Land space multi-level function data analysis processing method and device

Abstract

The invention relates to a territorial space multi-level function data analysis processing method and device, belongs to the technical field of land space distribution data processing, and solves the problems of inconsistent function positioning of different types of territorial spaces and insufficient accuracy of analysis result data in an existing territorial space function analysis method. The invention discloses a territorial space multi-level function data analysis processing method and device. A land parcel is strengthened according to a purpose-function conversion matrix so as to update a function identification unit of the land parcel; quantifying the spatial distribution density of each type of POI of the land parcel according to the kernel density of the POI data; calculating the distribution frequency of different POI types in each function type in the function identification unit; in combination with a kernel density analysis result and a frequency density analysis result, carrying out weighted calculation and standardization processing on the spatial function value of each function identification unit; and determining a data analysis result of each function identification unit in each function hierarchy framework. According to the method, the land space layering is analyzed and processed, so that the accuracy of analysis result data is improved.

Description

Technical Field

[0001] This application relates to the technical field of land spatial distribution data processing, and particularly to a method and device for analyzing and processing multi-level functions of national land space. Background Art

[0002] National land space planning is a long-term plan and overall arrangement made by the government departments of a country or region for the national land space resources and layout within its jurisdiction, aiming to achieve effective control and scientific governance of the national land space and promote the balance between development and protection. The analysis of national land space functions is a part of national land space planning, which requires the combination of human needs and the endowment of environmental resource elements. With the concept of national land development shifting from being dominated by production space to a coordinated spatial layout of production, life, and ecology, the importance of national land space function analysis has become increasingly prominent.

[0003] Currently, with the construction and development of big data analysis, the analysis of national land space functions can be quantitatively processed according to data analysis and processing models. However, due to the relatively complex concept of national land space and the inconsistent functional positioning of different types of national land space, the scientific nature and operability of using fixed-standard quantitative processing to deal with various national land space function analyses are poor, the analysis results are greatly different from the actual situation, and the reference and accuracy are insufficient. Summary of the Invention

[0004] In view of the above analysis, the embodiments of the present invention aim to provide a method and device for analyzing and processing multi-level functions of national land space, so as to solve the problems in the implementation results of the prior art that the functional positioning of different types of national land space is inconsistent, the scientific nature and operability of using fixed-standard quantitative processing to deal with various national land space function analyses are poor, the analysis results are greatly different from the actual situation, resulting in insufficient reference and accuracy.

[0005] In a first aspect, an embodiment of the present application provides a method for analyzing and processing multi-level functions of national land space, including the steps of: Dividing the national land space into multiple functional hierarchical frameworks, and determining the functional categories and functional recognition units of each functional hierarchical framework; Obtaining the spatial data of each plot within the functional recognition unit, constructing a use-function conversion matrix to strengthen the plot, so as to update the functional recognition unit; wherein, the spatial data includes land use data and POI data; the use-function conversion matrix is used to establish the mapping relationship between the plot and the functional category; Based on the updated functional recognition unit, quantifying the spatial distribution density of each type of POI in the plot according to the kernel density of the POI data, obtaining the kernel density analysis result, and calculating the spatial function value of each functional category within the functional recognition unit; Based on the updated function recognition unit, calculate the distribution frequency of different POI types in each function category in the function recognition unit to obtain the frequency density analysis result; Combine the kernel density analysis result and the frequency density analysis result, perform weighted calculation and normalization processing on the spatial function values of each function recognition unit, and calculate the comprehensive function value; Gradually perform weighted calculation of the comprehensive function values of each function recognition unit within the function hierarchy framework to determine the data analysis results of each function recognition unit in each function hierarchy framework.

[0006] The multi-level function data analysis and processing method for territorial space in the embodiments of the present application divides the territorial space into multiple function hierarchy frameworks, determines the function categories and function recognition units of each function hierarchy framework. Strengthen the plots according to the use-function conversion matrix to update the function recognition unit. Quantify the spatial distribution density of each type of POI in the plot according to the kernel density of the POI data to obtain the kernel density analysis result. Calculate the distribution frequency of different POI types in each function category in the function recognition unit to obtain the frequency density analysis result. Combine the kernel density analysis result and the frequency density analysis result, perform weighted calculation and normalization processing on the spatial function values of each function recognition unit, and calculate the comprehensive function value; gradually perform weighted calculation of the comprehensive function values of each function recognition unit within the function hierarchy framework to determine the data analysis results of each function recognition unit in each function hierarchy framework. Analyze and process the territorial space in layers, which improves the scientificity and operability of function analysis in planning and the reference value of the analysis results.

[0007] As one of the optional embodiments, the process of obtaining the spatial data of each plot in the function recognition unit and constructing a use-function conversion matrix to strengthen the plot to update the function recognition unit includes the steps of: Assign function categories to the main function, secondary function, and tertiary function of the plot according to the land use data; Assign weights to the main function, secondary function, and tertiary function respectively.

[0008] As one of the optional embodiments, based on the updated function recognition unit, the process of quantifying the spatial distribution density of each type of POI in the plot according to the kernel density of the POI data to obtain the kernel density analysis result and calculate the spatial function value of each function category in the function recognition unit includes: Perform kernel density estimation on the POI data in each function recognition unit, and calculate the density value of each type of POI in the function recognition unit; According to the position, bandwidth, and kernel function of the POI, perform smoothing processing through the standard kernel function to obtain the density distribution of each function category in the function recognition unit, that is, the kernel density analysis result; The boundary divides various POI kernel density maps, calculates the proportion of the kernel density of various POIs within the functional recognition unit, and defines the spatial function value.

[0009] As one of the optional embodiments, the kernel density calculation formula of the POI data is: ; Where is the kernel density of the POI data; represents the position of the evaluation point; represents the th position of the POI; is the total number of POIs; is the bandwidth, which determines the smoothness of the kernel function; K is the kernel function; Weight assignment is performed on the valid POI data, and the value of each POI is calculated , as follows: ; Where , , are the area coefficient, functional relevance, and functional level weight of the th type of POI respectively; The value of the bandwidth is the accessibility distance of various POIs, and hierarchical mapping is performed according to the POI functional weight level P3; for the same functional category with different service radii , the kernel density estimate is calculated as follows: ; Where is the position of the evaluation point, is the set of POI indices belonging to the functional category and the influence range ; is all those belonging to with a bandwidth of The sum of the values of the POIs, used for normalization; is the set of all influence ranges under the functional category ; is The position of the th point in the th type of POI, belonging to the bandwidth is the bandwidth of The total number of POIs; is the kernel function; Define the kernel density map containing all functional categories as a set Where is the set of all functional categories: ; For each functional category in the unit , calculate the total kernel density of the functional category within the unit Sc,u : ; is the kernel density map of the functional category , indicating integration within the range of the function recognition unit to calculate the total impact of the functional category; Find the sum of the functional values of all POI categories in the calculated plot , where is the set of all functional categories : is the set of all functional categories : ; The spatial functional value of this functional category in the plot is calculated as follows: .

