CN-121981349-A - Educational facility site selection method, apparatus, electronic device and storage medium

CN121981349ACN 121981349 ACN121981349 ACN 121981349ACN-121981349-A

Abstract

The disclosure provides an educational facility site selection method, an educational facility site selection device, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the steps of constructing an objective function by taking the sum of the population numbers of the proper ages of the educational facility demand points served by the educational facility supply points in the feasible solution set as the maximum, constructing constraint conditions by taking only one educational facility supply point as the service demand point, wherein the service number of the educational facility supply points does not exceed the upper limit of the service number, and solving the optimal feasible solution set based on the constraint conditions to obtain the optimal educational facility site selection scheme of the first area. By adopting the scheme disclosed by the invention, the site selection accuracy and site selection efficiency can be improved.

Inventors

CAI BOCHENG
YU SHUIPING
WU YUHAO
HU YAZHEN
LIAO YOUHUA
LUO LITING
DU LIANTAO
ZHENG XIAOBIN
LIANG YUQIU
WANG PENG
WANG TIANYING

Assignees

广州市城市规划勘测设计研究院有限公司

Dates

Publication Date: 20260505
Application Date: 20251211

Claims (10)

1. A method of locating an educational facility, comprising: Constructing an objective function with the aim of minimizing the average path from each educational facility demand point in a feasible solution set of educational facility site selection of a first area to a corresponding educational facility supply point, wherein the sum of the number of suitable age population of the educational facility demand points served by each educational facility supply point in the feasible solution set is the maximum, and the feasible solution set comprises a plurality of feasible solution groups, the feasible solution groups comprise a first educational facility demand point and a first educational facility supply point, and the first educational facility demand point is served by the first educational facility supply point; constructing constraint conditions by taking one educational facility demand point and only one educational facility supply point as constraints, wherein the service quantity of each educational facility supply point does not exceed the upper limit of the service quantity; and solving the optimal feasible solution set for the objective function based on the constraint condition to obtain an optimal educational facility site selection scheme of the first area.
2. The method of claim 1, wherein the objective function comprises a first objective function and a second objective function, and wherein constructing the objective function comprises: Constructing a first objective function by taking the shortest average path from each educational facility demand point in the feasible solution set to each corresponding educational facility supply point as a target; and constructing a second objective function by taking the minimum reciprocal of the sum of the number of suitable population of the educational facility demand points served by each educational facility supply point in the feasible solution set as the objective.
3. The method of claim 2, wherein the solving the set of best possible solutions for the objective function based on the constraint comprises: Generating an initial population and a supplemental population required for each iteration based on the educational facility demand points and the educational facility supply points in the first area, wherein the initial population and the supplemental population each comprise a plurality of individuals, each individual corresponds to an educational facility demand point feasible solution of one educational facility supply point, and each initial population and each supplemental population corresponds to one feasible solution set respectively; adding the initial population and the supplementary population required by the first iteration to obtain a first iteration population, starting from the first iteration population, executing population iteration operation to obtain a current iteration immunization population, and adding the current iteration immunization population and the supplementary population required by the next iteration to obtain a next iteration population, wherein the population iteration operation comprises the steps of evaluating antibody quality of the current iteration population based on antigens determined by the first objective function and the second objective function, screening the current iteration population based on antibody quality of each individual in the current iteration population to obtain a current iteration non-dominant solution set, and cloning variation of the current iteration non-dominant solution set to obtain the current iteration immunization population; Calculating a first target value and a second target value corresponding to the non-dominant solution set of the iteration based on the first target function and the second target function; and under the condition that a first target value and a second target value corresponding to the non-dominant solution set of the current iteration accord with preset conditions, taking the non-dominant solution set of the current iteration as the optimal feasible solution set.
4. The method of claim 3, wherein the performing antibody quality evaluation on the current iteration population based on the antigens determined by the first objective function and the second objective function, and performing screening on the current iteration population based on the antibody quality of each individual in the current iteration population, to obtain a current iteration non-dominant solution set, includes: Determining a first antigen function and a second antigen function based on the first target function and the second target function respectively, wherein the first antigen function is used for calculating the average distance from each demand point in an education facility demand point feasible solution of the education facility supply point corresponding to the individual to the education facility supply point, and the second antigen function is used for calculating the sum of the population numbers of the suitable ages of each demand point in the education facility demand point feasible solution of the education facility supply point corresponding to the individual; Based on the first antigen function and the second antigen function, performing antibody quality evaluation on each individual in the current iteration population to obtain a first antibody value and a second antibody value of each individual in the current iteration population; Based on the first antibody value and the second antibody value of each individual in the current iteration population, performing dominant and non-dominant analysis on each individual in the current iteration population to obtain a target non-dominant layer; And under the condition that the number of individuals in the target non-dominated layer is larger than N, determining the crowding degree of each individual in the target non-dominated layer based on the difference between the first antibody value and the second antibody value of the left adjacent individual and the right adjacent individual of each individual in the target non-dominated layer, and carrying out descending order on each individual in the target non-dominated layer according to the crowding degree of each individual, and intercepting the previous N individuals as the non-dominated solution set of the iteration, wherein N is an integer larger than 1.
5. The method of claim 4, wherein the subjecting each individual in the current iterative population to a dominant and non-dominant analysis based on the first and second antibody values for each individual in the current iterative population to obtain a target non-dominant layer comprises: Determining that a first individual in the iterative population is available to a second individual if both the first and second antibody values are better than the first and second antibody values of the second individual; determining that a first individual and a second individual in the iterative population are in a non-dominant relationship under the condition that only one of the first antibody value and the second antibody value of the first individual is superior to the corresponding antibody value of the second individual; according to a grading mode that each individual in the upper non-dominant layer is not subjected to each individual in the lower non-dominant layer, each individual in the same non-dominant layer is not subjected to mutual control, the iterative population is graded step by step, and a multi-level non-dominant layer is obtained; and determining the top non-dominant layer as a target non-dominant layer when the number of individuals of the top non-dominant layer in the multi-level non-dominant layers is greater than N.
6. The method of claim 5, wherein the number of individuals in the initial population and the supplemental population is N, and the number of individuals in the current iterative population is 2N.
7. A method according to claim 3, wherein said subjecting the non-dominant solution set of the current iteration to clonal variation results in a current iteration immune population, comprising: generating a random number corresponding to each element in the individuals in the non-dominant solution set of the iteration, wherein each element in the individuals corresponds to an educational facility demand point; And for each element in each individual in the iteration non-dominant solution set, mutating the element when the random number corresponding to the element in the individual is smaller than the mutation rate corresponding to the individual.
8. An educational facility site selection apparatus, comprising: An objective function construction module, configured to construct an objective function with an average path from each educational-facility demand point in a feasible solution set of educational-facility address selection of a first area to a corresponding educational-facility supply point, where the sum of the number of suitable age population of educational-facility demand points served by each educational-facility supply point in the feasible solution set is the largest, and the feasible solution set includes a plurality of feasible solution groups, and the feasible solution groups include a first educational-facility demand point and a first educational-facility supply point, and the first educational-facility demand point is served by the first educational-facility supply point; a constraint condition determining module, configured to construct constraint conditions by using an educational facility demand point to be served by only one educational facility supply point, wherein the number of services of each educational facility supply point does not exceed the upper limit of the number of services; and the addressing scheme solving module is used for solving the optimal feasible solution set for the objective function based on the constraint condition so as to obtain the optimal educational facility addressing scheme of the first area.
9. An electronic device, comprising: at least one processor, and A memory communicatively coupled to the at least one processor, wherein, The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.

