Quantitative evaluation methods and equipment for the implementation of compulsory education nearby enrollment

Abstract

The present application relates to a quantitative evaluation method, device and storage medium for the implementation of the nearest school enrollment for compulsory education, and the method includes: obtaining point-like geographical elements of student families and schools, obtaining a binary map of administrative divisions in the implementation area, and marking the grids corresponding to the schools in the binary map of administrative divisions; using the grids corresponding to each school as the initial area of ​​the school district, and taking the shortest walking distance from the student to the school as the goal, using a multi-region competitive growth algorithm to perform image segmentation on the binary map of the implementation area to obtain the school district range corresponding to each school; performing spatial overlay analysis on the point-like geographical elements of the school district range and the student family to determine the theoretical nearest school for each student; setting quantitative evaluation indicators based on the walking distance of the student to school, calculating the evaluation value of each quantitative evaluation indicator, and realizing the evaluation of the implementation effect of the nearest school enrollment. The present application has the technical effect of reasonable school district division and accurate implementation evaluation.

Description

Technical Field

[0001] The present application relates to the technical field of compulsory education enrollment management, and in particular to a quantitative evaluation method and equipment for the implementation of compulsory education nearby enrollment. Background Art

[0002] The 1986 Compulsory Education Law included "enrollment in the nearest school" in the law, but when implementing the school district division, local education administrative departments need to scientifically define the area for each compulsory education school based on factors such as the number of school-age students, school distribution, community, school size, and traffic conditions, in accordance with the principle of ensuring fairness and nearest school enrollment. The division of school districts for compulsory education nearest school enrollment is a problem of optimizing the spatial allocation of educational resources. The Thiessen polygon in the spatial analysis method of the geographic information system (GIS) is the main method currently studied. However, in this method, the Euclidean distance between the student's home address and the school address is defined as the student's school distance, and the Euclidean distance cannot fully reflect the actual distance of the student's school, so there is a possibility that the school district division is unreasonable.

[0003] At the same time, quantitative evaluation of the implementation of the policy of compulsory education nearby enrollment promoted by local education administrative departments can further promote the fairness and justice of compulsory education. The evaluation research on the implementation of compulsory education nearby enrollment focuses on the quantitative evaluation index system and index calculation method, among which the theoretical average school distance and the actual average school distance are two important indicators of the evaluation index system. However, the evaluation index system of this type of method is based on the entire district or county, rather than on the school district or school. The calculation method of distance-related indicators is based on the Euclidean distance between the student's home address and the school address, rather than the actual walking distance. The Euclidean distance cannot fully reflect the actual distance of students going to school, so it is impossible to accurately evaluate the implementation status. Summary of the invention

[0004] In view of this, the present application provides a quantitative evaluation method, device and storage medium for the implementation of compulsory education nearby enrollment, which is used to solve the technical problems of unreasonable school district division and inaccurate implementation evaluation.

[0005] In order to solve the above problems, in a first aspect, the present invention provides a quantitative evaluation method for the implementation of compulsory education nearby enrollment, comprising the following steps:

[0006] Obtain point-shaped geographic elements of the student's family and school, obtain a binary map of the administrative divisions of the implementation area, and mark the grids corresponding to the school in the binary map of the administrative divisions;

[0007] Taking the grid corresponding to each school as the initial area of ​​the school district and the shortest walking distance from students to school as the goal, a multi-region competitive growing algorithm is used to perform image segmentation on the binary image of the implementation area to obtain the school district range corresponding to each school;

[0008] Conduct spatial overlay analysis on the school district and the point-shaped geographical elements of the students' families to determine the theoretical nearest school for each student;

[0009] Quantitative evaluation indicators are set based on the walking distance of students to school. According to the theoretical nearest enrollment schools assigned to each student and the actual enrollment situation of the students, the evaluation value of each quantitative evaluation indicator is calculated to achieve the evaluation of the implementation effect of the nearest enrollment.

[0010] Optionally, obtain point geographic features of students' homes and schools, specifically:

[0011] Obtaining the student's home address information and school address information, geocoding the student's home address information and school address information respectively and obtaining geographic coordinates, and generating a point-like geographic element map of the student's home and school;

[0012] Get the binary map of administrative divisions in the implementation area, specifically:

[0013] Obtain an administrative division vector map with spatial reference of the implementation area, perform geographic space projection conversion on the administrative division vector map to obtain an administrative division vector map with the same spatial reference as the point-like geographic elements; perform vector-raster conversion on the administrative division vector map to generate an administrative division binary map of the implementation area.

[0014] Optionally, the grids corresponding to the schools are marked in the administrative division binary map, specifically:

[0015] According to the grid positioning point coordinates and grid spacing of the administrative division binary map, the point geographic elements of the school are spatially mapped, and the point geographic elements of the school are mapped to the grid space of the administrative division binary map, and each school corresponds to a grid in the administrative division binary map.

