significant, bringing many inconveniences to residents' lives. Therefore, how to scientifically and reasonably plan the green spaces in urban blocks has become a research hotspot. By optimizing the layout of urban green plants, not only can the heat island effect be effectively alleviated, but also the microclimate environment of the city can be improved, and the quality of life and health level of residents can be enhanced. In recent years, many countries and regions have introduced relevant policies and technical standards to promote the development of urban greening work and achieved positive results.

[0003] Currently, in the process of green space planning for urban blocks, GIS (Geographic Information System) and remote sensing technology are usually used to conduct an overall thermal environment analysis of the city to determine the types of vegetation to be planted. However, this method can only select the vegetation types according to the overall thermal environment of the city and cannot refine the analysis of the thermal environment at the block scale, resulting in unreasonable selection and layout of vegetation types and affecting the microclimate regulation ability of urban green spaces. Summary of the Invention

[0004] This application provides a method and device for urban block green space planning based on thermal environment big data, which is used to optimize the microclimate regulation effect of the city.

[0005] In the first aspect of this application, a method for urban block green space planning based on thermal environment big data is provided, which is applied to a server. The method includes: obtaining the thermal environment data, vegetation coverage data, and vegetation database of a target block, where the thermal environment data includes microclimate data, geospatial data, and human activity data, and the target block includes multiple sub-blocks; constructing a thermal environment map of the target block based on the thermal environment data, and obtaining the thermal environment feature vector of each sub-block according to the thermal environment map; obtaining the microclimate influence value of each vegetation type, where the vegetation types are all the vegetation types in the vegetation coverage data and the vegetation database; performing correlation analysis on the thermal environment feature vector of each sub-block and the microclimate influence value to obtain the microclimate regulation contribution degree of each vegetation type to each sub-block; determining the target vegetation type to be planted in each sub-block and the planting proportion of each target vegetation type according to the microclimate regulation contribution degree, so as to obtain the vegetation configuration plan of the target block.

[0006] Optionally, construct a heat environment map of the target block based on the heat environment data, specifically including: determining target heat environment impact factors of the target block based on the heat environment data to obtain a set of heat environment impact factors; constructing a heat environment map according to the set of heat environment impact factors.

[0007] Optionally, determining target heat environment impact factors of the target block based on the heat environment data to obtain a set of heat environment impact factors specifically includes: extracting heat environment impact factors based on the heat environment data, where the heat environment impact factors include meteorological factors, surface factors, and human emission factors; calculating the correlation strength between the heat environment impact factors to construct a factor correlation network; determining the target heat environment impact factors based on the factor correlation network to obtain a set of heat environment impact factors.

[0008] Optionally, determining the target heat environment impact factors based on the factor correlation network to obtain a set of heat environment impact factors specifically includes: calculating the heat environment impact degree of the target heat environment impact factors based on the correlation strength, and determining the target heat environment impact factors with the heat environment impact degree greater than a preset impact threshold as the first heat environment impact factors; calculating the heat environment impact stability of all the first heat environment impact factors within a preset historical time period according to the heat environment data, and determining the first heat environment impact factors with the heat environment impact stability greater than a preset stability threshold as the second heat environment impact factors; analyzing the collinearity relationship of all the second heat environment impact factors, and combining the second heat environment impact factors that meet the preset collinearity requirements screened out based on a preset screening criterion as the set of heat environment impact factors.

[0009] Optionally, obtaining a heat environment feature vector for each sub-block according to the heat environment map, specifically including: extracting a set of target heat environment impact factors corresponding to each sub-block from the heat environment map; calculating heat environment state parameters for each sub-block based on the set of target heat environment impact factors; constructing a heat environment feature function for each sub-block according to the heat environment state parameters to perform feature transformation on the heat environment state parameters to obtain a feature transformation result; extracting target heat environment feature variables from the feature transformation result through a feature dimensionality reduction method; generating a heat environment feature vector for each sub-block based on the target heat environment feature variables.

[0010] Optionally, perform correlation analysis on the thermal environment feature vector of each sub-block and the microclimate impact value to obtain the microclimate regulation contribution degree of each vegetation type to each sub-block, specifically including: based on the thermal environment feature vector and the microclimate impact value, construct a microclimate regulation contribution degree model, which is used to characterize the influence degree of different vegetation types on the thermal environment feature vector of the sub-block; based on the microclimate regulation contribution degree model, calculate the microclimate regulation contribution degree of each vegetation type to each sub-block.

[0011] Optionally, determine the target vegetation type to be planted in each sub-block and the planting proportion of each target vegetation type according to the microclimate regulation contribution degree to obtain the vegetation configuration plan of the target block, specifically including: based on the microclimate regulation contribution degree, construct a green space optimization objective function in combination with the thermal environment data; set constraint conditions in the green space optimization objective function, and the constraint conditions include vegetation growth characteristics, available land area and urban planning requirements; based on the green space optimization objective function, determine the target vegetation type to be planted in each sub-block and the planting proportion of each target vegetation type through a preset multi-objective optimization algorithm to obtain the vegetation configuration plan of the target block.

[0012] In the second aspect of the present application, a city block green space planning system based on thermal environment big data is provided, including: a first acquisition module, configured to acquire thermal environment data, vegetation coverage data and a vegetation database of a target block, where the thermal environment data includes microclimate data, geospatial data and human activity data, and the target block includes a plurality of sub-blocks; A construction module, configured to construct a thermal environment map of the target block based on the thermal environment data, and obtain a thermal environment feature vector of each sub-block according to the thermal environment map; A second acquisition module, configured to acquire the microclimate impact value of each vegetation type, where the vegetation type is all vegetation types in the vegetation coverage data and the vegetation database; An analysis module, configured to perform correlation analysis on the thermal environment feature vector of each sub-block and the microclimate impact value to obtain the microclimate regulation contribution degree of each vegetation type to each sub-block; A determination module, configured to determine the target vegetation type to be planted in each sub-block and the planting proportion of each target vegetation type according to the microclimate regulation contribution degree to obtain the vegetation configuration plan of the target block.