[0010] As one of the optional embodiments, the process of calculating the distribution frequency of different POI types in each functional category in the function recognition unit based on the updated function recognition unit to obtain the frequency density analysis result includes: For each function recognition unit, measure its frequency density : ; represents the weighted sum of all POI types belonging to the same functional category ; where is the function recognition unit in the number of POI types of the th functional category; is the number of the rd functional POI type in the entire region, is the corresponding weight or importance value; Calculate the type proportion of the function within the function recognition unit as the frequency of the function source : ; ; ​is the total number of function categories included in the function recognition unit and is the kind of multi-functional set of the function recognition unit

[0011] As one optional embodiment, combining the kernel density analysis result and the frequency density analysis result, the process of calculating the comprehensive function value by performing weighted calculation and normalization processing on the spatial function values of each function recognition unit includes: For each function category in the function recognition unit the POI function value is defined as: ; is the POI kernel density of the function category on the function recognition unit ; is the frequency density of the function category on the function recognition unit ; and are weight coefficients adjusted according to the presence or absence of POI data; For the function category in the unit the following relationship is satisfied: ; Among them, (1, 0) means when there is no POI data for the function category in the function recognition unit , and (0.4, 0.6) means when there is POI data for the function category in the function recognition unit .

[0012] As one optional embodiment, the function hierarchy framework includes a national function framework, a provincial function framework, a municipal function framework, a county function framework, and a township function framework

[0013] As one optional embodiment, the function categories of the national function framework include production, life, and ecology; The function categories of the provincial function framework include agricultural product production, industrial / service production, life function, ecological regulation, and special functions; The function categories of the municipal function framework include agricultural product production, industrial production, service production, transportation, residence, scientific and technological innovation, public services, culture, ecological regulation, and special functions; The functional categories of the county-level functional framework include crop production, forestry production, livestock production, fishery production, industrial production, commercial services, logistics warehousing, transportation services, residential, scientific and technological innovation, public services, historical and cultural inheritance, modern cultural exchanges, cultural tourism, ecosystem services, disaster bearing, national defense security, reserve functions, and other special functions; The functional categories of the township-level functional framework include food crop production, cash crop production, forestry production, livestock production, fishery production, manufacturing production, production and supply of electricity, heat, gas and water, energy and mineral supply, business and financial services, commercial trade services, catering services, entertainment services, logistics warehousing, transportation services, urban residence, rural residence, scientific research services, higher education services, basic education services, public infrastructure, social welfare, physical exercise services, health care services, public security services, public management services, historical and cultural inheritance, modern cultural exchanges, green space leisure services, natural landscape tourism services, cultural landscape tourism services, rural recreation services, soil and water conservation, water source conservation, biodiversity, windbreak and sand fixation, flood control and regulation, national defense security, reserve functions, and other special functions; The functional identification unit of the national-level functional framework is a district or a county; The functional identification unit of the provincial-level functional framework is a township; The functional identification unit of the municipal-level functional framework is a community or a village; The functional identification units of the county-level functional framework and the township-level functional framework are blocks.

[0014] In a second aspect, the embodiments of the present application further provide a multi-level functional data analysis and processing device for territorial space, including: A basic division module, configured to divide the territorial space into multiple functional hierarchical frameworks, and determine the functional categories and functional identification units of each functional hierarchical framework; A unit update module, configured to obtain the spatial data of each plot within the functional identification unit, construct a use-function conversion matrix to strengthen the plot, so as to update the functional identification unit; wherein, the spatial data includes land use data and POI data; the use-function conversion matrix is used to establish a mapping relationship between the plot and the functional category; A first analysis module, configured to, based on the updated functional identification unit, quantify the spatial distribution density of each type of POI of the plot according to the kernel density of the POI data, obtain a kernel density analysis result, so as to calculate the spatial function value of each functional category within the functional identification unit; A second analysis module, configured to, based on the updated functional identification unit, calculate the distribution frequency of different POI types in each functional category within the functional identification unit, and obtain a frequency density analysis result; A comprehensive calculation module is used to combine the results of kernel density analysis and frequency density analysis, perform weighted calculation and normalization processing on the spatial function values of each functional recognition unit, and calculate the comprehensive function value. A result output module is used to calculate the comprehensive function values of each functional recognition unit step by step within the functional hierarchy framework to determine the data analysis results of each functional recognition unit in each functional hierarchy framework.

[0015] The multi-level functional data analysis and processing device for territorial space in the embodiments of the present application divides the territorial space into multiple functional hierarchy frameworks, determines the functional categories and functional recognition units of each functional hierarchy framework. Strengthens the plots according to the use-function conversion matrix to update the functional recognition units. Quantifies the spatial distribution density of various types of POIs in the plots according to the kernel density of POI data to obtain the results of kernel density analysis. Calculates the distribution frequency of different POI types in each functional category in the functional recognition units to obtain the results of frequency density analysis. Combines the results of kernel density analysis and frequency density analysis, performs weighted calculation and normalization processing on the spatial function values of each functional recognition unit, and calculates the comprehensive function value; calculates the comprehensive function values of each functional recognition unit step by step within the functional hierarchy framework to determine the data analysis results of each functional recognition unit in each functional hierarchy framework. Analyzes and processes the territorial space in layers, which improves the scientificity and operability of functional analysis in planning and the reference value of the analysis results.

[0016] In a third aspect, at least one embodiment of the present application further provides a data control device, including: One or more memories that non-transiently store computer-executable instructions; One or more processors configured to run the computer-executable instructions, wherein when the computer-executable instructions are run by the one or more processors, the multi-level functional data analysis and processing method for territorial space according to any embodiment of the present application is implemented.

[0017] The above-mentioned data control device divides the national land space into multiple functional hierarchical frameworks, and determines the functional categories and functional identification units of each functional hierarchical framework. Strengthen the plots according to the use-function conversion matrix to update the functional identification units. Quantify the spatial distribution density of various types of POIs in the plots according to the kernel density of the POI data to obtain the kernel density analysis results. Calculate the distribution frequency of different POI types in the functional identification units in each functional category to obtain the frequency density analysis results. Combined with the results of the kernel density analysis and the frequency density analysis results, the spatial function values ​​of each functional identification unit are weighted and standardized to calculate the comprehensive function value; the comprehensive function values ​​of each functional identification unit are weighted and calculated step by step within the functional hierarchy framework to determine the data analysis results of each functional identification unit in each functional hierarchy framework. Analyzing and processing the national land space in layers improves the scientificity and operability of functional analysis in planning, as well as the reference value of the analysis results.