[0016] Optionally, the grid corresponding to each school is used as the initial area of ​​the school district, and the walking distance from students to school is the shortest. The multi-region competitive growing algorithm is used to perform image segmentation on the binary image of the implementation area to obtain the school district range corresponding to each school, specifically:

[0017] The grid corresponding to each school is used as the initial area;

[0018] The morphological expansion method based on four neighborhoods is used to independently grow each region in turn within the implementation area of ​​the administrative division binary map;

[0019] Determine whether all regions have no growth in this round of independent growth. If so, segment the administrative division binary map with the grids contained in each region to obtain the school district range corresponding to each school. Otherwise, conduct multi-region growth competition judgment for the situation where multiple regions compete for the same grid, and then go to the previous step for the next round of independent growth.

[0020] Optionally, a morphological expansion method based on four neighborhoods is used to independently grow each region in turn within the implementation area of ​​the administrative division binary map, specifically:

[0021] A regional growth matrix is ​​constructed according to the binary map of administrative divisions, and the regional growth matrix is ​​initialized, wherein: the element value corresponding to the unattached grid in the implementation area is set to the first value, the element value corresponding to the grid in the current area is set to the second value, the element value corresponding to the grid in this round of expansion is set to the third value, and the element value corresponding to the grid that failed the competition in the current area is set to the fourth value;

[0022] Taking the grid corresponding to the school as the initial area of ​​the school district, and taking the grid corresponding to the initial area as the root node to construct a regional growth tree;

[0023] Traversing the grids of each region according to the elements corresponding to the school in the regional growth matrix, morphological expansion is performed on each grid of the current region; the morphological expansion is specifically: acquiring adjacent grids in the four neighborhood directions of the current grid, querying the element values ​​of the adjacent grids in the regional growth matrix, judging whether the element values ​​of each adjacent grid are the first value, if so, expanding the current region to the corresponding adjacent grid, and setting the element value corresponding to the corresponding grid to the third value, querying the node of the current grid in the regional growth tree, and adding the adjacent grid as its child node to the regional growth tree, otherwise the corresponding adjacent grid is not processed;

[0024] Determine whether all adjacent grids have been traversed. If so, exit the processing of adjacent grids. Otherwise, proceed to the next round of processing of adjacent grids.

[0025] Determine whether the grid traversal of the current area is complete. If so, exit the morphological expansion, otherwise proceed to the morphological expansion of the next area.

[0026] Optionally, a multi-region growth competition judgement is performed for the case where multiple regions compete for the same grid, specifically:

[0027] Calculate the walking distance between the school grid and the grid being competed in each area participating in the competition, and place the grid being competed in the area where the school grid with the shortest walking distance is located;

[0028] According to the competition result, the regions participating in the competition are divided into competition winning regions and competition losing regions, wherein the competition losing regions include first-type competition losing regions and second-type competition losing regions, wherein the first-type competition losing regions refer to the elements of the grid being competed in its regional growth matrix having the third value, and the second-type competition losing regions refer to the elements of the grid being competed in its regional growth matrix having the second value;

[0029] For the competition winning area, the corresponding element value of the competing grid in its regional growth matrix is ​​set to the second value;

[0030] For the first type of competition failure area, the corresponding element value of the contested grid in its regional growth matrix is ​​set to the fourth value, and the corresponding node of the contested grid is removed from its regional growth tree;

[0031] For the second type of competition failure area, the corresponding element value of the contested grid in its regional growth matrix is ​​set to the fourth value, the corresponding node of the contested grid and its child nodes are removed from its regional growth tree, and the corresponding element value of the grid corresponding to the child node in its regional growth matrix is ​​set to the first value.

[0032] Optionally, a spatial overlay analysis is performed on the school district and the point-shaped geographical elements of the student's family to determine the theoretical nearest school for each student, specifically:

[0033] A spatial overlay analysis is performed on the point-like geographical elements of the student's family and the school districts mentioned above to determine the topological relationship between the point-like geographical elements of the student's family and the school districts corresponding to the schools in the geographic space. If the school district contains the point-like geographical elements of the student's family, the corresponding school is determined as the student's theoretical nearest school.

[0034] Optionally, set quantitative evaluation indicators based on the distance students need to walk to school, specifically:

[0035] Quantitative evaluation indicators are set at two levels: school level and district and county level based on the distance students walk to school.

[0036] In a second aspect, the present application provides a computer device, which adopts the following technical solution:

[0037] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the quantitative evaluation method for the implementation of the compulsory education nearby enrollment are implemented.

[0038] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0039] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a quantitative evaluation method for implementing nearby compulsory education enrollment.

