Digital identification method for urban marginal area rural margins

Abstract

The invention discloses a digital identification method for urban marginal area rural margins. The method comprises the following steps: acquiring basic related data for carrying out digital identification on the rural margins; determining boundaries, forms, layouts and scales of rural markets capable of being quantitatively compared; constructing a space quantification platform; creating a multi-dimensional information atlas and a multi-dimensional feature identifier; determining attributes of core segments or edge segments corresponding to the equidistant fragmented regions in the rural markets; constructing a hierarchical rural mart node network, a comprehensive evaluation model and a coupling analysis model; according to the method, multi-dimensional information of spatial features, functional features, node association features and the like of the rural markets can be comprehensively, deeply and accurately identified, a rural mart system is constructed, comprehensive evaluation is performed, and the method is suitable for popularization and application. And reliable decision support and optimization suggestions are provided for rural and urban construction.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of urban planning and digital technology, in particular to a digital identification method of rural market in urban fringe area. BACKGROUND

[0002] With the deepening of urbanization process, urban fringe area gradually becomes one of the most frequent areas of interaction between city and countryside. Rural market, as a unique public service space, widely exists in rural society in China, and carries unique social and economic functions with a long history. However, with the process of urbanization, rural market is facing a sharp decline. The identification and analysis of rural market can guide the scientific planning and construction of rural and urban areas.

[0003] At present, the identification and analysis method of rural market mainly includes field investigation method, such as field observation, interview and questionnaire survey, which can understand the basic characteristics of rural market in detail. With the help of geographic information technology method, the overall range of rural market can be roughly estimated by analyzing remote sensing image. The status and role of rural market in rural economy can be evaluated by economic data analysis method. However, this method only identifies and analyzes rural market in a general way, and there are problems such as fuzzy boundary definition of rural market. Therefore, it is difficult to make more in-depth and accurate identification and data quantitative analysis of rural market. SUMMARY

[0004] Therefore, the present application provides a digital identification method of rural market in urban fringe area, which comprises the following steps: The basic related data for digital identification of the rural market in the urban fringe area is obtained; the basic related data includes the data of the rural market and the street, village range, land use, and merchant stall in which the rural market is located; the data of the street, village range, land use, and merchant stall in which the rural market is located is analyzed and processed to determine the quantifiable and comparable rural market boundary, form, layout, and scale; the center point coordinate, central degree, compactness, and shape index of the rural market are calculated through the rural market boundary, form, layout, and scale and the geographic information system technology to construct a spatial quantification platform; the spatial feature map, industrial feature map, crowd feature map, and multi-dimensional feature identifier corresponding to the rural market are constructed based on the spatial quantification platform; the target segmented attribute corresponding to each different equidistant segmented area in the rural market is determined by using the industrial feature map and the crowd feature map; the target segmented attribute includes a core segment and an edge segment; the rural market is identified in a hierarchical manner based on the basic related data and the multi-dimensional feature identifier to construct a hierarchical rural market node network; a comprehensive evaluation model is established based on the spatial feature map, industrial feature map, crowd feature map, and multi-dimensional feature identifier, and a coupling analysis model is constructed based on the comprehensive evaluation model, and the advantages and disadvantages and weak links of the rural market are identified and analyzed according to the coupling analysis model to determine the sustainable development guiding strategy for the rural market; the spatial feature map, industrial feature map, crowd feature map, multi-dimensional feature identifier, target segmented attribute, hierarchical rural market node network, comprehensive evaluation model, coupling analysis model, and sustainable development guiding strategy and node type are taken as the digital identification result of the rural market.

[0005] Optionally, based on the spatial quantification platform, the spatial feature map, industrial feature map, crowd feature map, and multi-dimensional feature identifier corresponding to the rural market are constructed, including: based on the spatial quantification platform, obtaining land use information within a predetermined range around each rural market, and creating a spatial feature map corresponding to the rural market according to the land use information; based on the spatial quantification platform, determining the business information of shops and stalls and the crowd flow information in the rural market, creating an industrial feature map corresponding to the rural market according to the business information, and creating a crowd feature map corresponding to the rural market according to the crowd flow information; based on the spatial quantification platform, spatial feature map, industrial feature map, crowd feature map, creating a multi-dimensional feature identifier corresponding to the rural market.

[0006] Optionally, using industry feature maps and population feature maps, the target segmentation attributes corresponding to different equidistant segments in a rural market are determined, including: using kernel density estimation to perform spatial analysis on the population feature map to determine the initial segmentation attributes corresponding to each of the multiple equidistant segments in the rural market; the initial segmentation attributes include core segments and edge segments; determining the shop distribution density and vendor distribution density in each equidistant segment based on the industry feature map, and determining the pedestrian flow density in each equidistant segment based on the population feature map; calculating the segmentation feature index corresponding to each equidistant segment using the shop distribution density, vendor distribution density, and pedestrian flow density; and updating the initial segmentation attributes based on the magnitude of the segmentation feature index to obtain the target segmentation attributes corresponding to each equidistant segment.

[0007] Optionally, the rural market may include multiple markets. The digital identification method for rural markets in urban fringe areas may further include: identifying target equidistant segmented areas in multiple rural markets whose target segmentation attribute indicators are core segments; determining the average value of the segmentation feature indices corresponding to the target equidistant segmented areas as the standard segmentation feature index; sequentially determining the difference between the segmentation feature index corresponding to the target equidistant segmented areas and the standard segmentation feature index in each rural market; and using the difference as the digital identification result of the rural market.

[0008] Optionally, the basic relevant data includes the spatial location, physical scale, and functional importance of rural markets. Based on the basic relevant data and multidimensional feature identifiers, rural markets are classified and identified in a hierarchical manner, and a hierarchical rural market node network is constructed. This includes: using hierarchical clustering and fuzzy C-means algorithms to perform cluster analysis on spatial location, physical scale, functional importance, and multidimensional feature identifiers to determine the node type corresponding to each rural market; the node type represents the attributes of the rural market in terms of scale, functional richness, and service scope; and based on the node type corresponding to each rural market, a hierarchical rural market node network is constructed.

[0009] Optionally, a comprehensive evaluation model is established based on spatial feature maps, industry feature maps, population feature maps, and multidimensional feature identifiers. This includes: obtaining spatial feature indicators of rural markets based on multidimensional feature identifiers and spatial feature maps; obtaining functional feature indicators of rural markets based on multidimensional feature identifiers and industry feature maps; obtaining transaction feature indicators of rural markets based on multidimensional feature identifiers, population feature maps, and industry feature maps; obtaining node influence strength indicators of rural markets based on multidimensional feature identifiers, population feature maps, and industry feature maps; calculating a comprehensive evaluation index of rural markets based on spatial feature indicators, functional feature indicators, transaction feature indicators, and node influence strength indicators; and establishing a comprehensive evaluation model based on the comprehensive evaluation index corresponding to each rural market.

[0010] Optionally, a coupled analysis model is constructed based on the comprehensive evaluation model. Based on this model, the strengths and weaknesses of rural markets are identified and analyzed, and sustainable development guidance strategies for rural markets are determined. This includes: identifying specific rural markets whose comprehensive evaluation index is less than or equal to a threshold value from the comprehensive evaluation model; the threshold value is the average of the comprehensive evaluation indices of all rural markets; calculating the transaction intensity, transportation accessibility index, and business diversity index corresponding to the specific rural market; constructing a coupled analysis model based on these indices; and identifying and analyzing the strengths and weaknesses of the specific rural market based on the coupled analysis model, and determining sustainable development guidance strategies for the specific rural market.

