Method, system and equipment for identifying and delimiting town and village planning unit and medium

Abstract

The invention relates to the technical field of regional planning, and discloses a method, a system, equipment and a medium for identifying and delimiting a town and village planning unit, and the method comprises the steps: obtaining the multi-source data of a region, and obtaining the comprehensive strength score of the region based on the multi-source data; based on multi-source data, designing an improved gravitation model by integrating strength scores; selecting a plurality of central areas; gravitation values of each non-central area and the plurality of central areas are calculated through an improved gravitation model, and the non-central areas are delimited to the central area with the maximum gravitation value to form a town and village planning unit with the central area as the center; classifying the types of the delimited town and village planning units by adopting a K-means clustering analysis method; and executing the classified town and village planning units in combination with a territorial space planning system so as to optimize resource allocation of the town and village planning units. And a town and village planning unit. According to the method, the delimitation of the rural planning unit is more accurately supported.

Description

Technical Field

[0001] This invention relates to the field of regional planning technology, specifically to a method, system, equipment, and medium for identifying and delineating town and village planning units. Background Technology

[0002] Traditional urban and rural planning uses administrative divisions as boundaries for resource allocation and planning management, relying on manual experience to delineate town and village planning units, supplemented by static industry and population data. Common methods include:

[0003] ① The "town-village" hierarchical planning based on administrative divisions leads to the dispersion of resources and duplication of construction;

[0004] ② Static planning is conducted using basic geographic information and census data, lacking dynamic factor flow analysis;

[0005] ③ Relying on a single indicator (such as total economic output) to delineate town and village planning units ignores differences in resource endowment and cross-regional synergistic effects.

[0006] Its disadvantages include:

[0007] ① Administrative boundary restrictions: Planning is limited by the administrative boundaries of towns, making it difficult to achieve cross-regional integration of resources, resulting in prominent problems of "small, scattered and weak" resources;

[0008] ② Data uniformity and lag: Relying on static data, it is impossible to reflect dynamic factors such as population flow and industrial linkages in real time, resulting in a disconnect between planning results and actual needs;

[0009] ③ Insufficient scientific rigor in the model: Traditional gravity models only consider town size and spatial distance, neglecting key factors such as resource similarity and industrial complementarity, resulting in imprecise delineation results. The formula of the traditional gravity model is: ;

[0010] ④ Fragmented management: Planning and implementation are disconnected, and there is a lack of a full life cycle coordination mechanism, resulting in poor implementation results. Summary of the Invention

[0011] This invention provides a method for identifying and delineating town and village planning units, breaking down administrative boundaries and achieving efficient resource integration through multi-town collaboration; it constructs a dynamic data-driven planning model to improve the scientific nature of town and village planning unit delineation; and it establishes a collaborative mechanism for the entire process of "planning-implementation-optimization" to enhance the operability and adaptability of planning.

[0012] This invention is achieved through the following technical solution:

[0013] A method for identifying and delineating town and village planning units, comprising:

[0014] Acquire multi-source data for a region and obtain a comprehensive strength score for the region based on the multi-source data, wherein the multi-source data includes mobile data and non-mobile data;

[0015] An improved gravity model was designed based on the aforementioned multi-source data and comprehensive strength score.

[0016] Several central regions were selected based on the aforementioned comprehensive strength score;

[0017] The gravity values ​​of each non-central region and several central regions are calculated using an improved gravity model. The non-central regions are then designated as central regions with the largest gravity values ​​to form town and village planning units centered on these central regions.

[0018] K-means clustering analysis was used to classify the types of the delineated town and village planning units.

[0019] The categorized town and village planning units will be implemented in conjunction with the national land and space planning system to optimize the resource allocation of these units.

[0020] As an optimization, the mobile data includes population mobility data and population density data, while the non-mobile data includes village core density data, geographic information data, industry data, public service facility data, and land development potential data.

