Method and device for analyzing and processing multi-level function data of territorial space

Abstract

The application relates to a land space multi-level function data analysis processing method and device, and belongs to the technical field of land space distribution data processing. The application solves the problem of inconsistent function positioning of different types of land space in the existing land space function analysis method and the problem of insufficient accuracy of analysis result data. The land space multi-level function data analysis processing method and device of the application update the function recognition unit of a plot according to a use-function conversion matrix; quantize the spatial distribution density of each type of POI of the plot according to the kernel density of POI data; calculate the distribution frequency of different POI types in the function recognition unit in each function category; combine the kernel density analysis result and the frequency density analysis result to perform weighted calculation and standardization processing on the spatial function value of each function recognition unit; and determine the data analysis result of each function recognition unit in each function level framework. The application improves the accuracy of the analysis result data by analyzing and processing the layered land space.

Description

Technical Field

[0001] This application relates to the field of land spatial distribution data processing technology, and in particular to a method and apparatus for analyzing and processing multi-level functional data of national land space. Background Technology

[0002] Territorial spatial planning is a long-term plan and overall arrangement made by a national or regional government department for the spatial resources and layout of its territory. It aims to achieve effective control and scientific governance of territorial space and promote a balance between development and protection. Territorial spatial function analysis is a part of territorial spatial planning, which requires combining human needs with environmental resource endowments. As the concept of land development shifts from production-oriented space to a spatial layout that coordinates production, living, and ecology, the importance of territorial spatial function analysis is becoming increasingly prominent.

[0003] Currently, with the development of big data analytics, land space function analysis can be quantitatively processed based on data analysis and processing models. However, due to the complexity of the concept of land space and the inconsistent functional positioning of different types of land space, using fixed-standard quantitative processing to address various types of land space function analysis is not scientifically sound or practical, resulting in significant discrepancies between the analysis results and the actual situation, and insufficient reference value and accuracy. Summary of the Invention

[0004] In view of the above analysis, the embodiments of the present invention aim to provide a method and apparatus for analyzing and processing multi-level functional data of national land space, so as to solve the problems of inconsistent functional positioning of different types of national land space in the results of existing technologies, poor scientificity and operability of quantitative processing with fixed standards to deal with various types of national land space function analysis, large differences between analysis results and actual situation, resulting in insufficient reference and accuracy.

[0005] In a first aspect, embodiments of this application provide a method for analyzing and processing multi-level functional data of national land space, including the following steps:

[0006] The national land space is divided into multiple functional hierarchical frameworks, and the functional categories and functional identification units of each functional hierarchical framework are determined.

[0007] Spatial data of each parcel within the functional identification unit is acquired, and a land use-function transformation matrix is ​​constructed to reinforce the parcels and update the functional identification unit. The spatial data includes land use data and POI data. The land use-function transformation matrix is ​​used to establish the mapping relationship between parcels and functional categories.

[0008] Based on the updated functional identification unit, the spatial distribution density of each type of POI in the land parcel is quantified according to the kernel density of the POI data to obtain the kernel density analysis results, so as to calculate the spatial functional value of each functional category within the functional identification unit.

[0009] Based on the updated function identification unit, the distribution frequency of different POI types in the function identification unit in each function category is calculated, and a frequency density analysis result is obtained;

[0010] Combined with the kernel density analysis result and the frequency density analysis result, the spatial function value of each function identification unit is weighted and standardized to calculate a comprehensive function value;

[0011] The comprehensive function value of each function identification unit is weighted and calculated in each function level framework to determine the data analysis result of each function identification unit in each function level framework.

[0012] The land space multi-level function data analysis and processing method of the embodiments of the application divides the land space into multiple function level frameworks, determines the function categories and function identification units of each function level framework. According to the use-function conversion matrix, the plots are strengthened to update the function identification units. According to the kernel density of POI data, the spatial distribution density of each type of POI of the plot is quantified to obtain a kernel density analysis result. The distribution frequency of different POI types in the function identification unit in each function category is calculated to obtain a frequency density analysis result. Combined with the kernel density analysis result and the frequency density analysis result, the spatial function value of each function identification unit is weighted and standardized to calculate a comprehensive function value; the comprehensive function value of each function identification unit is weighted and calculated in each function level framework to determine the data analysis result of each function identification unit in each function level framework. The land space is analyzed and processed in layers, which improves the scientificity and operability of the function analysis in planning, and the reference value of the analysis result.

[0013] As one of the optional embodiments, the process of obtaining the spatial data of each plot in the function identification unit, constructing a use-function conversion matrix to strengthen the plot, and updating the function identification unit includes the following steps:

[0014] According to the land use data, the main function, secondary function and third function of the plot are assigned a function category;

[0015] The main function, secondary function and third function are respectively weighted.

[0016] As one of the optional embodiments, based on the updated function identification unit, the process of quantifying the spatial distribution density of each type of POI of the plot according to the kernel density of POI data to obtain a kernel density analysis result to calculate the spatial function value of each function category in the function identification unit includes:

[0017] The POI data in each function identification unit is estimated by kernel density to calculate the density value of each type of POI in the function identification unit;

[0018] Based on the location, bandwidth, and kernel function of the POI, smoothing is performed using a standard kernel function to obtain the density distribution of each functional category within the functional identification unit, i.e., the kernel density analysis result.

[0019] The boundary segmentation of various POI kernel density maps is performed, and the proportion of various POI kernel densities within the functional identification unit is calculated to define the spatial functional value.

[0020] As one optional embodiment, the kernel density calculation formula for the POI data is as follows:

[0021] ;

[0022] in, Kernel density of POI data; Represents the location of the assessment point; Representing the The location of each POI; This is the total number of POIs; Bandwidth determines the smoothness of the kernel function; K is the kernel function.

[0023] Weights were assigned to the valid POI data, and the value for each POI was calculated. ,as follows:

[0024] ;

[0025] in , , They are the first Area factor, functional relevance, and functional level weight of POIs;

[0026] The bandwidth value is the reachability distance of various POIs, and a hierarchical graph is plotted according to the POI functional weight level P3; for the same POI, different service radii are included. Functional categories Calculate kernel density estimation as follows:

[0027] ;

[0028] in, It is the location of the assessment point. It belongs to the functional category and scope of influence The set of POI indexes; It belongs to all Bandwidth is POI The sum of values, used for normalization; It is a functional category the union of all impact ranges below; is the location of the th point in the POI class, belonging to the bandwidth ; is the total number of POIs with bandwidth ; is the kernel function;

[0029] Define the kernel density plot of all functional classes as a set where is the set of all functional classes:

[0030] ;

[0031] For each functional class in the unit , calculate the sum of the kernel density of the functional class within the unit : Sc,u

[0032] ;

[0033] is the kernel density plot of the functional class , and represents the integration within the functional recognition unit to calculate the total impact of the functional class;

[0034] Calculate the sum of all POI class functional values in the calculation plot , where is the set of all functional classes :

[0035] ;

[0036] The spatial functional value of this functional class in the plot is calculated as follows:

[0037] .