[0040] The beneficial effect of the present invention is that the present invention adopts a unique standard, that is, only considers the walking distance factor, and uses the walking distance of students to school as a benchmark to quantitatively evaluate the implementation status of nearby school enrollment. The walking distance standard is closer to the common travel mode adopted by students to go to school: the distance of the walking mode, rather than the straight-line distance between two points in the flying mode, which can more accurately calculate the evaluation index of nearby school enrollment. At the same time, using the walking distance to school as the only evaluation standard is not affected by other factors, so it can be used for unified and fair evaluation and comparison of nearby school enrollment in different implementation areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A method flow chart of an embodiment of a quantitative evaluation method for implementing nearby compulsory education enrollment provided in this application;

[0042] Figure 2 A schematic diagram of student classification for an embodiment of a quantitative evaluation method for implementing nearby compulsory education enrollment provided in this application. DETAILED DESCRIPTION

[0043] The preferred embodiments of the present application are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the present application and are used together with the embodiments of the present application to illustrate the principles of the present application, and are not used to limit the scope of the present application.

[0044] In the description of the present application, “plurality” means two or more than two, unless otherwise clearly and specifically defined.

[0045] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0046] The present application provides a quantitative evaluation method, computer equipment and storage medium for the implementation of compulsory education nearby enrollment, which are described in detail below.

[0047] First, if Figure 1 As shown, the embodiment of the present application provides a quantitative evaluation method for the implementation of compulsory education nearby enrollment, including the following steps:

[0048] S1. Obtain point-shaped geographic elements of the student's family and school, obtain a binary map of the administrative divisions of the implementation area, and mark the grids corresponding to the school in the binary map of the administrative divisions;

[0049] S2. Taking the grid corresponding to each school as the initial area of ​​the school district and the shortest walking distance from students to school as the goal, a multi-region competitive growing algorithm is used to perform image segmentation on the binary image of the implementation area to obtain the school district range corresponding to each school;

[0050] S3, perform spatial overlay analysis on the school district and the point-shaped geographical elements of the students' families to determine the theoretical nearest school for each student;

[0051] S4. Quantitative evaluation indicators are set based on the walking distance of students to school. According to the theoretical nearest schools for each student and the actual enrollment situation of the students, the evaluation values ​​of each quantitative evaluation indicator are calculated to evaluate the effectiveness of the implementation of the nearest school.

[0052] The nearest enrollment of compulsory education is not the nearest enrollment that only considers the distance factor, but a comprehensive consideration of other factors on the basis of the distance factor to ensure fairness and the principle of nearest enrollment. However, other factors considered in different districts and counties, even in the same district and county at different times, are inconsistent, which makes it difficult to use the same standards for evaluation. The purpose of this embodiment is to provide a quantitative evaluation method for the implementation of the nearest enrollment of compulsory education, including a quantitative indicator system and an automatic calculation algorithm for indicators. The evaluation method can quantitatively evaluate the implementation of the nearest enrollment of compulsory education based on the actual walking distance to school.

[0053] In order to achieve the above purpose, this embodiment is specifically implemented as follows: obtaining point geographic elements of student families and schools; obtaining a binary map of administrative divisions in the implementation area; mapping the point geographic elements of schools to the grid space of the binary map of administrative divisions through geographic space, and obtaining the grid corresponding to the school; using the grids of each school in the implementation area as the initial area of ​​the school district, adopting a multi-region competitive growth algorithm, and performing image segmentation based on the principle of the shortest walking distance to the school grid, and extracting the school district range, wherein the grids contained in the school district range have the shortest school distance to the corresponding school grid; performing spatial overlay analysis on the extracted school district range and the point geographic elements of the student's family, and determining the school that each student should theoretically attend according to the principle of nearest enrollment; classifying students according to the information of the school that the student should theoretically attend and the school that the student actually attends, and calculating the indicator values ​​respectively according to the definition of the evaluation indicator.

[0054] The evaluation index system proposed in this embodiment adopts a single standard, that is, only considering the walking distance factor, and using the distance that students walk to school as the benchmark to quantitatively evaluate the implementation of nearby enrollment. Based on the common travel mode used by students to go to school under the nearby enrollment: the distance in walking mode, rather than the straight-line distance between two points in flying mode, the evaluation index of nearby enrollment can be calculated more accurately. At the same time, using the distance to school as the only evaluation criterion is not affected by other factors, so a unified and fair evaluation and comparison can be made for nearby enrollment in different implementation areas.

[0055] In one embodiment, point-shaped geographic elements of the student's home and school are obtained, specifically:

[0056] Obtaining the student's home address information and school address information, geocoding the student's home address information and school address information respectively and obtaining geographic coordinates, and generating a point-like geographic element map of the student's home and school;

[0057] Get the binary map of administrative divisions in the implementation area, specifically:

[0058] Obtain an administrative division vector map with spatial reference of the implementation area, perform geographic space projection conversion on the administrative division vector map to obtain an administrative division vector map with the same spatial reference as the point-like geographic elements; perform vector-raster conversion on the administrative division vector map to generate an administrative division binary map of the implementation area.

[0059] The implementation area is divided into districts and counties, and the information of all compulsory education schools in the implementation area, the information of students in each school, and the administrative division information of the implementation area are extracted from the information system of the government education management department. School information includes school name and school address, student information includes the student's unique identification code after desensitization, home address, and school, and administrative division information is a district and county administrative division vector map with spatial reference.