[0021] As an optimization, the specific process for obtaining the comprehensive strength score of the region based on the multi-source data is as follows:

[0022] Population flow data is calculated based on mobile phone signaling data; population density data is obtained based on statistical yearbooks and population census data; the number of municipal facilities and public service facilities is obtained based on the public service facility data; transportation location and transportation network density are obtained based on the geographic information data; and the proportion of ecological landscape land area, the number of cultural resource sites, the proportion of agricultural land area, the output value of secondary and tertiary industries, and the number of agritainment businesses are obtained based on industrial data and land development potential data.

[0023] Let the number of municipal facilities, the number of public service facilities, transportation location, transportation network density, population density data, population flow data, and village core density data be the basic construction indicators, and let the proportion of ecological landscape land area, the number of cultural resource sites, the proportion of agricultural land area, the output value of secondary and tertiary industries, and the number of agritainment businesses be the characteristic development indicators.

[0024] Principal component analysis was performed on the infrastructure and characteristic development indicators using SPSS statistical software to obtain the eigenvectors and eigenvalues ​​corresponding to several principal factors.

[0025] R-analysis was performed on several of the aforementioned eigenvectors and eigenvalues ​​to obtain an R-type loading matrix, and the contribution rate and cumulative contribution rate of several principal factors were calculated.

[0026] Based on the eigenvalues, contribution rates, and cumulative contribution rates of several principal factors, the comprehensive strength score M is obtained. Where n represents the number of principal factors, This represents the eigenvalue of the i-th principal factor. This represents the weight of the i-th principal factor, where weight = contribution rate / cumulative contribution rate.

[0027] As an optimization, the principal factors include a first principal factor, a second principal factor, and a third principal factor. The first principal factor includes the number of municipal facilities and the number of public service facilities. The second principal factor includes the proportion of ecological landscape land area. The third principal factor includes the output value of secondary and tertiary industries and the proportion of agricultural land area.

[0028] As an optimization, the improved gravity model is as follows:

[0029] ;

[0030] in, This represents the gravitational force between region i and region j, where k is a constant. This represents the driving time between region i and region j. , This represents the overall strength score of region i and region j. This indicates the degree of connection between region i and region j, representing the proportion of the population flow between region i and region j to the total outward population flow of region i. , This represents the volume of human traffic between region i and region j.

[0031] As an optimization, the specific process of implementing the categorized town and village planning units in conjunction with the territorial spatial planning system is as follows:

[0032] The categorized town and village planning units are embedded into the territorial spatial planning system, and the actual delineation of town and village planning units is adjusted based on the relevant special plans, important control lines, binding indicators, and layout requirements of the territorial spatial planning system.

[0033] As an optimization, the specific process of dynamically recalculating the comprehensive strength score of each region based on the newly collected flow data to optimize the resource allocation of the town and village planning units is as follows:

[0034] The flow data is monitored by IoT devices, and then the comprehensive strength score of each region is recalculated and the central region is redefined. Then, based on the improved gravity model, the gravity values ​​of each non-central region and the central region are recalculated to redefine the town and village planning units. Finally, the K-means clustering analysis method is used to classify the types of the delineated town and village planning units.

[0035] This invention also discloses a system for identifying and delineating town and village planning units, used to execute the aforementioned method for identifying and delineating town and village planning units, comprising:

[0036] The data acquisition module is used to acquire multi-source data of a region and obtain a comprehensive strength score of the region based on the multi-source data, wherein the multi-source data includes mobile data and non-mobile data;

[0037] The gravity model design module is used to design an improved gravity model based on the multi-source data and comprehensive strength score.

[0038] The central region selection module is used to select several central regions based on the comprehensive strength score;

[0039] The town and village planning unit delineation module is used to calculate the gravitational values ​​of each non-central area and several central areas respectively through an improved gravitational model, and delineate the non-central areas to the central area with the largest gravitational value to form a town and village planning unit centered on the central area.

[0040] The town and village planning unit classification module is used to classify the types of the delineated town and village planning units using the K-means clustering analysis method;

[0041] The execution monitoring module is used to combine the classified town and village planning units with the territorial spatial planning system for execution, so as to optimize the resource allocation of the town and village planning units.

