Urban updating unit updating mode selection method and system based on GIS

Abstract

The invention relates to the technical field of city planning, in particular to a GIS (Geographic Information System)-based city updating unit updating mode selection method and system, which comprises the following steps of: based on spatial distribution data of city updating units, extracting geographic position parameters, functional node distribution characteristics and social activity intensity parameters, calculating geographic proximity intensity, and calculating geographic position information; functional proximity intensity is quantified through a node distribution relation, social proximity intensity is analyzed according to population density and activity frequency, and a regional proximity comprehensive intensity distribution value is generated. According to the invention, by integrating the proximity comprehensive strength distribution and the dynamic meteorological data, the analysis logic combining time and space dimensions is constructed, and by quantifying the dynamic change of the meteorological parameters and the proximity strength distribution, the space interaction range is expanded, and the adaptive distribution and the function transition characteristics are combined in the boundary adjustment. The limitation of static division is reduced, and the scientificity and accuracy of updating mode selection are remarkably improved by matching dynamic parameters and function distribution in mode selection.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, and in particular, to a method and system for selecting an urban renewal unit renewal mode based on GIS. Background Art

[0002] The technical field of urban planning includes research and design in aspects such as urban land use, building space layout, infrastructure planning, and traffic network optimization. The core content of this technical field lies in coordinating the allocation of urban spatial resources through scientific planning means, optimizing urban functional zoning, and achieving the sustainability and coordination of urban development. The technical field of urban planning covers multiple levels from overall planning to detailed planning, including the formulation of regional development strategies, the optimization of urban land use layout, the organization of traffic systems, and the scientific design of the distribution of public facilities, emphasizing the combination of technology and policy to enhance the spatial order and resource utilization efficiency of the city.

[0003] Among them, the method for selecting an urban renewal unit renewal mode based on GIS refers to using a geographic information system as a technical support tool to complete the optimal selection of the renewal mode through the spatial data analysis of urban renewal units. This patent theme addresses the problem of selecting the renewal mode of urban renewal units, covering technical matters such as the collection and analysis of geographic information of urban renewal units, the classification of urban renewal modes and the establishment of corresponding evaluation criteria, and the selection of renewal modes by combining evaluation criteria with data analysis results. Specifically, it is based on geospatial analysis technology and realizes the optimization and application of renewal mode selection by establishing a renewal mode database and applying a classification evaluation model.

[0004] The prior art mainly relies on static geographic information and single-category analysis, and fails to fully consider the dynamic change characteristics in the time dimension. For the analysis of the influence of meteorological factors, more reliance is placed on the distribution of static data and the quantification of its time series trend is lacking, making it difficult to identify the change characteristics of adaptive parameters in a dynamic environment, resulting in the evaluation results not being able to meet the actual application requirements. In proximity analysis, the prior art often stays at the calculation of intensity in a single dimension, ignoring the contribution of functional interaction to the dynamic impact of the region, which leads to insufficient evaluation accuracy. In boundary demarcation, the prior art pays more attention to the static characteristics of regional demarcation and fails to fully combine dynamic characteristic parameters to adjust the boundary range, easily resulting in a mismatch between regional demarcation and functional distribution. In the mode selection link, the consideration of multi-dimensional parameter matching and optimization is lacking, and too much reliance is placed on a single indicator or static matching method, making it difficult to cover the complex requirements of function and adaptability in a dynamic environment. These deficiencies limit the adaptability of the prior art in complex environments and its ability to handle dynamic changes, and it is difficult to meet the high-precision requirements for urban renewal mode optimization. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose a method and system for selecting an urban renewal unit renewal mode based on GIS.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A method for selecting an urban renewal unit renewal mode based on GIS, comprising the following steps:

[0007] S1: Based on the spatial distribution data of the urban renewal unit, extract the geographical location parameters, the distribution characteristics of functional nodes and the social activity intensity parameters, calculate the geographical proximity intensity, quantify the functional proximity intensity through the node distribution relationship, analyze the social proximity intensity based on the population density and activity frequency, and generate the comprehensive intensity distribution value of regional proximity;

[0008] S2: Based on the comprehensive intensity distribution value of the regional proximity, call the temperature, humidity, and wind speed time series data of regional meteorological monitoring, calculate the time change trend of the meteorological parameters, analyze its spatial distribution characteristics, and combine with the proximity intensity distribution to generate the microclimate change trend distribution value;

[0009] S3: Based on the comprehensive intensity distribution value of the regional proximity and the microclimate change trend distribution value, extract the spatial areas with larger proximity intensity parameters and the areas with significant climate change, analyze the spatial interaction range and coverage relationship between the two, calculate the dynamic adaptability parameters within the region, and combine with the spatial influence intensity to generate the spatial dynamic adaptability evaluation value;

[0010] S4: Based on the spatial dynamic adaptability evaluation value, combine with the existing boundary data of the renewal unit, extract the areas with high adaptability values within the boundary, analyze the boundary change gradient, and identify the functional transition areas, adjust the boundary range according to the dynamic characteristics, and generate the optimized boundary distribution value of the renewal unit;

[0011] S5: Based on the optimized boundary distribution value of the renewal unit and the spatial dynamic adaptability evaluation value, extract the functional distribution characteristics within the boundary range, analyze the matching relationship between the mode and the adaptability parameters, calculate the intensity value covered by the region, and combine with the dynamic balance result to generate the dynamic selection result of the urban renewal unit mode.

[0012] As a further solution of the present invention, the comprehensive intensity distribution value of the regional proximity includes geographical proximity distribution, functional proximity distribution, and social proximity distribution, the microclimate change trend distribution value includes temperature change trend, humidity change trend, and wind speed change trend, the spatial dynamic adaptability evaluation value includes adaptability distribution intensity, spatial interaction area range, and dynamic coverage intensity value, the optimized boundary distribution value of the renewal unit includes the adaptability distribution after boundary adjustment, the range of the functional transition area, and the boundary change gradient distribution, and the dynamic selection result of the urban renewal unit mode includes functional matching parameter values, regional coverage distribution intensity, and dynamic mode optimization selection values.

[0013] As a further solution of the present invention, based on the spatial distribution data of urban renewal units, geographic location parameters, functional node distribution characteristics and social activity intensity parameters are extracted, geographic proximity intensity is calculated, functional proximity intensity is quantified through node distribution relationship, social proximity intensity is analyzed according to population density and activity frequency, and the specific steps of generating regional proximity comprehensive intensity distribution value are as follows:

[0014] S101: Based on the spatial distribution data of the urban renewal unit, the coordinate information of the geographical location is extracted, and the node categories and their geographical distribution characteristics are analyzed one by one in combination with the functional node data, the spatial characteristics and distribution ranges of the nodes are classified, and the functional characteristic parameters associated with the geographical coordinates are extracted to generate a set of geographical location and functional distribution parameters;

[0015] S102: Based on the geographic location and function distribution parameter set, the geometric space distance between adjacent nodes is analyzed, and according to the distance value between the nodes, the physical proximity between the nodes is calculated, the correlation degree and distribution compactness of the functional node categories are quantified, and the geographic proximity and functional proximity strength matrix is generated;

[0016] S103: Based on the geographic proximity and functional proximity intensity matrix, the population density and activity frequency data are integrated to perform spatial overlay analysis on the activity distribution intensity, calculate the regional social activity density, analyze the dynamic interaction intensity of the functional nodes, and generate a comprehensive intensity distribution value of regional proximity.