[0060] The addresses of all compulsory education schools in the implementation area and the home addresses of students attending each school are geocoded respectively to obtain the geographic coordinates under a certain spatial reference, and generate point geographic elements of school addresses and student home addresses.

[0061] The administrative division vector map of the implementation area with spatial reference is subjected to geospatial projection conversion so that it has the same spatial reference as the point geographic elements. Then, the polygons of the administrative divisions in the administrative division vector map after geospatial projection conversion are subjected to vector-raster conversion to generate an administrative division binary map with a certain grid spacing, in which the raster value outside the implementation area is 0 and the raster value within the implementation area is 1. The administrative division binary map has the same spatial reference as the point geographic elements.

[0062] In one embodiment, the grids corresponding to the schools are marked in the administrative division binary map, specifically:

[0063] According to the grid positioning point coordinates and grid spacing of the administrative division binary map, the point geographic elements of the school are spatially mapped, and the point geographic elements of the school are mapped to the grid space of the administrative division binary map, and each school corresponds to a grid in the administrative division binary map.

[0064] According to the grid positioning point coordinates and grid spacing of the administrative division binary map, the point-shaped geographic elements of the school are spatially mapped and mapped to the grid space of the administrative division binary map, corresponding to one of the grids. All schools in the district and county correspond to one grid in the grid space.

[0065] In one embodiment, the grid corresponding to each school is used as the initial area of ​​the school district, and the walking distance from students to school is the shortest. The multi-region competitive growing algorithm is used to perform image segmentation on the binary image of the implementation area to obtain the school district range corresponding to each school, specifically:

[0066] The grid corresponding to each school is used as the initial area;

[0067] The morphological expansion method based on four neighborhoods is used to independently grow each region in turn within the implementation area of ​​the administrative division binary map;

[0068] Determine whether all regions have no growth in this round of independent growth. If so, segment the administrative division binary map with the grids contained in each region to obtain the school district range corresponding to each school. Otherwise, conduct multi-region growth competition judgment for the situation where multiple regions compete for the same grid, and then go to the previous step for the next round of independent growth.

[0069] This embodiment adopts a multi-region competitive growth algorithm, which is implemented in the grid space of the administrative division binary map. The grid corresponding to the school is used as the initial area, and a morphological expansion method based on four neighborhoods is used to iteratively and independently grow in the implementation area. After each round of iteration, a spatial overlay analysis is performed on each area, the overlapping grids between areas are checked, and multi-region growth competition is performed on their affiliation. The overlapping grid is the same grid for multi-region competition. The overlapping grid is an insurmountable boundary for the area that failed the competition. The boundaries of the implementation area and the boundaries between areas resulting from the failure of multi-region competition gradually leave no room for further growth in each area. Eventually, the iterative growth ends and a stable area is formed, that is, a stable school district range.

[0070] Specifically: the grids corresponding to each school in the implementation area in the binary administrative division map are used as the initial areas of the school's corresponding school district; each area grows independently and iteratively toward the adjacent grids using morphological expansion on four neighborhoods, and during each round of growth, each area is morphologically expanded in turn; the growth of all areas in this round is counted, and if there is no growth in all areas, the iterative growth is exited; as long as there is growth in any area, a multi-area growth competition is judged, and then the next round of growth is entered; when all areas stop growing, the grids contained in each area respectively segment the binary administrative division map to extract the scope of each school district.

[0071] In the above algorithm, single-region independent growth and multi-region growth competition refereeing are the core of this algorithm, which are explained in detail below.

[0072] In one embodiment, the morphological expansion method based on four neighborhoods is used to independently grow each region in turn within the implementation area of ​​the administrative division binary map, specifically:

[0073] A regional growth matrix is ​​constructed according to the binary map of administrative divisions, and the regional growth matrix is ​​initialized, wherein: the element value corresponding to the unattached grid in the implementation area is set to the first value, the element value corresponding to the grid in the current area is set to the second value, the element value corresponding to the grid in this round of expansion is set to the third value, and the element value corresponding to the grid that failed the competition in the current area is set to the fourth value;

[0074] Taking the grid corresponding to the school as the initial area of ​​the school district, and taking the grid corresponding to the initial area as the root node to construct a regional growth tree;

[0075] Traversing the grids of each region according to the elements corresponding to the school in the regional growth matrix, morphological expansion is performed on each grid of the current region; the morphological expansion is specifically: acquiring adjacent grids in the four neighborhood directions of the current grid, querying the element values ​​of the adjacent grids in the regional growth matrix, judging whether the element values ​​of each adjacent grid are the first value, if so, expanding the current region to the corresponding adjacent grid, and setting the element value corresponding to the corresponding grid to the third value, querying the node of the current grid in the regional growth tree, and adding the adjacent grid as its child node to the regional growth tree, otherwise the corresponding adjacent grid is not processed;

[0076] Determine whether all adjacent grids have been traversed. If so, exit the processing of adjacent grids. Otherwise, proceed to the next round of processing of adjacent grids.