[0013] In a third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. Both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes the method described in any one of the above.

[0014] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions that, when executed, perform the method described in any one of the above.

[0015] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through microclimate data, geospatial data, and human activity data, the thermal environment data of the target block is collected. Combining with vegetation cover data and vegetation databases ensures the integrity of the data and the scientific nature of vegetation selection. Subsequently, spatial analysis is performed on the thermal environment data to construct a thermal environment map of the target block, and the thermal environment feature vectors of each sub-block are extracted, enabling the spatial distribution of the thermal environment and microclimate characteristics to be quantitatively expressed. Based on the vegetation cover data and vegetation databases of the target block, the influence of different vegetation types on microclimate factors such as temperature, humidity, and wind speed is further quantified, and the microclimate regulation contribution degree of each vegetation type under different block conditions is analyzed through data modeling, so as to accurately identify the vegetation types most suitable for each sub-block, optimize its microclimate regulation effects such as cooling and humidification, and avoid problems of over-reliance on subjective experience or low-efficiency matching in traditional green space planning. Finally, based on the microclimate regulation contribution degree of each vegetation type, the optimal target vegetation type and its planting proportion for each sub-block are scientifically determined, achieving precise matching of vegetation configuration and enhancing the microclimate regulation ability of urban green spaces.

[0016] 2. Through data-driven quantitative analysis, accurate identification and optimized screening of urban thermal environment impact factors are achieved. The complex interaction relationships of various thermal environment impact factors are comprehensively considered, and through factor association networks and collinearity analysis, the core factors with the most significant influence and high stability on microclimate regulation are screened out. Finally, the constructed thermal environment map can accurately reflect the thermal environment characteristics of the target block, providing a scientific basis for subsequent vegetation optimization configuration, urban green space planning, and thermal environment regulation strategies.

[0017] 3. Extract the target thermal environment impact factor set of the sub-blocks from the thermal environment map to ensure that the selected factors accurately reflect the regional microclimate characteristics. Subsequently, calculate the thermal environment state parameters (such as temperature, humidity, wind speed, etc.) based on these factors to comprehensively describe the thermal environment conditions of the sub-blocks. To improve the accuracy and stability of data analysis, further construct a thermal environment characteristic function, perform characteristic transformation on the state parameters, and extract the core thermal environment characteristic variables through feature dimensionality reduction to reduce redundancy and improve calculation efficiency. Finally, generate a thermal environment feature vector based on the extracted feature variables to ensure that the thermal environment characteristics of each sub-block are accurately expressed in the high-dimensional data space, avoiding the deficiencies of traditional thermal environment analysis relying on a single indicator or subjective experience. Description of the Drawings

[0018] Figure 1 is a schematic flowchart of a method for urban block green space planning based on thermal environment big data in an embodiment of the present application; Figure 2 is a schematic structural diagram of a system for urban block green space planning based on thermal environment big data in an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device in an embodiment of the present application.

[0019] Description of the reference numerals: 201, the first acquisition module; 202, the construction module; 203, the second acquisition module; 204, the analysis module; 205, the determination module; 301, the processor; 302, the communication bus; 303, the user interface; 304, the network interface; 305, the memory. Detailed Embodiments

[0020] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0021] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, using words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0022] In the description of the embodiments of the present application, the term "a plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0023] Figure 1 It is a process schematic diagram of a method for urban block green space planning based on big data of thermal environment in the embodiments of the present application.

[0024] Please refer to Figure 1 , a method for urban block green space planning based on big data of thermal environment in the embodiments of the present application, is applied to a server. The method includes: S101. Obtain the thermal environment data, vegetation coverage data and vegetation database of the target block. The thermal environment data includes microclimate data, geospatial data and human activity data, and the target block includes a plurality of sub-blocks; First, obtain the thermal environment data, vegetation coverage data and vegetation database of the target block to support subsequent thermal environment analysis and vegetation optimization configuration. Among them, the thermal environment data includes microclimate data, geospatial data and human activity data to comprehensively reflect the thermal environment conditions in the block. The microclimate data can be obtained through fixed weather stations, mobile monitoring devices or remote sensing means, covering elements such as temperature, humidity, wind speed, and radiation intensity. For example, high-precision temperature sensors can be used to measure the surface temperature changes at different time periods, or infrared thermal imaging devices carried by unmanned aerial vehicles can be used to capture the heat distribution. The geospatial data includes information such as the topography, building distribution, road network, and water body distribution of the block, which can be integrated through a geographic information system to analyze the impact of different spatial elements on the thermal environment. The human activity data involves aspects such as population density, traffic flow, and energy consumption. For example, the satellite night light data can be used to analyze the heat island effect in population aggregation areas, or mobile data can be used to evaluate the heat load in crowded areas.

