Educational facility site selection and configuration optimization method, system, computer device and medium

Abstract

The application discloses an education facility site selection and configuration optimization method and system, computer equipment and a medium. The method comprises the following steps: acquiring data of a target area and preprocessing; performing primary school education facility supply analysis and resident demand analysis based on township primary school education facility supply data and rural community life power supply data, and measuring the supply and demand accessibility of primary school education facilities; performing population flow measurement of different time scales based on the rural community life power supply data; performing K-means clustering analysis according to the supply and demand accessibility, the population monthly flow level and the population annual flow level, and obtaining clustering results; matching the clustering results with rural community units, identifying the layout characteristics and types of rural community primary school education facilities, and realizing targeted township primary school education facility site selection and configuration optimization in units of rural communities. The application can provide primary school education facility site selection and configuration optimization by replacing the previous population census data.

Description

Technical Field

[0001] This invention relates to a method, system, computer equipment, and medium for optimizing the site selection and configuration of educational facilities, belonging to the fields of urban and rural planning and urban geography. Background Technology

[0002] Equal access to public services is a crucial means to improve people's well-being, address imbalances in development, and support high-quality development. With my country's rapid urbanization, a large influx of rural residents into cities has exacerbated the imbalance between supply and demand for public services. While academic research on the site selection and optimization of public service facilities has been extensive, most studies focus on large cities and rely heavily on census data. However, in rural areas experiencing significant population loss, the data from decennial censuses cannot accurately and promptly reflect resident migration and changes. Consequently, the allocation of public services in these areas fails to meet the diverse needs of rural residents for primary school education facilities. Summary of the Invention

[0003] In view of this, the present invention provides a method, system, computer equipment and storage medium for the site selection and configuration optimization of educational facilities. It comprehensively considers both the supply and demand of facilities and the needs of residents, and uses rural community electricity supply data to reflect the demand level of township residents in areas with population outflow for primary school education facilities, replacing the previous population census data, and can provide optimization for the site selection and configuration of primary school education facilities.

[0004] The first objective of this invention is to provide a method for optimizing the site selection and configuration of educational facilities.

[0005] The second objective of this invention is to provide a system for optimizing the site selection and configuration of educational facilities.

[0006] A third objective of this invention is to provide a computer device.

[0007] A fourth objective of this invention is to provide a storage medium.

[0008] The first objective of this invention can be achieved by adopting the following technical solution:

[0009] A method for optimizing the site selection and configuration of educational facilities, the method comprising:

[0010] Data from the target area is acquired and preprocessed. The data includes data on the supply of educational facilities in rural primary schools and data on the electricity supply for rural communities.

[0011] Based on the supply data of primary school education facilities in townships and the electricity supply data of rural communities, the supply of primary school education facilities and residents' demand are analyzed separately to calculate the supply and demand accessibility of primary school education facilities.

[0012] Population mobility is calculated at different time scales based on rural community electricity supply data. The population mobility calculation includes monthly population mobility level calculation and annual population mobility level calculation.

[0013] Based on supply and demand availability, monthly population mobility levels, and annual population mobility levels, K-means cluster analysis was performed to obtain clustering results.

[0014] By matching clustering results with rural community units, the layout characteristics and types of primary school education facilities in rural communities can be identified, enabling targeted optimization of the site selection and configuration of primary school education facilities in rural communities.

[0015] Furthermore, the acquisition and preprocessing of data from the target area specifically includes:

[0016] Obtain supply data of primary school education facilities in townships within the target area. The supply data includes the point coordinates of primary school education facilities and the number of available places in each school.

[0017] Acquire residential power supply data for rural communities within the target area. The power supply data includes the name of each residential power supply station, monthly residential power supply data for multiple years, number of households served, the rural community to which it belongs, and coordinate information. Correct any missing or abnormal data, and summarize the residential power supply data by rural community.

[0018] Obtain vector data of the boundaries of rural communities in the target area.

[0019] Furthermore, based on the supply data of primary school education facilities in townships and the electricity supply data of rural communities, the supply and demand of primary school education facilities are analyzed separately to calculate the availability of primary school education facilities. Specifically, this includes:

[0020] Primary school education facilities are extracted as supply points. A search domain is established based on the extreme distance radius of students traveling to the school. All demand quantities within the search domain are aggregated, and weights are assigned according to the distance decay law using a Gaussian function. The demand quantities are then aggregated, and the supply-demand ratio is calculated.