[0077] Determine whether the grid traversal of the current area is complete. If so, exit the morphological expansion, otherwise proceed to the morphological expansion of the next area.

[0078] For each region, single-region independent growth is performed. Single-region independent growth refers to a single region that uses morphological expansion to grow independently toward adjacent grids on four neighborhoods. Although its growth is restricted by boundaries, it does not need to directly consider the growth of other regions. The boundaries that restrict growth include district and county boundaries and boundaries generated by the failure of multi-region growth competition. The regional growth matrix records the status of each grid in each round of growth from the perspective of the current regional growth. The element values ​​of the mth row and nth column in the regional growth matrix mark the status of the grid in the mth row and nth column in the administrative division binary map. In this embodiment: 0 represents a grid outside the implementation area; 1 represents a grid without affiliation in the implementation area; 2 represents a grid belonging to the current area; 3 represents a grid to be expanded in this round; 4 represents a grid that failed in the current regional competition; the current area refers to an area that is undergoing independent growth. The role of the regional growth matrix is ​​to avoid repeated growth on the one hand, and to mark the growth boundary on the other hand. The regional growth tree takes the school grid as the root node, constructs an incomplete quadtree in the order of regional growth, and records the path of morphological expansion during regional growth.

[0079] Specifically: First, a regional growth matrix is ​​constructed and initialized using a binary map of administrative divisions: the matrix element values ​​corresponding to the grids outside the implementation area are initialized to 0, the matrix element values ​​corresponding to the school grid are initialized to 2, and the matrix element values ​​corresponding to the grids in other implementation areas are initialized to 1; the school grid is used as the initial area for regional growth, and a regional growth tree is constructed with it as the root node; the grids in the current area are traversed according to the elements with a value of 2 in the regional growth matrix; the four adjacent grids in the four neighborhoods of the current grid are obtained, and the adjacent grids are traversed; the regional growth matrix element value G corresponding to each adjacent grid is queried; if G is equal to 1, the current area will be expanded to the adjacent grid; the regional growth matrix element value G=3 of the expanded adjacent grid is updated to mark the grid as expanded; the current regional grid is queried in the regional growth tree , and add the current adjacent grid as the child node of the node to the regional growth tree; after the processing of the currently expandable adjacent grid is completed, it is determined whether to exit the iteration of the adjacent grid processing; if the regional growth matrix element value G of the current adjacent grid is not equal to 1, the neighboring grid is not processed, and it directly jumps to the determination of whether to exit the iteration of the adjacent grid processing; the exit judgment of the adjacent grid processing iteration needs to check whether all adjacent grids have been traversed. If not all of them have been traversed, the next round of adjacent grid processing iteration is continued, otherwise the adjacent grid processing iteration is exited; after the adjacent grid processing of the current regional grid is completed, it is determined whether the regional grid is traversed. If it has been traversed, the iteration of the regional grid morphological expansion is exited, otherwise the next round of regional grid morphological expansion iteration is continued.

[0080] In one embodiment, a multi-region growth competition judgement is performed for a situation where multiple regions compete for the same grid, specifically:

[0081] Calculate the walking distance between the school grid and the grid being competed in each area participating in the competition, and place the grid being competed in the area where the school grid with the shortest walking distance is located;

[0082] According to the competition result, the regions participating in the competition are divided into competition winning regions and competition losing regions, wherein the competition losing regions include first-type competition losing regions and second-type competition losing regions, wherein the first-type competition losing regions refer to the elements of the grid being competed in its regional growth matrix having the third value, and the second-type competition losing regions refer to the elements of the grid being competed in its regional growth matrix having the second value;

[0083] For the competition winning area, the corresponding element value of the competing grid in its regional growth matrix is ​​set to the second value;

[0084] For the first type of competition failure area, the corresponding element value of the contested grid in its regional growth matrix is ​​set to the fourth value, and the corresponding node of the contested grid is removed from its regional growth tree;

[0085] For the second type of competition failure area, the corresponding element value of the contested grid in its regional growth matrix is ​​set to the fourth value, the corresponding node of the contested grid and its child nodes are removed from its regional growth tree, and the corresponding element value of the grid corresponding to the child node in its regional growth matrix is ​​set to the first value.

[0086] The referee of multi-region growth competition refers to the refereeing of the ownership of the same grid in multi-region competition according to the principle of the smallest walking distance between the school grid in the region and the contested grid after each region has undergone a round of growth. According to the results of the competition, these regions can be divided into competition winning regions and competition losing regions, and the competition losing regions can also be divided into two categories. The first category is that the value G=3 of the contested grid in its regional growth matrix indicates that the region has only expanded to the contested grid in the most recent round of growth, and its position on the regional growth tree is the terminal node and does not contain any child nodes. The second category is that the value G=2 of the contested grid in its regional growth matrix indicates that the region has expanded to the contested grid before the most recent round of growth, and its position on the regional growth tree may not be the terminal node, but may contain child nodes, and the grids corresponding to these child nodes are the grids that the region has grown from the current contested grid in the previous rounds.