[0018] In a fourth aspect, at least one embodiment of the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement a method for analyzing and processing multi-level functional data of land space according to any embodiment of the present application.

[0019] The above-mentioned non-transient computer-readable storage medium divides the national land space into multiple functional hierarchical frameworks, and determines the functional categories and functional identification units of each functional hierarchical framework. The land parcels are strengthened according to the use-function conversion matrix to update the functional identification units. The spatial distribution density of each type of POI of the land parcel is quantified according to the kernel density of the POI data to obtain the kernel density analysis results. The distribution frequency of different POI types in each functional category in the functional identification unit is calculated to obtain the frequency density analysis results. Combining the results of the kernel density analysis and the frequency density analysis results, the spatial function values ​​of each functional identification unit are weighted and standardized to calculate the comprehensive function value; the comprehensive function value of each functional identification unit is weighted and calculated step by step within the functional hierarchy framework to determine the data analysis results of each functional identification unit in each functional hierarchy framework. The analysis and processing of the national land space in layers improves the scientificity and operability of the functional analysis in planning, and the analysis results realize the effective transmission of macro-planning goals to micro-implementation, and the analysis results data are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart of a method for analyzing and processing multi-level functional data of national land space provided by the present invention; Figure 2 A flow chart of a method for analyzing and processing multi-level functional data of national land space according to a preferred embodiment of the present invention; Figure 3Structural diagram of the module for analyzing and processing multi-level functional data of the national land space provided by an embodiment of the present invention; Figure 4 Schematic block diagram of a data control device provided by the present invention; Figure 5 Schematic diagram of a non-transitory computer-readable storage medium provided by the present invention. Detailed implementation manners

[0021] In order to make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0022] Unless otherwise defined, the technical terms or scientific terms used in the present application shall have the ordinary meanings understood by those of ordinary skill in the art to which the present application belongs. The "first", "second", and similar terms used in the present application do not denote any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0023] In order to keep the following description of the embodiments of the present application clear and concise, the detailed descriptions of some known functions and known components are omitted in the present application.

[0024] The embodiments of the present application provide a method for analyzing and processing multi-level functional data of the national land space.

[0025] Figure 1 Flowchart of the method for analyzing and processing multi-level functional data of the national land space according to an embodiment of the present application, as Figure 1 shown, the method for analyzing and processing multi-level functional data of the national land space according to an embodiment of the present application includes steps S100 to S105: S100, divide the national land space into multiple functional level frameworks, and determine the functional categories and functional identification units of each functional level framework; S101, obtaining spatial data of each plot in the functional identification unit, constructing a use-function conversion matrix to strengthen the plot, and updating the functional identification unit; wherein the spatial data includes land use data and POI data; the use-function conversion matrix is ​​used to establish a mapping relationship between the plot and the functional category, and realize the conversion identification from the plot use to the function; wherein the use-function conversion value is .

[0026] S102, based on the updated function identification unit, quantify the spatial distribution density of each type of POI of the plot according to the kernel density of the POI data, obtain the kernel density analysis result, and calculate the spatial function value of each functional category in the function identification unit; S103, based on the updated function identification unit, calculating the distribution frequency of different POI types in each function category in the function identification unit to obtain a frequency density analysis result; S104, combining the results of kernel density analysis and frequency density analysis to improve the accuracy and reliability of the function identification results, weighted calculation and standardized processing are performed on the spatial function values ​​of each function identification unit, the comprehensive function value is calculated, and the overall function value (P) is calculated comprehensively; wherein, the spatial function value after weighted calculation and standardized processing is .

[0027] S105, weighted calculation of the comprehensive function value of each function identification unit in the function hierarchy framework step by step to determine the data analysis result of each function identification unit in each function hierarchy framework.

[0028] Based on the framework of "three functions" of production, life and ecology, combined with domestic and foreign research results and international experience, a multi-level and calculable functional spectrum is constructed, covering the five-level functional hierarchy framework of national, provincial, municipal, county and township levels, and the functional analysis of national land space is managed in layers. This framework divides various functions into different levels and ensures that the functional classifications at all levels are transparent and calculable in terms of spatial scale and functional applicability.

[0029] Preferably, the functional spectrum includes five levels: national, provincial, municipal, county, and township, and each level has its own unique functional requirements and division standards.

[0030] The functional level framework includes national functional framework, provincial functional framework, municipal functional framework, county functional framework and township functional framework; The functional categories of the national functional framework include production, living, ecology, and special functions; The functional categories of the provincial functional framework include agricultural product production, industrial / service production, living functions, ecological regulation, and special functions; The functional categories of the municipal functional framework include agricultural production, industrial production, service production, transportation, residence, scientific and technological innovation, public services, culture, ecological regulation, and special functions; The functional categories of the county-level functional framework include crop production, forestry production, livestock production, fishery production, industrial production, commercial services, logistics warehousing, transportation services, residence, scientific and technological innovation, public services, historical and cultural inheritance, modern cultural exchanges, cultural tourism, ecosystem services, disaster bearing, national defense security, reserve functions, and other special functions; The functional categories of the township-level functional framework include food crop production, cash crop production, forestry production, livestock production, fishery production, manufacturing production, production and supply of electricity, heat, gas, and water, energy and mineral supply, business and financial services, commercial trade services, catering services, entertainment services, logistics warehousing, transportation services, urban residence, rural residence, scientific research services, higher education services, basic education services, public infrastructure, social welfare, physical exercise services, health care services, public security services, public management services, historical and cultural inheritance, modern cultural exchanges, green space leisure services, natural landscape tourism services, cultural landscape tourism services, rural recreation services, soil and water conservation, water source conservation, biodiversity, windbreak and sand fixation, flood control and regulation, national defense security, reserve functions, and other special functions; The functional identification unit of the national-level functional framework is the district or county; The functional identification unit of the provincial-level functional framework is the township; The functional identification unit of the municipal-level functional framework is the community or village; The functional identification units of the county-level and township-level functional frameworks are the blocks.

[0031] Specifically, the national-level functional framework mainly considers the three basic functions of production, life, and ecology, and further considers special functions on this basis. At the national scale, the functional division can be divided into four major categories: production, life, ecology, and special functions.