[0011] According to the solution provided in this application, by analyzing and processing data on the streets, villages, land use, and vendors of rural markets, the boundaries, forms, layouts, and scales of rural markets that can be quantified and compared can be determined, clearly defining the boundaries of rural markets. Furthermore, based on the boundaries, forms, layouts, scales, and geographic information system (GIS) technology of rural markets, the coordinates of the center point, centrality, compactness, and shape index of the rural markets are calculated, constructing a spatial quantification platform that can comprehensively describe the spatial morphological characteristics of rural markets. Based on this spatial quantification platform, spatial feature maps, industry feature maps, population feature maps, and multi-dimensional feature identifiers of rural markets are constructed, enabling comprehensive data quantification analysis of the spatiotemporal and functional characteristics of rural markets. By determining the target segment attributes of different equidistant areas within the rural market through the industry feature map and population feature map, the distribution of pedestrian traffic and industries in each equidistant area of ​​the rural market can be refined, achieving accurate identification of the interior of the rural market. Finally, the rural markets are classified and identified using basic related data and multi-dimensional feature identifiers. Furthermore, constructing a hierarchical network of rural market nodes allows for a direct comparison of the scale, functional richness, and service scope of various rural markets. Based on spatial feature maps, industrial feature maps, population feature maps, and multi-dimensional feature identifiers, a comprehensive evaluation model can be established to assess the overall development level and potential of each rural market. Further, a coupled analysis model based on this comprehensive evaluation model can identify and analyze the strengths and weaknesses of rural markets, determining sustainable development guidance strategies. This clarifies the optimization and adjustment directions for disadvantaged rural markets, enhancing their overall competitiveness and sustainable development capabilities. Finally, by using spatial feature maps, industrial feature maps, population feature maps, multi-dimensional feature identifiers, target segmentation attributes, the hierarchical rural market node network, the comprehensive evaluation model, the coupled analysis model, sustainable development guidance strategies, and node types as digital identification results for rural markets, these results can be applied to rural and urban development planning, providing decision-makers with scientific basis and optimization guidance. Therefore, the technical solution provided in this application can comprehensively, deeply, and accurately identify multi-dimensional information such as the spatial characteristics, functional characteristics, and node association characteristics of rural markets, construct a rural market system, and conduct a comprehensive evaluation, providing reliable decision support and optimization suggestions for rural and urban construction. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 A flowchart illustrating a digital identification method for rural markets in urban fringe areas provided in this application; Figure 2 A schematic diagram illustrating some features of a rural market as provided in an embodiment of this application; Figure 3 A schematic diagram illustrating the spatial, industrial, and demographic characteristics of a rural market A, provided in this embodiment of the application. Figure 4 This is a schematic diagram illustrating the spatial, industrial, and demographic characteristics of a rural market B as provided in an embodiment of this application. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0015] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0016] Figure 1 This is a flowchart illustrating a digital identification method for rural markets in urban fringe areas provided in this application embodiment. The digital identification method for rural markets in urban fringe areas provided in this application embodiment can be executed by an electronic device, such as a computer or server. Figure 1 As shown, the digital identification method for rural markets in urban fringe areas provided in this application includes: S101. Obtain basic relevant data for digital identification of rural markets in urban fringe areas.

[0017] It should be noted that there may be multiple rural markets in urban fringe areas, and basic relevant data can be collected in advance for digital identification of each rural market. This basic relevant data includes information on the street and village boundaries where the rural market is located, land use, and vendors. This may include, but is not limited to, information on the spatial location of the rural market, the street or village boundaries, relevant land use types and building cluster distribution, shop and vendor distribution, pedestrian flow and surrounding transportation networks, as well as socio-economic data such as population, economy, and policies of the area where the rural market is located.

[0018] In some embodiments, basic data on rural markets in urban fringe areas can be collected using various methods such as field surveys, satellite remote sensing, geographic information systems, and socio-economic surveys. The collected basic data can be cleaned to remove noise and outliers, such as removing temporary rural markets like morning markets and temple fairs. Coordinate calibration and data format standardization can also be performed, such as unifying the location coordinates of each rural market under the same coordinate system and standardizing or normalizing the data on pedestrian traffic and shop distribution of each rural market.

[0019] S102. Analyze and process data on the streets, villages, land use, and vendors of rural markets to determine the boundaries, form, layout, and scale of rural markets that can be quantified and compared.

[0020] In some embodiments, high-resolution remote sensing satellite imagery, point of interest (POI) data acquisition and survey verification technologies can be used to analyze data on the streets, villages, land use, and vendors of rural markets. Spatial information such as land use types and building clusters in and around the rural market can be extracted. Through quantitative analysis and summarization of basic data related to multiple rural markets, the boundaries of rural markets that can be accurately delineated for in-depth quantitative comparison can be determined. The spatial form and functional layout within the rural market can be analyzed to clarify the form, layout, and scale of the rural market.

[0021] S103. Using the boundaries, form, layout, scale, and geographic information system technology of rural markets, calculate the coordinates of the center point, centrality, compactness, and shape index of rural markets, and construct a spatial quantification platform.

[0022] It should be noted that the spatial quantification platform can be a database, software system, or other system that records the spatial morphological characteristics of rural markets.

[0023] In some embodiments, the coordinates of the center point of the rural market, as well as its shape index and compactness, can be calculated based on the market's boundaries, form, layout, and scale, using Geographic Information System (GIS) technology for vectorization and spatial calibration. This quantifies the market's spatial utilization efficiency and functional agglomeration. Simultaneously, based on the market's spatial layout and surrounding transportation network data, its centrality can be calculated to assess its importance and accessibility within the regional transportation network.

[0024] Furthermore, the shape index, compactness, and centrality of rural markets can comprehensively describe the spatial attributes of rural markets, while the coordinates of the center point and the boundaries of rural markets can comprehensively describe their spatial location characteristics. A spatial quantification platform for rural markets can be created based on the shape index, compactness, centrality, and center point coordinates.

[0025] S104. Based on the spatial quantization platform, construct spatial feature maps, industry feature maps, population feature maps, and multi-dimensional feature identifiers corresponding to rural markets.

[0026] Here, multidimensional feature identifiers can be used to represent the attributes of rural markets in different feature dimensions (such as time periodicity, spatial scale, and functional business formats).

[0027] In some embodiments, based on the spatial quantification platform, the spatial distribution around the rural market, the industrial distribution within the rural market, and the population distribution within the rural market can be further determined, thereby sequentially creating a spatial feature map, an industrial feature map, and a population feature map of the rural market, and constructing a multidimensional feature map and multidimensional feature identifier for the rural market.

[0028] S105. Using industry feature maps and population feature maps, determine the target segment attributes corresponding to different equidistant areas in rural markets; based on basic relevant data and multi-dimensional feature identifiers, classify and identify rural markets, and construct a hierarchical rural market node network.

[0029] It should be noted that the target segmentation attribute is used to represent the pedestrian flow density and merchant distribution density within the equidistant segmentation area. Merchant distribution density can include shop distribution density, vendor distribution density, etc.

[0030] In some embodiments, the target segmentation attributes may include core segments and edge segments. The core segment may be a high-density area within a rural market, with high pedestrian density, shop density, and vendor density, while the edge segment may be a low-density area within a rural market, with low pedestrian density, shop density, and vendor density. For example, the core segment may be an area near the center of the rural market, while the edge segment may be an area at the edge of the rural market.