[0038] As one of the optional embodiments, based on the updated functional recognition unit, the distribution frequency of different POI types in the functional recognition unit in each functional class is calculated to obtain the process of frequency density analysis results, including:

[0039] For each functional recognition unit, measure its frequency density :

[0040] ​​​​

[0041] denotes the weighted sum of all POI types belonging to the same functional category ; wherein is the number of functional categories in the functional recognition unit ; is the number of POI types of the functional category in the functional recognition unit ; is the corresponding weight or importance value

[0042] The type proportion of the function in the functional recognition unit is calculated as the frequency of the functional source :

[0043] ;

[0044] ;

[0045] The total number of functional categories contained in the functional recognition unit ; is the number of multi-functional sets of the functional recognition unit . As one of the optional embodiments, the spatial function value of each functional recognition unit is calculated and standardized by combining the kernel density analysis result and the frequency density analysis result, and the process of calculating the comprehensive function value includes:

[0046] The POI function value of each functional category

[0047] in the functional recognition unit is defined as:

[0048] ;

[0049] 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 is the weight coefficient adjusted according to the presence or absence of POI data

[0050] For the functional category in the unit , the following relationship is met: ​​

[0051] ;

[0052] wherein (1, 0) is the function recognition unit the function category (0.4, 0.6) is the function recognition unit when there is no POI data the function category when there is POI data.

[0053] As one of the optional embodiments, 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.

[0054] As one of the optional embodiments, the function category of the national function framework includes production, life, and ecology.

[0055] The function category of the provincial function framework includes agricultural product production, industrial / service industry production, life function, ecological regulation, and special function.

[0056] The function category of the municipal function framework includes agricultural product production, industrial production, service industry production, transportation, residence, scientific innovation, public service, culture, ecological regulation, and special function.

[0057] The function category of the county function framework includes planting production, forestry production, livestock production, fishery production, industrial production, commercial service, logistics and warehousing, transportation service, residence, scientific innovation, public service, historical and cultural heritage, modern cultural exchange, cultural tourism, ecological system service, disaster bearing, national defense security, reserve function, and other special functions.

[0058] The function category of the township function framework includes grain crop production, economic crop production, forestry production, livestock production, fishery production, manufacturing production, electric heating and water production and supply, energy and mineral supply, commercial and financial service, commercial trade service, catering service, entertainment service, logistics and warehousing, transportation service, urban residence, rural residence, scientific research service, higher education service, basic education service, public infrastructure, social welfare, sports service, health care service, public security service, public management service, historical and cultural heritage, modern cultural exchange, green land leisure service, natural landscape tourism service, humanistic landscape tourism service, rural recreation service, water and soil conservation, water conservation, biodiversity, wind prevention and sand fixation, flood control and storage, national defense security, reserve function, and other special functions.

[0059] The function recognition unit of the national function framework is a district or a county.

[0060] The function recognition unit of the provincial function framework is a township.

[0061] The function identification unit of the municipal function framework is a community or a village;

[0062] The function identification unit of the county function framework and the township function framework is a block.

[0063] In a second aspect, the embodiments of the present application also provide a land space multi-level function data analysis processing device, which comprises:

[0064] A basic division module is configured to divide the land space into a plurality of function level frameworks, and determine the function categories and function identification units of the function level frameworks;

[0065] A unit updating module is configured to obtain spatial data of each plot in the function identification unit, construct a use-function conversion matrix to strengthen the plot, and update the function identification unit; wherein the spatial data comprises land use data and POI data; the use-function conversion matrix is used to establish a mapping relationship between the plot and the function category;

[0066] A first analysis module is configured to, 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;

[0067] A second analysis module is configured to, based on the updated function identification unit, calculate the distribution frequency of different POI types in each function category in the function identification unit, and obtain a frequency density analysis result;

[0068] A comprehensive calculation module is configured to combine the kernel density analysis result and the frequency density analysis result, and perform weighted calculation and standardization processing on the spatial function value of each function identification unit to calculate a comprehensive function value;

[0069] A result output module is configured to perform weighted calculation on the comprehensive function value of each function identification unit in the function level framework, and determine the data analysis result of each function identification unit in each function level framework.

[0070] The land space multi-level function data analysis processing device divides the land space into multiple function level frameworks, determines the function categories and function identification units of each function level framework. The land blocks are reinforced according to the use-function conversion matrix to update the function identification units. The spatial distribution density of each type of POI of the land block is quantified according to the kernel density of the POI data, and the kernel density analysis result is obtained. The distribution frequency of different POI types in the function identification unit in each function category is calculated, and the frequency density analysis result is obtained. The spatial function value of each function identification unit is weighted and standardized by combining the kernel density analysis result and the frequency density analysis result, and the comprehensive function value is calculated. The comprehensive function value of each function identification unit is weighted and calculated in the function level framework, so as to determine the data analysis result of each function identification unit in each function level framework. The land space is analyzed and processed in layers, which improves the scientificity and operability of the function analysis in planning, and the reference value of the analysis result.

[0071] In a third aspect, the at least one embodiment of the present application further provides a data control device, comprising:

[0072] one or more memories, which non-transitorily store computer executable instructions;

[0073] one or more processors configured to run the computer executable instructions, wherein the computer executable instructions, when run by the one or more processors, implement the land space multi-level function data analysis processing method according to any embodiment of the present application.

[0074] The data control device divides the land space into multiple function level frameworks, determines the function categories and function identification units of each function level framework. The land blocks are reinforced according to the use-function conversion matrix to update the function identification units. The spatial distribution density of each type of POI of the land block is quantified according to the kernel density of the POI data, and the kernel density analysis result is obtained. The distribution frequency of different POI types in the function identification unit in each function category is calculated, and the frequency density analysis result is obtained. The spatial function value of each function identification unit is weighted and standardized by combining the kernel density analysis result and the frequency density analysis result, and the comprehensive function value is calculated. The comprehensive function value of each function identification unit is weighted and calculated in the function level framework, so as to determine the data analysis result of each function identification unit in each function level framework. The land space is analyzed and processed in layers, which improves the scientificity and operability of the function analysis in planning, and the reference value of the analysis result.