[0025] In addition, vegetation cover data and a vegetation database are obtained for the target block to assess the existing vegetation status and the ecological effects of different vegetation types. Vegetation cover data can be obtained from remote sensing imagery, drone aerial photography, or field surveys. Specifically, it includes information on vegetation type (such as trees, shrubs, and grasslands), vegetation coverage, vegetation health (assessed using vegetation indices such as NDVI), and vegetation microclimate impact. For example, high-resolution satellite imagery can be used to identify tree canopy cover within a block, and the model can be calibrated using field data. The vegetation database contains information on the growth characteristics, ecological functions, heat tolerance, and transpiration of different vegetation types. For example, certain tree species (such as sycamore) have strong transpiration cooling capabilities, while certain lawns (such as bermudagrass) are suitable for surface cooling in high-temperature environments. The vegetation database contains all vegetation types suitable for planting in the target block and other related data. The vegetation database may include both existing vegetation and other vegetation types that are suitable for the target block but have not yet been planted.

[0026] S102, constructing a thermal environment map of the target block based on the thermal environment data, and obtaining a thermal environment feature vector of each sub-block according to the thermal environment map; Specifically, based on the thermal environment data, the target thermal environment impact factors of the target block are determined to obtain a thermal environment impact factor set; and a thermal environment map is constructed according to the thermal environment impact factor set.

[0027] Among them, based on the thermal environment data, the target thermal environment impact factors of the target block are determined to obtain the thermal environment impact factor set, which specifically includes: extracting thermal environment impact factors based on the thermal environment data, and the thermal environment impact factors include meteorological factors, surface factors and human emission factors; calculating the correlation strength between the thermal environment impact factors and constructing a factor correlation network; based on the factor correlation network, determining the target thermal environment impact factors to obtain the thermal environment impact factor set.

[0028] Based on the acquired thermal environment data, the core factors affecting the thermal environment of the target block are extracted. These factors mainly include meteorological factors, surface factors and anthropogenic emission factors. Meteorological factors include variables such as air temperature, relative humidity, wind speed, wind direction, and solar radiation intensity. For example, enhanced solar radiation under hot weather may cause the surface temperature to rise, thereby affecting the thermal environment of the block. Surface factors involve surface material type, vegetation coverage, water body distribution, building density, etc. For example, concrete pavement has a stronger heat storage capacity than vegetation-covered areas and releases more heat at night, thereby exacerbating the urban heat island effect. Anthropogenic emission factors include traffic exhaust emissions, industrial emissions, and waste heat from building air conditioning. For example, in commercially dense areas, due to the extensive use of air conditioning systems, local heat emissions increase significantly, resulting in high nighttime temperatures.

[0029] After extracting various thermal environment impact factors, calculate the correlation strength between them to reveal the interaction relationships among different factors. Correlation analysis (such as Pearson correlation coefficient) can be used to evaluate the relationships between air temperature and factors such as building density and surface material, and methods such as regression analysis and principal component analysis (PCA) can also be used to quantify the interactions of various thermal environment impact factors. After calculating the correlation strength between thermal environment impact factors, construct a correlation network of thermal environment impact factors based on the analysis results to visually display the complex relationships and their interactions among thermal environment impact factors. First, take all thermal environment impact factors as nodes in the network, including variables such as air temperature, humidity, wind speed, building density, surface material, vegetation coverage, traffic flow, and industrial emissions. Then, according to the previously calculated factor correlation strength (such as Pearson correlation coefficient, regression coefficient, grey correlation degree, or path coefficient in structural equation model), establish weighted connections between each node, where the weight of the edge reflects the degree of mutual influence between factors. For example, if the correlation between building density and surface temperature in a certain area is relatively high (such as 0.8), then establish a connection with a larger weight between "building density" and "surface temperature", and if the correlation between wind speed and surface temperature is relatively weak (such as 0.3), then the weight of the corresponding edge is smaller.

[0030] In addition, community detection algorithms (such as Louvain algorithm or K-Means clustering) can be used to perform modular analysis on the factor correlation network to identify highly correlated factor groups. For example, it may be found that "building density - surface material - traffic emissions" form a sub-network that mainly affects the urban heat island effect, while "vegetation coverage - air humidity - wind speed" form another sub-network that mainly affects the microclimate regulation ability.

[0031] After constructing the factor correlation network, further screen the core factors that have a significant impact on the thermal environment of the target block to obtain the set of thermal environment impact factors. Specifically, based on the correlation strength, calculate the thermal environment impact degree of the target thermal environment impact factors, and determine the target thermal environment impact factors with a thermal environment impact degree greater than the preset impact threshold as the first thermal environment impact factors; calculate the thermal environment impact stability of all the first thermal environment impact factors within the preset historical time period according to the thermal environment data, and determine the first thermal environment impact factors with a thermal environment impact stability greater than the preset stability threshold as the second thermal environment impact factors; analyze the collinearity relationship of all the second thermal environment impact factors, and based on the preset screening criteria, combine the screened second thermal environment impact factors that meet the preset collinearity requirements into the set of thermal environment impact factors.

[0032] After constructing the factor association network, it is necessary to further calculate the thermal environment impact degree of each target thermal environment impact factor to quantify its impact intensity on the thermal environment of the target block. The thermal environment impact degree can be calculated by various methods, such as the regression coefficient of the regression model, information gain entropy analysis, random forest feature importance ranking, etc. In a multiple regression model, the standardized regression coefficient of each factor can also be calculated. The larger the value, the more significant the impact of the factor on the thermal environment. In a decision tree or random forest model, the contribution degree of each factor to the thermal environment can be evaluated through variable importance. In addition, the grey relational analysis (GRA) or structural equation model (SEM) can be combined to evaluate the impact degree of the factors on key meteorological variables such as temperature, humidity, and wind speed.