[0087] The region that wins the competition needs to update the corresponding element value G=2 of the regional growth matrix of the contested grid and mark it as the grid within the region. The region that loses the competition needs to abandon the current grid, that is, update the corresponding element value G=4 of its regional growth matrix, mark it as the boundary of regional growth, and remove the node corresponding to the contested grid from the regional growth tree. For the second type of region that fails the competition mentioned above, when the node corresponding to the contested grid is removed from the regional growth tree, its child nodes such as branches and leaves are also removed from the regional growth tree. Accordingly, the values ​​of the grids corresponding to these child nodes in the regional growth matrix need to be reset to the initial state G=1.

[0088] Specifically: extract the grid corresponding to the element with value G=3 from each regional growth matrix as the grid to be competed in this round, and the area with element value G=3 or G=2 in the regional growth matrix of the competed grid is the participating competition area; judge each competed grid in turn to determine which area it belongs to; calculate the walking distance from the current competed grid to the school grid in each participating competition area respectively; judge the area with the minimum distance as the winning area according to the minimum walking distance principle, and the other areas are the losing areas; update the regional growth tree and regional growth matrix of each area in turn according to the judgment results; for the winning area, update the value G=2 of the current competed grid in its regional growth matrix; for the losing area, update the value G=2 of the current competed grid in its regional growth matrix The value G in the matrix is ​​4; query the grids corresponding to all branch and leaf nodes under the node corresponding to the current competition grid on the current regional growth tree, and update the values ​​of these grids in all regional growth matrices G=1; remove the node corresponding to the current competition grid from the regional growth tree of the current competition failure area; after the regional growth tree and regional growth matrix of the current area are updated, determine whether all areas have been updated. If not, update the next area according to the above steps. If all areas have been updated, the multi-region competition judgment for the current contested grid is completed; determine whether all contested grids have been judged. If not, judge the next contested grid according to the above steps, otherwise the multi-region growth competition judgment algorithm process ends.

[0089] In one embodiment, a spatial overlay analysis is performed on the school district range and the point-shaped geographical elements of the student's family to determine the theoretical nearest school for each student, specifically:

[0090] A spatial overlay analysis is performed on the point-like geographical elements of the student's family and the school districts mentioned above to determine the topological relationship between the point-like geographical elements of the student's family and the school districts corresponding to the schools in the geographic space. If the school district contains the point-like geographical elements of the student's family, the corresponding school is determined as the student's theoretical nearest school.

[0091] The point-shaped geographic elements of the student's family generated by geocoding are spatially overlaid with the school district range extracted by the multi-region competitive growth algorithm to determine the topological relationship between the point-shaped geographic elements of the student's family and each school district in the geographic space: that is, inclusion or non-inclusion. If the school district range contains the point-shaped geographic elements of the student's family, the school corresponding to the school district range is determined to be the theoretical nearest school for the student.

[0092] In one embodiment, a quantitative evaluation index is set based on the distance students walk to school, specifically:

[0093] Quantitative evaluation indicators are set at two levels: school level and district and county level based on the distance students walk to school.

[0094] In this embodiment, the evaluation indicators include school-level indicators and district-level indicators. The school-level indicators are shown in Table 1, and the district-level indicators are shown in Table 2.

[0095] Table 1. School-level indicators

[0096]

[0097]

[0098] Table 2: District and County Level Indicators

[0099]

[0100]

[0101] In order to calculate the index value conveniently, students are first classified according to the theoretical nearest school assigned to each student and the actual enrollment situation of the students. According to the single-school zoning of a school in the school district that only considers the distance factor and the actual student enrollment situation of the school, students can be divided into 2 major categories and 3 subcategories, such as Figure 2 As shown. The set M represented by the solid circle on the left represents the students in the school district corresponding to a certain school, and the set N represented by the dotted circle on the right represents the students actually enrolled in the school. The intersection of the two sets M∩N represents the students who actually enroll in the school and the corresponding school district, that is, Class B students. The difference set M﹣N of the two sets represents the students who choose to study in other school districts in the school district, that is, Class A students. The difference set NM of the two sets represents the students who choose to study in other school districts in the school, that is, Class C students. The details are shown in Table 3.

[0102] Table 3. Student classification table

[0103]

[0104] Specifically: Based on the theoretical nearest schools determined by spatial overlay analysis and the actual schools obtained by information extraction, the students are classified at the school level and the district and county level according to the indicator system; the theoretical nearest school walking distance and the actual school walking distance from the students' families to the theoretical nearest school are calculated respectively; according to the student classification results, according to the indicator system, the number of students of each category is counted at the school level and the district and county level to calculate two secondary indicators: the number of students and the ratio of students attending the nearest school. According to the student classification results and the students' nearest school and actual school walking distance, the average distance of each category of students is calculated at the school level and the district and county level to calculate two secondary indicators: absolute distance and relative distance.