[0075] In a fourth aspect, the at least one embodiment of the present application further provides a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the land space multi-level function data analysis processing method according to any embodiment of the present application.

[0076] The non-transitory computer readable storage medium divides the national space into a plurality of functional hierarchical frameworks, determines the functional categories and functional identification units of each functional hierarchical framework. The land blocks are reinforced 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 blocks is quantified according to the kernel density of the POI data, and the kernel density analysis result is obtained. The distribution frequency of different POI types in the functional identification units in each functional category is calculated, and the frequency density analysis result is obtained. The spatial function value of each functional identification unit is weighted and standardized by combining the kernel density analysis result and the frequency density analysis result, and the comprehensive function value is calculated. The comprehensive function value of each functional identification unit is weighted and calculated in the functional hierarchical framework, so as to determine the data analysis result of each functional identification unit in each functional hierarchical framework. The national space is divided into layers for analysis and processing, which improves the scientificity and operability of the functional analysis in planning, and the analysis result realizes the effective transmission of the macro planning target to the micro implementation, and the analysis result data is more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0077] Figure 1 A national space multi-level function data analysis processing method flowchart is provided for the application;

[0078] Figure 2 A national space multi-level function data analysis processing method flowchart of a preferred embodiment is provided for the application;

[0079] Figure 3 A national space multi-level function data analysis processing device module structure diagram is provided for an embodiment of the application;

[0080] Figure 4 A schematic block diagram of a data control device is provided for the application;

[0081] Figure 5 A schematic diagram of a non-transitory computer readable storage medium is provided for the application. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical scheme and advantages of the embodiments of the application more clear, the technical scheme of the embodiments of the application will be described clearly and completely below in combination with the drawings of the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments. Based on the described embodiments of the application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the application.

[0083] Unless otherwise defined, technical terms or scientific terms used in the present application shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The words "first", "second", and similar words of distinction do not by themselves indicate any order, quantity, or importance, but are used to distinguish different components. The words "include" or "contain" and similar words mean that the elements or objects before the words encompass the elements or objects listed after the words and their equivalents, and do not exclude other elements or objects. The words "connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The words "up", "down", "left", "right", and the like are used only to indicate relative positional relationships, which can change when the absolute positions of the described objects change.

[0084] In order to keep the following description of the embodiments of the present application clear and brief, detailed descriptions of some known functions and known components are omitted.

[0085] The embodiments of the present application provide a land space multi-level function data analysis processing method.

[0086] Figure 1 The land space multi-level function data analysis processing method of an embodiment of the present application is shown in a flowchart as Figure 1 The land space multi-level function data analysis processing method of an embodiment of the present application includes steps S100 to S105:

[0087] S100, dividing the land space into a plurality of function level frameworks, and determining the function categories and function identification units of the function level frameworks;

[0088] S101, obtaining the spatial data of each land block in the function identification unit, constructing a use-function conversion matrix to strengthen the land block, and updating the function 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 land block and the function category, and to realize the conversion identification of the land block use to the function; wherein the use-function conversion value is .

[0089] S102, based on the updated function identification unit, quantifying the spatial distribution density of each type of POI of the land block according to the kernel density of the POI data, obtaining the kernel density analysis result, and calculating the spatial function value of each function category in the function identification unit;

[0090] S103, based on the updated function identification unit, calculating the distribution frequency of different POI types in the function identification unit in each function category, and obtaining the frequency density analysis result;

[0091] S104, in combination with the results of the nuclear density analysis and the frequency density analysis, improving the accuracy and reliability of the function recognition result, weighting and normalizing the spatial function value of each function recognition unit, calculating the comprehensive function value, and calculating the overall function value (P); wherein the spatial function value after weighting and normalization is .

[0092] S105, weighting and calculating the comprehensive function value of each function recognition unit in the function level framework to determine the data analysis result of each function recognition unit in each function level framework.

[0093] Based on the "three functions" framework of production, life and ecology, combined with domestic and foreign research results and international experience, a multi-level and calculable function spectrum is constructed, covering the function level framework of five levels of country, province, city, county and township, and the function analysis of land space is managed in layers. This framework divides various functions into different levels and ensures that each level of function classification has transparency and calculability in spatial scale and functional applicability.

[0094] Preferably, the function spectrum includes five levels of country, province, city, county and township, each level having its unique functional requirements and division standards.

[0095] The function level framework includes a national function framework, a provincial function framework, a municipal function framework, a county function framework and a township function framework;

[0096] The function categories of the national function framework include production, life, ecology and special functions;

[0097] The function categories of the provincial function framework include agricultural production, industrial / service production, living function, ecological regulation and special function;

[0098] The function categories of the municipal function framework include agricultural production, industrial production, service production, transportation, residence, scientific and technological innovation, public service, culture, ecological regulation and special function;

[0099] The function categories of the county function framework include planting production, forestry production, livestock production, fishery production, industrial production, commercial service, logistics and warehousing, transportation service, residence, scientific and technological innovation, public service, historical and cultural heritage, modern cultural exchange, cultural tourism, ecological system service, disaster bearing, national security, reserve function and other special functions;

[0100] The function categories of the township-level function framework include grain crop production, economic crop production, forestry production, livestock production, fishery production, manufacturing production, electricity and heat production and supply, energy and mineral supply, business and financial services, commercial and 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, sports and exercise services, health care services, public security services, public management services, historical and cultural heritage, modern cultural exchange, green space leisure services, natural landscape tourism services, humanistic landscape tourism services, rural recreation services, water and soil conservation, water conservation, biodiversity, wind and sand prevention, flood control and storage, national security, reserve functions, and other special functions;

[0101] The function identification unit of the national-level function framework is a district or a county;

[0102] The function identification unit of the provincial-level function framework is a township;

[0103] The function identification unit of the municipal-level function framework is a community or a village;

[0104] The function identification unit of the county-level function framework and the township-level function framework is a block.

[0105] Specifically, the national-level function framework mainly considers three basic functions of production, life, and ecology, and further considers special functions. At the national level, the function division can be divided into four categories: production, life, ecology, and special functions.

[0106] The county (district) is selected as the function identification unit for the national-level function identification. The township is selected as the function identification unit for the provincial-level function identification. The community or village is selected as the function identification unit for the municipal-level function identification. The block (modular block, MB) is selected as the function identification unit for the county-level and township-level function identification. 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 performed to ensure the accuracy and connectivity of the data. Meanwhile, the MB is the smallest unit for function assignment, and the function spectrum conducts the function to the upper level. In addition, the boundaries of the upper level units are also generated from the MB units layer by layer, and the function value is the sum of the function transmission and transformation of the lower level units, and is normalized.