[0031] In some embodiments, the rural market can be pre-divided into multiple equidistant segments. Furthermore, the flow of people, number of vendors, and number of shops in different equidistant segments within the same time period can be obtained from the industry feature map. Based on the flow of people, number of vendors, and number of shops in each equidistant segment, the target segmentation attribute of each equidistant segment can be determined, i.e., whether each equidistant segment is a core segment or an edge segment.

[0032] In some embodiments, by hierarchically identifying rural markets, the node types of different rural markets can be determined. The node type represents the attributes of the rural market in terms of scale, functional richness, and service scope. There are various node types, and different rural markets may have different node types. Different node types correspond to different scales, functional richness, and service scopes of the rural markets. Data representing the spatial location, physical scale, and functional importance of rural markets can be obtained from basic related data. Using this data, along with multidimensional feature identifiers, rural markets can be hierarchically identified to determine their corresponding node types.

[0033] In some embodiments, the node types corresponding to rural markets may include core rural market nodes, primary nodes, and secondary nodes. The higher the level of the node type (core rural market nodes are the highest level, and secondary nodes are the lowest level), the larger the scale, the richer the functions, and the wider the service area of ​​the corresponding rural market. That is, the scale, richer the functions, and the wider the service area of ​​a core rural market node are all greater than those of a primary node, and the scale, richer the functions, and the wider the service area of ​​a primary node are all greater than those of a secondary node. For example, a core rural market node can be a large-scale rural market with complete functions and a wide coverage area, while a secondary node can be a smaller-scale rural market with relatively simple functions, mainly serving the surrounding residents.

[0034] In some embodiments, after classifying and identifying rural markets and determining the node types of each rural market, a hierarchical rural market node network can be constructed according to the node type level from low to high (secondary node, primary node, core rural market node) or from high to low (core rural market node, primary node, secondary node). Through the hierarchical rural market node network, the node type of each rural market can be intuitively obtained, and the scale, functional richness and service scope of each rural market can be clarified.

[0035] S106. Based on spatial feature maps, industry feature maps, population feature maps, and multi-dimensional feature identifiers, establish a comprehensive evaluation model, and construct a coupling analysis model based on the comprehensive evaluation model. According to the coupling analysis model, identify and analyze the differences in strengths and weaknesses of rural markets, and determine sustainable development guidance strategies for rural markets.

[0036] In some embodiments, spatial features, functional features, transaction features, and node influence strength of rural markets can be obtained based on spatial feature maps, industry feature maps, population feature maps, and multidimensional feature identifiers. Furthermore, a comprehensive evaluation model can be established based on the spatial features, functional features, transaction features, and node influence strength of rural markets. The comprehensive evaluation model can reflect the overall development and construction level and comprehensive potential of each rural market as a whole.

[0037] Furthermore, based on the comprehensive evaluation model, rural markets with low levels of comprehensive development and construction and low comprehensive potential can be identified, and a coupled analysis model can be constructed. By constructing the coupled analysis model, rural markets with low levels of comprehensive development and construction and low comprehensive potential can be identified and analyzed, the differences in strengths and weaknesses of such rural markets can be determined, and corresponding sustainable development guidance strategies can be determined in response to these differences in strengths and weaknesses.

[0038] S107. Spatial feature maps, industry feature maps, population feature maps, multi-dimensional feature identifiers, target segmentation attributes, hierarchical rural market node networks, comprehensive evaluation models, coupling analysis models, and sustainable development guidance strategies are used as the digital identification results of rural markets.

[0039] In some embodiments, the spatial feature map, industry feature map, population feature map, multi-dimensional feature identifiers of rural markets, as well as the target segmentation attributes of each rural market, hierarchical rural market node network, comprehensive evaluation model, coupling analysis model, and sustainable development guidance strategy, are all used as digital identification results of rural markets. These digital identification results can be visualized to facilitate researchers or staff to formulate systematic decision-making plans based on the digital identification results of rural markets and to carry out subsequent planning and management of rural markets.

[0040] In this embodiment, by analyzing and processing data on the streets, villages, land use, and vendors of the rural market, quantifiable and comparable boundaries, forms, layouts, and scales of the rural market can be determined, clearly defining its boundaries. Furthermore, based on the boundaries, forms, layouts, scales, and geographic information system (GIS) technology, the coordinates of the market's center point, centrality, compactness, and shape index are calculated, constructing a spatial quantification platform that comprehensively describes the spatial morphological characteristics of the rural market. On this platform, spatial feature maps, industry feature maps, population feature maps, and multidimensional feature identifiers are constructed, enabling comprehensive data quantification analysis of the spatiotemporal and functional characteristics of the rural market. By determining the target segment attributes of different equidistant areas within the rural market through the industry and population feature maps, the distribution of pedestrian traffic and industries in each equidistant area can be refined, achieving accurate identification of the rural market's interior. Finally, using basic relevant data and multidimensional feature identifiers, the rural market is classified and identified, constructing... Constructing a hierarchical network of rural market nodes allows for a direct comparison of the scale, functional richness, and service scope of various rural markets. Based on spatial feature maps, industry feature maps, population feature maps, and multi-dimensional feature identifiers, a comprehensive evaluation model can be established to assess the overall development level and potential of each rural market. Furthermore, a coupled analysis model based on this comprehensive evaluation model can identify and analyze the strengths and weaknesses of rural markets, determining sustainable development guidance strategies. This clarifies the optimization and adjustment directions for disadvantaged rural markets, enhancing their overall competitiveness and sustainable development capabilities. Finally, by using spatial feature maps, industry feature maps, population feature maps, multi-dimensional feature identifiers, target segmentation attributes, the hierarchical rural market node network, the comprehensive evaluation model, the coupled analysis model, sustainable development guidance strategies, and node types as digital identification results for rural markets, these results can be applied to rural and urban development planning, providing decision-makers with scientific basis and optimization guidance. Therefore, the technical solution provided in this application can comprehensively, deeply, and accurately identify multi-dimensional information such as the spatial characteristics, functional characteristics, and node association characteristics of rural markets, construct a rural market system, and conduct a comprehensive evaluation, providing reliable decision support and optimization suggestions for rural and urban construction.

[0041] In some embodiments of this application, the construction of spatial feature maps, industry feature maps, population feature maps, and multidimensional feature identifiers corresponding to rural markets based on the spatial quantization platform in S104 can be achieved through S1041 to S1042: S1041. Based on the spatial quantification platform, obtain land use information within a preset range around each rural market, and create a spatial feature map corresponding to the rural market based on the land use information; based on the spatial quantification platform, determine the business information and population flow information of shops and vendors in the rural market, create an industry feature map corresponding to the rural market based on the business information, and create a population feature map corresponding to the rural market based on the population flow information.

[0042] In some embodiments, the preset range can be 2 to 3 kilometers, so the preset range around the rural market can be 2 kilometers, 2.5 kilometers, 3 kilometers, etc. Land use information within the preset range around the rural market can be further supplemented or refined based on a spatial quantification platform. This land use information can include the occupied area and spatial form of industrial parks, agricultural bases, large-scale public service products, etc. Based on this land use information, a complete spatial feature map can be constructed, which can reveal the role and status of the rural market in the regional spatial structure.

[0043] In some embodiments, a spatial quantification platform can be used to obtain the operating information of shops and vendors in rural markets, including the number of venues providing services such as catering, entertainment, and culture, the industrial structure and functional characteristics of rural markets, and the layout of surrounding industries. Through this operating information of shops and vendors, an industrial feature map can be constructed to show the spatial distribution of different commodity trading areas and service function areas, so as to assess the driving effect of rural markets on regional economic development.