[0105] The regional competition referee algorithm proposed in the present invention is essentially a spatial overlay method based on distance grid data. Traditional spatial overlay methods need to calculate the global walking distance grid data of each school within the district and county respectively, which requires a large amount of walking distance calculation. This algorithm adopts a multi-regional competition growth method, takes the school as the initial area of ​​the school district, and adopts a morphological expansion method to iteratively grow to the four neighborhoods. On the one hand, the boundary constraint regional growth generated by multi-region competition prevents it from growing unlimitedly within the district and county. On the other hand, it is only necessary to calculate the walking distance when regional competition occurs in the boundary area. In the early stage of regional growth, that is, the growth of the region in the core area of ​​the school district, it is not necessary to calculate the walking distance. This greatly reduces the algorithm's demand for walking distance calculation, thereby reducing the algorithm's overhead and improving the algorithm's efficiency.

[0106] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0107] Based on the above-mentioned quantitative evaluation method for the implementation of compulsory education nearby enrollment, the present application also provides a computer device accordingly, which can be a computing device such as a mobile terminal, a desktop computer, a notebook, a PDA, and a server. The computer device includes a processor, a memory, and a display. In one embodiment, a quantitative evaluation program for the implementation of compulsory education nearby enrollment is stored on the memory, and the quantitative evaluation program for the implementation of compulsory education nearby enrollment can be executed by the processor, thereby realizing the quantitative evaluation method for the implementation of compulsory education nearby enrollment in each embodiment of the present application.

[0108] In one embodiment, when the processor executes the quantitative evaluation program for the implementation of compulsory education nearby enrollment in the memory, the following steps are implemented:

[0109] Obtain point-shaped geographic features of students' families and schools, and obtain a binary map of administrative divisions in the implementation area;

[0110] Mapping the point-shaped geographic elements to the administrative division binary map through a geographic space mapping algorithm to obtain an administrative division binary map with grids corresponding to the schools marked;

[0111] Taking the grid corresponding to each school as the initial area of ​​the school district, taking the shortest walking distance from students to school as the goal, a multi-region competitive growing algorithm is used to perform image segmentation on the binary image of the implementation area to obtain the school district range corresponding to each school;

[0112] Conduct spatial overlay analysis on the school district and the point geography of the students' families to determine the theoretical nearest school for each student;

[0113] Quantitative evaluation indicators are set based on the walking distance of students to school. According to the theoretical nearest enrollment schools assigned to each student and the actual enrollment situation of the students, the evaluation value of each quantitative evaluation indicator is calculated to achieve the evaluation of the implementation effect of the nearest enrollment.

[0114] This embodiment also provides a computer-readable storage medium, on which a quantitative evaluation program for the implementation of compulsory education nearby enrollment is stored. When the program is executed by a processor, the following steps are implemented:

[0115] Obtain point-shaped geographic features of students' families and schools, and obtain a binary map of administrative divisions in the implementation area;

[0116] Mapping the point-shaped geographic elements to the administrative division binary map through a geographic space mapping algorithm to obtain an administrative division binary map with grids corresponding to the schools marked;

[0117] Taking the grid corresponding to each school as the initial area of ​​the school district, taking the shortest walking distance from students to school as the goal, a multi-region competitive growing algorithm is used to perform image segmentation on the binary image of the implementation area to obtain the school district range corresponding to each school;

[0118] Conduct spatial overlay analysis on the school district and the point geography of the students' families to determine the theoretical nearest school for each student;

[0119] Quantitative evaluation indicators are set based on the walking distance of students to school. According to the theoretical nearest enrollment schools assigned to each student and the actual enrollment situation of the students, the evaluation value of each quantitative evaluation indicator is calculated to achieve the evaluation of the implementation effect of the nearest enrollment.