[0107] The provincial-level function framework focuses on the layout of production space, and also considers the influence of regional urbanization process on population carrying capacity and resource allocation, forming five main function categories: agricultural product production, industrial / service industry production, life function, ecological regulation, and special function.

[0108] The city-level functional framework is located between the provincial and county levels in the administrative system. As a key position in the administrative system, it directly implements the policies of the upper-level government (provincial level) and coordinates and guides the lower-level government (county level), ensuring the effective transmission of policies. Prefecture-level cities are usually the core of regional economic development and manage regional economic, social, and cultural affairs. The three-level land spatial function classification is a macro and micro function transmission medium, setting 10 city-level scale land spatial function classifications, including agricultural production, industrial production, service production, transportation, residence, scientific innovation, public service, culture, ecological regulation, and special function, which are used to decompose and measure "three-dimensional" functions.

[0109] The county-level functional framework is located between the provincial and county levels in the administrative system. As a key position in the administrative system, it directly implements the policies of the upper-level government (provincial level) and coordinates and guides the lower-level government (county level), ensuring the effective transmission of policies. Prefecture-level cities are usually the core of regional economic development and manage regional economic, social, and cultural affairs. The three-level land spatial function classification is a macro and micro function transmission medium, setting 10 city-level scale land spatial function classifications, including agricultural production, industrial production, service production, transportation, residence, scientific innovation, public service, culture, ecological regulation, and special function, which are used to decompose and measure "three-dimensional" functions.

[0110] Township-level administrative units are the grassroots administrative units under the county level, 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.

[0111] Therefore, the township-level functional framework provides basic education, health care and social welfare, directly serving the life needs of residents, while being responsible for the implementation of the policies of the county-level government, detailing into specific actions, and dealing with actual grassroots problems. As the basic level of the functional system, the five-level function, in combination with the functional categories of the standard unit of land space at this level, realizes the correlation of land use to spatial function, including grain crop production, economic crop production, forestry production, livestock production, fishery production, manufacturing production, electricity and hot water production and supply, 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, sports services, health care services, public security services, public management services, historical and cultural heritage, modern cultural exchange, green space leisure services, natural landscape tourism services, cultural landscape tourism services, rural recreation services, soil and water conservation, water conservation, biodiversity, wind prevention and sand fixation, flood control and storage, national security, reserve functions and other special functions, forming 39 types of township-level scale land space function classification.

[0112] The spatial data includes land use data and POI data, and can also include specific administrative boundary data and road network data. In order to ensure the accuracy and consistency of the spatial data, accurate spatial basis is provided for subsequent function identification. After the functional pedigree has been established and the functional hierarchy framework at different levels has 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 identification. Therefore, the original data of the spatial data needs to be strictly preprocessed to ensure its spatial consistency and accuracy.

[0113] Preferably, the specific preprocessing steps include boundary clipping and coding of land use data, topological correction and segmentation of administrative boundary and road network data, attribute inspection and spatial position verification of POI data. At the same time, in order to ensure the consistency of the data in space, all spatial data will be converted to the same coordinate system (such as WGS-84 coordinate system) and the same projection method will be used.

[0114] The division of the function identification unit needs to correspond to the different levels of administrative management, to ensure the effective implementation of the policy and the rational allocation of resources. In order to meet the management needs of the governments at all levels from the national to the local and the planning practice, the function identification unit is divided into 4-level units according to the different levels of function.

[0115] The county (district) is selected as the functional identification unit in the national functional framework. The county level is the basic unit of national administrative division, has relatively perfect policy implementation and resource integration capability, and can effectively implement national policies on a macro level, and has conditions for large-scale regional development planning and resource allocation. The division helps to reflect the unity, comprehensiveness and balance of national policy promotion.

[0116] The township is selected as the functional identification unit in the provincial functional framework. The township is the smallest level of local government, which is an important part of governance, and is directly responsible for specific affairs in the region, and is suitable for the specific implementation and adjustment of provincial policies at a more micro level. The provincial government makes regional development planning and resource allocation according to the actual needs of the local area, and ensures that provincial policies are properly implemented in each township.

[0117] The community or village is selected as the functional identification unit in the municipal functional framework. In the administrative system, the community and the village are the most basic autonomous organization units in urban and rural areas respectively. They belong to the grassroots and directly serve the daily life of residents, and are the forefront of implementing policies and public services. Using community / village as the functional identification unit is conducive to the municipal government to develop development strategies more accurately according to the needs of the specific community or village population, and to realize the integration of urban and rural development at the municipal level.

[0118] The block (modular block, MB) is selected as the functional identification unit in the county-level and township-level functional framework. The county-level and township-level functions are directly related to all aspects of residents' daily life, and the block as the basic unit of urban road network is the skeleton of urban development. Using the block as the basic unit can plan the urban spatial structure in detail, improve the quality of life of residents, and improve the efficiency of urban services.

[0119] 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 the present application perform topological correction to ensure the accuracy and connectivity of the data. At the same time, the MB is the smallest unit of functional assignment, and the functional spectrum conducts functions to the upper level. In addition, the boundaries of the upper level units are also generated layer by layer from the MB unit, and the function value is the sum of the function transmission and transformation of the lower level unit, and is normalized.

[0120] As one of the optional embodiments, Figure 2 The flowchart of the land space multi-level functional data analysis and processing method of an optional embodiment is shown in Figure 2 As shown in FIG. 1, in step S101, the spatial data of each plot in the functional identification unit is obtained, and a use-function conversion matrix is constructed to strengthen the plot and update the process of the functional identification unit, including steps S200 and S201:

[0121] S200, assigning function categories to the main function, secondary function and tertiary function of the plot according to the land use data, and identifying the multi-type function structure of the plot;

[0122] S201, assigning weights to the main function, secondary function and tertiary function, respectively.

[0123] By associating land use data with function attributes, a conversion matrix from use to function is constructed to realize the multi-function conversion of plots and convert plots of different uses into corresponding function categories.

[0124] Based on the processed land use data, by associating plot use categories with function attributes, a tool for mapping the relationship between plot use and function is constructed to perform multi-function conversion of use units. The core of this process is to convert plots of different uses into corresponding function categories according to land use data and function attributes.

[0125] Preferably, according to the "Guidelines for Land Space Investigation, Planning, and Use Control Land and Sea Classification", etc., the basic use of different types of land is defined and mapped with the corresponding function attributes.