[0033] After calculating the thermal environment impact degree of each factor, it is compared with the preset impact threshold to eliminate the factors with smaller impacts. For example, if the impact degree is lower than the preset impact threshold (such as a 20% contribution rate), it is considered that the factor has a smaller impact on the block's thermal environment and is not included in the subsequent analysis. The factors with an impact degree greater than the threshold are determined as the first thermal environment impact factors. For example, if the analysis results show that "building density, surface material, vegetation coverage, traffic flow" have a higher impact degree on local temperature, while "cloud cover change, long-distance industrial emissions" have a smaller impact, the former are retained as the first thermal environment impact factors, and the latter are eliminated.

[0034] After screening out the first thermal environment impact factors, it is necessary to further evaluate whether these factors have a stable influence during the preset historical period (such as the past 5 years), that is, to calculate their thermal environment impact stability. The calculation of stability can adopt methods such as time series analysis (such as moving average, coefficient of variation calculation), analysis of variance (ANOVA), or trend consistency test. For example, if the impact degree of a certain factor fluctuates greatly in the past 5 years, it may indicate that its impact is unstable and it is not suitable as a core factor.

[0035] If the impact stability of a certain first thermal environment impact factor is greater than the preset stability threshold (such as coefficient of variation CV < 0.3), it is considered that its impact is relatively stable and is determined as the second thermal environment impact factor; otherwise, it is eliminated. For example, if the analysis finds that "building density, vegetation coverage, road heat radiation" have remained stable in their impact on the urban thermal environment in the past few years, while the impact of "short-term extreme weather events" on the thermal environment fluctuates greatly, the former are determined as the second thermal environment impact factors, and the latter are eliminated.

[0036] After selecting the second most significant environmental impact factors, it is necessary to further analyze the collinearity relationship among these factors to avoid redundancy and multicollinearity problems. Collinearity analysis can be performed using methods such as variance inflation factor (VIF) calculation, Pearson correlation coefficient matrix, principal component analysis (PCA), and ridge regression analysis. For example, if the correlation coefficient between two factors (such as "building density" and "impervious surface rate") is as high as 0.9, it indicates that they may represent similar impact mechanisms. In this case, one of the factors can be retained to reduce data redundancy. According to the preset screening criteria (such as VIF < 5 or correlation coefficient < 0.7), factors that meet the collinearity requirements are selected and combined into the final set of environmental impact factors.

[0037] After determining the set of environmental impact factors, an environmental map is constructed based on this set to visually display the environmental characteristics and their spatial distribution of the target block. The process of constructing the environmental map mainly includes key steps such as data spatialization, interpolation calculation, and environmental zoning analysis. First, based on the obtained microclimate data, geospatial data, and human activity data, the selected environmental impact factors (such as land surface temperature, vegetation coverage rate, building density, wind speed, humidity, traffic heat emissions, etc.) are spatially processed, that is, these data are matched with the geographical information of the block (such as longitude and latitude, plot boundaries) to form a rasterized or vectorized spatial data set. For example, using GIS (Geographic Information System) software, the land surface temperature data in the remote sensing image can be converted into a raster layer and overlaid with data such as building density and green space distribution to form multi-layer spatial information.

[0038] Next, spatial interpolation methods (such as Kriging interpolation, inverse distance weighted interpolation IDW) are used to fill in the missing data and calculate the spatial distribution characteristics of each factor. For example, the Kriging interpolation method can be used to interpolate the temperature data at multiple monitoring points to generate a continuous temperature distribution map of the entire block, or the NDVI (Normalized Difference Vegetation Index) can be used to evaluate the vegetation coverage in different regions. Then, based on the factor weights (such as principal component analysis or regression weights), the comprehensive environmental index of each region is calculated to quantify the overall environmental situation. For example, an environmental index model can be constructed, where "land surface temperature" accounts for 40%, "vegetation coverage rate" accounts for 30%, "wind speed" accounts for 20%, and "human emissions" account for 10%, and finally the environmental index values of each sub-block are obtained.

[0039] Finally, GIS visualization tools (such as ArcGIS, QGIS) or Python geospatial data processing libraries (such as GeoPandas, Matplotlib) can be used to plot heat environment maps, forming spatial distribution maps of different heat environment indicators, such as surface temperature distribution maps, heat environment index maps, vegetation cooling effect maps, etc., and conduct heat environment zoning analysis. For example, high-temperature hotspots (such as densely built-up areas, transportation hubs) and low-temperature regulation areas (such as park green spaces, water bodies) of the heat environment can be identified through K-Means clustering or spatial autocorrelation analysis, and different heat environment levels (such as high heat risk areas, medium heat load areas, comfortable areas) can be divided.

[0040] After obtaining the heat environment map, the heat environment characteristic vectors of each sub-block can be obtained according to the heat environment map. Specifically, the target heat environment impact factor set corresponding to each sub-block is extracted from the heat environment map; based on the target heat environment impact factor set, the heat environment state parameters of each sub-block are calculated; according to the heat environment state parameters, the heat environment characteristic function of each sub-block is constructed to perform feature transformation on the heat environment state parameters to obtain the feature transformation result; the feature transformation result is used to extract the target heat environment feature variables through a feature dimensionality reduction method; based on the target heat environment feature variables, the heat environment characteristic vectors of each sub-block are generated.

[0041] After constructing the heat environment map, it is necessary to further extract the target heat environment impact factor set of each sub-block to quantify the key factors affecting the microclimate in this area. First, according to the spatial data of the heat environment map, the geographical scope of each sub-block is spatially matched with the heat environment data, and the main environmental variables in this area are extracted, such as surface temperature, air temperature, humidity, wind speed, building density, surface material, green space coverage rate, traffic flow, heat emission source distribution, etc. Then, based on the previous factor correlation analysis, the factors with the most significant impact on the heat environment are screened out. For example, in high-density building areas, the main factors that may be extracted include building density, surface temperature, wind speed, traffic heat emissions, while in areas with more park green spaces, the factors that may be extracted include vegetation coverage rate, transpiration cooling effect, air humidity, etc. Finally, each sub-block will obtain an independent target heat environment impact factor set.