[0042] The present invention also discloses an electronic device, including at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method for identifying and delineating town and village planning units as described above.

[0043] The present invention also discloses a storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for identifying and delineating town and village planning units.

[0044] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0045] (1) Precise delineation: The improved gravity model reduces the error rate of delineation of town and village planning units and improves resource matching.

[0046] Traditional gravity models only consider the influence of township level and spatial distance on the strength of spatial connections. Combining the characteristics and functions of rural town and village planning units—"breaking boundaries, central leadership, convenient connections, and resource integration"—this invention, based on the traditional gravity model, introduces connection density to modify the model and uses the reciprocal of the actual driving commute time between two locations instead of the spatial distance indicator. This more realistically reflects the degree of spatial proximity and more accurately supports the delineation of rural town and village planning units.

[0047] (2) Management efficiency: Through resource integration, the utilization efficiency of public service facilities and infrastructure in rural areas is significantly improved;

[0048] Planning for a single town often results in insufficient allocation and duplication of public services and infrastructure due to the large number of towns and villages. This leads to fragmented resource investment and low service levels in public services and infrastructure (for example, each town is equipped with primary and secondary schools and health centers according to the supporting standards, making it difficult to coordinate the allocation of high-quality teachers and doctors; inconsistent standards for infrastructure construction such as roads and transportation, and poor information sharing, all affect overall service efficiency). By developing territorial spatial plans based on town and village planning units, it is possible to promote the rational layout and precise allocation of public services and infrastructure on a larger scale, avoiding problems such as uneven distribution and poor quality of rural public resources, and achieving the goal of concentrating superior resources to improve quality and achieving balanced resource sharing.

[0049] (3) Economic benefits: First, it improves the efficiency of planning compilation and review. Planning based on individual towns results in each town having its own rules, making it difficult to coordinate planning elements; the large number of plans makes it difficult to match high-quality planning resources, and it is difficult to improve approval efficiency. Planning based on rural town planning units is conducive to concentrating high-quality planning resources, organizing experts from multiple industries to provide detailed guidance, focusing on key tasks within a certain period of time, reducing planning setbacks, improving the overall quality of planning compilation, and improving compilation and review efficiency.

[0050] Second, it promotes high-quality development of the county economy. Traditional planning often focuses on individual towns and districts, failing to consider the insufficient support of rural hinterlands for the overall economic development of the county. Under limited resource conditions, there is little room for maneuver in development. Developing territorial spatial plans on a regional basis can guide the full flow, rational aggregation, and optimal allocation of public resources and market factors on a larger scale, thereby strengthening the county economy. Attached Figure Description

[0051] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0052] Figure 1 This is a flowchart of a method for identifying and delineating town and village planning units according to the present invention;

[0053] Figure 2 To improve the schematic diagram of the gravity model;

[0054] Figure 3 Example diagram of a planning unit for agricultural rural towns;

[0055] Figure 4 Example diagram of a planning unit for scenic rural towns and villages;

[0056] Figure 5 This is an example diagram of a comprehensive rural town planning unit. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0058] This embodiment 1 provides a method for identifying and delineating town and village planning units, such as... Figure 1 As shown, it includes:

[0059] S1. Obtain multi-source data for the region and obtain a comprehensive strength score for the region based on the multi-source data, wherein the multi-source data includes mobile data and non-mobile data.

[0060] In some embodiments, the mobility data includes population mobility data and population density data, while the non-mobility data includes village core density data, geographic information data, industry data, public service facility data, and land development potential data.

[0061] In some embodiments, the specific process of obtaining the comprehensive strength score of the region based on the multi-source data is as follows:

[0062] S1.1. Population flow data is calculated based on the mobile phone signaling data; population density data is obtained based on statistical yearbooks and population census data; the number of municipal facilities and public service facilities is obtained based on the public service facility data; transportation location and transportation network density are obtained based on the geographic information data; and the proportion of ecological landscape land area, the number of cultural resource sites, the proportion of agricultural land area, the output value of secondary and tertiary industries, and the number of agritainment businesses are obtained based on industrial data and land development potential data.