[0126] To achieve this mapping, a use-function conversion matrix is constructed, through which the main function, secondary function and tertiary function of each plot can be accurately determined. For example, a plot of agricultural land can be determined as "agricultural product production" as its main function, "ecological regulation" as its secondary function, and "cultural protection" as its tertiary function according to its potential function attributes. Through this mapping, the function categories of each plot at different function level frameworks can be divided in detail.

[0127] In addition, in multi-level function division, the weight of different functions also needs to be considered. For example, the weight of the first-level function is set to 1, the weight of the second-level function is set to 0.6, and the weight of the third-level function is set to 0.4. In this way, the function category and intensity of each function identification unit are quantified, and finally the close relationship between land use and spatial function is formed.

[0128] As one of the optional embodiments, as shown in Figure 2 the process of calculating the spatial function value of each function category in the function identification unit based on the updated function identification unit in step S102 according to the kernel density of POI data to obtain the kernel density analysis result, includes steps S300 to S302:

[0129] S300, kernel density estimation is performed on the POI data in each function identification unit to calculate the density value of each type of POI in the function identification unit;

[0130] S301, according to the position, bandwidth and kernel function of the POI, the standard kernel function is used for smoothing processing to obtain the density distribution of each functional category in the functional recognition unit, that is, the kernel density analysis result;

[0131] S302, the boundary of each type of POI kernel density map is segmented, and the proportion of each type of POI kernel density in the functional recognition unit is calculated to define the spatial function value.

[0132] The spatial distribution density of different types of POIs in the plot is quantitatively analyzed by POI kernel density analysis, which reflects the aggregation degree of each functional category in the functional recognition unit, and the spatial function value of each functional category in the functional recognition unit is calculated based on the kernel density estimation

[0133] After the multi-function transformation of the functional recognition unit is completed, the POI kernel density analysis is first performed. The kernel density analysis is used to quantitatively analyze the spatial distribution density of different types of POIs (points of interest) in the target plot, which can reflect the spatial aggregation degree of the functions in the plot. Specifically, the density value of each POI category in the functional recognition unit is calculated by performing kernel density estimation on the POI data in each functional recognition unit. This process relies on the position, bandwidth (service radius) and kernel function of the POI, and is smoothed by using standard kernel functions such as Gaussian kernel function, so as to obtain the density distribution of each functional category in the functional recognition unit. This analysis helps to accurately identify the aggregation of each type of POI in the functional recognition unit, and lays a foundation for subsequent function optimization.

[0134] Preferably, given a set of POI data points, the kernel density estimation can be calculated by the following formula:

[0135] ;

[0136] wherein, represents the position of the evaluation point; represents the position of the th POI; is the total number of POIs; is the bandwidth, which determines the smoothing degree of the kernel function; K is the kernel function;

[0137] The effective POI data is given a weight value, and the value of each POI is calculated as follows:

[0138] ;

[0139] wherein, , , are the area coefficient, the function correlation degree and the function level weight of the th POI, respectively;

[0140] The bandwidth value is the reachability distance of various POIs, and a hierarchical graph is plotted according to the POI functional weight level P3; for the same POI, different service radii are included. Functional categories Calculate kernel density estimation as follows:

[0141] ;

[0142] in, It is the location of the assessment point. It belongs to the functional category and scope of influence The set of POI indexes; It belongs to all Bandwidth is POI The sum of values, used for normalization; It is a functional category The set of all areas of influence below; yes In the POI class, the first The location of each point belongs to the bandwidth. ; It is bandwidth The total number of POIs; It is a kernel function;

[0143] Define the kernel density map containing all functional categories as a set. ,in It is all functional categories The set of:

[0144] ;

[0145] For unit Each functional category in Calculate the sum of kernel densities Sc for each function category within the cell. u :

[0146] ;

[0147] in, It is a functional category The kernel density map, Indicated in the function recognition unit Integrate within the range to calculate the total impact of the functional category;

[0148] Find the plot of land to be calculated The sum of all POI category function values ,in is the set of all functional categories:

[0149] ;

[0150] This functional category has a spatial functional value in the plot which is calculated as follows:

[0151] .

[0152] The ratio form in the above equation represents the relative importance or influence of the functional category in the plot .

[0153] After completing the POI kernel density analysis, the frequency of different POI types in each functional category in the functional recognition unit is calculated by frequency density, revealing the proportion and influence of each type of function in the spatial structure.

[0154] 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 POI of that type in the entire area, and the corresponding weight value. By weighting the number of each POI type within each functional category, the relative density of each functional type in the unit is obtained. This analysis can reflect the frequency of occurrence of a specific POI type in the functional recognition unit, further revealing the distribution characteristics of each type of function and its influence on the spatial structure. This step provides the necessary basis for determining the proportion of different functional categories within the functional recognition unit.

[0155] For each functional recognition unit, the frequency density is measured:

[0156] ;

[0157] represents the weighted sum of all POI types belonging to the same functional category ; where is the number of functional category POI types in the functional recognition unit ; is the number of functional POI types in the entire area, is the corresponding weight or importance value; The type proportion of the function in the functional recognition unit is calculated as the frequency of the functional source:

[0158]

[0159] ​​​;

[0160] ;

[0161] is a function recognition unit the total number of function categories contained, is a function recognition unit of a multi-functional set.

[0162] wherein the final function value of each function recognition unit is the weighted sum of all independent function categories values thereon, the function recognition unit defines its function set as follows:

[0163] ;

[0164] wherein, is the set of all function categories on the function recognition unit , and are respectively two different attribute values of the function category , namely the purpose-function conversion value and the spatial function value after weighting calculation and standardization processing; and are the weights applied to and respectively. Preferably, set , .

[0165] On the basis of considering the land use-purpose-function conversion, the land block function is optimized and adjusted in combination with POI (Point of Interest) data, the kernel density and frequency density are fused, the dimension and depth of land space information are enriched, and the accuracy and reliability of the function recognition result are improved. Ensure and respectively assign each function category on each land block unit independently, and finally synthesize these values at the land block unit level.

[0166] According to the land use data and the POI data, the function categories are respectively assigned to the main function, the secondary function and the third function of the unit; the main function, the secondary function and the third function are respectively weighted.

[0167] Assign the purpose-function conversion value ( ). An innovative qualitative analysis method of land use is proposed by constructing a basic use-function transformation matrix. The basic use of different plots is associated with its potential multi-level function transformation relationship, and the land space function category of plot unit at different levels is clarified. The following is the detailed use-multi-function transformation correspondence. The use-function transformation value of each function category c in unit u is defined as:

[0168] ;

[0169] is the weight of function category c in unit u, is the original data value of function category c in unit u. For function category c in plot unit u, the original function data value is calculated according to the following formula :

[0170] ;

[0171] represents the area of function category c in unit u, is the total area of unit u. Since the original function data value exists multi-level function, according to this unit, this function category belongs to 1-level function, 2-level function, 3-level function, and the weight value is 1, 0.6, 0.4.