[0044] In some embodiments, a spatial quantification platform can be used to obtain information on the flow of people in rural markets, including population density, origin of people, and consumption behavior. Based on the obtained population flow information, the distribution and fluctuation patterns, flow trends and transaction activity of people in rural markets can be analyzed. Combined with the socioeconomic characteristics of the population, such as age, gender, occupation and origin, a population characteristic distribution map of the rural market can be created.

[0045] S1042. Based on the spatial quantization platform, spatial feature map, industry feature map and population feature map, create multi-dimensional feature identifiers corresponding to rural markets.

[0046] In some embodiments, the multidimensional feature identifiers include feature identifiers of rural markets in terms of time periodicity, spatial scale, and functional formats. It should be noted that the feature identifiers of rural markets in terms of time periodicity, spatial scale, and functional formats only represent some characteristics of rural markets, not all characteristics. This embodiment uses the feature identifiers of rural markets in terms of time periodicity, spatial scale, and functional formats as an example. Based on a spatial quantification platform, as well as spatial feature maps, industry feature maps, and population feature maps, information about rural markets in multiple aspects such as time, scale, and management methods can be obtained, i.e., determining the time periodicity, spatial scale, and functional formats of rural markets. Furthermore, the characteristics of rural markets in these three different aspects are identified, thereby obtaining the feature identifiers of rural markets in terms of time periodicity, spatial scale, and functional formats. Among them, the characteristic identifiers corresponding to the time periodicity of rural markets can be any one of daily cycle, weekly cycle, and festival cycle; the characteristic identifiers of rural markets in terms of spatial scale can include specific values ​​of rural market area, number of stalls, and radiation radius; the characteristic identifiers of rural markets in terms of functional business formats can include at least one of commercial transactions, social services, and cultural heritage.

[0047] For example, such as Figure 2 The diagram shown is a schematic representation of some features of a rural market according to an embodiment of this application. Figure 2 It can be seen that the characteristic identifier corresponding to the time periodicity of rural market A is daily cycle; if the area of ​​rural market A is 100,000 square meters, the number of stalls is 100, and the radius of influence is 600 meters, then the characteristic identifier corresponding to the spatial scale of rural market A is: 100,000 square meters, 100 stalls, and a radius of influence of 600 meters; the characteristic identifier corresponding to the functional business format of rural market A is commercial transaction, then some of the characteristic identifiers corresponding to rural market A can be: (daily cycle; 100,000 square meters, 100 stalls, radius of influence of 600 meters; commercial transaction). The characteristic identifier of rural market B in terms of time periodicity is a weekly cycle; if rural market B has an area of ​​80,000 square meters, 50 stalls, and a radius of 500 meters, then its spatial scale characteristics are: 80,000 square meters, 50 stalls, and a radius of 500 meters; the characteristic identifier of rural market B in terms of functional business format is social services and cultural heritage, then some of the characteristic identifiers of rural market B can be: (weekly cycle; 80,000 square meters, 50 stalls, and a radius of 500 meters; social services, cultural heritage).

[0048] Understandably, by combining spatial quantification platforms, spatial feature maps, industry feature maps, and population feature maps, a multi-dimensional feature identifier system can be constructed for the refined management and dynamic monitoring of the rural market system. This system can assign each rural market a unique, comprehensive, and comparative "composite feature identifier" and multiple "multi-dimensional feature identifiers" with different focuses. This system can not only intuitively reflect the spatiotemporal dynamic characteristics and functional focuses of rural markets, but also enable rapid retrieval and cross-analysis of multi-dimensional data, providing a standardized data foundation for the refined management and dynamic monitoring of the rural market system.

[0049] In some embodiments of this application, the determination of the target segmentation attributes corresponding to different equidistant segmented areas in the rural market using industry feature maps and population feature maps in step S105 can be achieved through the following steps S1051A to S1054A, and each step will be described below.

[0050] S1051A. Spatial analysis of population feature maps is performed using the kernel density estimation method to determine the initial segmentation attributes corresponding to each of the multiple equidistant segmented areas of a rural market.

[0051] In some embodiments, a rural market can be divided into multiple equal segments (equidistant partitioned areas) based on a preset area division rule. This preset area division rule can be based on the length of the market. For example, for a long and narrow rural market, every 100 meters within the market can be considered as an equidistant partitioned area. If a rural market is 1000 meters long, it can be divided into 10 equidistant partitioned areas. Here, the preset area division rule and the division method are merely illustrative examples, and this application does not limit them.

[0052] Here, the initial segmentation attributes can include core segments and edge segments. These attributes represent the pedestrian density within equidistant segments; areas with high pedestrian density are core segments, and areas with low pedestrian density are edge segments. Therefore, based on pedestrian density, it can be determined whether an equidistant segment is a core segment or an edge segment. In some embodiments, spatial analysis of the crowd feature map using kernel density estimation can identify high-density and low-density equidistant segments within the rural market, thereby initially determining the segment boundaries of the rural market.

[0053] S1052A. Determine the shop distribution density and vendor distribution density in each equidistant area based on the industry characteristic map, and determine the pedestrian flow density in each equidistant area based on the population characteristic map.

[0054] In some embodiments, after determining the equidistant segments within the rural market, the number of shops, segment area, and number of vendors in each equidistant segment can be obtained from the industry feature map, and the pedestrian flow in each equidistant segment during the same time period can be obtained from the crowd feature map. The shop distribution density is calculated based on the number of shops and segment area, the vendor distribution density is calculated based on the number of vendors and segment area, and the pedestrian flow density is calculated based on the pedestrian flow and segment area.

[0055] S1053A. Calculate the segmentation characteristic index corresponding to each equidistant segmented area using the density of shop distribution, the density of vendor distribution, and the density of pedestrian traffic.

[0056] In some embodiments, the weighted sum of shop distribution density, vendor distribution density, and pedestrian flow density can be used as the segmented characteristic index corresponding to the grading region. For example, the segmented characteristic index... The calculation can be performed using the following formula (1): (1); in, The pedestrian density index is calculated by dividing the total pedestrian flow over each time period by the area of ​​the segment. The pedestrian flow is derived from the estimated results of instantaneous pedestrian flow statistics. The density of street vendors is obtained by dividing the number of vendors by the area of ​​the segment. This represents the density of shop distribution, calculated by dividing the number of shops by the segment area. These represent the respective weights of the pedestrian density index, vendor distribution density, and shop distribution density.

[0057] For example, it can be set Since pedestrian traffic directly reflects the activity level of rural markets and has a significant impact on their spatial characteristics; the weight of vendor density is... As an important component of rural markets, the distribution and density of street vendors significantly impact segmentation characteristics; the weight of shop density is... Because the distribution of shops is relatively inflexible, it contributes little to the dynamic changes in segmentation characteristics.

[0058] Furthermore, to ensure the comparability of different indicators in the calculation, the density of shop distribution, the density of vendor distribution, and the density of pedestrian traffic can be planned and processed, and the standardized formula is shown in formula (2): (2); in, For any indicator ( , or ), These are the normalized values. After normalization, the range of values ​​for each variable is [value missing]. This ensures the scientific validity and reliability of the calculation results.

[0059] In practical applications, it is first necessary to calculate the corresponding density values ​​based on the statistical data of pedestrian flow, shop and vendor distribution in each section of the rural market; then, through standardization, all indicators are unified to the same dimension range, and the segmented characteristic index is calculated by substituting the weights into formula (1). Finally, according to The size of each segment is used to sort the segments, thereby clarifying whether each equidistant segment is a core segment or an edge segment.