[0120] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0121] Any reference to memory, storage, database or other medium used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0122] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A quantitative evaluation method for the implementation of compulsory education nearby enrollment, characterized in that: The following steps are involved: Obtain point-shaped geographic elements of the student's family and school, obtain a binary map of the administrative divisions of the implementation area, and mark the grids corresponding to the school in the binary map of the administrative divisions; Taking the grid corresponding to each school as the initial area of ​​the school district and the shortest walking distance from students to school as the goal, a multi-region competitive growing algorithm is used to perform image segmentation on the binary image of the implementation area to obtain the school district range corresponding to each school; Conduct spatial overlay analysis on the school district and the point-shaped geographical elements of the students' families to determine the theoretical nearest school for each student; Quantitative evaluation indicators are set based on the walking distance of students to school, and the evaluation values ​​of each quantitative evaluation indicator are calculated according to the theoretical nearest school for each student and the actual enrollment of the student, so as to evaluate the implementation effect of the nearest school; Among them, the grid corresponding to each school is used as the initial area of ​​the school district, the walking distance from students to school is the shortest, and the multi-region competitive growing algorithm is used to perform image segmentation on the binary image of the implementation area to obtain the school district range corresponding to each school, specifically: The grid corresponding to each school is used as the initial area; A regional growth matrix is ​​constructed according to the binary map of administrative divisions, and the regional growth matrix is ​​initialized, wherein: the element value corresponding to the unattached grid in the implementation area is set to the first value, the element value corresponding to the grid in the current area is set to the second value, the element value corresponding to the grid in this round of expansion is set to the third value, and the element value corresponding to the grid that failed the competition in the current area is set to the fourth value; Taking the grid corresponding to the school as the initial area of ​​the school district, and taking the grid corresponding to the initial area as the root node to construct a regional growth tree; Traversing the grids of each region according to the elements corresponding to the school in the regional growth matrix, morphological expansion is performed on each grid of the current region; the morphological expansion is specifically: acquiring adjacent grids in the four neighborhood directions of the current grid, querying the element values ​​of the adjacent grids in the regional growth matrix, judging whether the element values ​​of each adjacent grid are the first value, if so, expanding the current region to the corresponding adjacent grid, and setting the element value corresponding to the corresponding grid to the third value, querying the node of the current grid in the regional growth tree, and adding the adjacent grid as its child node to the regional growth tree, otherwise the corresponding adjacent grid is not processed; Determine whether all adjacent grids have been traversed. If so, exit the processing of adjacent grids. Otherwise, proceed to the next round of processing of adjacent grids. Determine whether the grid traversal of the current area is complete. If so, exit the morphological expansion, otherwise proceed to the morphological expansion of the next area; Determine whether all regions in this round of independent growth have no growth. If so, segment the administrative division binary map with the grids contained in each region to obtain the school district range corresponding to each school. Otherwise, conduct a multi-region growth competition judgement for the situation where multiple regions compete for the same grid, and then go to the previous step for the next round of independent growth. The multi-region growth competition refereeing for the situation where multiple regions compete for the same grid is specifically as follows: Calculate the walking distance between the school grid and the grid being competed in each area participating in the competition, and place the grid being competed in the area where the school grid with the shortest walking distance is located; According to the competition result, the regions participating in the competition are divided into competition winning regions and competition losing regions, wherein the competition losing regions include first-type competition losing regions and second-type competition losing regions, wherein the first-type competition losing regions refer to the elements of the grid being competed in its regional growth matrix having the third value, and the second-type competition losing regions refer to the elements of the grid being competed in its regional growth matrix having the second value; For the competition winning area, the corresponding element value of the competing grid in its regional growth matrix is ​​set to the second value; For the first type of competition failure area, the corresponding element value of the contested grid in its regional growth matrix is ​​set to the fourth value, and the corresponding node of the contested grid is removed from its regional growth tree; For the second type of competition failure area, the corresponding element value of the contested grid in its regional growth matrix is ​​set to the fourth value, the corresponding node of the contested grid and its child nodes are removed from its regional growth tree, and the corresponding element value of the grid corresponding to the child node in its regional growth matrix is ​​set to the first value.

2. The quantitative evaluation method for the implementation of nearby compulsory education enrollment according to claim 1 is characterized in that: Get the point geographic features of students' families and schools, specifically: Obtaining the student's home address information and school address information, geocoding the student's home address information and school address information respectively and obtaining geographic coordinates, and generating a point-like geographic element map of the student's home and school; Get the binary map of administrative divisions in the implementation area, specifically: Obtain an administrative division vector map with spatial reference of the implementation area, perform geographic space projection conversion on the administrative division vector map to obtain an administrative division vector map with the same spatial reference as the point-like geographic elements; perform vector-raster conversion on the administrative division vector map to generate an administrative division binary map of the implementation area.

3. The quantitative evaluation method for the implementation of nearby compulsory education enrollment according to claim 1 is characterized in that: Mark the grids corresponding to the schools in the administrative division binary map, specifically: According to the grid positioning point coordinates and grid spacing of the administrative division binary map, the point geographic elements of the school are spatially mapped, and the point geographic elements of the school are mapped to the grid space of the administrative division binary map, and each school corresponds to a grid in the administrative division binary map.

4. The quantitative evaluation method for the implementation of nearby compulsory education enrollment according to claim 1 is characterized in that: The spatial overlay analysis of the school district and the point-shaped geographical elements of the students' families is carried out to determine the theoretical nearest school for each student, which is as follows: A spatial overlay analysis is performed on the point-like geographical elements of the student's family and the school districts mentioned above to determine the topological relationship between the point-like geographical elements of the student's family and the school districts corresponding to the schools in the geographic space. If the school district contains the point-like geographical elements of the student's family, the corresponding school is determined as the student's theoretical nearest school.

5. The quantitative evaluation method for the implementation of nearby compulsory education enrollment according to claim 1 is characterized in that: Quantitative evaluation indicators are set based on the distance students walk to school, specifically: Quantitative evaluation indicators are set at two levels: school level and district and county level based on the distance students walk to school.

6. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the quantitative evaluation method for implementing the nearby enrollment of compulsory education as claimed in any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a quantitative evaluation method for implementing nearby compulsory education enrollment as described in any one of claims 1 to 5.

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