[0032] For national-level functional identification, the county (district) is selected as the functional identification unit. For provincial-level functional identification, the township is used as the functional identification unit. For municipal-level functions, the community or village is used as the functional identification unit. For county-level and township-level functional identification, the block (modular block, MB) is used as the functional identification unit. Since there may be suspended roads or isolated roads not connected to the main road network in the obtained road network data, topological correction is carried out to ensure the accuracy and connectivity of the data. At the same time, the MB is the smallest unit for function assignment, and the function pedigree conducts the function to the upper level. In addition, the boundaries of the upper-level units are also aggregated layer by layer from the MB units, and the function values are the sum of the function transmissions and conversions of the lower-level units, and normalization is carried out.

[0033] The provincial-level functional framework focuses on the layout of production space, taking into account the impact of regional urbanization on population carrying capacity and resource allocation, and forms five main functional categories: agricultural production, industrial / service production, living functions, ecological regulation, and special functions.

[0034] The municipal-level functional framework is between the provincial and county levels in the administrative management hierarchy. As a key position connecting the upper and lower levels in the administrative system, they directly implement the policies of the superior government (provincial level) and coordinate and guide the subordinate government (county level) to ensure the effective transmission of policies. Prefecture-level cities are usually the core of regional economic development, coordinating the management of economic, social, and cultural affairs within the region. The three-level national territorial space functional classification is the medium for macro-micro function transmission. Ten types of national territorial space functional classifications at the prefecture-level scale are set, including agricultural production, industrial production, service production, transportation, residence, scientific and technological innovation, public services, culture, ecological regulation, and special functions, which are used to decompose and measure the "production-living-ecology" functions.

[0035] The county-level functional framework connects the upper and lower levels in the administrative system and is responsible for the comprehensive management of the areas under the jurisdiction of the province. It is responsible for economic, social, cultural, and environmental affairs within its jurisdiction, including promoting local economic development, providing public services, producing agricultural products, building infrastructure, and protecting the environment. At this level, industries are further divided, emphasizing basic agricultural and industrial production while meeting the basic living and production needs of residents within the county. The four-level national territorial space functional classification further refines the functional classification at the micro scale, including 19 types of functions such as crop production, forestry production, livestock production, fishery production, industrial production, commercial services, logistics warehousing, transportation services, residence, scientific and technological innovation, public services, historical and cultural inheritance, modern cultural exchange, cultural tourism, ecosystem services, disaster bearing, national defense security, reserve functions, and other special functions, forming a four-level functional classification system that matches the county-level national territorial space zoning system.

[0036] The township-level administrative units are the grass-roots administrative units under the county level and are mainly responsible for the most direct local management and services. They focus on specific rural and urban affairs, including basic agricultural production, local public services, and daily infrastructure maintenance.

[0037] Therefore, the township-level functional framework provides basic education, healthcare, and social welfare, directly serving the living needs of residents. At the same time, it is responsible for implementing the policies of the county-level government, refining them into specific actions, and handling actual grass-roots problems. As the basic level of the functional system, the five-level function combines the functional categories of the national territorial space standard units at this level to realize the association from land use to spatial function, including food crop production, cash crop production, forestry production, livestock production, fishery production, manufacturing production, production and supply of electricity, heat, gas, and water, energy and mineral supply, business and financial services, commercial trade services, catering services, entertainment services, logistics and warehousing, transportation services, urban residence, rural residence, scientific research services, higher education services, basic education services, public infrastructure, social welfare, physical exercise services, healthcare services, public security services, public management services, historical and cultural inheritance, modern cultural exchanges, green space and leisure services, natural landscape tourism services, cultural landscape tourism services, rural recreation services, soil and water conservation, water source conservation, biodiversity, windbreak and sand fixation, flood control and regulation, national defense security, reserve functions, and other special functions, forming 39 types of national territorial space function classifications at the township level.

[0038] Spatial data includes land use data and POI data, and may also include specific administrative boundary data and road network data. To ensure the accuracy and consistency of spatial data and provide an accurate spatial basis for subsequent function recognition. After the function pedigree has been established and the functional hierarchy frameworks at different levels have been clarified in step S101, the next step is to obtain the spatial data of the target area. The accuracy and consistency of these spatial data are directly related to the accuracy of function recognition. Therefore, the original spatial data needs to undergo strict preprocessing to ensure its spatial consistency and accuracy.

[0039] Preferably, the specific preprocessing steps include: cropping and coding the boundaries of land use data, topologically correcting and segmenting administrative boundary and road network data, and checking the attributes and verifying the spatial positions of POI data. At the same time, to ensure the spatial consistency of the data, all spatial data will be uniformly converted into the same coordinate system (such as the WGS-84 coordinate system) and uniformly adopt the same projection method.

[0040] The division of function recognition units needs to correspond to different levels of administrative management to ensure the effective implementation of policies and the rational allocation of resources. To meet the management needs and planning realities of governments at all levels from the national to the local level, the function recognition units are divided into four-level units according to different function levels.

[0041] The national-level functional framework selects counties (districts) as the functional identification units. As the basic units of the national-level administrative divisions, counties have relatively perfect policy implementation capabilities and resource integration capabilities, can effectively implement national policies macroscopically, and at the same time have the conditions for large-scale regional development planning and resource allocation. The division helps to reflect the unity, comprehensiveness, and balance of the promotion of national policies.

[0042] The provincial-level functional framework uses townships as the functional identification units. Townships are the smallest local governments and an important link in governance, directly responsible for specific affairs within the region, and are suitable for the specific implementation and adjustment of provincial policies at a more micro level. The provincial government conducts regional development planning and resource allocation implementation according to local actual needs to ensure that provincial policies are properly implemented in each township.

[0043] The municipal-level functional framework uses communities or villages as the functional identification units. In the administrative system, communities and villages are the most basic autonomous organizational units in urban and rural areas respectively. They belong to the grass-roots level, directly serve the daily lives of residents, and are the forefront of implementing policies and public services. Using communities / villages as the functional identification units is conducive to the municipal government formulating development strategies more precisely according to the population needs of specific communities or villages, and realizing urban-rural integration and coordinated development at the municipal level.

[0044] The county-level functional framework and the township-level functional framework use blocks (modular blocks, MB) as the functional identification units. The county-level and township-level functions are directly related to all aspects of residents' daily lives, and blocks, as the basic units of the urban road network and the framework of urban development, can be used as the basic units to carefully plan the urban spatial structure, improve the quality of residents' lives, and enhance the efficiency of urban services.

[0045] Since there may be suspended roads or isolated roads not connected to the main road network in the obtained road network data, the embodiments of this application perform topological correction to ensure the accuracy and connectivity of the data. At the same time, MB is the smallest unit for function assignment, and the function pedigree conducts functions to the upper levels. In addition, the boundaries of the upper-level units are also aggregated layer by layer from the MB units, and the function values are the sum of the function conductions and conversions of the lower-level units, and are normalized.