[0017] As a further solution of the present invention, based on the regional proximity comprehensive intensity distribution value, calling the temperature, humidity, and wind speed time series data of regional meteorological monitoring, calculating the time change trend of meteorological parameters, analyzing its spatial distribution characteristics, and combining the proximity intensity distribution, the specific steps of generating the microclimate change trend distribution value are as follows:

[0018] S201: Based on the regional proximity comprehensive intensity distribution value, calling the temperature, humidity, and wind speed time series data of regional meteorological monitoring, analyzing the time variation characteristics of the temperature, humidity, and wind speed data one by one, performing dynamic trend calculation on each group of parameters in chronological order, extracting the variation amplitude of the meteorological parameters, and generating a feature set of the time variation trend of the meteorological parameters;

[0019] S202: Based on the time change trend feature set of the meteorological parameters, the time trend characteristics of the meteorological parameters are matched with the spatial distribution coordinates one by one, the change amplitude and difference of the meteorological parameters in the space are analyzed, the spatial distribution intensity is calculated, the change value and distribution relationship associated with the position are extracted, and the spatial distribution feature set of the meteorological parameters is generated;

[0020] S203: Based on the set of meteorological parameter spatial distribution characteristics, combined with the comprehensive intensity distribution value of regional proximity, analyze the distribution relationship between meteorological parameters and proximity, combine and calculate the interaction between the meteorological change trend and proximity intensity within the region, and generate a micro-climate change trend distribution value.

[0021] As a further solution of the present invention, the specific formula for the distribution intensity is:

[0022]

[0023] Wherein, S ij represents the distribution intensity between two nodes i and j in space, V ijk represents the observed value of meteorological parameter k between spatial nodes i and j, represents the average value of meteorological parameter k between spatial nodes i and j, n represents the total number of meteorological parameters, represents the absolute deviation of the meteorological parameter, |X i -X j | and |Y i -Y j | respectively represent the absolute distances of spatial nodes i and j in the X and Y coordinates.

[0024] As a further solution of the present invention, based on the comprehensive intensity distribution value of regional proximity and the micro-climate change trend distribution value, the specific steps for extracting spatial regions with larger proximity intensity parameters and regions with significant climate change, analyzing the spatial interaction range and coverage relationship between the two, calculating the dynamic adaptability parameters within the region, and combining the spatial influence intensity to generate a spatial dynamic adaptability evaluation value are as follows:

[0025] S301: Based on the comprehensive intensity distribution value of regional proximity and the micro-climate change trend distribution value, set a threshold for the proximity intensity parameter, screen one by one the regions with high values in the proximity intensity parameter, analyze the regions with large parameter fluctuation amplitudes in the meteorological change trend, match the superimposed range of the two types of regions in space, and generate a set of spatial interaction and coverage range characteristics;

[0026] S302: Based on the set of spatial interaction and coverage range characteristics, analyze one by one the proximity intensity and climate change intensity values within the interaction region, calculate the dynamic change characteristics of the interaction region, summarize the dynamic matching parameters within the region, classify and quantify the range of the matching parameters, and generate a set of regional dynamic matching parameters;

[0027] S303: Based on the set of regional dynamic matching parameters, combined with the distribution characteristics of proximity intensity in the spatial range, analyze the correlation characteristics between dynamic matching and spatial coverage range, evaluate the change and distribution intensity of dynamic matching, and generate a spatial dynamic adaptability evaluation value.

[0028] As a further solution of the present invention, the specific formula for the dynamic change characteristic value is as follows:

[0029]

[0030] D ij represents the dynamic change characteristic value between interaction regions i and j. represents the proximity intensity of the k-th meteorological parameter in interaction region i. represents the change intensity value of the k-th meteorological parameter in interaction region j. represents the observed value of the k-th meteorological parameter between interaction regions i and j. represents the average value of the k-th meteorological parameter between interaction regions i and j. represents the total number of meteorological parameters participating in the calculation between interaction regions i and j.

[0031] As a further solution of the present invention, based on the spatial dynamic adaptability evaluation value, combined with the existing boundary data of the update unit, extract the high-adaptability value regions within the boundary, analyze the boundary change gradient, and identify the functional transition regions. According to the dynamic characteristics, adjust the boundary range, and the specific steps for generating the optimized boundary distribution value of the update unit are as follows:

[0032] S401: Based on the spatial dynamic adaptability evaluation value and the existing boundary data of the update unit, set the adaptability evaluation value threshold, extract the regions with higher numerical values in the dynamic adaptability evaluation value one by one, compare the spatial distribution of the high-value regions with the boundary data, calculate the dynamic adaptability distribution range of the regions within the boundary, analyze the adjacent spatial change characteristics of the high-value regions, and generate the distribution of high-adaptability value regions within the boundary;

[0033] S402: Based on the distribution of high-adaptability value regions within the boundary, calculate the change gradient of the adaptability numerical values in the boundary regions, analyze the spatial regions with significant adaptability gradient changes within the boundary range, summarize the functional characteristics of the positions with significant gradients, and combine with the high-adaptability value regions to generate the distribution of functional transition region characteristics;

[0034] S403: Based on the distribution of functional transition region characteristics, combined with the existing boundary and the dynamic adaptability distribution value, analyze the adaptive dynamic changes at the boundary positions, adjust the regions with prominent dynamic changes in the boundary range one by one, re-match the spatial relationship between the boundary and the adaptability distribution, and generate the optimized boundary distribution value of the update unit.

[0035] As a further solution of the present invention, based on the optimized boundary distribution value of the update unit and the spatial dynamic adaptability evaluation value, extract the functional distribution characteristics within the boundary range, analyze the matching relationship between the pattern and the adaptability parameters, calculate the intensity value of the area coverage, and combine with the dynamic balance result, and the specific steps for generating the dynamic selection result of the urban update unit pattern are as follows:

[0036] S501: Optimize the boundary distribution value and the spatial dynamic adaptability evaluation value based on the update unit, extract the distribution characteristics of the functional areas within the boundary one by one, analyze the spatial range and type characteristics of the functional areas, compare the spatial characteristics matching the functional areas in the dynamic adaptability parameters, extract the distribution law of the matching between function and adaptability, and generate a set of characteristics of the matching between function distribution and adaptability;

[0037] S502: Calculate the coverage intensity parameters within the functional areas based on the set of characteristics of the matching between function distribution and adaptability, analyze the distribution law of the coverage intensity values in different functional partitions, summarize the coverage characteristics of the functional areas one by one, and perform a hierarchical analysis of the spatial distribution of the coverage intensity in combination with the dynamic change characteristics of the functional areas to generate a characteristic distribution of the regional coverage intensity;

[0038] S503: Based on the characteristic distribution of the regional coverage intensity, combine the distribution characteristics of the coverage range in the dynamic balance result, analyze the dynamic change law of the coverage intensity value and the functional areas, match the dynamic adaptability of the functional distribution and the intensity distribution within the region, and generate a dynamic selection result of the urban renewal unit mode.