[0021] Calculate the Gaussian decay function considering spatial friction.

[0022] Using the central point of a rural community as the demand point, a search domain is established based on the extreme distance radius of students traveling to schools. All primary schools within the search domain are identified, and the supply-demand ratios are summed based on a Gaussian decay function to obtain the supply-demand accessibility of primary school educational facilities at the central point of the rural community.

[0023] Furthermore, the monthly population mobility level is calculated as follows:

[0024]

[0025] Among them, M s P represents the monthly population mobility level. m and Based on data on electricity supply in rural communities, P was calculated. m This represents the total monthly electricity supply for residential use in rural community unit k in a given year, where 1 ≤ m ≤ 11. The average monthly electricity supply value of rural community unit k in that year is represented by the following formula:

[0026]

[0027] Where n = 12.

[0028] Furthermore, the annual population mobility level is calculated as follows:

[0029]

[0030] Among them, M c P represents the annual level of population mobility. xy and Based on data on electricity supply in rural communities, P was calculated. xy Let l represent the total electricity supply for residential use in each rural community unit k in the yth quarter of year x, where l ≥ 1, 1 ≤ y ≤ 4. Let (x+l) represent the average electricity supply for each rural community unit k in the fourth quarter of the entire year. The formula is as follows:

[0031]

[0032] Where n = 4.

[0033] Furthermore, based on supply and demand availability, monthly population mobility levels, and annual population mobility levels, K-means cluster analysis is performed to obtain clustering results, specifically including:

[0034] The availability of supply and demand, monthly population mobility, annual population mobility, and rural community unit numbers are compiled into a table. The K-means clustering algorithm is run to initialize k cluster centers and calculate the distance from each object to the cluster center.

[0035] Compare the distance of each object to the cluster center in turn, and assign the object to the cluster with the nearest cluster center to obtain k clusters. Calculate the K-means cluster center.

[0036] Input the K-means clustering analysis system with supply and demand accessibility, monthly population mobility level, and annual population mobility level as variables, and with rural community units as case annotation basis, and output the clustering results.

[0037] Furthermore, the process of matching clustering results with rural community units to identify the layout characteristics and types of primary school education facilities in rural communities enables targeted optimization of the site selection and configuration of primary school education facilities in townships, based on rural communities. Specifically, this includes:

[0038] The clustering results are matched with rural community units to identify the layout characteristics and types of primary school education facilities in rural communities. Visual analysis is performed, and site selection and configuration optimization strategies for primary school education facilities are proposed for rural communities with different characteristics.

[0039] The second objective of this invention can be achieved by adopting the following technical solution:

[0040] An educational facility site selection and configuration optimization system, the system comprising:

[0041] The acquisition module is used to acquire data from the target area and perform preprocessing. The data includes data on the supply of educational facilities in rural primary schools and data on the electricity supply for rural communities.

[0042] The first calculation module is used to analyze the supply of primary school education facilities and residents' demand based on the supply data of primary school education facilities in townships and the power supply data of rural communities, and to calculate the supply and demand accessibility of primary school education facilities.

[0043] The second calculation module is used to calculate population flow at different time scales based on rural community electricity supply data. The population flow calculation includes monthly population flow level calculation and annual population flow level calculation.

[0044] The clustering module is used to perform K-means clustering analysis based on supply and demand accessibility, monthly population mobility level, and annual population mobility level to obtain clustering results;

[0045] The identification module is used to match clustering results with rural community units, identify the layout characteristics and types of primary school education facilities in rural communities, and realize targeted site selection and configuration optimization of primary school education facilities in rural communities.

[0046] The third objective of this invention can be achieved by adopting the following technical solution:

[0047] A computer device includes a processor and a memory for storing processor-executable programs, characterized in that, when the processor executes the program stored in the memory, it implements the above-described method for optimizing the location and configuration of educational facilities.

[0048] The fourth objective of this invention can be achieved by adopting the following technical solution:

[0049] A storage medium storing a program that, when executed by a processor, implements the above-described method for optimizing the location and configuration of educational facilities.