[0046] As one of the optional embodiments, Figure 2 It is a flowchart of a method for analyzing and processing multi-level functions of national territorial space in an optional embodiment, as Figure 2 shown. In step S101, the spatial data of each plot within the functional identification unit is obtained, and a use-function conversion matrix is constructed to strengthen the plot, and the process of updating the functional identification unit includes step S200 and step S201: S200, assign function categories to the main function, secondary function, and tertiary function of the plot according to the land use data, and identify the multi-type function structure of the plot; S201, assign weights to the primary function, secondary function, and tertiary function respectively.

[0047] By associating land use data with functional attributes, construct a conversion matrix from land use to function to achieve the multi-functional conversion of plots, and convert plots of different land uses into corresponding functional categories.

[0048] Based on the processed land use data, by associating the land use category of plots with functional attributes, construct a tool for mapping the relationship from land use to function of plots to perform the multi-functional conversion of land use units. The core of this process is to convert plots of different land uses into corresponding functional categories according to land use data and functional attributes.

[0049] Preferably, according to specifications such as the "Classification Guide for Land and Sea Use in National Territory Spatial Survey, Planning, and Use Control", define the basic land uses of different types of land and map them to the corresponding functional attributes.

[0050] To achieve this mapping, construct a land use - function conversion matrix, through which the primary function, secondary function, and tertiary function of each plot can be accurately determined. For example, if a certain plot belongs to agricultural land, its primary function can be judged as "agricultural product production", secondary function as "ecological regulation", and the tertiary function may be "cultural protection" according to the potential attributes of its functional category. Through this mapping, the functional categories of each plot in the functional hierarchy framework can be detailedly divided.

[0051] In addition, in the multi-level function division, the weights of different functions also need to be considered. For example, set the weight of the primary function to 1, the secondary function to 0.6, and the tertiary function to 0.4. In this way, the functional categories and intensities of each functional recognition unit are quantified, and finally a close association between land use and spatial function is formed.

[0052] As one of the optional embodiments, as Figure 2 shown, the process of quantifying the spatial distribution density of various types of POIs of plots based on the updated functional recognition units in step S102 according to the kernel density of POI data to obtain the kernel density analysis result to calculate the spatial function value of each functional category within the functional recognition unit includes S300 to step S302: S300, perform kernel density estimation on the POI data within each functional recognition unit, and calculate the density values of various types of POIs within the functional recognition unit; S301, according to the position, bandwidth, and kernel function of the POI, perform smoothing processing through the standard kernel function to obtain the density distribution of each functional category within the functional recognition unit, that is, the kernel density analysis result; S302. Segment the kernel density maps of various POIs at the boundary, calculate the proportion of the kernel density of various POIs within the function recognition unit, and define the spatial function value.

[0053] Quantify the spatial distribution density of different types of POIs within the plot through POI kernel density analysis, so as to reflect the aggregation degree of various function categories in the function recognition unit, and calculate the spatial function value of each function category within the function recognition unit based on kernel density estimation. After completing the multi-functional transformation of the function recognition unit, first perform POI kernel density analysis. Kernel density analysis is used to quantify the spatial distribution density of different types of POIs (points of interest) in the target plot, and can reflect the spatial aggregation degree of functions within the plot. Specifically, it estimates the kernel density of the POI data within each function recognition unit and calculates the density value of each POI category within the function recognition unit. This process depends on parameters such as the location of the POI, bandwidth (service radius), and kernel function, and is smoothed through standard kernel functions such as the Gaussian kernel function, so as to obtain the density distribution of each function category within the function recognition unit. This analysis helps to accurately identify the aggregation of various POIs in the function recognition unit and lay a foundation for subsequent function optimization.

[0054] Preferably, given a set of POI data points, its kernel density estimate can be calculated by the following formula: ; where represents the location of the evaluation point; represents the th location of the POI; is the total number of POIs; is the bandwidth, which determines the smoothness of the kernel function; K is the kernel function; Assign weights to the valid POI data and calculate the value of each POI , as follows: ; where , , are the area coefficient, function relevance, and function level weight of the th type of POI respectively; The value of the bandwidth is the reachable distance of various POIs, and hierarchical mapping is performed according to the POI function weight level P3; for the same function category with different service radii , calculate the kernel density estimate as follows: ; where is the position of the evaluation point, belongs to the functional category and the influence range is the set of POI indexes; is all those belonging to with a bandwidth of of the POIs the sum of the values, used for normalization; is the functional category the set of all influence ranges below; is the position of the th point in the category of POIs, belonging to the bandwidth is the total number of POIs with a bandwidth of ; is the kernel function; defines the kernel density map containing all functional categories as a set where is the set of all functional categories : ; For each functional category in the cell , calculate the total kernel density Sc of the functional category within the cell, u : ; where, is the kernel density map of the functional category , indicating integration within the range of the functional recognition unit to calculate the total influence of the functional category; Find the sum of the functional values of all POI categories in the calculated plot where is the set of all functional categories: ; The spatial functional value of this functional category in the plot is calculated as follows: .

[0055] The ratio form in the above formula represents the relative importance or influence of the functional category in the plot

[0056] After completing the POI kernel density analysis, calculate the distribution frequency of different POI types in each functional category within the functional recognition unit through frequency density, revealing the proportion and influence of various functions in the spatial structure.

[0057] In actual operation, for each functional recognition unit, the calculation of frequency density takes into account the number of POI types within each functional category, the total number of such POIs in the entire region, and the corresponding weight values. By performing a weighted calculation on the number of various POIs within each functional category, the relative density of each functional type in the unit is obtained. This analysis can reflect the occurrence frequency of a specific POI type in the functional recognition unit, further revealing the distribution characteristics of various functions and their impact on the spatial structure. This step provides a necessary basis for judging the proportion of different functional categories within the functional recognition unit.

[0058] Measure the frequency density for each functional recognition unit : ; represents the weighted sum of all POI types belonging to the same functional category ; where is the functional recognition unit in the number of POI types of the th functional category; is the number of the th functional POI type in the entire region, is the corresponding weight or importance value; Calculate the type proportion of the functions within the functional recognition unit as the frequency of the functional source : ; ; is the total number of functional categories included in the functional recognition unit , is the of the functional recognition unit multifunctional set.

[0059] Among them, the final function value of each functional recognition unit is the set of the weighted sum of the values of all independent functional categories on it, and the functional recognition unit defines its functional set as follows: ; Among them, is the function recognition unit all functional categories in the set of and are two different attribute values of the functional category namely the use - function conversion value and the spatial function value after weighted calculation and normalization; and are the weights applied to and respectively. Preferably, let , .