[0039] A system for selecting an urban renewal unit renewal mode based on GIS, comprising:

[0040] The proximity analysis module extracts geographical location parameters, functional node distribution characteristics and social activity intensity parameters based on the spatial distribution data of urban renewal units, quantifies the functional proximity intensity through the node distribution relationship, analyzes the social proximity intensity based on the population density and activity frequency, and generates a comprehensive regional proximity intensity distribution value;

[0041] The climate trend module calls the temperature, humidity and wind speed time series data of regional meteorological monitoring based on the comprehensive regional proximity intensity distribution value, calculates the time change trend of meteorological parameters, and combines the proximity intensity distribution to generate a microclimate change trend distribution value;

[0042] The dynamic evaluation module extracts spatial areas with larger proximity intensity parameters and areas with significant climate change based on the comprehensive regional proximity intensity distribution value and the microclimate change trend distribution value, calculates the dynamic adaptability parameters within the region, and combines the spatial influence intensity to generate a spatial dynamic adaptability evaluation value;

[0043] The boundary optimization module extracts high-adaptability value areas within the boundary based on the spatial dynamic adaptability evaluation value and combines the existing boundary data of the renewal unit, analyzes the boundary change gradient, and adjusts the boundary range according to the dynamic characteristics to generate an optimized boundary distribution value of the renewal unit;

[0044] The pattern matching module optimizes the boundary distribution value and the spatial dynamic adaptability evaluation value based on the update unit, extracts the functional distribution characteristics within the boundary range, analyzes the matching relationship between the pattern and the adaptability parameters, calculates the intensity value of the area coverage, and generates the dynamic selection result of the urban renewal unit pattern.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] In the present invention, by integrating the comprehensive intensity distribution of proximity and dynamic meteorological data, an analysis logic combining time and space dimensions is constructed. By quantifying the dynamic changes of meteorological parameters and the intensity distribution of proximity, the spatial interaction range is improved. In the boundary adjustment, the adaptability distribution and functional transition characteristics are combined to reduce the limitations of static division. The pattern selection matches the dynamic parameters with the functional distribution, significantly improving the scientificity and accuracy of the update pattern selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0048] Figure 1 It is a schematic diagram of the step flow of the present invention;

[0049] Figure 2 It is a flowchart of step S1 of the present invention;

[0050] Figure 3 It is a flowchart of step S2 of the present invention;

[0051] Figure 4 It is a flowchart of step S3 of the present invention;

[0052] Figure 5 It is a flowchart of step S4 of the present invention;

[0053] Figure 6 It is a flowchart of step S5 of the present invention;

[0054] Figure 7 It is a system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The following will describe the technical solutions in the present invention in conjunction with the drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to give examples, illustrations or explanations. Any embodiment or design described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or designs. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0059] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0060] Please refer to Figure 1 , a method for selecting an urban renewal unit renewal mode based on GIS, comprising the following steps:

[0061] S1: Based on the spatial distribution data of urban renewal units, extract geographical location parameters, functional node distribution characteristics and social activity intensity parameters, calculate the geographical proximity intensity, quantify the functional proximity intensity through the node distribution relationship, analyze the social proximity intensity based on population density and activity frequency, and generate the comprehensive intensity distribution value of regional proximity;

[0062] S2: Based on the comprehensive intensity distribution value of regional proximity, call the temperature, humidity and wind speed time series data of regional meteorological monitoring, calculate the time change trend of meteorological parameters, analyze its spatial distribution characteristics, and combine with the proximity intensity distribution to generate the microclimate change trend distribution value;

[0063] S3: Based on the comprehensive intensity distribution value of regional proximity and the microclimate change trend distribution value, extract the spatial regions with larger proximity intensity parameters and the regions with significant climate change, analyze the spatial interaction range and coverage relationship between the two, calculate the dynamic adaptability parameters within the region, and combine with the spatial influence intensity to generate the spatial dynamic adaptability evaluation value;

[0064] S4: Based on the spatial dynamic adaptability evaluation value, combined with the existing boundary data of the update unit, extract the high-value adaptability area within the boundary, analyze the boundary change gradient, and identify the functional transition area. Adjust the boundary range according to the dynamic characteristics and generate the optimized boundary distribution value of the update unit;

[0065] S5: Based on the optimization boundary distribution value and spatial dynamic adaptability evaluation value of the renewal unit, extract the functional distribution characteristics within the boundary range, analyze the matching relationship between the pattern and adaptability parameters, calculate the intensity value of the regional coverage, and combine the dynamic balance results to generate the dynamic selection results of the urban renewal unit model.

[0066] The comprehensive intensity distribution values of regional proximity include geographical proximity distribution, functional proximity distribution, and social proximity distribution. The distribution values of microclimate change trends include temperature change trends, humidity change trends, and wind speed change trends. The spatial dynamic adaptability assessment values include adaptability distribution intensity, spatial interaction area range, and dynamic coverage intensity values. The optimized boundary distribution values of renewal units include the adaptability distribution after boundary adjustment, the functional transition area range, and the boundary change gradient distribution. The dynamic selection results of urban renewal unit models include functional matching parameter values, regional coverage distribution intensity, and dynamic model optimization selection values.

[0067] See also Figure 2 , the specific steps of S1 are:

[0068] S101: Based on the spatial distribution data of the urban renewal unit, the coordinate information of the geographical location is extracted, and the node categories and their geographical distribution characteristics are analyzed one by one in combination with the functional node data, the spatial characteristics and distribution ranges of the nodes are classified, and the functional characteristic parameters associated with the geographical coordinates are extracted to generate a set of geographical location and functional distribution parameters;

[0069] The urban renewal units extracted from the file are subjected to geometric spatial processing. First, their geographic coordinate data are subjected to unified standardization correction and projection transformation. The functional node data are hierarchically analyzed using geographic information system tools. According to the node type and functional identification, node sets of different categories are screened out and their relevant attributes are extracted. Subsequently, the spatial characteristics of each node are quantitatively calculated, including node coverage area, average adjacent node density, spatial boundary determination and boundary range calculation. A distribution feature table is established in combination with the node type data. The functional distribution parameter table is generated by associating and mapping the geographic coordinate information of the nodes to form a set of geographic location and functional distribution parameters.