[0060] Understandably, by using the density of shop distribution, the density of vendor distribution, and the density of pedestrian traffic, the corresponding segmented characteristic index of each equidistant segmented area can be calculated, thereby enabling quantitative analysis of the distribution of vendors, transaction scale, and pedestrian traffic in the equidistant segmented areas within a rural market.

[0061] S1054A. Based on the magnitude of the segmentation feature index, the initial segmentation attributes are updated to obtain the target segmentation attributes corresponding to each equidistant segmentation region.

[0062] In some embodiments, the segmentation indices of equidistant segments within the same rural market can be arranged in descending order, or equidistant segments with the same or similar segmentation indices can be grouped into one region, ultimately determining the core segment and the edge segment. Furthermore, the initial segmentation attributes of each equidistant segment can be updated. This update can be an adjustment or correction. For example, if the initial segmentation attribute of equidistant segment A is a core segment with high pedestrian density, and the calculated segmentation index of equidistant segment A ranks last among the segmentation indices of multiple equidistant segments within the same rural market, then the initial segmentation attribute of equidistant segment A can be updated to a core segment with high pedestrian density, shop density, and vendor density (target segmentation attribute).

[0063] In some embodiments, after obtaining the target segmentation attributes corresponding to each equidistant segmentation area, equidistant segmentation areas in different rural markets with the same target segmentation attribute (e.g., all being core areas with high pedestrian density, shop density, and vendor density) can be compared to achieve in-depth analysis of the internal data of different rural markets.

[0064] For example, such as Figure 3 The diagram shows the spatial, industrial, and demographic characteristics of rural market A. The horizontal axis represents the length of rural market A, and the vertical axis represents its width. Figure 4As can be seen, the rural market A is 1300 meters long. Based on the total length of rural market A, it is divided into 13 equidistant sections, each 100 meters long. Equidistant sections A1, A2, A3, A4, and A5 constitute the core section (core area) of rural market A. The width of A1, A2, A3, A4, and A5 is 150 meters. The number of shops in A1... A1 has 50 shops and 45 stalls, with a peak foot traffic of 168 people; A2 has 52 shops and 36 stalls, with a peak foot traffic of 220 people; A3 has 65 shops and 32 stalls, with a peak foot traffic of 286 people; A4 has 55 shops and 48 stalls, with a peak foot traffic of 260 people; A5 has 70 shops and 54 stalls, with a peak foot traffic of 300 people.

[0065] Accordingly, such as Figure 4 The diagram shows the spatial, industrial, and demographic characteristics of rural market B. The horizontal axis represents the length of rural market B, and the vertical axis represents its width. Figure 3 As can be seen, rural market B is 800 meters long and is divided into 8 equidistant sections, each 100 meters long. Sections B1 and B2 are the core sections (core areas) of rural market B. Both B1 and B2 are 150 meters wide. B1 has 50 shops and 40 stalls, with a peak foot traffic of 300 people per hour; B2 has 30 shops and 35 stalls, with a peak foot traffic of 200 people per hour. It should be noted that the peak foot traffic figures for each core section in rural market A and rural market B refer to the foot traffic during the same time period.

[0066] Based on the number of shops and the area of ​​the equidistant zones in each core section (A1, A2, A3, A4, and A5) of rural market A, the shop distribution density for each core section can be calculated; based on the number of vendors and the area of ​​the equidistant zones in each core section of rural market A, the vendor distribution density for each core section can be calculated; and based on the time-of-day pedestrian flow and the area of ​​the equidistant zones in each core section of rural market A, the pedestrian flow density for each core section can be calculated. Similarly, based on the number of shops and the area of ​​the equidistant zones in each core section (B1 and B2) of rural market B, the shop distribution density for each core section can be calculated; based on the number of vendors and the area of ​​the equidistant zones in each core section of rural market B, the vendor distribution density for each core section can be calculated; and based on the time-of-day pedestrian flow and the area of ​​the equidistant zones in each core section of rural market B, the pedestrian flow density for each core section can be calculated. Then, the density of shops, vendors, and pedestrian traffic in each core section of rural market A was compared with the density of shops, vendors, and pedestrian traffic in each core section of rural market B, in order to obtain comparative results on the spatial characteristics, industrial characteristics, and population characteristics of each core section of rural market A and rural market B.

[0067] It is understandable that by dividing the rural market into multiple equidistant segments, determining the initial segmentation attributes corresponding to the equidistant segments based on kernel density estimation, and updating the initial segmentation attributes based on the calculated segmentation feature index, it is possible to quantify the functional and activity differences between the core and peripheral segments of the rural market.

[0068] In some embodiments of this application, the rural markets in the urban fringe area include multiple ones. After obtaining the target segmentation attributes corresponding to each equidistant segmentation area in each rural market, the following steps S108 to S110 can be performed. Each step is described below.

[0069] S108. Determine the target equidistant segmented areas in multiple rural markets whose target segmentation attribute indicators are core segments.

[0070] In some embodiments, the target segmentation attribute can represent the pedestrian flow density within the equidistant segmentation area. Based on the pedestrian flow density, it can be determined whether each equidistant segmentation area is a core segment or an edge segment. Therefore, a target equidistant segmentation area whose target segmentation attribute indicates that the equidistant segmentation area is a core segment can be determined from multiple equidistant segmentation areas in each rural market. That is, the target equidistant segmentation area is a core segment.

[0071] S109. The average value of the segmented feature index corresponding to the target equidistant segmented region is determined as the standard segmented feature index.

[0072] In some embodiments, the segmentation feature indices corresponding to the target equidistant segments of different rural markets may differ. The average value of the segmentation feature indices corresponding to each target equidistant segment can be used as the standard segmentation feature index. The standard segmentation feature index represents the average level of the segmentation feature index corresponding to the core segment, and can be used subsequently to compare the deviation of the segmentation feature index of the core segment in different rural markets from the standard segmentation feature index.

[0073] In some embodiments, the segmentation indices corresponding to each equidistant segmented region can be arranged in ascending order to obtain a sorting result. Then, the average of the maximum and minimum segmentation feature indices in the sorting result can be used as the standard segmentation feature index.

[0074] S110. Sequentially determine the difference between the segment feature index and the standard segment feature index corresponding to the target equidistant segmented area in each rural market, and use the difference as the digital recognition result of the rural market.

[0075] In some embodiments, the segment feature index corresponding to the target equidistant segmented area in each rural market can be compared with the standard segment feature index in turn, the difference between the segment feature index corresponding to the target equidistant segmented area and the standard segment feature index can be calculated, and the difference can be used as the digital recognition result of the rural market.

[0076] Understandably, by identifying target equidistant segments in multiple rural markets where the target segmentation attribute indicates the core segment, and determining the average value of the segmentation feature index corresponding to the target equidistant segments as the standard segmentation feature index, and then successively determining the difference between the segmentation feature index corresponding to the target equidistant segments in each rural market and the standard segmentation feature index, the differences between the core segments and the standard segments in each rural market can be determined, obtaining quantitative results regarding the association of the core segments, which facilitates subsequent comparative analysis of multiple different rural markets.

[0077] In some embodiments of this application, the basic related data includes the spatial location, physical scale, and functional importance of the rural market; the hierarchical identification of the rural market based on the basic related data and multi-dimensional feature identifiers in step S105, and the construction of a hierarchical rural market node network, can be achieved through the following steps S1051B to S1052B: S1051B. Using systematic clustering and fuzzy C-means clustering algorithms, cluster analysis is performed on spatial location, physical scale, functional importance, and multidimensional feature identifiers to determine the node type corresponding to the rural market.