[0060] Based on considering the land use - function conversion, combined with POI (Point of Interest) data, optimize and adjust the land parcel function, integrate kernel density and frequency density, enrich the dimension and depth of national land spatial information, and improve the accuracy and reliability of function recognition results. Ensure and assign independent values to each functional category on each land parcel unit respectively, and finally synthesize these values at the land parcel unit level.

[0061] Assign the functional category to the main function, secondary function and tertiary function of the unit according to land use data and POI data; assign weights to the main function, secondary function and tertiary function respectively.

[0062] Assign the use - function conversion value ( ). By constructing a basic land use - function conversion matrix, an innovative land use qualitative analysis method is proposed. Systematically associate the basic uses of different land parcels with their potential multi - level function conversion relationships, and clarify the national land spatial functional categories of land parcel units at different levels. The following is the detailed use - multi - function conversion correspondence. The use - function conversion value of each functional category c in unit u is defined as the following formula: ; where is the weight of functional category c in unit u, is the original data value of functional category c in unit u. For the functional category c in land parcel unit u, calculate the original functional data value according to the following formula: ; represents the area of functional category c in unit u, is the total area of unit u. Since the original functional data value has multi - level functions, According to this unit, this functional category belongs to the first-level function, the second-level function, and the third-level function, and the weight assignment is carried out according to 1, 0.6, and 0.4.

[0063] By combining the results of kernel density analysis and frequency density analysis, the functional values of each functional recognition unit are weighted and standardized to reflect the relative importance and spatial influence of each functional category within the plot.

[0064] Based on the results of kernel density analysis and frequency density analysis, the calculation and standardization of functional values are carried out. The functional values within each functional recognition unit will be comprehensively calculated through weighting and standardization by combining the results of kernel density and frequency density analysis. First, the functional values of each POI are weighted, considering factors such as its area coefficient, functional relevance, and functional level weight. Subsequently, the obtained kernel density and frequency density results are normalized, and finally, the standardized functional values of each functional category within the plot are obtained. These standardized functional values reflect the relative importance and spatial influence of each functional category within the plot, providing an accurate basis for subsequent functional optimization and decision-making.

[0065] Each functional category in the functional recognition unit of the POI functional value is defined as: ; where is the POI kernel density of the functional category in the functional recognition unit ; is the frequency density of the functional category in the functional recognition unit ; and are the weight coefficients adjusted according to the presence or absence of POI data; For the functional category in the unit satisfies the following relationship: ; where, (1,0) is when there is no POI data for the functional category in the functional recognition unit , and (0.4,0.6) is when there is POI data for the functional category in the functional recognition unit .

[0066] According to the functional pedigree framework, the comprehensive functional values of each functional recognition unit are calculated step by step to ensure that the functional characteristics and spatial efficiency of different-level units are accurately reflected.

[0067] After completing the functional assignment calculation of the minimum unit, a hierarchical comprehensive function value calculation is performed on the function recognition unit to better reflect the functional characteristics and spatial efficiency of units at different levels. Through the set functional pedigree framework, combined with the specific requirements and hierarchical requirements of different function recognition units, the comprehensive function values of each function recognition unit are aggregated and weighted step by step. By the weighted average method, all relevant function values of each function recognition unit are integrated according to the weights, and finally the comprehensive function performance of each function recognition unit at different levels is calculated.

[0068] Preferably, by sorting the comprehensive function values of each function recognition unit, the dominant function, secondary function, and tertiary function are identified.

[0069] After completing the comprehensive function value calculation, the dominant function, secondary function, and tertiary function of each function recognition unit are identified to provide a basis for subsequent resource allocation and policy formulation. Specifically, traverse all function recognition units, sort the function values of each unit, determine the function categories with the largest, second largest, and third largest function values, and assign corresponding function category codes to each function recognition unit. Through sorting, the dominant function of each function recognition unit is identified.

[0070] Preferably, a dynamic adjustment and feedback mechanism is established to continuously monitor and optimize the function recognition results through real-time data collection and update.

[0071] The dynamic adjustment and feedback mechanism is used to ensure that the function recognition results are continuously effective and can respond to external changes in a timely manner. Specifically, by establishing a data collection and update mechanism, the changes of each function recognition unit are continuously monitored, including dynamic data such as land use, population flow, economic activities, and environmental changes. Whenever new data is collected, the system will re-evaluate and optimize the existing function recognition results to ensure that they are consistent with the current actual situation. For example, if the ecological environment of a certain area changes significantly due to climate change, or the economic activities in a certain place transform due to policy adjustments, the system will adjust the function classification of this area according to the new data. In addition, the feedback mechanism can also be used for data interaction with relevant government departments and expert teams. Through regular data communication with decision-makers and technical experts, it is ensured that the function recognition results not only conform to data-driven analysis but also incorporate the actual needs at the social, economic, and policy levels.

[0072] The beneficial effects brought by the technical solution of the embodiment of this application are specifically reflected in solving the key technical problems in the existing national territorial space function classification system and providing practical technical support. Aiming at the deficiencies of the existing multi-level function classification of national territorial space, a hierarchical system is constructed, and an innovative method for function recognition is proposed.

[0073] Existing methods mainly focus on the macro - classification of the three major functions of "production - life - ecology", paying less attention to regional differences, functional subdivision, and their dynamic identification. By constructing a five - level function system from macro to micro, this study clarifies the classification system of functions at the national, provincial, municipal, county, and township levels, solves the problem of disjointed planning goals between levels, and improves the scientificity and operability of functional analysis in planning. In addition, the functions of territorial space often have complex characteristics, especially the complex intersection and interaction of production, life, and ecological functions. By integrating multiple function sources, the intersection and complex characteristics of different functions are demonstrated, and practical analysis methods for complex functions are proposed, promoting the research on the evaluation and management of complex functions.

[0074] Considering the six principles of scientificity, rationality, forward - looking, systematicness, regionality, and dynamics, a complete multi - level function classification system is constructed. Using geospatial big data analysis technology, integrating land use data and POI data, through the combination of function source discrimination methods and kernel density analysis, the accuracy of function identification and the ability to analyze cross - scale dynamic changes are improved. Ensuring the multi - level of function classification and the refinement of function identification provides more solid data support for territorial space planning and management.

[0075] The embodiment of this application also provides a device for data analysis and processing of multi - level functions of territorial space.