[0172] The function value of each function recognition unit is calculated and standardized by combining the results of kernel density analysis and frequency density analysis to reflect the relative importance and spatial influence of each function category in the plot.

[0173] Based on the results of kernel density analysis and frequency density analysis, the function value is calculated and standardized. The function value in each function recognition unit will be calculated by weighting and standardization by combining the results of kernel density and frequency density analysis. First, the function value of each POI is weighted, considering the area coefficient, function correlation degree and function level weight, etc. Then, the obtained kernel density and frequency density results are normalized, and finally the standardized function value of each function category in the plot is obtained. These standardized function values reflect the relative importance and spatial influence of each function category in the plot, and provide accurate basis for subsequent function optimization and decision-making.

[0174] Each function category in the function recognition unit POI function value is defined as:

[0175] ; ​​

[0176] wherein, is the function category POI core density on the function recognition unit ; is the function category frequency density on the function recognition unit ; and is the weight coefficient adjusted according to the presence or absence of POI data;

[0177] for the function category in the unit satisfies the following relationship:

[0178] ;

[0179] wherein, (1, 0) is the function category in the function recognition unit without POI data, (0.4, 0.6) is the function category in the function recognition unit with POI data.

[0180] According to the function spectrum framework, the comprehensive function value of each function recognition unit is calculated by weighting step by step, ensuring that the function characteristics and spatial performance of different level units are accurately reflected.

[0181] After completing the function assignment calculation of the minimum unit, the hierarchical comprehensive function value calculation of the function recognition unit is performed to better reflect the function characteristics and spatial performance of different level units. Through the set function spectrum framework, combined with the specific needs and level requirements of different function recognition units, the comprehensive function value of each function recognition unit is calculated by weighting step by step. Through the weighted average method, all related function values of each function recognition unit are integrated according to the weight, and the comprehensive function performance of each function recognition unit at different levels is finally calculated.

[0182] Preferably, by sorting the comprehensive function value of each function recognition unit, the dominant function, secondary function and third function are identified.

[0183] After completing the comprehensive function value calculation, the dominant function, secondary function and third function of each function recognition unit are identified in order to provide a basis for subsequent resource allocation and policy making. Specifically, all function recognition units are traversed, the function value of each unit is sorted, the function categories with the largest, second largest and third largest function values are determined, and the corresponding function category codes are allocated to each function recognition unit. Through sorting, the dominant function of each function recognition unit is identified.

[0184] Preferably, a dynamic adjustment and feedback mechanism is established to continuously monitor and optimize the functional identification results through real-time data collection and updating.

[0185] The dynamic adjustment and feedback mechanism is used to ensure that the functional identification results are continuously effective and timely respond to external changes. Specifically, by establishing a data collection and updating mechanism, the changes of each functional identification unit are continuously monitored, including dynamic data such as land use, population flow, economic activity, and environmental change. Whenever new data is collected, the system will re-evaluate and optimize the existing functional identification 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 activity of a certain area changes due to policy adjustment, the system will adjust the functional classification of the region 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 functional identification results not only meet the data-driven analysis, but also take into account the actual needs of the social, economic and policy levels.

[0186] The beneficial effects brought by the technical solutions of the embodiments of the present application are specifically embodied in solving the key technical problems in the existing land space functional classification system and providing practical operational technical support. In view of the deficiencies of the existing multi-level functional classification of land space, a hierarchical system is constructed, and an innovative method of functional identification is proposed.

[0187] The existing method is mostly focused on the macro classification of the three functions of "production, life and ecology", and less attention is paid to regional differences, functional subdivision and dynamic identification. By constructing a five-level functional system from macro to micro, the classification system of functions at the national, provincial, municipal, county and township levels is clarified, the problem of disconnection between planning targets at different levels is solved, and the scientificity and operability of functional analysis in planning are improved. In addition, the functions of land space often have complex characteristics, especially the cross and interaction of production, life and ecological functions. By integrating multiple functional sources, the cross and complex characteristics between different functions are realized, and practical analysis methods for complex functions are proposed, which promotes the research of complex function evaluation and management.

[0188] Considering the six principles of scientificity, rationality, forward-looking, systematization, regionalization and dynamics, a perfect multi-level functional classification system is constructed, and geographic spatial big data analysis technology is used to integrate land use data and POI data. Through the combination of functional source discrimination method and kernel density analysis, the functional identification precision and cross-scale dynamic change analysis capability are improved. The multi-level of functional classification and the refinement of functional identification are ensured, which provides more solid data support for land space planning and management.

[0189] The embodiments of the present application also provide a land space multi-level function data analysis processing device.

[0190] Figure 3 A module structure diagram of the land space multi-level function data analysis processing device of an embodiment is shown in FIG. 1. As shown in FIG. 1, the land space multi-level function data analysis processing device of an embodiment includes: Figure 3

[0191] a basic division module 100 configured to divide a land space into a plurality of function level frameworks and determine function categories and function identification units of the function level frameworks;

[0192] a unit updating module 101 configured to obtain spatial data of each plot in the function identification unit, construct a use-function conversion matrix to strengthen the plot, and update the function identification unit; wherein the spatial data includes land use data and POI data; and the use-function conversion matrix is used to establish a mapping relationship between the plot and the function category;

[0193] a first analysis module 102 configured to, based on the updated function identification unit, quantify spatial distribution density of each type of POI of the plot according to kernel density of the POI data, obtain a kernel density analysis result, and calculate a spatial function value of each function category in the function identification unit;

[0194] a second analysis module 103 configured to, based on the updated function identification unit, calculate a distribution frequency of different POI types in each function category in the function identification unit, and obtain a frequency density analysis result;

[0195] a comprehensive calculation module 104 configured to combine the kernel density analysis result and the frequency density analysis result, and perform weighted calculation and standardization processing on the spatial function value of each function identification unit to calculate a comprehensive function value;

[0196] a result output module 105 configured to perform weighted calculation on the comprehensive function value of each function identification unit in each function level framework to determine a data analysis result of each function identification unit in each function level framework.