[0050] The present invention has the following advantages over the prior art:

[0051] This invention comprehensively considers three aspects: supply, demand, and population flow. It is more accurate than the traditional per capita index allocation standard. Furthermore, it uses rural community electricity data to reflect the actual demand level of residents in rural areas with population outflow for educational facilities, and can provide optimization for the site selection and allocation of primary school educational facilities. In addition, this invention is based on multi-factor evaluation indicators and uses commonly used analysis methods such as K-means clustering, which has the advantages of rapid calculation and iteration and easy promotion and application. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0053] Figure 1 This is a simplified flowchart of the educational facility site selection and configuration optimization method according to Embodiment 1 of the present invention.

[0054] Figure 2 This is a detailed flowchart of the educational facility site selection and configuration optimization method according to Embodiment 1 of the present invention.

[0055] Figure 3 This is a distribution map of primary school educational facilities according to Embodiment 1 of the present invention.

[0056] Figure 4 This is a graph showing the supply and demand availability calculation results of Embodiment 1 of the present invention.

[0057] Figure 5 This is a graph showing the results of the monthly population mobility level calculation in Embodiment 1 of the present invention.

[0058] Figure 6 This is a graph showing the results of the annual population mobility level calculation in Embodiment 1 of the present invention.

[0059] Figure 7 This is a clustering result diagram of Embodiment 1 of the present invention.

[0060] Figure 8 This is a structural block diagram of the educational facility site selection and configuration optimization system of Embodiment 2 of the present invention.

[0061] Figure 9 This is a structural block diagram of the computer device according to Embodiment 3 of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Example 1:

[0064] like Figure 1 and Figure 2 As shown in the figure, this embodiment provides a method for optimizing the site selection and configuration of educational facilities. The educational facilities are rural primary school educational facilities in areas with population outflow. The method includes the following steps:

[0065] S201. Obtain data from the target area and perform preprocessing.

[0066] The target area in this embodiment is a town in Lufeng City, Shanwei City, Guangdong Province. The total land area of ​​the target area is approximately 110 square kilometers, with 39 rural community units. The data of the target area is obtained through a multi-source data open platform, including data on the supply of educational facilities in township primary schools and data on the power supply for rural communities.

[0067] Furthermore, step S201 specifically includes:

[0068] S2011. Obtain the supply data of primary school education facilities in the target area. The supply data includes the point coordinates of the primary school education facilities and the number of places in each school. In this embodiment, there are 34 primary schools with a number of places ranging from 50 to 5000, distributed as follows: Figure 3 As shown.

[0069] S2012. Obtain the power supply data for rural communities within the target area. The power supply data includes the name of each power supply station, monthly power supply data for multiple years, number of households served, the rural community to which it belongs, and coordinate information. Correct any missing or abnormal data and summarize the power supply data by rural community.

[0070] S2013. Obtain vector data of the boundaries of rural communities in the target area.

[0071] S202. Based on the supply data of primary school education facilities in townships and the power supply data of rural communities, the supply of primary school education facilities and residents' demand are analyzed separately to calculate the supply and demand accessibility of primary school education facilities.

[0072] Furthermore, step S202 specifically includes:

[0073] S2021. Extract primary school educational facilities as supply points j, establish a search domain j with the radius d0 of the extreme distance students travel to the school, summarize all demand quantities within the search domain j, assign weights according to the distance decay law using a Gaussian function, sum and aggregate the demand quantities, and calculate the supply-demand ratio R. j The calculation formula is:

[0074]

[0075] Among them, E k The total annual power supply, d, is calculated based on rural community electricity data. kj The distance between positions k and j is given by d. Unit k must fall within the search domain, i.e., d kj ≤d0, where d0 is the farthest reachable distance from the student to the primary school; in this embodiment, the threshold d0 is set to 1000 meters; Nj is the number of school places; G(d ij ) is a Gaussian decay function.

[0076] S022, Calculate the Gaussian decay function G(d) considering spatial friction. ij The calculation formula is:

[0077]

[0078] Where e represents the natural constant.

[0079] S023. Taking the rural community center point i as the demand point, establish a search domain I with the radius d0 of the extreme distance d0 from students to the school, find all primary schools j within the search domain, and apply the supply-demand ratio R obtained in step S021. j Based on the Gaussian decay function, summation is performed to obtain the supply and demand accessibility of primary school education facilities at the central point i of the rural community. The higher the value, the higher the accessibility. The calculation formula is:

[0080]

[0081] The supply and demand availability calculation results of this embodiment Figure 4 As shown.