Description

Educational facility site selection method, apparatus, electronic device and storage medium Technical Field The present disclosure relates to the field of data processing technology. The present disclosure relates specifically to educational facility location methods, apparatus, electronic devices, and storage media. Background Educational facilities, such as kindergartens, elementary school, junior middle school, are space carriers of public educational services in cities, determining the number and quality of public educational services enjoyed by urban residents. The layout and the configuration of the educational facilities are more emphasized in social fairness, which is different from private service facilities such as markets and supermarkets. The public education facility layout and the resource allocation are equalized, so that residents can obtain public education services meeting the demands of the residents uniformly and equally, and the public education facility layout and the resource allocation are important guarantees of high-quality and sustainable development of cities. The method of planning, site selection and layout optimization employed for an underlying educational facility is based primarily on qualitative analysis, multi-criteria configuration models and mathematical models. The advantages and disadvantages of this approach come more from the rationality of expert knowledge, are too subjective, not sufficiently high in automation and intelligence, and the quality of the results obtained is relatively poor. With the continuous development of machine learning, various heuristic algorithms such as a particle swarm algorithm, a genetic algorithm, an ant colony optimization algorithm, a tabu search algorithm and the like are widely used for solving a space layout optimization problem, and the method has stronger openness and solving capability and higher automation and intelligent degree. However, most of intelligent optimization algorithms obtain optimal solutions through random search and repeated iteration, and the solving efficiency is drastically reduced along with the increase of variables. Therefore, how to improve the address accuracy and the address efficiency is a technical problem solved in the art. Disclosure of Invention The present disclosure provides an educational facility site selection method, apparatus, electronic device, and storage medium. According to an aspect of the present disclosure, there is provided an educational facility location method comprising: Constructing an objective function with the aim of minimizing the average path from each educational facility demand point in a feasible solution set of educational facility site selection of a first area to a corresponding educational facility supply point, wherein the sum of the number of suitable age population of the educational facility demand points served by each educational facility supply point in the feasible solution set is the maximum, and the feasible solution set comprises a plurality of feasible solution groups, the feasible solution groups comprise a first educational facility demand point and a first educational facility supply point, and the first educational facility demand point is served by the first educational facility supply point; constructing constraint conditions by taking one educational facility demand point and only one educational facility supply point as constraints, wherein the service quantity of each educational facility supply point does not exceed the upper limit of the service quantity; and solving the optimal feasible solution set for the objective function based on the constraint condition to obtain an optimal educational facility site selection scheme of the first area. According to another aspect of the present disclosure, there is provided an educational facility site selection apparatus comprising: An objective function construction module, configured to construct an objective function with an average path from each educational-facility demand point in a feasible solution set of educational-facility address selection of a first area to a corresponding educational-facility supply point, where the sum of the number of suitable age population of educational-facility demand points served by each educational-facility supply point in the feasible solution set is the largest, and the feasible solution set includes a plurality of feasible solution groups, and the feasible solution groups include a first educational-facility demand point and a first educational-facility supply point, and the first educational-facility demand point is served by the first educational-facility supply point; a constraint condition determining module, configured to construct constraint conditions by using an educational facility demand point to be served by only one educational facility supply point, wherein the number of services of each educational facility supply point does not exceed the upper lim