[0197] ​The multi-level functional data analysis and processing device for national land space in this application divides national land space into multiple functional hierarchical frameworks, determining the functional categories and functional identification units of each functional hierarchical framework. It strengthens land parcels based on the use-function transformation matrix to update functional identification units. It quantifies the spatial distribution density of various POI types in land parcels using kernel density data, obtaining kernel density analysis results. It calculates the distribution frequency of different POI types in functional identification units within each functional category, obtaining frequency density analysis results. Combining the kernel density analysis results and frequency density analysis results, it performs weighted calculation and standardization on the spatial functional values ​​of each functional identification unit to calculate the comprehensive functional value. Within the functional hierarchical framework, it calculates the comprehensive functional value of each functional identification unit with progressively weighted calculations to determine the data analysis results of each functional identification unit within each functional hierarchical framework. This layered analysis and processing of national land space enhances the scientific rigor and operability of functional analysis in planning, as well as the reference value of the analysis results.

[0198] At least one embodiment of this application also provides a data control device. Figure 4 This is a schematic block diagram of a data control device provided for at least one embodiment of this application. For example, such as... Figure 4 As shown, the data control device 20 may include one or more memories 200 and one or more processors 201. The memories 200 are used to store computer-executable instructions non-transitory; the processors 201 are used to run the computer-executable instructions, which, when run by the processors 201, can cause the processors 201 to perform one or more steps in the multi-level functional data analysis and processing method for territorial space according to any embodiment of this application.

[0199] For the specific implementation and related explanations of each step of the multi-level functional data analysis and processing method for territorial space, please refer to the relevant content in the embodiments of the above-mentioned multi-level functional data analysis and processing method for territorial space, which will not be repeated here. It should be noted that Figure 4 The components of the data control device 20 shown are merely exemplary and not limiting. The data control device 20 may have other components depending on the actual application requirements.

[0200] In one of the embodiments, 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 with each other 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, and the type and function of the network are not limited herein. For another example, the processor 201 and the memory 200 can also communicate with each other 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 arranged at a remote data server end (cloud end) or a distributed energy system end (local end), and can also be arranged at a client end (for example, a mobile device such as a mobile phone, etc.). 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 a data processing capability and / or an instruction execution capability, and can control other components in the data prediction apparatus 20 to perform desired functions. The central processing unit (CPU) can be X86 or ARM architecture, etc.

[0201] In one of the embodiments, the memory 200 can include one or more computer program products in any combination, and the computer program product can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. For example, the volatile memory can include random access memory (RAM), cache memory, etc. For example, the non-volatile memory can include read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), compact disc read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer executable instructions can be stored on the computer readable storage medium, and the processor 201 can run the computer executable instructions to implement various functions of the data prediction apparatus 20. Various application programs and various data, and various data used and / or generated by the application programs, etc. can also be stored in the memory 200.

[0202] It should be noted that the data control apparatus 20 can achieve similar technical effects as the aforementioned method for analyzing and processing multi-level function data of territorial space, and the repeated parts will not be described herein.

[0203] At least one embodiment of the present application also provides a non-transitory computer readable storage medium. Figure 5 A schematic diagram of a non-transitory computer readable storage medium provided by at least one embodiment of the present application is shown in FIG. 8. For example, as shown in FIG. 8, the non-transitory computer readable storage medium can include a computer program product 800. The computer program product 800 can include a computer readable storage medium 801. The computer readable storage medium 801 can include one or more computer readable program instructions 802. The one or more computer readable program instructions 802 can be executable by a computer or a processor to cause the computer or the processor to perform functions of the data prediction apparatus 20. Figure 5The one or more computer-executable instructions 301 can be non-transitorily stored in the non-transitory computer-readable storage medium 30, as shown. For example, the one or more computer-executable instructions 301, when executed by a computer, can cause the computer to perform one or more steps of the method for analyzing and processing multi-level function data of territorial space according to any one of the embodiments of the present application.

[0204] In one of the embodiments, the non-transitory computer-readable storage medium 30 can be applied to the data control device 20 described above, for example, it can be the memory 200 in the data control device 20.

[0205] In one of the embodiments, the description about the non-transitory computer-readable storage medium 30 can refer to the description about the memory 200 in the embodiments of the data control device 20, and the repeated parts will not be described herein.

[0206] It should be noted that the memory 200 stores different non-transitory computer-executable instructions, and the data control device 20 corresponds to a firmware upgrade device, which can cause the processor 201 to perform one or more steps of the method for analyzing and processing multi-level function data of territorial space according to any one of the embodiments of the present application when the computer-executable instructions are executed by the processor 201.

[0207] For the present application, the following points need to be explained:

[0208] (1) The drawings of the embodiments of the present application only involve the structures involved in the embodiments of the present application, and other structures can refer to the general design.

[0209] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present application, the thickness and size of the layers or structures are exaggerated. 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, it can be "directly" on or under the other element, or there can be an intermediate element.

[0210] (3) The embodiments of the present application and the features in the embodiments can be combined with each other to obtain new embodiments without conflict. The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and the protection scope of the present application should be subject to the protection scope of the claims.

[0211] The technical features of the above embodiments can be combined arbitrarily, and for the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not conflict, they should be considered as the scope of the present application.

[0212] The above embodiments only express several implementation ways of the present application, and the description is specific and detailed, but it should not be understood as a limitation to the patent scope of the application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for analyzing and processing multi-level functional data of national land space, characterized in that, Including the following steps: The national land space is divided into multiple functional hierarchical frameworks, and the functional categories and functional identification units of each functional hierarchical framework are determined. Spatial data of each plot within the functional identification unit are acquired, and a land use-function transformation matrix is ​​constructed to strengthen the plots and update the functional identification unit; wherein, the spatial data includes land use data and POI data; the land use-function transformation matrix is ​​used to establish a mapping relationship between the plots and the functional categories; Based on the updated functional identification unit, the spatial distribution density of each type of POI in the land parcel is quantified according to the kernel density of the POI data to obtain kernel density analysis results, so as to calculate the spatial functional value of each functional category in the functional identification unit. Based on the updated function identification unit, the distribution frequency of different POI types in each function category in the function identification unit is calculated to obtain the frequency density analysis results. Combining the kernel density analysis results 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, including: The POI function value P of each function category c in the function identification unit u 2,c,u for: P 2,c,u =k 1,c,u ·P core,c,u +k 2,c,u ·PF c,u ; Among them, P core,c,u It is the POI kernel density of functional category c on functional recognition unit u; PF c,u It is the frequency density of functional category c on functional recognition unit u; k 1,c,u and k 2,c,u These are weighting coefficients adjusted based on whether or not POI data exists; For functional category c, the following relationship is satisfied in element u: Where (1,0) represents the case where there is no POI data for function category c in function identification unit u, and (0.4,0.6) represents the case where there is POI data for function category c in function identification unit u. The comprehensive functional value of each functional identification unit is calculated by weighting each level within the functional hierarchy framework to determine the data analysis results of each functional identification unit within each functional hierarchy framework.