[0076] Figure 3 For the module structure diagram of the device for data analysis and processing of multi - level functions of territorial space in an embodiment, as Figure 3 shown, the device for data analysis and processing of multi - level functions of territorial space in one embodiment includes: The basic division module 100 is used to divide the territorial space into multiple functional hierarchical frameworks and determine the functional categories and functional identification units of each functional hierarchical framework; The unit update module 101 is used to obtain the spatial data of each plot within the functional identification unit, construct a use - function conversion matrix to strengthen the plot, and update the functional identification unit; wherein, the spatial data includes land use data and POI data; the use - function conversion matrix is used to establish the mapping relationship between the plot and the functional category; The first analysis module 102 is used to, based on the updated functional identification unit, quantify the spatial distribution density of various types of POIs in the plot according to the kernel density of the POI data, obtain the kernel density analysis result, and calculate the spatial function value of each functional category within the functional identification unit; The second analysis module 103 is used to, based on the updated functional identification unit, calculate the distribution frequency of different POI types in each functional category within the functional identification unit, and obtain the frequency density analysis result; The comprehensive calculation module 104 is configured to combine the kernel density analysis result and the frequency density analysis result, perform weighted calculation and normalization processing on the spatial function values of each function recognition unit, and calculate the comprehensive function value; The result output module 105 is configured to perform weighted calculation on the comprehensive function values of each function recognition unit level by level within the function hierarchy framework to determine the data analysis results of each function recognition unit in each function hierarchy framework.

[0077] The land spatial multi-level function data analysis and processing device according to the embodiment of the present application divides the land space into multiple function hierarchy frameworks, determines the function categories and function recognition units of each function hierarchy framework. Strengthens the plots according to the use-function conversion matrix to update the function recognition units. Quantifies the spatial distribution density of each type of POI in the plots according to the kernel density of the POI data to obtain the kernel density analysis result. Calculates the distribution frequency of different POI types in each function category in the function recognition unit to obtain the frequency density analysis result. Combines the kernel density analysis result and the frequency density analysis result, performs weighted calculation and normalization processing on the spatial function values of each function recognition unit, and calculates the comprehensive function value; performs weighted calculation on the comprehensive function values of each function recognition unit level by level within the function hierarchy framework to determine the data analysis results of each function recognition unit in each function hierarchy framework. Analyzes and processes the land space layer by layer, improving the scientificity and operability of the function analysis in the planning, as well as the reference value of the analysis results.

[0078] At least one embodiment of the present application further provides a data control device. Figure 4 It is a schematic block diagram of a data control device provided by at least one embodiment of the present application. For example, as Figure 4 shown, the data control device 20 may include one or more memories 200 and one or more processors 201. The memory 200 is used to non-transiently store computer-executable instructions; the processor 201 is used to run the computer-executable instructions, and when the computer-executable instructions are run by the processor 201, the processor 201 can be made to execute one or more steps in the land spatial multi-level function data analysis and processing method according to any embodiment of the present application.

[0079] For the specific implementation of each step of the land spatial multi-level function data analysis and processing method and the related explanatory content, reference can be made to the relevant content in the embodiments of the land spatial multi-level function data analysis and processing method described above, which will not be elaborated here. It should be noted that Figure 4 the components of the data control device 20 shown are exemplary and not restrictive. According to actual application needs, the data control device 20 may also have other components.

[0080] In one embodiment, the processor 201 and the memory 200 can communicate with each other directly or indirectly. For example, the processor 201 and the memory 200 can communicate through a network connection. The network can include a wireless network, a wired network, and / or any combination of a wireless network and a wired network. The type and function of the network are not limited herein. For another example, the processor 201 and the memory 200 can also communicate through a bus connection. The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. For example, the processor 201 and the memory 200 can be set at a remote data server end (cloud) or a distributed energy system end (local end), or can also be set at a client end (such as a mobile device like a mobile phone). For example, the processor 201 can be a Central Processing Unit (CPU), a Tensor Processing Unit (TPU), or a Graphics Processing Unit (GPU), etc., which has data processing capabilities and / or instruction execution capabilities, and can control other components in the data prediction device 20 to perform desired functions. The Central Processing Unit (CPU) can be of an X86 or ARM architecture, etc.

[0081] In one embodiment, the memory 200 can include any combination of one or more computer program products. The computer program products can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory can include, for example, Random Access Memory (RAM) and / or a cache, etc. Non-volatile memory can include, for example, Read-Only Memory (ROM), a hard disk, Erasable Programmable Read-Only Memory (EPROM), a Portable Compact Disc Read-Only Memory (CD-ROM), a USB memory, a flash memory, etc. One or more computer-executable instructions can be stored on the computer-readable storage media. The processor 201 can run the computer-executable instructions to implement various functions of the data prediction device 20. Various application programs and various data can also be stored in the memory 200, as well as various data used and / or generated by the application programs, etc.

[0082] It should be noted that the data control device 20 can achieve technical effects similar to those of the aforementioned multi-level functional data analysis and processing method for national land space. Repeated parts will not be elaborated here.

[0083] At least one embodiment of the present application also provides a non-transitory computer-readable storage medium. Figure 5 It is a schematic diagram of a non-transitory computer-readable storage medium provided by at least one embodiment of the present application. For example, as Figure 5As shown, one or more computer-executable instructions 301 can be non-transiently stored on a non-transient computer-readable storage medium 30. For example, when the computer-executable instructions 301 are executed by a computer, the computer can be caused to execute one or more steps in the method for analyzing and processing multi-level function data of national land space according to any embodiment of the present application.

[0084] In one embodiment, the non-transient computer-readable storage medium 30 can be applied to the above-mentioned data control device 20. For example, it can be the memory 200 in the data control device 20.

[0085] In one embodiment, the description of the non-transient computer-readable storage medium 30 can refer to the description of the memory 200 in the embodiment of the data control device 20, and the repeated parts will not be elaborated.

[0086] It should be noted that when the memory 200 stores different computer-executable instructions non-transiently, the data control device 20 correspondingly serves as a firmware upgrade device. When the computer-executable instructions are run by the processor 201, the processor 201 can be caused to execute one or more steps in the method for analyzing and processing multi-level function data of national land space according to any embodiment of the present application.

[0087] For the present application, the following points also need to be explained: (1) The accompanying drawings of the embodiments of the present application only relate to the structures involved in the embodiments of the present application, and other structures can refer to the general design.

[0088] (2) For the sake of clarity, in the accompanying drawings used to describe the embodiments of the present invention, the thickness and size of layers or structures are enlarged. It can be understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element can be "directly" on or under the other element, or there can be intermediate elements.

[0089] (3) Without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other to obtain new embodiments. The above are only the specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. The protection scope of the present application shall be subject to the protection scope of the claims.

[0090] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combinations of these technical features do not conflict, they should all be considered as the scope described in this specification.

