Regional education data processing method and system, and storage medium

Abstract

The invention provides a regional education data processing method and system and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: collecting education data of a plurality of regions, carrying out the preprocessing of the education data, dividing the education data according to the regions, obtaining a region data set, and carrying out the calculation of the region data set according to the region data set; obtaining a resource feature data set and a score feature data set, calculating education quality scores of different regions according to the resource feature data set and the score feature data set, obtaining an education quality score data set, dividing the region data set into different sets according to subject types, analyzing education resource demand types and quantities in different regions, and obtaining an education resource demand data set; and determining a resource allocation weight of each region by combining the educational quality score data set and the educational resource demand data set of all regions, calculating a resource allocation quantity which should be allocated to each region, and generating a resource allocation scheme. According to the invention, education data can be processed through a regionalized management method.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for processing regional education data, and a storage medium. Background Art

[0002] With the development of information technology, regionalized data management models have gradually been introduced into the field of education. However, existing regional education data processing methods mostly rely on traditional manual entry and independent data management systems, which may lack the ability to centralize and automate data processing.

[0003] For example, in the process of allocating educational resources, the current system performs fixed analysis and prediction based on historical data, which may not be able to process new data in a timely manner or conduct real-time dynamic analysis. Therefore, the system cannot respond and adjust quickly, resulting in uneven distribution of educational resources and failure to effectively meet the actual needs of different regions. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for processing regional education data, and a storage medium, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a method for processing regional education data comprises:

[0007] Collect education data from multiple regions, including education resource data and student performance data;

[0008] By preprocessing the education data, removing redundant data, unifying the data format, and dividing it by region, we get the regional data set;

[0009] According to the educational resource data in the regional dataset, the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers and the utilization rate of hardware equipment in the region are calculated to obtain the resource feature dataset;

[0010] According to the student performance data in the regional data set, the average performance of students in the region, the performance distribution range, performance volatility and subject bias are calculated to obtain the performance feature data set;

[0011] According to the resource feature dataset and the achievement feature dataset, the education quality scores of different regions are calculated to obtain the education quality score dataset;

[0012] According to the subject type, the regional data set is divided into different sets to obtain the subject group set;

[0013] According to the subject group set and the preset threshold data set, the types and quantities of educational resource demands in different regions are analyzed to obtain the educational resource demand data set. The preset threshold data set includes the preset teacher allocation ratio, the preset excellent teacher ratio and the preset hardware equipment allocation ratio.

[0014] By combining the education quality score dataset and the education resource demand dataset of all regions, the resource allocation weight of each region is determined to obtain the weight dataset;

[0015] According to the weight data set, the amount of resources that should be allocated to each region is calculated and a resource allocation plan is generated.

[0016] Furthermore, based on the educational resource data in the regional dataset, the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers and the utilization rate of hardware equipment in the region are calculated to obtain a resource feature dataset, including:

[0017] By counting the total number of teachers and students in the region, the number of teachers per 100 students is calculated to determine the teacher staffing ratio;

[0018] By counting the number of excellent teachers in the region and determining the proportion of excellent teachers based on the total number of teachers;

[0019] According to the teachers’ years of employment, the teachers’ years of teaching experience are determined, and the average years of teaching experience of the teachers is obtained;

[0020] Calculate hardware equipment utilization by recording the total number and daily usage of computers and laboratory equipment in the area.

[0021] Furthermore, based on the student score data in the regional dataset, the average score, score distribution range, score volatility and subject bias of students in the region are calculated to obtain a score feature dataset, including:

[0022] According to the total number of students in the area and the total score of each student, the average score of students in the area is calculated to obtain the average score;

[0023] Determine the distribution range of students' scores in the region based on the lowest and highest total scores of students;

[0024] According to the historical transcripts of students in the region, the historical average scores of students are calculated, and the standard deviation of students' scores is calculated based on it to obtain the volatility of scores;

[0025] By calculating the average of all students' scores in different subjects, we get the subject mean;

[0026] By calculating the average of different students' scores in all subjects, we can get the individual mean and compare it with the subject mean to determine whether there is any subject bias;

[0027] According to the subject mean and individual mean, the performance of different students in different subjects is evaluated to obtain the degree of subject bias. The calculation formula for the degree of subject bias is: ,

[0028] in, The degree of students' partiality in one subject. Index for students, is the total number of students in the region, Index of subjects, is the total number of subjects, For the The subject mean of each subject, For the The individual mean of the students, For the The weight of each subject, For the The standard deviation of the scores of each subject, For student performance volatility, is the coefficient, is a constant.