[0042] After obtaining the target heat environment impact factor set for each sub-block, it is necessary to calculate its corresponding heat environment state parameters to further quantify the microclimate characteristics of the area. Heat environment state parameters are numerical expressions of the target factors and usually include average air temperature, diurnal temperature difference, surface temperature, air humidity, wind speed, heat flux, urban heat island intensity, vegetation cooling contribution, building heat capacity, etc. For example, if a sub-block has a high building density, low wind speed, and high surface temperature, its heat environment state parameters may show a high average air temperature, a large urban heat island intensity, and a low wind speed, while another sub-block with a high vegetation coverage rate may have a low surface temperature and high air humidity. These parameters can be calculated using methods such as **measured data (e.g., meteorological station data), remote sensing image analysis (e.g., MODIS, Landsat surface temperature data), numerical simulation (e.g., ENVI-met), etc.

[0043] After calculating the heat environment state parameters for each sub-block, it is necessary to construct a heat environment characteristic function to perform characteristic transformation on these state parameters to make them more comparable and pattern recognition capable, and finally obtain the characteristic transformation result. First, for different types of heat environment state parameters (such as surface temperature, air humidity, wind speed, building density, etc.), select appropriate mathematical transformation methods to reduce the data scale difference and improve the data stability. For example, for parameters with a large numerical range (such as surface temperature 20 - 60 °C), normalization transformation (min-max scaling) can be used to limit the numerical range of the parameter between 0 - 1. Similarly, for variables with an obvious skewed data distribution (such as heat island intensity, traffic heat emissions), logarithmic transformation can be used to make the data distribution closer to normal and improve the calculation stability. In addition, for time series data (such as diurnal temperature difference), exponential smoothing method can be used to weaken the influence of short-term fluctuations and highlight the long-term trend.

[0044] After completing the characteristic transformation, the obtained characteristic transformation result is a standardized, noise-reduced or enhanced version of all heat environment state parameters, making them directly comparable between different sub-blocks and suitable for subsequent data dimensionality reduction and pattern recognition. For example, the original "surface temperature 40 °C, air humidity 50%, wind speed 3 m / s" may be transformed into a dimensionless vector such as "0.75, 0.5, 0.6".

[0045] After the characteristic transformation of the thermal environment state parameters, a set of standardized and normalized characteristic transformation results are obtained. However, these data may contain a large amount of redundant information, or there may be a high correlation between certain features. Therefore, it is necessary to adopt a feature reduction method to extract the most representative target thermal environment feature variables. The main purpose of feature reduction is to reduce the data dimension, improve the calculation efficiency, and at the same time retain the key thermal environment information. For example, through linear transformation, multiple related variables are synthesized into several uncorrelated new variables (principal components), and several of the most important principal components are selected according to the variance contribution rate. For example, the original data may contain multiple variables such as "surface temperature, air temperature, building density, green space coverage rate, wind speed". Through principal component analysis, it is found that 80% of the variance information can be represented by three variables: "heat island intensity principal component", "vegetation cooling principal component", and "wind speed ventilation principal component". Then these three principal components can be used as the target thermal environment feature variables.

[0046] After extracting the target thermal environment feature variables, it is necessary to further generate corresponding thermal environment feature vectors for each sub-block based on these variables for subsequent analysis and optimization. The thermal environment feature vector is a numerical array, and each element corresponds to the value of a target thermal environment feature variable, representing the characteristics of the sub-block in different thermal environment dimensions. For example, if three target thermal environment feature variables are finally selected: heat island intensity, vegetation cooling effect, and wind speed regulation ability, then the thermal environment feature vector of a certain sub-block may be expressed as: V=(0.85, 0.40, 0.25), where 0.85 represents a relatively high heat island intensity in this sub-block (the larger the value, the more serious the heat island effect); 0.40 represents a medium vegetation cooling ability (the larger the value, the more obvious the contribution of vegetation to cooling); 0.25 represents a weak wind speed regulation ability (the smaller the value, the poorer the air fluidity in this area).

[0047] S103. Obtain the microclimate impact value of each vegetation type, where the vegetation types are all vegetation types in the vegetation coverage data and the vegetation database; The microclimate impact value is an inherent property of vegetation, that is, under specific climate conditions, the ability of each vegetation to affect the local microclimate (such as cooling, humidifying, shading, ventilation regulation, etc.). These data can be directly obtained from the vegetation coverage data and the vegetation database. The vegetation coverage data refers to the current existing vegetation distribution in the target block, while the vegetation database contains all vegetation types suitable for planting in this block, which may include both the currently existing vegetation and other vegetation that is suitable but not yet planted.