[0063] S1.2 Let the number of municipal facilities, the number of public service facilities, transportation location, transportation network density, population density data, population flow data, and village core density data be the basic construction indicators, and let the proportion of ecological landscape land area, the number of cultural resource sites, the proportion of agricultural land area, the output value of secondary and tertiary industries, and the number of agritainment businesses be the characteristic development indicators.

[0064] Based on population data such as mobile phone signaling data, statistical yearbooks, and population census data, geographic information data (such as topography and transportation network), industry data (such as the distribution of agricultural parks and scenic spots), public service facility data (such as the location of hospitals and schools), and land development potential data (based on the main functional area strategy), two sets of indicators are formed: infrastructure (number of municipal facilities, number of public service facilities, transportation location, transportation network density, and population density) and characteristic development (proportion of ecological landscape land area, number of cultural resource sites, proportion of agricultural land area, output value of secondary and tertiary industries, and number of agritainment businesses).

[0065] Population flow data can be calculated from mobile phone signaling data. If mobile phone signaling data cannot be obtained, it can be replaced by public transportation card swipe data or social media location data.

[0066] S1.3. Use SPSS statistical software to perform principal component analysis on the infrastructure and characteristic development indicators to obtain the eigenvectors and eigenvalues ​​corresponding to several principal factors; that is, input the infrastructure and characteristic development indicators into the SPSS statistical software. Each principal factor will correspond to one eigenvector and one eigenvalue.

[0067] S1.4 Perform R analysis on the aforementioned eigenvectors and eigenvalues ​​to obtain the R-type loading matrix, and calculate the contribution rate and cumulative contribution rate of several principal factors using the "Factor Analysis" module of SPSS statistical software.

[0068] Each feature vector Multiply by the square root of its corresponding eigenvalue, i.e.:

[0069] ;

[0070] k is the number of principal factors. This represents the eigenvalue of the j-th principal factor. Let represent the eigenvector of the j-th principal factor.

[0071] Assume that the correlation coefficient matrix R is obtained after decomposition as follows: Eigenvalues: ; Feature vector: ;

[0072] The load matrix is ​​then:

[0073] ;

[0074] The first principal factors (loadings 0.95 and 1.26) explained most of the variance ( The variable is highly correlated with the first principal factor.

[0075] The second principal factors (loadings of -0.57 and 0.42) explained less variance. ), which is of low importance.

[0076] S1.5. Based on the eigenvalues, contribution rates, and cumulative contribution rates of several principal factors, the comprehensive strength score M is obtained. Where n represents the number of principal factors, This represents the eigenvalue of the i-th principal factor. This represents the weight of the i-th principal factor. Weight = contribution rate / cumulative contribution rate, which is the ratio of contribution rate to cumulative contribution rate.

[0077] Taking a region as an example (e.g., a township or town), the main operations described above are as follows:

[0078] Principal component analysis was performed on the aforementioned infrastructure and characteristic development indicators using SPSS statistical software to obtain eigenvectors and eigenvalues. R-analysis was then performed on the eigenvectors and eigenvalues ​​to obtain R-type loading matrices, and the contribution rate and cumulative contribution rate were calculated. Based on the eigenvalues, contribution rates, and cumulative contribution rates of each principal factor (the first principal factor is the number of municipal facilities and public service facilities; the second principal factor is the proportion of ecological landscape land area; the third principal factor is the output value of secondary and tertiary industries and the proportion of agricultural land area), and the factor loading matrices of each principal factor on each evaluation factor, the comprehensive evaluation result of the township's overall strength was further calculated. Where M represents the evaluation result of the comprehensive strength of the township; X represents the principal factor eigenvalue; X represents the weight, which is the ratio of contribution rate to cumulative contribution rate.

[0079] In some embodiments, the principal factors include a first principal factor, a second principal factor, and a third principal factor. The first principal factor includes the number of municipal facilities and the number of public service facilities. The second principal factor includes the proportion of ecological landscape land area. The third principal factor includes the output value of secondary and tertiary industries and the proportion of agricultural land area.