[0082] S203. Based on rural community electricity supply data, population mobility is calculated at different time scales, including monthly population mobility level calculation and annual population mobility level calculation.

[0083] The calculation formula for the monthly population mobility level in this embodiment is as follows:

[0084]

[0085] Among them, Ms P represents the monthly population mobility level. m and Based on data on electricity supply in rural communities, P was calculated. m This represents the total monthly electricity supply for residential use in rural community unit k in a given year, where 1 ≤ m ≤ 11. The average monthly electricity supply value of rural community unit k in that year is represented by the following formula:

[0086]

[0087] Where n = 12.

[0088] In this embodiment, the calculation of monthly population mobility level is based on rural community electricity supply data from February and March 2019. Figure 5 As shown.

[0089] The formula for calculating the annual population mobility level in this embodiment is as follows:

[0090]

[0091] Among them, M c P represents the annual level of population mobility. xy and Based on data on electricity supply in rural communities, P was calculated. xy Let l represent the total electricity supply for residential use in each rural community unit k in the yth quarter of year x, where l ≥ 1, 1 ≤ y ≤ 4. Let (x+l) represent the average electricity supply for each rural community unit k in the fourth quarter of the entire year. The formula is as follows:

[0092]

[0093] Where n = 4.

[0094] In this embodiment, rural community electricity supply data from the fourth quarter of 2015 and the fourth quarter of 2019 are used as examples for calculation. The results of the annual population mobility level are as follows: Figure 6 As shown.

[0095] S204. Based on supply and demand accessibility, monthly population mobility level, and annual population mobility level, perform K-means cluster analysis to obtain clustering results.

[0096] Furthermore, step S204 specifically includes:

[0097] S2041. Collect indicators such as supply and demand accessibility, monthly population mobility level, and annual population mobility level along with the rural community unit number into a table, import it into SPSS software, and run the K-means clustering algorithm.

[0098] Suppose a data sample X includes n objects X = {X1, X2, X3, ..., X...} n In a clustering algorithm, n objects are grouped into k specified clusters based on their similarity. Each object belongs to exactly one cluster whose distance to the cluster center is minimized. The k cluster centers are initialized as follows: {C1, C2, C3, ..., C...}. k}, 1 < k ≤ n, further, by calculating the Euclidean distance from each object to the cluster center, the calculation formula is as follows:

[0099]

[0100] Among them, X i Let C represent the i-th object, where 1 ≤ i ≤ n. j Let X represent the j-th cluster center, where 1 ≤ j ≤ k. it Let C represent the t-th attribute of the i-th object, where 1 ≤ t ≤ m. jt This represents the t-th attribute of the j-th cluster center.

[0101] In this embodiment, a given number of samples is 39, where each object has 3-dimensional attributes. The K-means clustering algorithm is used to cluster the 39 objects into 3 designated clusters based on the similarity between the objects.

[0102] S2042. Compare the distance of each object to the cluster center in turn, and assign the object to the cluster with the nearest cluster center, resulting in k clusters {S1, S2, S3, ..., S...}. k}

[0103] K-means cluster centers are the mean of all objects within a cluster, and their calculation formula is as follows:

[0104]

[0105] Among them, C l Represents the center of the l-th cluster, 1≤l≤k, |S l | represents the number of objects in the l-th cluster, Xi represents the i-th object in the l-th cluster, 1≤i≤|S l |

[0106] Further input variables: supply and demand accessibility, monthly population mobility level, and annual population mobility level; input case annotation basis: rural community unit;

[0107] Furthermore, the clustering results are shown in Table 1-3.

[0108] Table 1 Final Cluster Centers

[0109] Clustering 1 2 3 Supply and demand availability .00073315 .00032096 .00101137 Monthly liquidity level -.0229 -.0328 -.0173 Annual mobility level .7172 .0499 .3658

[0110] Table 2 Distances between final cluster centers

[0111] Clustering 1 2 3 1 .667 .351 2 .667 .316 3 .351 .316

[0112] Table 3 Number of cases in each cluster

[0113] S205. Match the clustering results with rural community units to identify the layout characteristics and types of primary school education facilities in rural communities, so as to achieve targeted site selection and configuration optimization of primary school education facilities in rural communities.