[0029] Furthermore, the education quality scores of different regions are calculated based on the resource feature dataset and the achievement feature dataset to obtain an education quality score dataset, including:

[0030] The calculation formula for the education quality score is: ,

[0031] in, For the The education quality score of each region, is the index of the region, The proportion of excellent teachers, is the average teaching experience of teachers, is the average score of the students, For student performance volatility, is the average deviation of the distribution range of students’ scores, is the hardware device utilization rate, The degree of students' partiality in one subject. For teacher staffing ratio, is the coefficient.

[0032] Furthermore, based on the subject group set and the preset threshold data set, the types and quantities of educational resource demands in different regions are analyzed to obtain an educational resource demand data set, including:

[0033] Determine whether there is a demand for teacher resources in different disciplines based on the preset teacher staffing ratio and the preset ratio of excellent teachers;

[0034] When there is a demand for teacher resources, the number of teacher shortages and the proportion of excellent teachers in different disciplines are identified based on the teacher staffing rate and the proportion of excellent teachers, and the demand for teacher resources is obtained;

[0035] According to the total number of computers and laboratory equipment, select the subjects with low hardware equipment ratio to determine whether they have hardware equipment resource needs;

[0036] When there is a demand for hardware equipment resources, the demand for computers and laboratory equipment of the discipline is calculated according to the preset hardware equipment configuration ratio to obtain the hardware equipment resource demand.

[0037] Furthermore, by combining the education quality score dataset and education resource demand dataset of all regions, the resource allocation weight of each region is determined to obtain a weight dataset, including:

[0038] Based on the education quality score dataset, calculate the education quality score weight of each region;

[0039] According to the education resource demand data set, calculate the education resource demand weight of each region;

[0040] The resource allocation weight of each region is calculated by combining the education quality score weight with the education resource demand weight. The calculation formula for the resource allocation weight is: ,

[0041] in, For the The resource allocation weight of each region, is the index of the region, For the The weight of the education quality score of each region, , For the The education quality score of each region, For the The education quality score of each region, For the The educational resource demand weight of each region, , For the The total demand for educational resources in a region For the The total demand for educational resources in a region For the The weight of the education quality score of each region, For the The educational resource demand weight of each region, is the index of the region, is the total number of regions, is the coefficient;

[0042] By normalizing the resource allocation weights to ensure that the sum of the resource allocation weights of all regions is 1, a weighted data set is obtained.

[0043] Furthermore, according to the weight data set, the resource allocation amount that should be allocated to each region is calculated, and a resource allocation plan is generated, including:

[0044] By summarizing all the unallocated educational resource data, we get the total educational resource data set. The types of educational resources include teacher resources and hardware equipment resources.

[0045] Determine the allocation ratio of each type of educational resources based on the proportion of educational resource types in the total educational resource data set;

[0046] By combining the resource allocation weight of each region and the allocation ratio of the educational resource type, the resource allocation amount of each educational resource type in each region is calculated to obtain the resource allocation data set;

[0047] The resource allocation dataset is adjusted according to the education resource demand dataset to ensure that the minimum education resource demand of each region is met and a resource allocation plan is generated.

[0048] In a second aspect, a system for processing regional education data comprises:

[0049] The data collection module is used to collect educational data from multiple regions, including educational resource data and student performance data;

[0050] The preprocessing module is used to preprocess the education data, remove redundant data, unify the data format, and divide it by region to obtain a regional data set;

[0051] The resource feature module is used to calculate the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers and the utilization rate of hardware equipment in the region based on the educational resource data in the regional data set, and obtain the resource feature data set;

[0052] The score feature module is used to calculate the average score, score distribution range, score volatility and subject bias of students in the region based on the student score data in the regional data set, and obtain the score feature data set;

[0053] The education quality module is used to calculate the education quality scores of different regions based on the resource feature dataset and the achievement feature dataset to obtain the education quality score dataset;

[0054] The subject division module is used to divide the regional data set into different sets according to the subject type to obtain the subject group set;

[0055] The resource demand module is used to analyze the types and quantities of educational resource demands in different regions according to the subject group set and the preset threshold data set, and obtain the educational resource demand data set. The preset threshold data set includes: preset teacher allocation ratio, preset excellent teacher ratio and preset hardware equipment allocation ratio;

[0056] An allocation weight module is used to determine the resource allocation weight of each region by combining the education quality score data set and the education resource demand data set of all regions to obtain a weight data set;

[0057] The allocation plan module is used to calculate the resource allocation amount that should be allocated to each area based on the weight data set and generate a resource allocation plan.