[0078] Among them, node type represents the attributes of rural markets in terms of scale, functional richness, and service scope.

[0079] In some embodiments, clustering methods such as hierarchical clustering and fuzzy C-means clustering can be used to classify and identify each rural market according to its spatial location, physical scale, functional importance, and multidimensional feature identifiers. This can determine the node type of the rural market, i.e., whether each rural market is a core rural market node, a first-level node, or a second-level node.

[0080] S1052B: Construct a hierarchical rural market node network based on the node types corresponding to each rural market.

[0081] In some embodiments, a hierarchical rural market node network can be constructed based on the level of the node type of each rural market, for example, in order of level from high to low or from low to high.

[0082] It is understandable that by determining the corresponding node type for each rural market and constructing a hierarchical rural market node network based on the node type of each rural market, the characteristics of rural markets in terms of space, scale, and function can be clearly identified through this hierarchical node network. This allows for horizontal comparison of different rural markets, thereby determining the differences between rural markets in terms of space, scale, and function, and realizing quantitative data analysis between rural markets.

[0083] In some embodiments of this application, the establishment of a comprehensive evaluation model based on spatial feature maps, industry feature maps, population feature maps and multidimensional feature identifiers in step S106 can be achieved through the following steps S1061A to S1063A.

[0084] S1061A. Based on multidimensional feature identifiers and spatial feature maps, obtain spatial feature indicators of rural markets; based on multidimensional feature identifiers and industrial feature maps, obtain functional feature indicators of rural markets; based on multidimensional feature identifiers, population feature maps, and industrial feature maps, obtain transaction feature indicators of rural markets; based on multidimensional feature identifiers, population feature maps, and industrial feature maps, obtain node role strength indicators of rural markets.

[0085] It should be noted that spatial characteristic indicators include the centrality, compactness, and shape index of rural markets; functional characteristic indicators include the commercial function diversity index and the completeness of social service functions; transaction characteristic indicators include transaction intensity, transaction activity, and transaction stability; and nodal role intensity indicators include the intensity of people flow, commodity circulation, and information exchange.

[0086] In some embodiments, spatial characteristic indicators such as centrality, compactness, and shape index of each rural market can be obtained based on multidimensional feature identifiers and spatial feature maps; commercial function diversity index and social service function completeness of each rural market can be obtained based on multidimensional feature identifiers and industry feature maps; transaction characteristic indicators such as transaction intensity, transaction activity, and transaction stability of each rural market can be obtained based on multidimensional feature identifiers, population feature maps, and industry feature maps; and node role strength indicators such as the intensity of people flow connections, commodity circulation connections, and information exchange connections of rural markets can be obtained based on multidimensional feature identifiers, population feature maps, and industry feature maps.

[0087] In some embodiments, the intensity index of human flow connection can be constructed based on the multidimensional feature identifiers of rural markets and the human flow data of rural markets in the human feature map; the intensity index of commodity circulation connection can be constructed based on the multidimensional feature identifiers of rural markets and the commodity circulation data in the industrial feature map; and the intensity index of resource matching can be constructed based on the multidimensional feature identifiers of rural markets and the resource matching data in the industrial feature map.

[0088] In some embodiments, population flow data may include the trajectory and scale of population flow between rural markets and primary and secondary areas, which can be obtained through mobile signaling and video surveillance data. By analyzing population flow data, a population flow connection strength index can be constructed.

[0089] In some embodiments, commodity circulation data may include commodity circulation paths, efficiency, and dependencies, which can be obtained through vendor and shop inquiries, logistics platform transaction data, and other means. By analyzing commodity circulation data, a commodity circulation connection strength index can be constructed.

[0090] In some embodiments, resource matching data may include the scale and level of surrounding industries, recreation, public services, and other resources, as well as the level of rural market node networks. By exploring the correlation and influence between various types of resource matching data, a resource matching intensity index can be constructed.

[0091] In some embodiments, after constructing the three indicators mentioned above—the intensity of human flow connections, the intensity of commodity flow connections, and the intensity of resource matching—these three indicators can be transformed into standardized indicators using methods such as gravity models and social network analysis. This will construct a quantitative system for the connection intensity of rural market nodes, accurately reveal the regional connection characteristics and factor flow patterns of the rural market network, and assess the intensity of interaction and dependence between different regions.

[0092] S1062A. Calculate the comprehensive evaluation index of rural markets based on spatial characteristic indicators, functional characteristic indicators, transaction characteristic indicators, and node role intensity indicators.

[0093] In some embodiments, a comprehensive evaluation index for a rural market can be calculated based on factors such as the market's shape index, compactness, centrality, commercial function diversity index, social service function completeness index, pedestrian flow intensity index, commodity circulation intensity index, and resource matching intensity index.

[0094] For example, weights can be assigned to various indicators to obtain a comprehensive evaluation index (the comprehensive development and construction level or comprehensive potential guidance index of rural markets), which can be calculated using the following formula (3). The comprehensive evaluation index of each rural market : (3); in, It is the first rural market Entropy weight of the indicator Indicates the first The first rural market The dimensionless values ​​of the indicators 'm' represents the number of indicators.

[0095] Furthermore, the comprehensive evaluation index can be standardized using Z-Score. Z-Score standardization, also known as zero-mean normalization or standard deviation standardization, uses standardized data to determine the quadrants of supply and demand, and plots a scatter plot based on their high and low levels. The formulas for Z-Score standardization of the comprehensive evaluation index are shown in formulas (4) and (5) below: (4); (5); in, This indicates the total number of rural markets. This is a standardized comprehensive evaluation index; The average value of the evaluation or assessment of rural markets across the entire research scope; The standard deviation is used to guide the development, construction, or potential of rural markets within the research scope.

[0096] Furthermore, by calculating the comprehensive evaluation index of each rural market, rural markets in urban fringe areas can be divided into high-level balanced rural markets (such as those with high supply and demand, development and utilization levels, and high comprehensive evaluation scores), lagging-development rural markets (such as those with high demand but insufficient supply, average development and utilization, and need to further optimize supply capacity and construction level), low-level balanced rural markets (such as those with low supply and demand, and construction that meets basic needs, and whose sustainable development strategies should be comprehensively considered), and advanced-development rural markets (such as those with high supply, some space for construction, but insufficient demand, and need to adjust supply strategies to match demand).

[0097] S1063A. Based on the comprehensive evaluation index corresponding to each rural market, a comprehensive evaluation model is established.

[0098] In some embodiments, after calculating the corresponding comprehensive evaluation index based on the spatial characteristic index, functional characteristic index, transaction characteristic index and node role intensity index of each rural market, a comprehensive evaluation model can be established based on each comprehensive evaluation index.

[0099] Understandably, the comprehensive evaluation index of rural markets, calculated based on spatial characteristic indicators, functional characteristic indicators, transaction characteristic indicators, and node role intensity indicators, encompasses factors such as the spatial characteristics, functional characteristics, transaction characteristics, and node role intensity of rural markets. This enables a comprehensive evaluation of rural markets, allowing for precise segmentation of them. Furthermore, a comprehensive evaluation model can be constructed based on the comprehensive evaluation index, which can then clearly define the level of comprehensive development and construction and the magnitude of comprehensive potential of each rural market.

[0100] In some embodiments of this application, a coupled analysis model is constructed based on a comprehensive evaluation model. According to the coupled analysis model, the differences in strengths and weaknesses of rural markets are identified and analyzed. The sustainable development guidance strategy for rural markets can be determined through the following steps S1061B to S1063B.