[0114] The clustering results were imported into ArcGIS, and the "Spatial Linkage" tool was used to match the clustering results with rural community units. This identified the layout characteristics and types of primary school facilities in rural communities, performed visualization analysis, and further proposed site selection and configuration optimization strategies for primary school facilities in rural communities with different characteristics. The output results are shown in Table 4, and the final result diagram is shown below. Figure 7 As shown.

[0115] Table 4. Layout Characteristics and Type Identification Results of Primary School Education Facilities in Rural Communities

[0116]

[0117]

[0118] It should be noted that although the method operations of the above embodiments are described in a specific order, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the described steps may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0119] Example 2:

[0120] like Figure 8 As shown, this embodiment provides an educational facility site selection and configuration optimization system. The system includes an acquisition module 801, a first calculation module 802, a second calculation module 803, a clustering module 804, and an identification module 805. The specific functions of each module are as follows:

[0121] The acquisition module 801 is used to acquire data from the target area and perform preprocessing. The data includes data on the supply of educational facilities in township primary schools and data on the power supply for rural communities.

[0122] The first calculation module 802 is used to analyze the supply of primary school education facilities and residents' demand based on the supply data of primary school education facilities in townships and the power supply data of rural communities, and to calculate the supply and demand accessibility of primary school education facilities.

[0123] The second calculation module 803 is used to calculate population flow at different time scales based on rural community electricity supply data. The population flow calculation includes monthly population flow level calculation and annual population flow level calculation.

[0124] Clustering module 804 is used to run K-means clustering analysis based on supply and demand accessibility, monthly population mobility level and annual population mobility level to obtain clustering results.

[0125] The identification module 805 is used to match the clustering results with rural community units, identify the layout characteristics and types of primary school education facilities in rural communities, and realize targeted site selection and configuration optimization of primary school education facilities in rural communities.

[0126] It should be noted that the system provided in this embodiment is only an example of the above-described division of functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0127] It is understood that the terms "first," "second," etc., used in the above system can be used to describe various modules, but these modules are not limited by these terms. These terms are only used to distinguish the first module from another module. For example, without departing from the scope of the invention, the first calculation module can be referred to as the second calculation module, and similarly, the second calculation module can be referred to as the first calculation module. Both the first and second calculation modules are calculation modules, but they are not the same calculation module.

[0128] Example 3:

[0129] This embodiment provides a computer device, which may be a computer, such as... Figure 9 As shown, it includes a processor 902, a memory, an input device 903, a display 904, and a network interface 905 connected via a system bus 901. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 906 and internal memory 907. The non-volatile storage medium 906 stores an operating system, computer programs, and a database. The internal memory 907 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. When the processor 902 executes the computer programs stored in the memory, it implements the educational facility site selection and configuration optimization method of Embodiment 1 described above, as follows:

[0130] Data from the target area is acquired and preprocessed. The data includes data on the supply of educational facilities in rural primary schools and data on the electricity supply for rural communities.

[0131] Based on the supply data of primary school education facilities in townships and the electricity supply data of rural communities, the supply of primary school education facilities and residents' demand are analyzed separately to calculate the supply and demand accessibility of primary school education facilities.

[0132] Population mobility is calculated at different time scales based on rural community electricity supply data. The population mobility calculation includes monthly population mobility level calculation and annual population mobility level calculation.

[0133] Based on supply and demand availability, monthly population mobility levels, and annual population mobility levels, K-means cluster analysis was performed to obtain clustering results.

[0134] By matching clustering results with rural community units, the layout characteristics and types of primary school education facilities in rural communities can be identified, enabling targeted optimization of the site selection and configuration of primary school education facilities in rural communities.

[0135] Example 4:

[0136] This embodiment provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, it implements the educational facility site selection and configuration optimization method of Embodiment 1 above, as follows:

[0137] Data from the target area is acquired and preprocessed. The data includes data on the supply of educational facilities in rural primary schools and data on the electricity supply for rural communities.

[0138] Based on the supply data of primary school education facilities in townships and the electricity supply data of rural communities, the supply of primary school education facilities and residents' demand are analyzed separately to calculate the supply and demand accessibility of primary school education facilities.

[0139] Population mobility is calculated at different time scales based on rural community electricity supply data. The population mobility calculation includes monthly population mobility level calculation and annual population mobility level calculation.

[0140] Based on supply and demand availability, monthly population mobility levels, and annual population mobility levels, K-means cluster analysis was performed to obtain clustering results.