[0058] The above solution of the present invention includes at least the following beneficial effects:

[0059] The present invention generates a resource feature data set by calculating the teacher staffing rate, the proportion of excellent teachers, the average teaching years and the utilization rate of hardware equipment in the region, and can realize accurate quantitative analysis of educational resources. This method, on the basis of existing technologies, supplements the comprehensive evaluation of the regional educational resource distribution status. It can not only calculate the number of teachers corresponding to every 100 students, but also further refine it into the proportion of excellent teachers and the average teaching years of teachers, quantify the teaching ability of teacher resources, and can also identify idle or scarce resources through hardware equipment utilization analysis, so as to avoid waste of resources due to underutilization of equipment.

[0060] The present invention generates a performance feature data set by calculating the average performance, performance distribution range, performance volatility and degree of subject bias of students in a region. This method effectively makes up for the limitations of traditional performance analysis. The traditional method only reflects student performance through simple average score statistics, ignoring the academic fluctuations of individual students and the differences in subject performance. This method accurately measures the stability of student performance by calculating the standard deviation of historical performance, so as to identify students with large performance fluctuations and provide personalized tutoring. At the same time, by using a quantitative formula for the degree of subject bias, individual performance can be compared with the mean of subject performance to scientifically evaluate the comprehensive ability of students. This comprehensive evaluation method improves the depth of data analysis.

[0061] The present invention calculates the education quality score of each region by combining the resource feature data set and the performance feature data set. This calculation method comprehensively considers multiple indicators such as teacher resources, student performance, and hardware equipment utilization rate, making the evaluation of education quality more comprehensive and accurate. Traditional evaluation methods may only be based on students' admission rates, while ignoring the influence of teachers' professional level and hardware resources. By adding dimensions such as teacher staffing rate, proportion of excellent teachers, and student performance volatility, the new method can reflect the multidimensional characteristics of education quality.

[0062] The present invention combines the education quality score data set and the preset threshold data set to analyze the resource needs of each region and generate an education resource demand data set. This method significantly improves the real-time and flexibility of resource allocation. By analyzing the regional education data collected in real time, the method can promptly discover areas with resource shortages or surpluses. At the same time, the preset threshold data set is used as a benchmark to accurately identify the demand type and quantity of each region.

[0063] The present invention determines the resource allocation weight of each region by comprehensively considering the education quality score weight and the education resource demand weight. This method is a further refinement of the traditional method. It not only takes into account the differences in regional education quality, but also combines the actual resource demand, thus avoiding the problem of uneven distribution caused by single-dimensional decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flowchart of a method for processing regional education data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0066] like Figure 1 As shown, an embodiment of the present invention provides a method for processing regional education data, the method comprising:

[0067] Collect education data from multiple regions, including education resource data and student performance data;

[0068] By preprocessing the education data, removing redundant data, unifying the data format, and dividing it by region, we get the regional data set;

[0069] According to the educational resource data in the regional dataset, the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers and the utilization rate of hardware equipment in the region are calculated to obtain the resource feature dataset;

[0070] According to the student performance data in the regional data set, the average performance of students in the region, the performance distribution range, performance volatility and subject bias are calculated to obtain the performance feature data set;

[0071] According to the resource feature dataset and the achievement feature dataset, the education quality scores of different regions are calculated to obtain the education quality score dataset;

[0072] According to the subject type, the regional data set is divided into different sets to obtain the subject group set;

[0073] According to the subject group set and the preset threshold data set, the types and quantities of educational resource demands in different regions are analyzed to obtain the educational resource demand data set. The preset threshold data set includes the preset teacher allocation ratio, the preset excellent teacher ratio and the preset hardware equipment allocation ratio.

[0074] By combining the education quality score dataset and the education resource demand dataset of all regions, the resource allocation weight of each region is determined to obtain the weight dataset;

[0075] According to the weight data set, the amount of resources that should be allocated to each region is calculated and a resource allocation plan is generated.