2. The method for analyzing and processing multi-level functional data of national land space according to claim 1, characterized in that, The process of acquiring spatial data of each plot within the functional identification unit, constructing a use-function transformation matrix to enhance the plots, and updating the functional identification unit includes the following steps: Based on the land use data, the primary, secondary, and tertiary functions of the land parcel are assigned functional categories. The primary, secondary, and tertiary functions are assigned weights respectively.

3. The method for analyzing and processing multi-level functional data of national land space according to claim 1, characterized in that, The process by which the updated function identification unit quantifies the spatial distribution density of each type of POI in the land parcel based on the kernel density of the POI data, obtains kernel density analysis results, and calculates the spatial function value of each function category within the function identification unit includes: Kernel density estimation is performed on the POI data in each of the aforementioned functional identification units to calculate the density value of each type of POI in the functional identification unit; Based on the location, bandwidth, and kernel function of the POI, smoothing is performed using a standard kernel function to obtain the density distribution of each functional category within the functional identification unit, i.e., the kernel density analysis result. The boundary segmentation of various POI kernel density maps is used to calculate the proportion of various POI kernel densities within the functional identification unit, thereby defining spatial functional values.

4. The method for analyzing and processing multi-level functional data of national land space according to claim 3, characterized in that, The formula for calculating the kernel density of the POI data is as follows: Among them, P core Here, represents the kernel density of the POI data; x represents the location of the evaluation point; X i Represents the position of the i-th POI; n is the total number of POIs; h is the bandwidth; K is the kernel function; Weights are assigned to the POI data, with each POI having a value f. i : F i =P1 i ×P2 i ×P3 i ; Among them, P1 i P2 i P3 i These are the area coefficient, functional relevance, and functional level weight of the i-th type of POI, respectively. The bandwidth is taken as the reachability distance of each POI, and a hierarchical graph is plotted according to the POI function weight level P3; for the same function category c containing different bandwidths h, the kernel density P core,c (x) is as follows: Where x is the location of the evaluation point, I c,h It is a set of POI indexes belonging to functional category c and scope of influence h; N c,h f is all POIs belonging to functional category c and with bandwidth h. i The sum of values; H c It is the set of all influence areas under functional category c; X m It is the position of the m-th point in POI of class i, belonging to bandwidth h. i ;n h It represents the total number of POIs with bandwidth h; K is the kernel function. Define the kernel density map of POIs containing all functional categories c as a set. as follows: Where C is the set of all functional categories c; For each functional category c in cell u, the sum of kernel densities S of the functional category within the cell is calculated according to the following formula. c,u : S c,u =∫ u P core,c (x)dx; Among them, P core,c (x) is the kernel density map of functional category c, ∫ u This indicates that integration is performed within the functional identification unit u to calculate the total impact of the functional category; The sum of the functional values ​​of all POI categories in plot u, T, is obtained using the following formula. u : Calculate the spatial function value P of function category c in plot u according to the following formula. core,c,u :

5. The method for analyzing and processing multi-level functional data of national land space according to claim 1, characterized in that, The process of calculating the distribution frequency of different POI types in each functional category within the updated functional identification unit, and obtaining the frequency density analysis results, includes: The frequency density F is calculated according to the following formula. c,u : Where, N i This represents the weighted sum of all POI types belonging to the same functional category c; n j,u N is the number of POI types of function c in the function identification unit u, which is the j-th type of POI; j f is the number of the j-th functional POI type in the entire region. j This is the corresponding weight or importance value; The frequency PF of the function source is calculated as the proportion of function types within the function identification unit u according to the following formula. c,u : PF d,u ={PF c,u |c∈C}; Where d is the total number of function categories contained in the function recognition unit u, and PF d,u Let d be the set of d multifunctional units for the function identification unit u.

6. The method for analyzing and processing multi-level functional data of national land space according to claim 1, characterized in that, The functional hierarchy framework includes a national-level functional framework, a provincial-level functional framework, a municipal-level functional framework, a county-level functional framework, and a township-level functional framework.

7. A multi-level functional data analysis and processing device for national land space, characterized in that, include: The basic division module is used to divide the national land space into multiple functional hierarchical frameworks and determine the functional categories and functional identification units of each functional hierarchical framework. The unit update module is used to acquire spatial data of each plot within the function identification unit, construct a use-function transformation matrix to strengthen the plot, and update the function identification unit; wherein, the spatial data includes land use data and POI data; the use-function transformation matrix is ​​used to establish a mapping relationship between the plot and the function category; The first analysis module is used to quantify the spatial distribution density of each type of POI in the land parcel based on the kernel density of the POI data according to the updated functional identification unit, and obtain kernel density analysis results to calculate the spatial functional value of each functional category in the functional identification unit. The second analysis module is used to calculate the distribution frequency of different POI types in each of the functional categories in the functional identification unit based on the updated functional identification unit, and obtain the frequency density analysis results. The comprehensive calculation module is used to combine the kernel density analysis results and the frequency density analysis results to perform weighted calculation and standardization on the spatial function values ​​of each functional identification unit, and calculate the comprehensive function value, including: The POI function value P of each function category c in the function identification unit u 2,c,u for: P 2,c,u =k 1,c,u ·P core,c,u +k 2,c,u ·PF c,u ; Among them, P core,c,u It is the POI kernel density of functional category c on functional recognition unit u; PF c,u It is the frequency density of functional category c on functional recognition unit u; k 1,c,u and k 2,c,u These are weighting coefficients adjusted based on whether or not POI data exists; For functional category c, the following relationship is satisfied in element u: Where (1,0) represents the case where there is no POI data for function category c in function identification unit u, and (0.4,0.6) represents the case where there is POI data for function category c in function identification unit u. The result output module is used to calculate the comprehensive functional value of each functional identification unit in a weighted manner within the functional hierarchy framework, so as to determine the data analysis result of each functional identification unit in each functional hierarchy framework.

8. A non-transitory computer-readable storage medium, characterized in that, A non-transitory computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the multi-level functional data analysis and processing method for territorial space as described in any one of claims 1 to 6.

9. A data control device, characterized in that, include: One or more memories that store computer-executable instructions non-transitory; One or more processors are configured to run computer-executable instructions, wherein the computer-executable instructions are executed by the one or more processors to implement the multi-level functional data analysis and processing method for territorial space as described in any one of claims 1 to 6.

Images