[0080] After principal component screening and simplification, the most important influencing factors are selected, and the remaining factors will not be identified as principal factors.

[0081] S2. Based on the multi-source data and comprehensive strength score, design an improved gravity model.

[0082] like Figure 2 As shown, in some embodiments, the improved gravity model is specifically as follows:

[0083] ;

[0084] in, This represents the gravitational force between region i and region j, where k is a constant. This represents the driving time between region i and region j. , This represents the overall strength score of region i and region j. This indicates the degree of connection between region i and region j; , This indicates the strength of the human flow connection between region i and region j.

[0085] Traditional gravity models only consider two factors: the energy level of the central town and the spatial distance. The improved gravity model in this invention introduces a connection density coefficient on the basis of the traditional gravity model, and uses the actual driving time between the two places to replace the spatial distance.

[0086] Let's take townships as an example.

[0087] 𝛽(𝑖,𝑗): The degree of connection between township i and township j. Based on population flow data, the intensity of population flow between townships i and j within the same county within 24 hours is calculated. 𝛽(𝑖,𝑗) is the proportion of the total population flow between townships i and j to the total population flow of township i to the outside world. .

[0088] : Comprehensive strength score of townships.

[0089] : Comprehensive strength score of township J.

[0090] Driving time between town i and town j. Obtained by calling the Baidu Maps API.

[0091] K: constant.

[0092] This represents the gravitational force between township i and township j.

[0093] It should be noted that the total number of people-to-people connections within townships i and j (including the town center and surrounding rural areas) accounts for the total number of people-to-people flows from township i to the outside.

[0094] S3. Select several central regions based on the comprehensive strength score.

[0095] Specifically, this step involves selecting the center of the town / village planning unit, taking the region as an example:

[0096] Based on the comprehensive strength score of townships, central towns with strong radiating influence are selected. A higher comprehensive strength score indicates a stronger ability to attract resources and factors, enabling it to radiate and drive the economic and social development of surrounding townships and rural areas, and promote regional new urbanization. OD radiation intensity expresses the spatial radiation level of a township; the more frequent the township's connections and activities with surrounding areas, the stronger its ability to attract people and factors, making it more suitable as a regional center to generate a radiating effect. Village density expresses the degree of village agglomeration in each township, calculated using the "kernel density analysis tool" in ArcGIS analysis software. Townships with higher village agglomeration levels have a greater demand for improving rural production and living standards.

[0097] S4. Calculate the gravitational values ​​of each non-central region and several central regions using the improved gravitational model, and delineate the non-central regions to the central region with the largest gravitational value to form a town and village planning unit centered on the central region.

[0098] This step involves defining the scope of town and village planning units, taking townships as an example.

[0099] After selecting the central area (i.e., the central town), the gravitational force between each non-central town and each central town is calculated using an improved gravity model. The non-central towns are then assigned to the central town with the largest gravitational force, thus forming a town and village planning unit composed of the central town and its corresponding non-central town.

[0100] S5. Use K-means clustering analysis to classify the types of the delineated town and village planning units.

[0101] This step involves classifying town and village planning units, which means using K-means cluster analysis to determine the type of town and village planning units based on the dominant industry (agricultural, scenic, comprehensive, etc.).

[0102] like Figure 4 As shown, agricultural-type rural town and village planning units are mostly located in areas with a certain agricultural foundation, concentrated and contiguous farmland, and relatively abundant paddy field resources. The development model of agricultural-type rural town and village planning units mainly involves using the local advantageous agricultural industry to drive the development of secondary and tertiary industries. The dominant driving force is large-scale agriculture as the main focus, driving the development of secondary and tertiary industries; achieving "agriculture leading production, and production revitalizing agriculture" through the integration of the three industries. The planning guidance method is to build a distinctive and innovative agricultural industrial system based on the Tianfu agricultural civilization; to integrate rural resources and promote rural self-renewal. The implementation organization method is mainly a combination of government guidance and spontaneous town and village initiatives.