[0076] In an embodiment of the present invention, education data of multiple regions are collected, and the education data include education resource data and student performance data. Fully automated data collection replaces traditional manual input, thereby significantly improving data collection efficiency and accuracy. Education data is preprocessed to remove redundant data, unify the data format, and divide it by region to obtain a regional data set. Through redundancy removal and standardization operations, data quality is improved to ensure the accuracy and reliability of subsequent analysis results. Based on the education resource data in the regional data set, the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers, and the utilization rate of hardware equipment in the region are calculated to obtain a resource feature data set, which quantifies the configuration of education resources, comprehensively reflects the utilization level of teacher resources and hardware resources in the region, and helps identify the advantages and disadvantages of resource allocation.

[0077] Based on the student performance data in the regional dataset, the average student performance, performance distribution range, performance volatility and degree of subject bias in the region are calculated to obtain the performance characteristic dataset, which provides a comprehensive academic performance evaluation for education managers, can accurately locate students' learning characteristics and existing problems, help formulate targeted teaching plans, and improve regional education levels; based on the resource characteristic dataset and the performance characteristic dataset, the education quality scores of different regions are calculated to obtain the education quality score dataset, which comprehensively considers the multi-dimensional factors of resources and performance, provides quantitative indicators for the comparison of education levels between regions, helps to accurately identify problem areas, and provides a scientific basis for resource adjustment; according to the subject type, the regional dataset is divided into different sets to obtain the subject group set. The subject division facilitates the subsequent analysis of educational resource needs in the region.

[0078] Based on the subject group set and the preset threshold data set, the types and quantities of educational resource demands in different regions are analyzed to obtain the educational resource demand data set. The preset threshold data set includes: the preset teacher staffing rate, the preset excellent teacher ratio and the preset hardware equipment ratio, which accurately predicts the educational resource demand in each region; by combining the education quality score data set and the education resource demand data set of all regions, the resource allocation weight of each region is determined to obtain the weighted data set, which ensures the scientificity and fairness of educational resource allocation and can balance the differences in education quality and resource demands between regions; based on the weighted data set, the amount of resources that should be allocated to each region is calculated, and a resource allocation plan is generated, which maximizes resource utilization while taking into account fairness and efficiency, providing support for the balanced development of regional education.

[0079] Among them, the educational data of multiple regions are collected, and the educational data includes educational resource data and student performance data, including:

[0080] Educational data is collected through intelligent terminal devices deployed in multiple areas, such as computers, tablets, and smart sensors. Educational data includes the following two categories:

[0081] Educational resource data: collect teacher's employment information, types of hardware equipment and usage, etc., and record them in real time through the education management platform;

[0082] Student performance data: historical performance is imported through the student management system, combined with real-time updated test scores, to automatically generate a student performance database.

[0083] Among them, by preprocessing the education data, removing redundant data, unifying the data format, and dividing it by region, we get the regional data set, which includes:

[0084] The collected educational data may contain redundant items and inconsistent formats, so the data needs to be processed as follows:

[0085] Remove redundant data: Filter duplicate records by uniquely identifying teacher information and student scores;

[0086] Unified data format: converting education data from different sources into a unified standard format, for example, unifying the number of years of service for teachers into a year-month system;

[0087] Regional division: According to the region to which the data belongs, it is classified and stored as an independent regional dataset.

[0088] In a preferred embodiment of the present invention, based on the educational resource data in the regional data set, the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers and the utilization rate of hardware equipment in the region are calculated to obtain a resource feature data set, including:

[0089] By counting the total number of teachers and students in the region, the number of teachers per 100 students is calculated to determine the teacher staffing ratio;

[0090] By counting the number of excellent teachers in the region and determining the proportion of excellent teachers based on the total number of teachers;

[0091] According to the teachers’ years of employment, the teachers’ years of teaching experience are determined, and the average years of teaching experience of the teachers is obtained;

[0092] Calculate hardware equipment utilization by recording the total number and daily usage of computers and laboratory equipment in the area.

[0093] In an embodiment of the present invention, by counting the total number of teachers and the total number of students in a region, calculating the number of teachers per 100 students, and determining the teacher staffing rate, the adequacy of teacher resources in each region can be intuitively reflected by quantifying the teacher staffing rate; by counting the number of excellent teachers in a region and determining the proportion of excellent teachers based on the total number of teachers, the overall teaching level of the regional teaching team is reflected, and education managers can evaluate the quality of the teaching staff; based on the teachers' years of employment, the teachers' years of teaching experience are determined, and the average years of teaching experience of the teachers are obtained, reflecting the teaching experience level of the regional teaching team; by recording the total number and daily usage records of computers and laboratory equipment in the region, the utilization rate of hardware equipment is calculated, reflecting the actual utilization efficiency of the hardware equipment.