[0048] S104. Conduct a correlation analysis on the thermal environment feature vector and the microclimate impact value of each sub-block to obtain the microclimate regulation contribution degree of each vegetation type to each sub-block; After obtaining the thermal environment feature vectors of each sub-block and the microclimate impact values of different vegetation types, it is necessary to construct a microclimate regulation contribution model to quantify the regulation ability of different vegetation types on the thermal environment of sub-blocks. The core goal of the microclimate regulation contribution model is to establish the relationship between vegetation types and thermal environment characteristic variables, that is, the impact degree on the local microclimate (such as cooling, humidifying, shading, wind speed regulation, etc.) after planting different types of vegetation in different sub-blocks. The construction of the model can be based on methods such as regression analysis, machine learning models, or numerical simulations. For example, using a multiple regression model, setting thermal environment characteristic variables (such as heat island intensity, wind speed regulation ability) as dependent variables, and the microclimate impact values of vegetation types (such as cooling effect, transpiration, canopy coverage) as independent variables, training the model with historical data to obtain the impact coefficients of each vegetation type on thermal environment variables. In addition, random forest regression or structural equation models can also be used to analyze the contribution of vegetation characteristics (such as leaf area index, transpiration rate) to different thermal environment characteristic variables. For example, in a high-density building area, planting tall trees with high transpiration ability (such as Platanus orientalis) may significantly reduce the surface temperature, while in an open area with strong wind speed, planting low shrubs (such as Ilex chinensis) may help reduce the wind speed and improve local comfort.

[0049] After constructing the microclimate regulation contribution model, it is necessary to use this model to calculate the specific microclimate regulation contribution of each vegetation type in different sub-blocks, that is, to quantify the impact degree of different vegetation types on the thermal environment characteristic variables of this area. The calculation process usually includes inputting the thermal environment feature vectors of sub-blocks, inputting the microclimate impact values of different vegetation types, and combining the model to calculate the contribution value. Specifically, when calculating, the regression coefficients of the linear regression model can be used, or based on the prediction results of machine learning, to obtain the contribution scores of each vegetation type in different sub-blocks. For example, if the thermal environment feature vector of a certain sub-block is (0.85, 0.40, 0.25), indicating that the heat island effect in this area is strong, the vegetation cooling ability is average, and the wind speed regulation ability is weak, then the contribution calculation of planting different vegetation types in this area is as follows: Platanus orientalis (tall tree with high transpiration): predicted cooling contribution of 2.8 °C, humidity increase of 5%, wind speed reduction of 10%, and finally obtaining a contribution vector of (0.75, 0.60, 0.35); Lawn (low herb): predicted cooling contribution of 1.2 °C, humidity increase of 2%, wind speed reduction of 3%, and finally a contribution vector of (0.90, 0.45, 0.27); Bamboo (high-density shrub): predicted cooling contribution of 2.0 °C, humidity increase of 4%, wind speed reduction of 15%, and finally a contribution vector of (0.80, 0.55, 0.40).

[0050] Through the above calculations, the microclimate regulation contribution of each vegetation type in a specific sub-block can be obtained. For example, if the goal of a certain sub-block is to reduce the heat island effect while enhancing the wind speed regulation ability, vegetation types with higher contributions can be preferentially selected, such as bamboo with strong transpiration and wind speed reduction capabilities, rather than lawns or low shrubs.

[0051] S105. Determine the target vegetation types to be planted in each sub-block and the planting proportion of each target vegetation type according to the microclimate regulation contribution, so as to obtain the vegetation configuration plan of the target block. Specifically, based on the microclimate regulation contribution, combine the thermal environment data to construct a green space optimization objective function; set constraint conditions in the green space optimization objective function, and the constraint conditions include vegetation growth characteristics, available land area and urban planning requirements; based on the green space optimization objective function, determine the target vegetation types to be planted in each sub-block and the planting proportion of each target vegetation type through a preset multi-objective optimization algorithm, so as to obtain the vegetation configuration plan of the target block.

[0052] Among them, after calculating the microclimate regulation contribution of each vegetation type to each sub-block, it is necessary to further combine the thermal environment data to construct a green space optimization objective function. The objective function should comprehensively consider multiple key thermal environment indicators, such as reducing the surface temperature, increasing the air humidity, optimizing the wind speed, etc., and assign different weights to meet the needs of different blocks. For example, in a commercial area with a serious heat island effect, the objective function can be set to maximize the cooling effect and air humidity, while in an open area with a large wind speed, the optimization objective can be adjusted to focus on reducing the wind speed and enhancing the shading effect.

[0053] After constructing the green space optimization objective function, it is necessary to introduce constraint conditions in the optimization calculation process to ensure that the optimization results meet the actual feasibility. These constraint conditions mainly include vegetation growth characteristics, available land area and urban planning requirements, so as to ensure that the selected vegetation can not only meet the microclimate regulation requirements, but also grow normally in the urban environment and comply with the urban planning policies. The vegetation growth characteristics constraint requires that the selected plants adapt to the local hydrology, climate and soil conditions. For example, high-transpiration arbors (such as Platanus orientalis) are suitable for humid environments, while drought-tolerant herbaceous plants (such as Carex) are more suitable for dry areas. The available land area constraint ensures that the optimization plan meets the spatial conditions of the block. For example, the greening space in high-density building areas is limited, and vertical greening or street trees may need to be given priority, while the planting proportion of arbors and shrubs can be increased in open spaces. The urban planning requirements constraint ensures that the vegetation configuration complies with the government's greening policies, such as the greening rate requirement in the commercial area ≥ 20%, or the planting of deep-rooted tree species is prohibited in some areas to protect underground pipelines.