[0141] By matching clustering results with rural community units, the layout characteristics and types of primary school education facilities in rural communities can be identified, enabling targeted optimization of the site selection and configuration of primary school education facilities in rural communities.

[0142] It should be noted that the computer-readable storage medium in this embodiment can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0143] In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this embodiment, the computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0144] The computer-readable storage medium described above can be used to write computer programs for executing this embodiment in one or more programming languages ​​or combinations thereof. These programming languages ​​include object-oriented programming languages—such as Java, Python, and C++—and conventional procedural programming languages—such as C or similar programming languages. The program can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0145] In summary, this invention comprehensively considers supply, demand, and population flow, making it more accurate than the traditional per capita index allocation standard. Furthermore, it uses rural community electricity data to reflect the actual demand for educational facilities among residents in rural areas experiencing population outflow, thus providing optimization for the site selection and allocation of primary school educational facilities. In addition, this invention is based on multi-factor evaluation indicators and utilizes commonly used analysis methods such as K-means clustering, offering advantages such as rapid calculation and iteration, and ease of promotion and application.

[0146] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the site selection and configuration of educational facilities, characterized in that, The method includes: Data from the target area is acquired and preprocessed, including data on the supply of educational facilities in rural primary schools and data on the electricity supply for rural communities. Based on data on the supply of primary school education facilities in townships and the electricity supply for rural communities, the supply of primary school education facilities and residents' demand are analyzed separately to calculate the availability of primary school education facilities. Population mobility is calculated at different time scales based on rural community electricity supply data, including monthly population mobility level calculation and annual population mobility level calculation. Based on supply and demand availability, monthly population mobility levels, and annual population mobility levels, K-means cluster analysis was performed to obtain clustering results. By matching clustering results with rural community units, the layout characteristics and types of primary school education facilities in rural communities can be identified, enabling targeted optimization of the site selection and configuration of primary school education facilities in rural communities. Based on data on the supply of primary school education facilities in townships and the electricity supply data for rural communities, the supply and demand of primary school education facilities are analyzed separately to calculate the availability of primary school education facilities. Specifically, this includes: Primary school facilities are selected as supply points. A search domain is established based on the extreme distance radius from students to the school. All demand quantities within the search domain are aggregated, and weights are assigned according to the distance decay law using a Gaussian function. The supply-demand ratio is calculated by summing the demand quantities. The calculation formula is as follows: Among them, R j Let i be the supply-demand ratio, j be the demand point, and E be the supply point. k The total annual power supply, d, is calculated based on rural community electricity data. kj The distance between k and j is given by d. For a rural community unit k to fall within the search domain, d is the distance between k and j. kj ≤d0, where d0 is the farthest reachable distance from the student to the primary school, Nj is the number of school places, and G(d ij ) is the Gaussian decay function, d ij Let d be the distance between i and j, and d ij <d0; The Gaussian decay function considering spatial friction is calculated using the following formula: Where e represents the natural constant; Using the central point of a rural community as the demand point, and establishing a search domain based on the extreme distance radius from students to schools, all primary schools within the search domain are identified. The supply-demand ratios are then summed using a Gaussian decay function to obtain the supply-demand accessibility of primary school educational facilities at the central point of the rural community. The calculation formula is as follows: in, Accessibility of primary school education facilities in rural community centers.

2. The method for optimizing the site selection and configuration of educational facilities according to claim 1, characterized in that, The acquisition and preprocessing of data from the target area specifically includes: Obtain supply data of primary school education facilities in townships within the target area. The supply data includes the point coordinates of primary school education facilities and the number of available places in each school. Acquire residential power supply data for rural communities within the target area. The power supply data includes the name of each residential power supply station, monthly residential power supply data for multiple years, number of households served, the rural community to which it belongs, and coordinate information. Correct any missing or abnormal data, and summarize the residential power supply data by rural community. Obtain vector data of the boundaries of rural communities in the target area.

3. The method for optimizing the site selection and configuration of educational facilities according to claim 1, characterized in that, The monthly population mobility level is calculated using the following formula: Among them, M s P represents the monthly population mobility level. m and Based on data on electricity supply in rural communities, P was calculated. m Let P represent the total monthly electricity supply for rural community unit k in a given year, where 1 ≤ m ≤ 11. Let P represent the average monthly electricity supply for rural community unit k in that year. The calculation formula is: Where n = 12.