[0070] S102: Based on the geographic location and function distribution parameter set, the geometric space distance between adjacent nodes is analyzed, and according to the distance value between the nodes, the physical proximity between the nodes is calculated, the correlation degree and distribution compactness of the functional node categories are quantified, and the geographic proximity and functional proximity strength matrix is generated;

[0071] Analyze the geometric spatial distance and physical proximity between adjacent nodes, and according to the formula

[0072]

[0073] calculate the Euclidean distance between nodes, and based on the proximity intensity calculation formula

[0074]

[0075] quantify the physical proximity between adjacent nodes, and generate a geographical proximity and functional proximity intensity matrix.

[0076] In the formula, d ij represents the geometric distance between node i and node j, x i , y i are the geographical coordinates of node i respectively, x j , y j are the geographical coordinates of node j, P ij is the proximity intensity between nodes, and k is the distance attenuation coefficient.

[0077] In the formula, the node geographical coordinates x i , y i and x j , y j are extracted from the functional node distribution parameter set through a geographic information system, and two sets of node coordinate values collected actually are adopted: node A (x = 100, y = 200) and node B (x = 120, y = 180).

[0078] Calculate the geometric distance between the two nodes:

[0079]

[0080] Substitute the distance attenuation coefficient k = 2 into the proximity intensity formula:

[0081]

[0082] The result shows that the physical proximity intensity between node A and node B is 0.00125, and this value is used to construct a geographical proximity and functional proximity intensity matrix, which reflects the spatial and functional association degree between different nodes and provides basic data support for subsequent analysis of the tightness of functional distribution.

[0083] S103: Based on the geographical proximity and functional proximity intensity matrix, fuse the population density and activity frequency data, conduct a spatial overlay analysis on the activity distribution intensity, calculate the regional social activity density, analyze the dynamic interaction intensity of functional nodes, and generate a regional proximity comprehensive intensity distribution value;

[0084] Analyze the activity distribution intensity of adjacent functional nodes. By fusing population density and activity frequency data, establish a dynamic activity distribution table for corresponding nodes. Dynamically superimpose the activity frequencies according to time segments, use an accumulation algorithm to quantitatively process the activity frequencies of different nodes, and normalize the activity distribution intensity values based on the overall population distribution ratio data within the region to quantify the social activity density within the region, and further generate a comprehensive intensity distribution value of proximity reflecting the regional social activity distribution.

[0085] Please refer to Figure 3 , and the specific steps of S2 are as follows:

[0086] S201: Based on the comprehensive intensity distribution value of regional proximity, call the temperature, humidity, and wind speed time series data of regional meteorological monitoring, analyze the time variation characteristics of temperature, humidity, and wind speed data one by one, perform dynamic trend calculations on each group of parameters in chronological order, extract the change amplitude of meteorological parameters, and generate a time change trend feature set of meteorological parameters;

[0087] Segment the time series of temperature, humidity, and wind speed data collected by meteorological monitoring stations, use the sliding window technique to analyze the time variation trend, select the maximum value, minimum value, and average value within each window to characterize the local variation characteristics of the time series, and at the same time calculate the change amplitude between adjacent time segments, extract the change rate and acceleration of the time series through the derivative method, and finally generate a time change trend feature set of meteorological parameters from the processed time series data.

[0088] S202: Based on the time change trend feature set of meteorological parameters, match the time trend characteristics and spatial distribution coordinates of meteorological parameters one by one, analyze the change amplitude and difference of meteorological parameters in space, calculate the spatial distribution intensity, extract the change values and distribution relationships associated with positions, and generate a spatial distribution characteristic set of meteorological parameters;

[0089] The specific formula for the distribution intensity is as follows:

[0090]

[0091] Among them, S ij represents the distribution intensity between two nodes i and j in space, V ijk represents the observed value of meteorological parameter k between spatial nodes i and j, represents the average value of meteorological parameter k between spatial nodes i and j, n represents the total number of meteorological parameters, represents the absolute deviation of the meteorological parameter, |X i -X j | and |Y i -Y j | respectively represent the absolute distances of spatial nodes i and j on the X coordinate and Y coordinate.

[0092] S ij is the distribution intensity between two nodes i and j in space, calculated by measuring the changes in meteorological parameters and the differences in spatial coordinates. The specific acquisition and derivation of parameters in the calculation process are as follows:

[0093] Observed value V of node meteorological parameters ijk : represents the observed value of the kth meteorological parameter between spatial nodes i and j, taken from the actual monitoring data of meteorological monitoring stations. Assume that the observed value of the first meteorological parameter of node i is 20, and the observed value of the first meteorological parameter of node j is 25, then the corresponding V ijk is 20 and 25.

[0094] Average value of node meteorological parameters Calculate the arithmetic mean through the observed values of the kth meteorological parameter between nodes i and j. The formula is

[0095]

[0096] For the first meteorological parameter,

[0097] Absolute deviation of meteorological parameters Calculate the absolute deviation value of each meteorological parameter to represent the degree of deviation of meteorological data. For the first meteorological parameter of nodes i and j, |20 - 22.5| = 2.5, |25 - 22.5| = 2.5.

[0098] Sum of squared deviations of meteorological parameters Sum the squares of the deviations of all meteorological parameters. Assume that there are two meteorological parameters observed between nodes. The absolute deviation of the first meteorological parameter is 2.5, and the observed values of the second meteorological parameter are 30 and 35, and its absolute deviation is |30 - 32.5| = 2.5. The sum of squared deviations is calculated as follows:

[0099]

[0100] Mean squared deviation: Divide the sum of squared deviations by the total number n of meteorological parameters. Assume that the total number of meteorological parameters monitored between nodes is 2:

[0101]

[0102] Standard deviation of meteorological parameters Take the square root of the mean of the squared deviations to represent the degree of dispersion of meteorological parameters:

[0103]

[0104] Absolute distance of spatial coordinates: The coordinate difference between nodes i and j. Assume the coordinate of node i is (10, 20) and the coordinate of node j is (30, 40):

[0105] |X i -X j | = |10 - 30| = 20, |Y i -Y j | = |20 - 40| = 20;

[0106] Calculation of the final distribution intensity: Substitute into the formula to calculate the distribution intensity:

[0107]

[0108] Substitute the calculation results:

[0109]

[0110] This result indicates that the distribution intensity between nodes i and j is 2.5, representing the comprehensive characteristics of the variation degree of meteorological parameters between nodes and the spatial distance difference, used to generate the set of spatial distribution characteristics of meteorological parameters to support subsequent analysis and extraction of distribution laws.