[0054] After constructing the objective function and setting the constraints, it is ultimately necessary to use multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, simulated annealing algorithms, etc.) to calculate the optimal vegetation configuration plan for each sub-block, that is, to determine the target vegetation types to be planted and the planting proportions. The execution process of the optimization algorithm usually includes initializing the population, calculating the fitness, evolutionary iteration, and convergence judgment to gradually optimize the vegetation configuration plan. For example, assuming that the thermal environment characteristic vector of a certain sub-block shows high temperature, low humidity, and high heat island effect, the optimization algorithm may recommend configuring 60% high-transpiration trees (such as ginkgo), 30% shrubs (such as rhododendron), and 10% lawns in this area to maximize the cooling and humidity regulation effects. Similarly, in areas with high wind speeds, the optimization algorithm may recommend increasing bamboo forests and high-density shrubs (such as holly) to reduce the wind speed, while in enclosed blocks, it may suggest optimizing the tree spacing to promote air circulation. Finally, the optimization results of all sub-blocks will be integrated to form a complete vegetation configuration plan for the target block.

[0055] Please refer to Figure 2 , which is a schematic structural diagram of an urban block green space planning system based on thermal environment big data provided by an embodiment of the present application. An urban block green space planning system 200 based on thermal environment big data specifically includes: A first acquisition module 201, configured to acquire the thermal environment data, vegetation coverage data, and vegetation database of the target block. The thermal environment data includes microclimate data, geospatial data, and human activity data, and the target block includes multiple sub-blocks; A construction module 202, configured to construct a thermal environment map of the target block based on the thermal environment data, and obtain the thermal environment characteristic vector of each sub-block according to the thermal environment map; A second acquisition module 203, configured to acquire the microclimate influence value of each vegetation type, and the vegetation types are all vegetation types in the vegetation coverage data and the vegetation database; An analysis module 204, configured to perform correlation analysis on the thermal environment characteristic vector and the microclimate influence value of each sub-block to obtain the microclimate regulation contribution degree of each vegetation type to each sub-block; A determination module 205, configured to determine the target vegetation types to be planted in each sub-block and the planting proportion of each target vegetation type according to the microclimate regulation contribution degree, so as to obtain the vegetation configuration plan of the target block.

[0056] Optionally, the construction module 202 is specifically configured to: Based on the thermal environment data, determine the target thermal environment impact factors of the target block to obtain a set of thermal environment impact factors; construct a thermal environment map according to the set of thermal environment impact factors. Optionally, the construction module 202 is further specifically configured to: extract thermal environment impact factors based on the thermal environment data, where the thermal environment impact factors include meteorological factors, surface factors, and human emission factors; calculate the correlation strength between the thermal environment impact factors, and construct a factor correlation network; based on the factor correlation network, determine the target thermal environment impact factors to obtain a set of thermal environment impact factors.

[0057] Optionally, the construction module 202 is further specifically configured to: Based on the correlation strength, calculate the thermal environment impact degree of the target thermal environment impact factors, and determine the target thermal environment impact factors with the thermal environment impact degree greater than the preset impact threshold as the first thermal environment impact factors; calculate the thermal environment impact stability of all the first thermal environment impact factors within a preset historical time period according to the thermal environment data, and determine the first thermal environment impact factors with the thermal environment impact stability greater than the preset stability threshold as the second thermal environment impact factors; analyze the collinearity relationship of all the second thermal environment impact factors, and based on a preset screening criterion, combine the second thermal environment impact factors that meet the preset collinearity requirements into a set of thermal environment impact factors.

[0058] Optionally, the construction module 202 is further specifically configured to: Extract the set of target thermal environment impact factors corresponding to each sub-block from the thermal environment map; calculate the thermal environment state parameters of each sub-block based on the set of target thermal environment impact factors; construct a thermal environment characteristic function for each sub-block according to the thermal environment state parameters to perform a characteristic transformation on the thermal environment state parameters to obtain a characteristic transformation result; extract the target thermal environment characteristic variables through a feature dimensionality reduction method for the characteristic transformation result; generate a thermal environment characteristic vector for each sub-block based on the target thermal environment characteristic variables.

[0059] Optionally, the analysis module 204 is specifically configured to: Based on the thermal environment characteristic vector and the microclimate impact value, construct a microclimate regulation contribution degree model, where the microclimate regulation contribution degree model is used to characterize the influence degree of different vegetation types on the thermal environment characteristic vector of the sub-block; calculate the microclimate regulation contribution degree of each vegetation type to each sub-block based on the microclimate regulation contribution degree model.

[0060] Optionally, the determination module 205 is specifically configured to: Based on the contribution degree of microclimate regulation, a green space optimization objective function is constructed by combining thermal environment data; constraint conditions are set in the green space optimization objective function, and the constraint conditions include vegetation growth characteristics, available land area, and urban planning requirements; based on the green space optimization objective function, the target vegetation types to be planted in each sub-block and the planting proportion of each target vegetation type are determined through a preset multi-objective optimization algorithm, and a vegetation configuration plan for the target block is obtained.

[0061] It should be noted that when the device provided in the above embodiment realizes its functions, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0062] This embodiment also discloses an electronic device. Referring to Figure 3 , the electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0063] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0064] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0065] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0066] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and circuits to connect various parts within the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by invoking the data stored in the memory 305, it performs various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0067] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As Figure 3 shown, the memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for an urban block green space planning method based on big data of the thermal environment.

[0068] In Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; while the processor 301 can be used to call an application program stored in the memory 305 for an urban block green space planning method based on big data of the thermal environment. When executed by one or more processors 301, the electronic device is caused to execute the method of one or more of the above embodiments.