4. The method for optimizing the site selection and configuration of educational facilities according to claim 1, characterized in that, The annual population mobility level is calculated using the following formula: Among them, M c P represents the annual level of population mobility. xy and Based on data on electricity supply in rural communities, P was calculated. xy Let l represent the total electricity supply for residential use in each rural community unit k in the yth quarter of year x, where l ≥ 1, 1 ≤ y ≤ 4. Let (x+l) represent the average electricity supply for each rural community unit k in the fourth quarter of the entire year. The formula is as follows: Where n = 4.

5. The method for optimizing the site selection and configuration of educational facilities according to any one of claims 1-4, characterized in that, The K-means clustering analysis is performed based on supply and demand availability, monthly population mobility levels, and annual population mobility levels to obtain clustering results, specifically including: The availability of supply and demand, monthly population mobility, annual population mobility, and rural community unit numbers are compiled into a table. The K-means clustering algorithm is run to initialize k cluster centers and calculate the distance from each object to the cluster center. Compare the distance of each object to the cluster center in turn, and assign the object to the cluster with the nearest cluster center to obtain k clusters. Calculate the K-means cluster center. Input the K-means clustering analysis system with supply and demand accessibility, monthly population mobility level, and annual population mobility level as variables, and with rural community units as case annotation basis, and output the clustering results.

6. The method for optimizing the site selection and configuration of educational facilities according to any one of claims 1-4, characterized in that, The process of matching clustering results with rural community units to identify the layout characteristics and types of primary school education facilities in rural communities, and to achieve targeted optimization of the site selection and configuration of primary school education facilities in townships at the rural community level, specifically includes: The clustering results are matched with rural community units to identify the layout characteristics and types of primary school education facilities in rural communities. Visual analysis is performed, and site selection and configuration optimization strategies for primary school education facilities are proposed for rural communities with different characteristics.

7. A system for optimizing the site selection and configuration of educational facilities, characterized in that, The system includes: The acquisition module is used to acquire data from the target area and perform preprocessing. The data includes data on the supply of educational facilities in rural primary schools and data on the electricity supply for rural communities. The first calculation module is used to analyze the supply of primary school education facilities and residents' demand based on the supply data of primary school education facilities in townships and the power supply data of rural communities, and to calculate the supply and demand accessibility of primary school education facilities. The second calculation module is used to calculate population flow at different time scales based on rural community electricity supply data. The population flow calculation includes monthly population flow level calculation and annual population flow level calculation. The clustering module is used to perform K-means clustering analysis based on supply and demand accessibility, monthly population mobility level, and annual population mobility level to obtain clustering results; The identification module is used to match clustering results with rural community units, identify the layout characteristics and types of primary school education facilities in rural communities, and realize targeted site selection and configuration optimization of primary school education facilities in rural communities. Based on data on the supply of primary school education facilities in townships and the electricity supply data for rural communities, the supply and demand of primary school education facilities are analyzed separately to calculate the availability of primary school education facilities. Specifically, this includes: Primary school facilities are selected as supply points. A search domain is established based on the extreme distance radius from students to the school. All demand quantities within the search domain are aggregated, and weights are assigned according to the distance decay law using a Gaussian function. The supply-demand ratio is calculated by summing the demand quantities. The calculation formula is as follows: Among them, R j Let i be the supply-demand ratio, j be the demand point, and E be the supply point. k The total annual power supply, d, is calculated based on rural community electricity data. kj The distance between k and j is given by d. For a rural community unit k to fall within the search domain, d is the distance between k and j. kj ≤d0, where d0 is the farthest reachable distance from the student to the primary school, Nj is the number of school places, and G(d ij ) is the Gaussian decay function, d ij Let d be the distance between i and j, and d ij <d0; The Gaussian decay function considering spatial friction is calculated using the following formula: Where e represents the natural constant; Using the central point of a rural community as the demand point, and establishing a search domain based on the extreme distance radius from students to schools, all primary schools within the search domain are identified. The supply-demand ratios are then summed using a Gaussian decay function to obtain the supply-demand accessibility of primary school educational facilities at the central point of the rural community. The calculation formula is as follows: in, Accessibility of primary school education facilities in rural community centers.

8. A computer device comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the educational facility site selection and configuration optimization method according to any one of claims 1-6.

9. A storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the method for optimizing the location and configuration of educational facilities as described in any one of claims 1-6.

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