[0111] S203: Based on the set of spatial distribution characteristics of meteorological parameters, combined with the comprehensive intensity distribution value of regional proximity, analyze the distribution relationship between meteorological parameters and proximity, combine and calculate the interaction between the meteorological change trend and proximity intensity within the region to generate the micro-climate change trend distribution value;

[0112] Analyze the distribution relationship between meteorological parameters and proximity, according to the formula

[0113]

[0114] Calculate the micro-climate change trend distribution value.

[0115] In the formula, T i represents the micro-climate change trend value of node i, P ij is the proximity intensity between node i and node j, V j is the meteorological parameter change value of node j, and n is the number of nodes adjacent to node i.

[0116] In the formula, the node proximity intensity P ij is obtained from the geographical proximity and functional proximity intensity matrix, and the meteorological parameter change value V j is calculated through regional meteorological monitoring data. Taking the data of specific regional monitoring points as an example, assume there are three nodes in a certain region, and the proximity intensities between node 1 and nodes 2 and 3 are P 12 = 0.8, P 13= 0.6, the meteorological parameter change values of Node 2 and Node 3 are V2 = 2.5 and V3 = 3.0 respectively.

[0117] Substitute into the formula for calculation:

[0118]

[0119] The result shows that the microclimate change trend value of Node 1 is 2.71. This value is used to reflect the interaction intensity between meteorological parameters and proximity, providing a reference basis for the quantitative analysis of the regional microclimate change trend.

[0120] Please refer to Figure 4 , the specific steps of S3 are as follows:

[0121] S301: Based on the comprehensive intensity distribution value of regional proximity and the distribution value of microclimate change trend, set the threshold of the proximity intensity parameter, screen one by one the regions with high values in the proximity intensity parameter, analyze the regions with large parameter fluctuation ranges in the meteorological change trend, match the overlapping range of the two types of regions in space, and generate the spatial interaction and coverage range characteristic set;

[0122] By screening the regions with relatively high values in the proximity intensity parameter, dividing the regions using the set proximity intensity threshold, gradually screening the region ranges that meet the proximity intensity threshold, analyzing the fluctuation range of the meteorological change trend of the high-proximity intensity regions after screening, calculating the maximum fluctuation range of the time series in the region using the difference method, combining with the spatial geometry calculation of the overlapping range, determining the overlapping range of the two types of regions, and generating the spatial interaction and coverage range characteristic set through spatial topology analysis.

[0123] S302: Based on the spatial interaction and coverage range characteristic set, analyze one by one the proximity intensity and climate change intensity values in the interaction regions, calculate the dynamic change characteristic values of the interaction regions, summarize the dynamic matching parameter within the regions, classify and quantify the range of the matching parameters, and generate the regional dynamic matching parameter set;

[0124] The specific formula for the dynamic change characteristic value is:

[0125]

[0126] D ij represents the dynamic change characteristic value between interaction regions i and j, represents the proximity intensity of the k-th meteorological parameter in interaction region i, represents the change intensity value of the k-th meteorological parameter in interaction region j, represents the observed value of the k-th meteorological parameter between interaction regions i and j, represents the average value of the k-th meteorological parameter between interaction regions i and j, Represents the total number of meteorological parameters involved in the calculation between interaction regions i and j.

[0127] D ij Is the dynamic change characteristic value between interaction regions i and j, which quantifies the dynamic matching relationship between nodes through the calculation of meteorological data and spatial correlation intensity. The specific parameter acquisition and derivation are as follows:

[0128] Proximity intensity

[0129] The proximity intensity represents the relationship intensity between the k-th meteorological parameter in interaction region i and its surrounding regions. Through the geographical proximity calculation formula, assume that the proximity intensity of the first meteorological parameter in region i is 0.8, and the second meteorological parameter is 0.6.

[0130] Change intensity

[0131] The change intensity refers to the change amplitude of the k-th meteorological parameter in interaction region j. The difference between the maximum and minimum values of each meteorological parameter is calculated through time series data. Assume that the change intensity of the first meteorological parameter is 2.0, and the second meteorological parameter is 1.5.

[0132] Observed value

[0133] The observed value refers to the measured value of the k-th meteorological parameter between interaction regions i and j. It is obtained through actual monitoring by meteorological stations. Assume that the observed values of the first meteorological parameter are 20 and 25, and the observed values of the second meteorological parameter are 30 and 35.

[0134] Average value

[0135] The average value represents the mean value of the k-th meteorological parameter between interaction regions i and j, calculated by the formula:

[0136]

[0137] For the first meteorological parameter, For the second meteorological parameter,

[0138] Absolute deviation of meteorological parameter

[0139] The absolute deviation is used to calculate the difference between the observed value and the average value. For the first meteorological parameter, the deviation is |20 - 22.5| = 2.5 and |25 - 22.5| = 2.5. For the second meteorological parameter, the deviation is |30 - 32.5| = 2.5 and |35 - 32.5| = 2.5.

[0140] Total number of parameters

[0141] The total number of parameters is the number of meteorological parameters involved in the calculation. Assume that the total number of meteorological parameters involved in the calculation is 2.

[0142] Calculation of dynamic change characteristic values:

[0143] First, calculate the sum of the products of the squared deviations of each meteorological parameter:

[0144] The first meteorological parameter:

[0145] The second meteorological parameter:

[0146] Add the two together:

[0147]

[0148] Substitute the result into the formula for calculation:

[0149]

[0150] Result description:

[0151] This result indicates that the dynamic change characteristic value between the interaction regions i and j is 3.25, representing the coupling strength between the dynamic matching degree between regions and the change of meteorological parameters. Through this value, the dynamic matching of the interaction regions can be further analyzed, providing a basic basis for the classification and quantification of regional dynamic matching parameters.

[0152] S303: Based on the set of regional dynamic matching parameters, combined with the distribution characteristics of the proximity strength within the spatial range, analyze the correlation characteristics between the dynamic matching and the spatial coverage range, evaluate the change and distribution intensity of the dynamic matching, and generate a spatial dynamic adaptability evaluation value;

[0153] Use the weighted average method to analyze the correlation characteristics between the dynamic matching and the spatial coverage range, calculate the distribution intensity through the coefficient of variation of the dynamic matching parameter values in the spatial distribution, adopt the calculation method of the centroid distribution of the regional range coverage, gradually match the distribution intensity with the regional centroid position, summarize the change situation of the dynamic matching within the spatial coverage range, and generate a spatial dynamic adaptability evaluation value based on the statistical results of the distribution of the change values.

[0154] Please refer to Figure 5 , the specific steps of S4 are as follows:

[0155] S401: Based on the spatial dynamic adaptability evaluation value and the existing boundary data of the update unit, set the threshold of the adaptability evaluation value, extract the regions with higher numerical values in the dynamic adaptability evaluation value one by one, compare the spatial distribution of the high-value regions with the boundary data, calculate the dynamic adaptability distribution range of the regions within the boundary, analyze the adjacent spatial change characteristics of the high-value regions, and generate the distribution of the high-adaptability regions within the boundary;

[0156] Select the regions where the adaptability evaluation value is higher than the threshold, use spatial analysis tools to compare the spatial distribution of the high-value regions with the boundary data, calculate the spatial coverage rate of the high adaptability values in the regions within the boundary, extract the distribution of the high-adaptability regions within the boundary range through geometric overlay methods, analyze the adjacent distribution characteristics of these high-value regions in space, and combine the spatial position centroid and geometric form to generate the distribution of the high-adaptability regions within the boundary.