[0101] S1061B. Identify specific rural markets whose comprehensive evaluation index is less than or equal to the comprehensive evaluation index threshold from the comprehensive evaluation model.

[0102] In some embodiments, the comprehensive evaluation index threshold can be the average of the comprehensive evaluation indices of each rural market. The comprehensive evaluation index corresponding to each rural market can be compared with the comprehensive evaluation index threshold to determine a specific rural market whose comprehensive evaluation index is less than or equal to the comprehensive evaluation index threshold. The specific rural market can be one or more of a plurality of rural markets.

[0103] S1062B Calculate the transaction intensity, traffic accessibility index, and business diversity index corresponding to a specific rural market, and construct a coupled analysis model based on the transaction intensity, traffic accessibility index, and business diversity index corresponding to a specific rural market.

[0104] In some embodiments, the transaction intensity and volatility of a specific rural market can be calculated. Transaction intensity and its volatility are common and important indicators for assessing the activity level of a rural market. They refer to the intensity of trading activities of vendors and shops in a rural market during a specific period, and are usually determined by factors such as pedestrian density, vendor density, and shop density. To quantify transaction intensity, this application uses the following formula (6) to calculate transaction intensity. : (6); in, S represents pedestrian density (the number of pedestrians per unit area, usually expressed as people / m²). 2 (represented by) vendor density (referring to the number of vendors per unit area, usually expressed as vendors / m²) 2 (Indicated by) Shop density (referring to the number of shops per unit area, usually expressed as shops / m²) 2 (represented) and unit area ( / m²) 2 ).

[0105] The volatility of trading intensity reflects the range of change in trading intensity at rural markets over different time periods. To measure this variation, we can calculate the standard deviation of trading intensity for each period. A larger standard deviation indicates greater volatility in trading intensity during that period, suggesting stronger fluctuations in rural market activity; a smaller standard deviation indicates more stable rural market activity and lower volatility in trading intensity. It can be calculated using the following formula (7): (7); in, , for Trading intensity values ​​for different time periods. By calculating the standard deviation of trading intensity for each time period, the stability and volatility of trading activity can be quantified.

[0106] In studies of rural market spaces, transportation accessibility is a crucial indicator for assessing the connectivity between the rural market and its surrounding area. The transportation accessibility index reflects the ease of transportation access to the rural market area, specifically the accessibility from the market area to key transportation nodes (such as main roads or intersections). A higher transportation accessibility index indicates that the rural market area is easily accessible and more likely to attract consumers. To quantify the transportation accessibility of rural markets, the transportation accessibility index can be defined as a comprehensive score of the accessibility of the surrounding transportation network. This score combines factors such as the distance from the rural market to main roads or intersections, road width, and road classification. The calculation can be performed using the following formula (8): (8); in, The number of major transportation facilities around a rural market (such as road intersections, bus stops, etc.). For the first The weight of a transportation facility is usually determined based on factors such as road grade, lane width, and traffic flow. A transportation facility with a higher weight indicates that it has a greater impact on the accessibility of rural markets. For the rural market to the first The distance between transportation facilities is usually measured in meters. Shorter distance transportation facilities have a greater impact on the accessibility of rural markets.

[0107] In some embodiments, business format diversity reflects not only the richness of the variety of goods in rural markets, but also the impact of the operating methods of rural markets (such as the various business forms of shops and vendors) on the vitality of rural markets. The aim is to comprehensively measure the richness of the variety of goods and the diversity of operating methods in rural markets. These two dimensions respectively reflect the diversity of the variety of goods and the diversity of operating methods in rural markets. A comprehensive evaluation value is obtained from both parts, resulting in the business format diversity index. The specific calculation method is shown in the following formula (9): (9); in, The quantity of different types of goods in a rural market; For the first Frequency of occurrence of each product type; The total number of vendors or shops for all types of goods; The number of different business forms in rural markets (including various types of vendors and shop types). For the first Frequency of occurrence of various business models; The total number of vendors or shops across all business formats; These are weighting coefficients used to balance the impact of product variety and business model diversity on the final index. .

[0108] In some embodiments, after calculating the transaction intensity, traffic accessibility index, and business diversity index of a specific rural market, a coupled analysis model corresponding to the specific rural market can be constructed based on the transaction intensity, traffic accessibility index, and business diversity index.

[0109] S1063B. Based on the coupling analysis model, the advantages and disadvantages and weaknesses of a specific rural market are identified and analyzed, and sustainable development guidance strategies for the specific rural market are determined.

[0110] In some embodiments, based on a coupled analysis model, commonalities and differences among different specific rural markets can be explored and assessed from dimensions such as transaction intensity, accessibility, and business diversity. This can identify the strengths and weaknesses of different specific rural markets and thus formulate appropriate sustainable development guidance strategies for different specific rural markets.

[0111] Understandably, by identifying specific rural markets whose comprehensive evaluation index is less than or equal to the threshold from the comprehensive evaluation model, we can pinpoint rural markets with weaker overall performance (specific rural markets) from among multiple rural markets. Further calculations of the transaction intensity, accessibility index, and business diversity index of these specific rural markets allow for further analysis of their transaction intensity, accessibility, and business diversity. Based on these factors, a coupled analysis model can be constructed to conduct specialized analyses of relevant elements, identify the strengths and weaknesses of specific rural markets, explore specific impacts, and optimize and adjust these markets. For example, for underdeveloped rural markets, analyzing their transaction intensity and accessibility can identify key factors hindering development.

[0112] In some embodiments of this application, 3D modeling software and geographic information visualization technology can be used, combined with a spatial quantification platform for rural markets, spatial feature maps, industry feature maps, population feature maps, multi-dimensional feature identifiers, target segmentation attributes of each equidistant segment of the rural market, hierarchical rural market node network, comprehensive evaluation model, coupling analysis model, and sustainable development guidance strategies, to construct a 3D digital model of the rural market and its surrounding environment for visualization. Through virtual reality (VR) and online design (Figma) technology, users can intuitively browse information such as the spatial layout, architectural form, stall distribution, and pedestrian flow of the rural market, realizing an interactive experience and observation of the rural market.

[0113] Furthermore, the results of digital identification (spatial feature maps, industry feature maps, population feature maps, multi-dimensional feature identifiers, target segment attributes of each equidistant area of ​​the rural market, node types of the rural market, comprehensive evaluation index, etc.) can be applied to rural and urban construction planning, providing decision-makers with scientific basis and optimization guidance. Based on the comprehensive evaluation results of the rural market system, the development scale and functional positioning of rural markets can be rationally planned, the spatial layout and traffic organization of rural markets can be optimized, the coordination and cooperation among rural markets can be strengthened, and the overall competitiveness and sustainable development capacity of rural markets can be enhanced.

[0114] For example, the configuration of stall types and passageway design in the core and peripheral sections can be optimized based on segmented characteristic indices: by quantitatively analyzing the differences in function and activity between the core and peripheral sections of a rural market, the configuration of stall types can be optimized. For instance, high-popularity, high-value-added stall types, such as specialty foods and handicrafts, can be added to the core section; while some basic life service stalls, such as vegetables, fruits, and daily necessities, can be appropriately configured in the peripheral section. At the same time, the width and layout of passageways can be rationally designed according to the pedestrian density and vendor distribution to ensure smooth pedestrian flow and avoid congestion.