[0069] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0070] In the above embodiments, the descriptions of the various embodiments each have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0071] In the several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0072] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] In addition, in each embodiment of this application, the various functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0074] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 305. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a memory 305 and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned memory 305 includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0075] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other implementation manners of the present disclosure. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for urban block green space planning based on big data of thermal environment, characterized in that, Applied to a server, the method includes: Obtain the thermal environment data, vegetation coverage data, and vegetation database of the target block. The thermal environment data includes microclimate data, geospatial data, and human activity data. The target block includes multiple sub-blocks; Construct a thermal environment map of the target block based on the thermal environment data, and obtain the thermal environment feature vectors of each sub-block according to the thermal environment map; Obtain the microclimate impact value of each vegetation type, where the vegetation types are all the vegetation types in the vegetation coverage data and the vegetation database; Perform correlation analysis on the thermal environment feature vectors and the microclimate impact values of each sub-block to obtain the microclimate regulation contribution degree of each vegetation type to each sub-block; Determine the target vegetation type to be planted in each sub-block and the planting proportion of each target vegetation type according to the microclimate regulation contribution degree, and obtain the vegetation configuration plan of the target block.

2. The method according to claim 1, wherein The constructing a thermal environment map of the target block based on the thermal environment data specifically includes: Based on the thermal environment data, determine the target thermal environment impact factors of the target block to obtain a set of thermal environment impact factors; Construct a thermal environment map according to the set of thermal environment impact factors.

3. The method according to claim 2, wherein The based on the thermal environment data, determining the target thermal environment impact factors of the target block to obtain a set of thermal environment impact factors specifically includes: Extract thermal environment impact factors based on the thermal environment data. The thermal environment impact factors include meteorological factors, surface factors, and human emission factors; Calculate the correlation strength between the thermal environment impact factors and construct a factor correlation network; Based on the factor correlation network, determine the target thermal environment impact factors to obtain a set of thermal environment impact factors.

4. The method according to claim 3, wherein The based on the factor correlation network, determining the target thermal environment impact factors to obtain a set of thermal environment impact factors specifically includes: Based on the correlation strength, calculate the thermal environment impact degree of the target thermal environment impact factors, and determine the target thermal environment impact factors with the thermal environment impact degree greater than the preset impact threshold as the first thermal environment impact factors; Calculate the thermal environment impact stability of all the first thermal environment impact factors within a preset historical time period according to the thermal environment data, and determine the first thermal environment impact factors with the thermal environment impact stability greater than the preset stability threshold as the second thermal environment impact factors; Analyze the collinearity relationship of all the second thermal environment impact factors, and based on a preset screening criterion, combine the selected second thermal environment impact factors that meet the preset collinearity requirements into the set of thermal environment impact factors.

5. The method according to claim 1, wherein The obtaining the thermal environment feature vectors of each sub-block according to the thermal environment map specifically includes: Extract the set of target thermal environment impact factors corresponding to each sub-block from the thermal environment map; Based on the set of target thermal environment impact factors, calculate the thermal environment state parameters of each sub-block; According to the thermal environment state parameters, construct a thermal environment feature function for each sub-block to perform feature transformation on the thermal environment state parameters to obtain a feature transformation result; Extract the target thermal environment feature variables from the feature transformation results through a feature dimensionality reduction method; Generate the thermal environment feature vectors for each of the sub-blocks based on the target thermal environment feature variables.

6. The method according to claim 1, wherein The correlation analysis of the thermal environment feature vectors of each of the sub-blocks and the microclimate influence values to obtain the microclimate regulation contribution degrees of each of the vegetation types to each of the sub-blocks specifically includes: Construct a microclimate regulation contribution degree model based on the thermal environment feature vectors and the microclimate influence values, and the microclimate regulation contribution degree model is used to characterize the influence degrees of different vegetation types on the thermal environment feature vectors of the sub-blocks; Calculate the microclimate regulation contribution degrees of each of the vegetation types to each of the sub-blocks based on the microclimate regulation contribution degree model.

7. The method according to claim 1, wherein The determination of the target vegetation types to be planted in each of the sub-blocks and the planting proportions of each of the target vegetation types according to the microclimate regulation contribution degrees to obtain the vegetation configuration plan of the target block specifically includes: Construct a green space optimization objective function based on the microclimate regulation contribution degrees in combination with the thermal environment data; Set constraint conditions in the green space optimization objective function, and the constraint conditions include vegetation growth characteristics, available land area, and urban planning requirements; Based on the green space optimization objective function, determine the target vegetation types to be planted in each of the sub-blocks and the planting proportions of each of the target vegetation types through a preset multi-objective optimization algorithm to obtain the vegetation configuration plan of the target block.

8. An urban block green space planning system based on big data of thermal environment, characterized in that, Include: A first acquisition module for acquiring the thermal environment data, vegetation coverage data, and vegetation database of the target block, where the thermal environment data includes microclimate data, geospatial data, and human activity data, and the target block includes a plurality of sub-blocks; A construction module for constructing the thermal environment map of the target block based on the thermal environment data and obtaining the thermal environment feature vectors of each of the sub-blocks according to the thermal environment map; A second acquisition module for acquiring the microclimate influence values of each of the vegetation types, and the vegetation types are all the vegetation types in the vegetation coverage data and the vegetation database; An analysis module for performing correlation analysis on the thermal environment feature vectors and the microclimate influence values of each of the sub-blocks to obtain the microclimate regulation contribution degrees of each of the vegetation types to each of the sub-blocks; A determination module for determining the target vegetation types to be planted in each of the sub-blocks and the planting proportions of each of the target vegetation types according to the microclimate regulation contribution degrees to obtain the vegetation configuration plan of the target block.

9. An urban block green space planning device based on big data of thermal environment, characterized in that Include: One or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable a device for urban block green space planning based on thermal environment big data to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on an urban block green space planning device based on big data of thermal environment, the urban block green space planning device based on big data of thermal environment is caused to execute the method according to any one of claims 1-7.

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