[0157] S402: Based on the distribution of the high-adaptability regions within the boundary, calculate the change gradient of the regional adaptability values in the boundary, analyze the spatial regions with significant adaptability gradient changes within the boundary range, summarize the functional characteristics of the positions with significant gradients, and combine with the high-adaptability regions to generate the distribution of the characteristics of the functional transition regions;

[0158] Calculate the change gradient of the regional adaptability values in the boundary, according to the formula

[0159]

[0160] Calculate the spatial regions with significant adaptability gradient changes within the boundary range.

[0161] In the formula, G i represents the adaptability change gradient of the ith region within the boundary, ΔA i represents the change amount of the adaptability value within region i, and ΔD i represents the corresponding spatial distance change amount within region i.

[0162] In the formula, the change amount of the adaptability value ΔA i is extracted through the numerical distribution of the high-adaptability regions within the boundary and calculated by comparing the adaptability values of each sampling point within the region; the spatial distance change amount ΔD i is obtained by calculating the spatial distance between the sampling points within the region. Taking an actual example calculation, assuming that the adaptability values of two sampling points in region i are 4.5 and 3.0 respectively, and the corresponding spatial distance is 2.0 kilometers:

[0163] Calculate the change amount of the adaptability value:

[0164] ΔA i =|4.5 - 3.0| = 1.5;

[0165] Calculate the spatial distance change amount:

[0166] ΔD i = 2.0;

[0167] Substitute into the formula to calculate the gradient:

[0168]

[0169] The result shows that the adaptive gradient change value of region i is 0.75, which is used to identify the spatial region with significant adaptive changes within the boundary range, providing basic data for the induction and classification of functional transition regions.

[0170] S403: Based on the characteristic distribution of functional transition regions, combined with the existing boundary and dynamic adaptive distribution values, analyze the adaptive dynamic changes at the boundary position, adjust one by one the regions with prominent dynamic changes within the boundary range, re-match the spatial relationship between the boundary and the adaptive distribution, and generate an update unit to optimize the boundary distribution value;

[0171] Adjust one by one the regions with prominent dynamic changes within the boundary range, partition and identify the regions with high dynamic changes within the boundary, analyze their spatial coincidence with the existing boundary, gradually adjust the spatial matching range of the dynamic adaptive distribution value, re-calculate the matching degree between the adjusted boundary data and the adaptive distribution value using geometric topology operations, and combine the boundary optimization distribution relationship of the regions with high dynamic changes to generate an update unit to optimize the boundary distribution value.

[0172] Please refer to Figure 6 , the specific steps of S5 are as follows:

[0173] S501: Based on the optimized boundary distribution value of the update unit and the spatial dynamic adaptive evaluation value, extract one by one the distribution characteristics of the functional regions within the boundary range, analyze the spatial range and type characteristics of the functional regions, compare the spatial characteristics matching the functional regions in the dynamic adaptive parameters, extract the distribution law of the matching between the function and the adaptability, and generate a set of characteristics of the functional distribution and adaptability matching;

[0174] Analyze the range and type of the functional regions, use spatial analysis methods to extract the geometric characteristics of each region, calculate the shape coefficient and area parameters of each functional region in combination with the functional attribute data, compare the spatial characteristics of the dynamic adaptive parameters, analyze the adaptive distribution law of the functional regions through one-by-one matching of the parameters, associate and match the geometric range of the functional regions with the adaptive characteristic values, extract the adaptive characteristics and distribution law of the functional regions, and generate a set of characteristics of the functional distribution and adaptability matching.

[0175] S502: Calculate the coverage intensity parameters within the functional area based on the functional distribution and adaptability matching feature set, analyze the distribution law of the coverage intensity values in different functional partitions, summarize the coverage characteristics of the functional area one by one, and perform hierarchical analysis and processing of the spatial distribution of the coverage intensity in combination with the dynamic change characteristics of the functional area to generate the regional coverage intensity characteristic distribution;

[0176] Calculate the coverage intensity parameters within the functional area according to the formula

[0177]

[0178] Analyze the distribution law of the coverage intensity values in different functional partitions.

[0179] In the formula, C i represents the coverage intensity value of functional area i, A ij is the coverage area of the j-th sub-area within area i, A i is the total area of functional area i, and w is the number of sub-areas within functional area i.

[0180] In the formula, the sub-area coverage area A ij is extracted from the spatial analysis and obtained through geometric calculation; the total area A of the functional area i is obtained through the spatial calculation method of the boundary range. Taking an actual example for calculation, assume that there are three sub-areas within functional area i, and their areas are A i1 = 20, A i2 = 15, A i3 = 25, and the total area of functional area i is A i = 70:

[0181] Calculate the coverage intensity:

[0182]

[0183] The result shows that the coverage intensity of functional area i is 0.857, which is used to measure the ratio relationship between the internal coverage area and the total area of the functional area, and reflects the distribution law of the coverage intensity in different functional partitions.

[0184] S503: Based on the regional coverage intensity characteristic distribution, combined with the distribution characteristics of the coverage range in the dynamic balance result, analyze the dynamic change law between the coverage intensity value and the functional area, match the dynamic adaptability of the functional distribution within the area and the intensity distribution, and generate the dynamic selection result of the urban renewal unit model;

[0185] Analyze the dynamic change law of the coverage intensity value and the functional area. Through the spatial distribution statistics of the standard deviation and dynamic change value of the coverage intensity of each sub-region within the functional area, gradually match the dynamic adaptability of the internal functional distribution of the region. Combine the regional coverage intensity value with the dynamic adaptability parameters of the functional distribution. Generate a hierarchical structure of adaptive distribution by calculating the dynamic change range within the region. Extract the spatial correlation characteristics of regional coverage and adaptive distribution. Finally, generate the dynamic selection result of the urban renewal unit mode.