[0091] The above embodiments only represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for data analysis and processing of multi-level functions of territorial space, characterized in that, Includes steps: Divide the national territory into multiple functional hierarchical frameworks and determine the functional categories and functional identification units of each functional hierarchical framework; Acquire spatial data of each plot within the functional identification unit, construct a use-function conversion matrix to strengthen the plot, and update the functional identification unit; wherein the spatial data includes land use data and POI data; the use-function conversion matrix is ​​used to establish a mapping relationship between the plot and the functional category; Based on the updated function identification unit, quantify the spatial distribution density of each type of POI of the plot according to the kernel density of the POI data, obtain a kernel density analysis result, and calculate the spatial function value of each function category in the function identification unit; Based on the updated function identification unit, calculating the distribution frequency of different POI types in each function category in the function identification unit to obtain a frequency density analysis result; Combining the kernel density analysis result and the frequency density analysis result, weighted calculation and standardization are performed on the spatial function value of each function identification unit to calculate the comprehensive function value; The comprehensive function value of each function identification unit is calculated step by step by weight within the function hierarchy framework to determine the data analysis result of each function identification unit in each function hierarchy framework.

2. The method for analyzing and processing multi-level functional data of territorial space according to claim 1, wherein The process of obtaining spatial data of each plot within the functional identification unit, constructing a use-function conversion matrix to strengthen the plot, and updating the functional identification unit includes the steps of: assigning the functional categories to the primary function, secondary function and tertiary function of the land parcel according to the land use data; Weights are assigned to the primary function, secondary function and tertiary function respectively.

3. The method for analyzing and processing multi-level functional data of territorial space according to claim 1, wherein, The process of quantifying the spatial distribution density of each type of POI of the plot based on the updated function identification unit according to the kernel density of the POI data, obtaining a kernel density analysis result, and calculating the spatial function value of each function category in the function identification unit includes: Performing kernel density estimation on the POI data in each of the function identification units to calculate the density value of each type of POI in the function identification unit; According to the location, bandwidth and kernel function of the POI, a smoothing process is performed by a standard kernel function to obtain the density distribution of each functional category in the functional identification unit, that is, the kernel density analysis result; The kernel density maps of various POIs are segmented by boundaries, and the proportion of the kernel density of various POIs in the function identification unit is calculated to define the spatial function value.

4. The method for analyzing and processing multi-level functional data of territorial space according to claim 3, wherein The kernel density calculation formula of the POI data is: ； in, is the kernel density of POI data; represents the location of the assessment point; Representative The location of the POI; is the total number of POIs; is bandwidth; is the kernel function; Assign weights to POI data, and the value of each POI : ； Among them, , , are the area coefficient, function relevance, and function level weight of the th type of POI, respectively; The value of the bandwidth is the accessibility distance of various POIs, and hierarchical mapping is performed according to the POI function weight level P3; for the same one containing different bandwidths of the functional categories , the kernel density is as follows: ； Among them, is the position of the evaluation point, belongs to the functional category and the influence range is the set of POI indexes; is all of those belonging to the functional category and with a bandwidth of of the POI the sum of the values; is the set of all influence ranges under the functional category ; is in the class of POI, the position of the th point, belonging to the bandwidth ; is the total number of POI with a bandwidth of ; is the kernel function; Include all functional categories Define the POI kernel density map containing all of them as a set as follows: ; where is the set of all functional categories ; For each functional category in the unit , calculate the total kernel density of the functional category within the unit according to the following formula Sc,u : ； Among them, is the kernel density plot of the functional category, indicating integration within the range of the function recognition unit to calculate the total impact of the functional category; Calculate the total functional values of all POI category in the plot according to the following formula :​ ； Calculate the functional category according to the following formula In the plot The spatial function value of : 。 5. The method for analyzing and processing multi-level functional data of territorial space according to claim 1, wherein The process of calculating the distribution frequency of different POI types in each functional category in the function identification unit based on the updated function identification unit to obtain the frequency density analysis result includes: Calculate the frequency density according to the following formula : ； Among them, represents the weighted sum of POI types belonging to the same functional category below; is the function recognition unit in the number of POI types of the function category is the number of the th functional POI type in the entire region, is the corresponding weight or importance value; Calculate the function recognition unit according to the following formula The type ratio of the internal function is used as the frequency of the function source : ； ； Among them, is the total number of function categories contained in the function recognition unit, is the kind of multi-function set of the function recognition unit.

6. The method for analyzing and processing multi-level functional data of territorial space according to claim 1, wherein The process of combining the kernel density analysis result and the frequency density analysis result, performing weighted calculation and standardization processing on the spatial function value of each function identification unit, and calculating the comprehensive function value includes: Each functional category In the function recognition unit The POI function value Is: ； Among them, is the functional category of the POI kernel density on the function recognition unit ; is the functional category of the frequency density on the function recognition unit ; and are the weight coefficients adjusted according to the presence or absence of POI data. For the functional category in the unit the following relationship is satisfied: ； Among them, (1, 0) is the function recognition unit Function category When there is no POI data, (0.4, 0.6) is the function recognition unit Function category When there is POI data 7. The method for analyzing and processing multi-level functional data of territorial space according to claim 1, wherein The functional hierarchy framework includes a national functional framework, a provincial functional framework, a municipal functional framework, a county functional framework and a township functional framework.

8. A device for data analysis and processing of multi-level functions of national territorial space, characterized in that, include: A basic division module for dividing the national territorial space into multiple functional hierarchical frameworks and determining the functional categories and functional recognition units of each functional hierarchical framework; A unit update module for obtaining the spatial data of each plot within the functional recognition unit, constructing a use-function conversion matrix to strengthen the plot, and updating the functional recognition unit; wherein the spatial data includes land use data and POI data; the use-function conversion matrix is used to establish the mapping relationship between the plot and the functional category; A first analysis module for quantifying the spatial distribution density of each type of POI of the plot based on the POI data according to the updated functional recognition unit, obtaining a kernel density analysis result, and calculating the spatial function value of each functional category within the functional recognition unit; A second analysis module for calculating the distribution frequency of different POI types in each functional category within the functional recognition unit based on the updated functional recognition unit, obtaining a frequency density analysis result; A comprehensive calculation module for combining the kernel density analysis result and the frequency density analysis result, performing weighted calculation and standardization processing on the spatial function value of each functional recognition unit, and calculating a comprehensive function value; A result output module for hierarchically weighting and calculating the comprehensive function value of each functional recognition unit within the functional hierarchical framework to determine the data analysis result of each functional recognition unit in each functional hierarchical framework.

9. A non-transitory computer-readable storage medium, characterized in that, A non-transitory computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the multi-level functional data analysis and processing method of the national territorial space as described in any one of claims 1 to 7 is implemented.

10. A data control device, characterized in that, Comprising: One or more memories non-transitorily storing computer-executable instructions; One or more processors configured to run the computer-executable instructions, wherein when the computer-executable instructions are run by the one or more processors, the multi-level functional data analysis and processing method of the national territorial space as described in any one of claims 1 to 7 is implemented.

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