[0115] For example, a collaborative scheme of "main village market radiation + complementary characteristic village markets" can be proposed to promote the linkage with functions such as direct supply from agricultural production bases, industrial product exhibitions and sales, and cultural tourism experiences. This involves constructing a radiation network centered on the core village market and strengthening collaborative cooperation between the core village market and surrounding characteristic village markets. For instance, village market A, as the core village market, can link up with surrounding characteristic village markets such as village market B and village market C. Through activities such as direct supply of agricultural products, industrial product exhibitions, and cultural tourism experiences, resource sharing and complementary advantages can be achieved, promoting the coordinated development of the regional economy.

[0116] For example, key transportation nodes can be optimized to address weaknesses and improve logistics efficiency, thereby enhancing market competitiveness and sustainability. For rural markets with poor accessibility, such as Rural Market D and Rural Market E, the focus should be on optimizing their connection to the surrounding major transportation networks. This can be achieved by improving road conditions, adding bus stops, and constructing parking lots to enhance the accessibility of rural markets. Simultaneously, introducing modern logistics and distribution systems can improve efficiency, reduce transportation costs, and strengthen the market competitiveness and sustainable development capabilities of rural markets.

[0117] The above measures can effectively improve the operational efficiency and service quality of rural markets in urban fringe areas, providing strong support for rural and urban development. At the same time, the application of digital display and application technologies also provides new means and methods for the management and planning of rural markets.

[0118] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of this application can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of this application.

[0119] The methods described in the embodiments of this application can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code downloaded over a network that is originally stored in a remote recording medium or a non-transitory machine-readable medium and will be stored in a local recording medium. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0120] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application.

[0121] The above embodiments are only used to illustrate the embodiments of this application, and are not intended to limit the embodiments of this application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of this application. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of this application, and the patent protection scope of the embodiments of this application should be defined by the claims.

Claims

1. A digital identification method for rural markets in urban fringe areas, characterized in that, include: Acquire basic relevant data for the digital identification of rural markets in urban fringe areas; The basic related data includes data on the street and village where the rural market is located, land use, and merchants and vendors. The data on the streets, villages, land use, and vendors of the rural markets are analyzed and processed to determine the quantifiable and comparable boundaries, forms, layouts, and scale of the rural markets. Using the boundaries, form, layout, scale, and geographic information system technology of the rural market, the coordinates of the center point, centrality, compactness, and shape index of the rural market are calculated, and a spatial quantification platform is constructed. Based on the aforementioned spatial quantization platform, spatial feature maps, industry feature maps, population feature maps, and multidimensional feature identifiers corresponding to the rural market are constructed. Using the industry feature map and the population feature map, the target segmentation attributes corresponding to different equidistant segments in the rural market are determined; the target segmentation attributes include core segments and edge segments; based on the basic related data and the multidimensional feature identifiers, the rural market is hierarchically identified, and a hierarchical rural market node network is constructed. Based on the spatial feature map, the industry feature map, the population feature map, and the multidimensional feature identifier, a comprehensive evaluation model is established, and a coupling analysis model is constructed based on the comprehensive evaluation model. According to the coupling analysis model, the advantages and disadvantages and weaknesses of the rural market are identified and analyzed, and sustainable development guidance strategies for the rural market are determined. The spatial feature map, the industry feature map, the population feature map, the multidimensional feature identifier, the target segmentation attribute, the hierarchical rural market node network, the comprehensive evaluation model, the coupling analysis model, and the sustainable development guidance strategy are used as the digital identification results of the rural market.

2. The method according to claim 1, characterized in that, Based on the spatial quantization platform, the construction of spatial feature maps, industry feature maps, population feature maps, and multi-dimensional feature identifiers corresponding to the rural market includes: Based on the spatial quantification platform, land use information within a preset range surrounding each rural market is obtained, and a spatial feature map corresponding to the rural market is created based on the land use information; based on the spatial quantification platform, the business information and population flow information of shops and vendors within the rural market are determined, and an industry feature map corresponding to the rural market is created based on the business information, and a population feature map corresponding to the rural market is created based on the population flow information. Based on the spatial quantization platform, the spatial feature map, the industry feature map, and the population feature map, a multi-dimensional feature identifier corresponding to the rural market is created.

3. The method according to claim 1, characterized in that, The step of using the industry feature map and the population feature map to determine the target segment attributes corresponding to different equidistant areas in the rural market includes: The kernel density estimation method is used to perform spatial analysis on the population feature map to determine the initial segmentation attributes corresponding to each of the multiple equidistant segments of the rural market; the initial segmentation attributes include core segments and edge segments; The density of shops and vendors in each equidistant area is determined based on the industry characteristic map, and the population density in each equidistant area is determined based on the population characteristic map. Using the shop distribution density, vendor distribution density, and pedestrian flow density, calculate the segmentation characteristic index corresponding to each equidistant segmented area; Based on the magnitude of the segmentation feature index, the initial segmentation attributes are updated to obtain the target segmentation attributes corresponding to each equidistant segmentation region.

4. The method according to claim 3, characterized in that, The rural markets include multiple ones, and the method further includes: Identify the target equidistant segmented regions in the multiple rural markets whose target segmentation attribute indicates a core segment; The average value of the segmented feature indices corresponding to the target equidistant segmented regions is determined as the standard segmented feature index; The difference between the segment feature index corresponding to the target equidistant segmented area in each rural market and the standard segment feature index is determined sequentially, and the difference is used as the digital identification result of the rural market.

5. The method according to claim 1, characterized in that, The basic related data includes the spatial location, physical scale, and functional importance of the rural market; The process of hierarchically identifying rural markets based on the aforementioned basic relevant data and the multidimensional feature identifiers, constructing a hierarchical rural market node network, determining the node type corresponding to each rural market, and constructing the hierarchical rural market node network includes: Using hierarchical clustering and fuzzy C-means clustering algorithms, cluster analysis is performed on the spatial location, physical scale, functional importance, and multidimensional feature identifiers to determine the node type corresponding to the rural market; the node type represents the attributes of the rural market in terms of scale, functional richness, and service scope. A hierarchical rural market node network is constructed based on the node types corresponding to each rural market.

6. The method according to claim 1, characterized in that, The comprehensive evaluation model established based on the spatial feature map, industry feature map, population feature map, and multidimensional feature identifiers includes: Based on the multidimensional feature identifier and the spatial feature map, spatial feature indicators of the rural market are obtained; based on the multidimensional feature identifier and the industry feature map, functional feature indicators of the rural market are obtained; based on the multidimensional feature identifier, the population feature map, and the industry feature map, transaction feature indicators of the rural market are obtained; based on the multidimensional feature identifier, the population feature map, and the industry feature map, node influence strength indicators of the rural market are obtained. Based on the spatial characteristic indicators, the functional characteristic indicators, the transaction characteristic indicators, and the node effect strength indicators, the comprehensive evaluation index of the rural market is calculated. The comprehensive evaluation model is established based on the comprehensive evaluation index corresponding to each rural market.

7. The method according to claim 6, characterized in that, The process involves constructing a coupled analysis model based on the comprehensive evaluation model, identifying and analyzing the strengths and weaknesses of the rural market using this model, and determining sustainable development guidance strategies for the rural market, including: The specific rural markets whose comprehensive evaluation index is less than or equal to the comprehensive evaluation index threshold are identified from the comprehensive evaluation model; the comprehensive evaluation index threshold is the average of the comprehensive evaluation indices of all rural markets. Calculate the transaction intensity, traffic accessibility index, and business diversity index corresponding to the specific rural market, and construct the coupled analysis model based on the transaction intensity, traffic accessibility index, and business diversity index corresponding to the specific rural market; Based on the coupling analysis model, the strengths and weaknesses of the specific rural market are identified and analyzed, and sustainable development guidance strategies for the specific rural market are determined.

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