[0186] Please refer to Figure 7 , a system for selecting the renewal mode of urban renewal units based on GIS, including:

[0187] The proximity analysis module extracts geographical location parameters, functional node distribution characteristics, and social activity intensity parameters based on the spatial distribution data of urban renewal units. Quantify the functional proximity intensity through the node distribution relationship. Analyze the social proximity intensity based on population density and activity frequency, and generate the comprehensive intensity distribution value of regional proximity;

[0188] The climate trend module calls the time series data of temperature, humidity, and wind speed monitored by regional meteorology based on the comprehensive intensity distribution value of regional proximity, calculates the time change trend of meteorological parameters, and combines the proximity intensity distribution to generate the micro-climate change trend distribution value;

[0189] The dynamic evaluation module extracts the spatial regions with larger proximity intensity parameters and the regions with significant climate change based on the comprehensive intensity distribution value of regional proximity and the micro-climate change trend distribution value, calculates the dynamic adaptability parameters within the region, and combines the spatial influence intensity to generate the spatial dynamic adaptability evaluation value;

[0190] The boundary optimization module extracts the regions with high adaptability values within the boundary based on the spatial dynamic adaptability evaluation value and combines the existing boundary data of the renewal unit, analyzes the boundary change gradient, and adjusts the boundary range according to the dynamic characteristics to generate the optimized boundary distribution value of the renewal unit;

[0191] The mode matching module extracts the functional distribution characteristics within the boundary based on the optimized boundary distribution value of the renewal unit and the spatial dynamic adaptability evaluation value, analyzes the matching relationship between the mode and the adaptability parameters, calculates the intensity value of regional coverage, and generates the dynamic selection result of the urban renewal unit mode.

[0192] As mentioned above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claimed rights.

Claims

1. A method for selecting an urban renewal unit renewal mode based on GIS, characterized in that It includes the following steps: S1: Based on the spatial distribution data of urban renewal units, extract geographical location parameters, functional node distribution characteristics, and social activity intensity parameters, calculate the geographical proximity intensity, quantify the functional proximity intensity through the node distribution relationship, analyze the social proximity intensity based on population density and activity frequency, and generate the comprehensive intensity distribution value of regional proximity; S2: Based on the comprehensive intensity distribution value of regional proximity, call the temperature, humidity, and wind speed time series data of regional meteorological monitoring, calculate the time variation trend of meteorological parameters, analyze their spatial distribution characteristics, and combine with the proximity intensity distribution to generate the microclimate change trend distribution value; S3: Based on the comprehensive intensity distribution value of regional proximity and the microclimate change trend distribution value, extract the spatial regions with larger proximity intensity parameters and the regions with significant climate change, analyze the spatial interaction range and coverage relationship between the two, calculate the dynamic adaptability parameters within the region, and combine with the spatial influence intensity to generate the spatial dynamic adaptability evaluation value; S4: Based on the spatial dynamic adaptability evaluation value, combine with the existing boundary data of the renewal unit, extract the high-adaptability regions within the boundary, analyze the boundary change gradient, and identify the functional transition regions, adjust the boundary range according to the dynamic characteristics, and generate the optimized boundary distribution value of the renewal unit; S5: Based on the optimized boundary distribution value of the renewal unit and the spatial dynamic adaptability evaluation value, extract the functional distribution characteristics within the boundary range, analyze the matching relationship between the pattern and the adaptability parameters, calculate the intensity value covered by the region, and combine with the dynamic balance result to generate the dynamic selection result of the urban renewal unit pattern.

2. The method for selecting an urban renewal unit renewal mode based on GIS according to claim 1, wherein The comprehensive intensity distribution value of regional proximity includes geographical proximity distribution, functional proximity distribution, and social proximity distribution. The microclimate change trend distribution value includes temperature change trend, humidity change trend, and wind speed change trend. The spatial dynamic adaptability evaluation value includes adaptability distribution intensity, spatial interaction region range, and dynamic coverage intensity value. The optimized boundary distribution value of the renewal unit includes the adaptability distribution after boundary adjustment, the range of functional transition regions, and the boundary change gradient distribution. The dynamic selection result of the urban renewal unit pattern includes functional matching parameter values, regional coverage distribution intensity, and dynamic pattern optimization selection values.

3. The method for selecting an urban renewal unit renewal mode based on GIS according to claim 1, wherein The specific steps for generating the comprehensive intensity distribution value of regional proximity based on the spatial distribution data of urban renewal units, extracting geographical location parameters, functional node distribution characteristics, and social activity intensity parameters, calculating the geographical proximity intensity, quantifying the functional proximity intensity through the node distribution relationship, and analyzing the social proximity intensity based on population density and activity frequency are as follows: S101: Based on the spatial distribution data of urban renewal units, extract the coordinate information of geographical locations, combine with the functional node data to parse the node categories and their geographical distribution characteristics one by one, summarize the spatial characteristics and distribution ranges of the nodes, extract the functional characteristic parameters associated with the geographical coordinates, and generate the geographical location and functional distribution parameter set; S102: Based on the geographical location and functional distribution parameter set, analyze the geometric spatial distance between adjacent nodes. According to the distance values between nodes, calculate the physical proximity between nodes, quantify the association degree and distribution tightness of functional node categories, and generate a geographical proximity and functional proximity intensity matrix; S103: Based on the geographical proximity and functional proximity intensity matrix, fuse the population density and activity frequency data, conduct a spatial overlay analysis on the activity distribution intensity, calculate the regional social activity density, analyze the dynamic interaction intensity of functional nodes, and generate a regional proximity comprehensive intensity distribution value.

4. The method for selecting an urban renewal unit renewal mode based on GIS according to claim 1, wherein Based on the regional proximity comprehensive intensity distribution value, the specific steps for calling the temperature, humidity, and wind speed time series data of regional meteorological monitoring, calculating the time change trend of meteorological parameters, analyzing their spatial distribution characteristics, and combining with the proximity intensity distribution to generate a microclimate change trend distribution value are as follows: S201: Based on the regional proximity comprehensive intensity distribution value, call the temperature, humidity, and wind speed time series data of regional meteorological monitoring, analyze the time change characteristics of temperature, humidity, and wind speed data one by one, conduct dynamic trend calculations on each group of parameters in chronological order, extract the change amplitude of meteorological parameters, and generate a meteorological parameter time change trend feature set; S202: Based on the meteorological parameter time change trend feature set, match the time trend characteristics and spatial distribution coordinates of meteorological parameters one by one, analyze the change amplitude and differences of meteorological parameters in space, calculate the spatial distribution intensity, extract the change values and distribution relationships of position associations, and generate a meteorological parameter spatial distribution characteristic set; S203: Based on the meteorological parameter spatial distribution characteristic set, combine with the regional proximity comprehensive intensity distribution value, analyze the distribution relationship between meteorological parameters and proximity, combine and calculate the interaction between the meteorological change trend and proximity intensity in the region, and generate a microclimate change trend distribution value.

5. The method for selecting an urban renewal unit renewal mode based on GIS according to claim 4, wherein, The specific formula for the distribution intensity is as follows: Among them, S ij represents the distribution intensity between two nodes i and j in space, V ijk represents the observed value of meteorological parameter k between space nodes i and j, represents the average value of meteorological parameter k between space nodes i and j, n represents the total number of meteorological parameters, represents the absolute deviation of the meteorological parameter, |X i - X j | and |Y i - Y j | respectively represent the absolute distances of space nodes i and j in the X and Y coordinates.