[0103] The planning units for agricultural rural towns and villages mainly adopt a "primary, secondary, and tertiary" integrated model with primary industry as the leading sector. First, attracting enterprises and vertical integration: relying on the entry of enterprises to achieve modern mechanized deep processing and recycling. Second, strengthening talent and promoting secondary industry: training traditional technical personnel in handicrafts to create diverse and distinctive products such as intangible cultural heritage and specialty foods. Third, relying on resources and expanding functions: leveraging the original high-quality resources such as village architecture and rural environment to promote service industries such as tourism, science education, and experiential learning.

[0104] like Figure 5 As shown, the development model of scenic town and village planning units relies on unique natural resources to drive the tourism industry, achieving agricultural development through scenic spots. The primary driving force is the urgent need to protect and improve the ecological environment through town and village planning units, while simultaneously increasing land use quotas for tourism services and other construction projects through land-use linkage policies. The planning guidance aims to promote ecological civilization construction, meet recreational needs, enhance ecological space functions, and satisfy the diverse leisure and tourism needs of urban and rural residents. The implementation is primarily government-led, supported by major cultural and tourism projects within the scenic area. Scenic town and village planning units mainly adopt a "tertiary-secondary-primary" integrated model, with the tertiary industry as the mainstay. By leveraging the attraction of visitors to scenic areas, the consumption value of agricultural and sideline products and processed agricultural products is enhanced, thereby driving industrial development, brand formation, and the ecological and organic upgrading of agriculture, achieving on-site experiential consumption.

[0105] like Figure 5 As shown, comprehensive rural town and village planning units are generally located in the suburbs of urban areas, with convenient transportation, relatively developed urban economies, and relatively diversified industrial structures. The development model of comprehensive rural town and village planning units combines the locational advantages of being adjacent to urban areas with relatively complete infrastructure and diversified factors such as rail transit and industrial parks to achieve integrated and coordinated development between central towns, general towns, and central urban areas. The dominant driving factor is the comprehensive agglomeration of multiple elements under the guidance of industry, technology, and culture. The planning guidance method is to promote new urbanization, improve the quality of urbanization, promote urban-rural integration, improve farmers' living conditions, and implement community-based management. The implementation organization method is mainly government-led.

[0106] Comprehensive rural town planning units mainly adopt a "secondary-tertiary-primary" integration model, with secondary and tertiary industries as the mainstays. Combining the locational advantages of being adjacent to urban areas, and relying on industrial parks, upstream, they require raw material supply based on primary industry, while downstream, they require supporting service industries (such as warehousing, e-commerce, transportation, etc.) and supporting service industries such as technology, information, and finance.

[0107] The types of town and village planning units can be set according to the leading industries, and there are not only these few leading industries, which will not be elaborated here.

[0108] S6. Implement the categorized town and village planning units in conjunction with the national land spatial planning system to optimize resource allocation within these units. Town and village planning units.

[0109] Specifically as follows:

[0110] ① Planning Transmission: Integrate town and village planning unit plans into the territorial spatial planning system, connecting with the city and county-level territorial spatial master plans and related special plans to implement important control lines, binding indicators, layout requirements, etc.; and guiding the preparation of detailed plans to enhance the implementability of plans.

[0111] ② Implementation Feedback: Monitor the flow of elements (such as people and goods) through IoT devices, and dynamically adjust the resource allocation of town and village planning units (specifically, break down the administrative boundaries of townships to form town and village planning units, and promote the rational layout and precise allocation of land resources and supporting facilities on a larger scale).

[0112] ③Benefit-sharing mechanism: Establish cross-town tax revenue sharing and ecological compensation agreements to ensure coordinated development among multiple towns.

[0113] In some embodiments, the specific process of implementing the categorized town and village planning units in conjunction with the territorial spatial planning system is as follows:

[0114] The categorized town and village planning units are embedded into the territorial spatial planning system, and the actual delineation of town and village planning units is adjusted based on the relevant special plans, important control lines, binding indicators, and layout requirements of the territorial spatial planning system.