6. The method for selecting an urban renewal unit renewal mode based on GIS according to claim 1, wherein Based on the regional proximity comprehensive intensity distribution value and the microclimate change trend distribution value, the specific steps for extracting the spatial regions with larger proximity intensity parameters and the regions with significant climate change, analyzing the spatial interaction range and coverage relationship between the two, calculating the regional dynamic adaptability parameters, and combining with the spatial influence intensity to generate a spatial dynamic adaptability evaluation value are as follows: S301: Based on the regional proximity comprehensive intensity distribution value and the microclimate change trend distribution value, set a proximity intensity parameter threshold, screen the regions with high values in the proximity intensity parameters one by one, analyze the regions with large parameter fluctuation amplitudes in the meteorological change trend, match the overlay range of the two types of regions in space, and generate a spatial interaction and coverage range characteristic set; S302: Based on the spatial interaction and coverage range characteristic set, analyze the proximity intensity and climate change intensity values in the interaction regions one by one, calculate the dynamic change characteristics of the interaction regions, summarize the dynamic matching parameters within the regions, classify and quantify the range of the matching parameters, and generate a regional dynamic matching parameter set; S303: Based on the set of regional dynamic matching parameter sets, combined with the distribution characteristics of the proximity intensity within the spatial range, analyze the correlation characteristics between the dynamic matching and the spatial coverage range, evaluate the changes and distribution intensity of the dynamic matching, and generate a spatial dynamic adaptability evaluation value.

7. The method for selecting an urban renewal unit renewal mode based on GIS according to claim 6, wherein The specific calculation formula for the dynamic change characteristic value is as follows: D ij represents the dynamic change characteristic value between interaction regions i and j represents the proximity intensity of the k-th meteorological parameter in interaction region i represents the change intensity value of the k-th meteorological parameter in interaction region j represents the observed value of the k-th meteorological parameter between interaction regions i and j represents the average value of the k-th meteorological parameter between interaction regions i and j represents the total number of meteorological parameters participating in the calculation between interaction regions i and j 8. The method for selecting an urban renewal unit renewal mode based on GIS according to claim 1, wherein Based on the spatial dynamic adaptability evaluation value, combined with the existing boundary data of the update unit, extract the high-adaptability value regions within the boundary, analyze the boundary change gradient, and identify the functional transition regions. The specific steps for adjusting the boundary range according to the dynamic characteristics and generating the optimized boundary distribution value of the update unit are as follows: S401: Based on the spatial dynamic adaptability evaluation value and the existing boundary data of the update unit, set the threshold of the adaptability evaluation value, extract the regions with higher numerical values in the dynamic adaptability evaluation value one by one, compare the spatial distribution of the high-value regions with the boundary data, calculate the dynamic adaptability distribution range of the regions within the boundary, analyze the adjacent spatial change characteristics of the high-value regions, and generate the distribution of the high-adaptability value regions within the boundary; S402: Based on the distribution of the high-adaptability value regions within the boundary, calculate the change gradient of the adaptability numerical values in the regions of the boundary, analyze the spatial regions with significant adaptability gradient changes within the boundary range, summarize the functional characteristics of the positions with significant gradients, and combine with the high-adaptability value regions to generate the distribution of the functional transition region characteristics; S403: Based on the distribution of the functional transition region characteristics, combined with the existing boundary and the dynamic adaptability distribution value, analyze the adaptive dynamic changes at the boundary positions, adjust the regions with prominent dynamic changes in the boundary range one by one, re-match the spatial relationship between the boundary and the adaptability distribution, and generate the optimized boundary distribution value of the update unit.

9. The method for selecting an urban renewal unit renewal mode based on GIS according to claim 1, wherein Based on the optimized boundary distribution value of the update unit and the spatial dynamic adaptability evaluation value, extract the functional distribution characteristics within the boundary range, analyze the matching relationship between the pattern and the adaptability parameters, calculate the intensity value of the regional coverage, and combine with the dynamic balance result to generate the specific steps of the dynamic selection result of the urban renewal unit pattern are as follows: S501: Based on the optimized boundary distribution value of the update unit and the spatial dynamic adaptability evaluation value, extract the distribution characteristics of the functional regions within the boundary range one by one, analyze the spatial range and type characteristics of the functional regions, compare the spatial characteristics matching the functional regions in the dynamic adaptability parameters, extract the distribution law of the function and adaptability matching, and generate the set of function distribution and adaptability matching characteristics; S502: Based on the set of function distribution and adaptability matching characteristics, calculate the coverage intensity parameters within the functional regions, analyze the distribution law of the coverage intensity values within the differentiated functional partitions, summarize the coverage characteristics of the functional regions one by one, and perform a hierarchical analysis of the spatial distribution of the coverage intensity in combination with the dynamic change characteristics of the functional regions to generate the distribution of the regional coverage intensity characteristics; S503: Based on the distribution of the regional coverage intensity characteristics, combined with the distribution characteristics of the coverage range in the dynamic balance result, analyze the dynamic change law between the coverage intensity value and the functional region, match the dynamic adaptability of the functional distribution within the region and the intensity distribution, and generate the dynamic selection result of the urban renewal unit pattern.

10. A system for selecting an urban renewal unit renewal mode based on GIS, characterized in that, A method for selecting an urban renewal unit renewal mode based on GIS according to any one of claims 1-9, the system comprising: The proximity analysis module extracts geographical location parameters, functional node distribution characteristics and social activity intensity parameters based on the spatial distribution data of urban renewal units, quantifies the functional proximity intensity through the node distribution relationship, analyzes the social proximity intensity based on population density and activity frequency, and generates a comprehensive regional proximity intensity distribution value; The climate trend module calls the temperature, humidity, and wind speed time series data of regional meteorological monitoring based on the comprehensive regional proximity intensity distribution value, calculates the time change trend of meteorological parameters, and combines the proximity intensity distribution to generate a microclimate change trend distribution value; The dynamic evaluation module extracts spatial regions with larger proximity intensity parameters and regions with significant climate change based on the comprehensive regional proximity intensity distribution value and the microclimate change trend distribution value, calculates the dynamic adaptability parameters within the region, and combines the spatial influence intensity to generate a spatial dynamic adaptability evaluation value; The boundary optimization module extracts high-adaptability value regions within the boundary based on the spatial dynamic adaptability evaluation value and combines the existing boundary data of the renewal unit, analyzes the boundary change gradient, adjusts the boundary range according to dynamic characteristics, and generates an optimized boundary distribution value for the renewal unit; The mode matching module extracts the functional distribution characteristics within the boundary based on the optimized boundary distribution value of the renewal unit and the spatial dynamic adaptability evaluation value, analyzes the matching relationship between the mode and the adaptability parameters, calculates the intensity value covered by the region, and generates a dynamic selection result for the urban renewal unit mode.

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