[0115] In some embodiments, the specific process of dynamically recalculating the comprehensive strength score of each region based on the newly collected flow data to optimize the town and village planning units and resource allocation is as follows:

[0116] The flow data is monitored by IoT devices, and then the comprehensive strength score of each region is recalculated and the central region is redefined. Then, based on the improved gravity model, the gravity values ​​of each non-central region and the central region are recalculated to redefine the town and village planning units. Finally, the K-means clustering analysis method is used to classify the types of the delineated town and village planning units.

[0117] In summary, the entire process of this invention is as follows:

[0118] Multi-source data acquisition and comprehensive scoring: Integrate mobile data (such as mobile phone signaling) and non-mobile data (such as village kernel density) to generate a comprehensive regional strength score.

[0119] Specifically, the multi-source data fusion of this invention is a dynamic integration of mobile signaling, geographic information, and industry data.

[0120] Improved gravity model design: Optimize traditional gravity models based on multi-source data to quantify interactions between regions.

[0121] Introducing closeness of connection Multi-factor correction.

[0122] Central area selection and town / village planning unit delineation: Central areas are selected by combining scoring and data analysis, and non-central areas are allocated to corresponding zones through a gravity model.

[0123] Clustering analysis and dynamic adjustment: The K-means algorithm is used to classify the regions, and the boundaries and resource allocation are dynamically optimized based on real-time data.

[0124] End-to-end collaborative mechanism: a dynamic optimization link of planning, implementation, and feedback.

[0125] The method of this invention solves technical problems in land spatial planning (such as resource allocation efficiency and dynamic adaptability) through data processing, model optimization and algorithm application, and is an innovation in the field of technology.

[0126] Example 2 discloses a system for identifying and delineating town and village planning units, used to execute the method for identifying and delineating town and village planning units described in Example 1, including:

[0127] The data acquisition module is used to acquire multi-source data of a region and obtain a comprehensive strength score of the region based on the multi-source data, wherein the multi-source data includes mobile data and non-mobile data;

[0128] The gravity model design module is used to design an improved gravity model based on the multi-source data and comprehensive strength score.

[0129] The central region selection module is used to select several central regions based on the comprehensive strength score;

[0130] The town and village planning unit delineation module is used to calculate the gravitational values ​​of each non-central area and several central areas respectively through an improved gravitational model, and delineate the non-central areas to the central area with the largest gravitational value to form a town and village planning unit centered on the central area.

[0131] The town and village planning unit classification module is used to classify the types of the delineated town and village planning units using the K-means clustering analysis method;

[0132] The execution monitoring module is used to combine the classified town and village planning units with the territorial spatial planning system for execution, so as to optimize the resource allocation of the town and village planning units.

[0133] Example 3 discloses an electronic device, including at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method for identifying and delineating town and village planning units as described in Example 1.

[0134] Example 4 discloses a storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method for identifying and delineating town and village planning units as described in Example 1.

[0135] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying and delineating town and village planning units, characterized in that, include: Acquire multi-source data for a region and obtain a comprehensive strength score for the region based on the multi-source data, wherein the multi-source data includes mobile data and non-mobile data; An improved gravity model was designed based on the aforementioned multi-source data and comprehensive strength score. Several central regions were selected based on the aforementioned comprehensive strength score; The gravity values ​​of each non-central region and several central regions are calculated using an improved gravity model. The non-central regions are then designated as central regions with the largest gravity values ​​to form town and village planning units centered on these central regions. K-means clustering analysis was used to classify the types of the delineated town and village planning units. The categorized town and village planning units will be implemented in conjunction with the national land and space planning system to optimize the resource allocation of these units.

2. The method for identifying and delineating town and village planning units according to claim 1, characterized in that, The mobile data includes population mobility data and population density data, while the non-mobile data includes village core density data, geographic information data, industry data, public service facility data, and land development potential data.

3. The method for identifying and delineating town and village planning units according to claim 2, characterized in that, The specific process for obtaining the comprehensive strength score of the region based on the multi-source data is as follows: Population flow data is calculated based on mobile phone signaling data; population density data is obtained based on statistical yearbooks and population census data; the number of municipal facilities and public service facilities is obtained based on the public service facility data; transportation location and transportation network density are obtained based on the geographic information data; and the proportion of ecological landscape land area, the number of cultural resource sites, the proportion of agricultural land area, the output value of secondary and tertiary industries, and the number of agritainment businesses are obtained based on industrial data and land development potential data. Let the number of municipal facilities, the number of public service facilities, transportation location, transportation network density, population density data, population flow data, and village core density data be the basic construction indicators, and let the proportion of ecological landscape land area, the number of cultural resource sites, the proportion of agricultural land area, the output value of secondary and tertiary industries, and the number of agritainment businesses be the characteristic development indicators. Principal component analysis was performed on the infrastructure and characteristic development indicators using SPSS statistical software to obtain the eigenvectors and eigenvalues ​​corresponding to several principal factors. R-analysis was performed on several of the aforementioned eigenvectors and eigenvalues ​​to obtain an R-type loading matrix, and the contribution rate and cumulative contribution rate of several principal factors were calculated. Based on the eigenvalues, contribution rates, and cumulative contribution rates of several principal factors, the comprehensive strength score M is obtained. Where n represents the number of principal factors, This represents the eigenvalue of the i-th principal factor. This represents the weight of the i-th principal factor, where weight = contribution rate / cumulative contribution rate.

4. The method for identifying and delineating town and village planning units according to claim 3, characterized in that, The principal factors include a first principal factor, a second principal factor, and a third principal factor. The first principal factor includes the number of municipal facilities and the number of public service facilities. The second principal factor includes the proportion of ecological landscape land area. The third principal factor includes the output value of secondary and tertiary industries and the proportion of agricultural land area.

5. The method for identifying and delineating town and village planning units according to claim 3, characterized in that, The improved gravitational model is as follows: ； in, This represents the gravitational force between region i and region j, where k is a constant. This represents the driving time between region i and region j. , This represents the overall strength score of region i and region j. This indicates the degree of connection between region i and region j, representing the proportion of the population flow between region i and region j to the total outward population flow of region i. , This represents the volume of human traffic between region i and region j.

6. The method for identifying and delineating town and village planning units according to claim 1, characterized in that, The specific process of implementing the categorized town and village planning units in conjunction with the national land spatial planning system is as follows: The categorized town and village planning units are embedded into the territorial spatial planning system, and the actual delineation of town and village planning units is adjusted based on the relevant special plans, important control lines, binding indicators, and layout requirements of the territorial spatial planning system.

7. The method for identifying and delineating town and village planning units according to claim 1, characterized in that, The specific process of dynamically recalculating the comprehensive strength score of each region based on the newly collected flow data to optimize the resource allocation of the town and village planning units is as follows: The flow data is monitored by IoT devices, and then the comprehensive strength score of each region is recalculated and the central region is redefined. Then, based on the improved gravity model, the gravity values ​​of each non-central region and the central region are recalculated to redefine the town and village planning units. Finally, the K-means clustering analysis method is used to classify the types of the delineated town and village planning units.

8. A system for identifying and delineating town and village planning units, used to execute the method for identifying and delineating town and village planning units as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire multi-source data of a region and obtain a comprehensive strength score of the region based on the multi-source data, wherein the multi-source data includes mobile data and non-mobile data; The gravity model design module is used to design an improved gravity model based on the multi-source data and comprehensive strength score. The central region selection module is used to select several central regions based on the comprehensive strength score; The town and village planning unit delineation module is used to calculate the gravitational values ​​of each non-central area and several central areas respectively through an improved gravitational model, and delineate the non-central areas to the central area with the largest gravitational value to form a town and village planning unit centered on the central area. The town and village planning unit classification module is used to classify the types of the delineated town and village planning units using the K-means clustering analysis method; The execution monitoring module is used to combine the classified town and village planning units with the territorial spatial planning system for execution, so as to optimize the resource allocation of the town and village planning units.

9. An electronic device, characterized in that, It includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method for identifying and delineating town and village planning units as described in any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for identifying and delineating town and village planning units as described in any one of claims 1 to 7.

Images