[0094] The hardware equipment utilization rate is calculated by recording the total number and daily usage records of computers and laboratory equipment in the area, including:

[0095] The utilization rate of hardware equipment is calculated by counting the actual usage time of the equipment over a period of time and dividing it by the total available time of the equipment. For example, there are 50 computers in a certain area, and each computer is available for 10 hours a day for a total of 30 days, with a total available time of 15,000 hours. If the actual usage time is 9,000 hours, the utilization rate is 0.6.

[0096] In a preferred embodiment of the present invention, based on the student score data in the regional data set, the average score, score distribution range, score volatility and subject bias of students in the region are calculated to obtain a score feature data set, including:

[0097] According to the total number of students in the area and the total score of each student, the average score of students in the area is calculated to obtain the average score;

[0098] Determine the distribution range of students' scores in the region based on the lowest and highest total scores of students;

[0099] According to the historical transcripts of students in the region, the historical average scores of students are calculated, and the standard deviation of students' scores is calculated based on it to obtain the volatility of scores;

[0100] By calculating the average of all students' scores in different subjects, we get the subject mean;

[0101] By calculating the average of different students' scores in all subjects, we can get the individual mean and compare it with the subject mean to determine whether there is any subject bias;

[0102] According to the subject mean and individual mean, the performance of different students in different subjects is evaluated to obtain the degree of subject bias. The calculation formula for the degree of subject bias is: ,

[0103] in, The degree of students' partiality in one subject. Index for students, is the total number of students in the region, Index of subjects, is the total number of subjects, For the The subject mean of each subject, For the The individual mean of the students, For the The weight of each subject, For the The standard deviation of the scores of each subject, For student performance volatility, is the coefficient, is a constant.

[0104] In an embodiment of the present invention, the average of the scores of students in the region is calculated based on the total number of students in the region and the total score of each student to obtain the average score, which is a concise reflection of the overall academic level of the region; the score distribution range of students in the region is determined based on the lowest and highest values ​​of the students' total scores, which can reveal the differences in the scores of students in the region; based on the historical transcripts of students in the region, the historical average scores of the students are calculated, and based on the standard deviation of the students' scores, the score volatility is obtained, which effectively reflects the stability of the students' scores.

[0105] By calculating the average of all students' scores in different subjects, we get the subject mean, which reflects the overall academic level of students in different subjects. By calculating the average of different students' scores in all subjects, we get the individual mean, and compare it with the subject mean to determine whether there is any subject bias, which reflects the students' academic level in different subjects and whether there is any subject bias among students. Based on the subject mean and the individual mean, we evaluate the scores of different students in different subjects to get the degree of subject bias, revealing the students' strengths and weaknesses in each subject, and can effectively identify which subjects have large gaps.

[0106] In a preferred embodiment of the present invention, the education quality scores of different regions are calculated according to the resource feature data set and the achievement feature data set to obtain the education quality score data set, including:

[0107] The calculation formula for the education quality score is: ,

[0108] in, For the The education quality score of each region, is the index of the region, The proportion of excellent teachers, is the average teaching experience of teachers, is the average score of the students, For student performance volatility, is the average deviation of the distribution range of students’ scores, is the hardware device utilization rate, The degree of students' partiality in one subject. For teacher staffing ratio, is the coefficient.

[0109] In an embodiment of the present invention, the calculation formula for the education quality score first accurately evaluates the education resource allocation situation in each region based on the education resource data, then analyzes the students' academic performance, including the volatility of grades, subject bias and distribution range, to ensure the comprehensiveness of the evaluation, and finally takes into account the resource allocation and student grades to derive a scientific and objective education quality score. The implementation of each step not only improves the accuracy of the data, but also enhances the dynamic and intelligent nature of education management, ensuring the scientificity and fairness of education resource allocation.

[0110] in, is the average deviation of the students' score distribution range. The average deviation of the students' score distribution range is obtained by dividing the sum of the highest score and the lowest score in the students' score distribution range by 2. For example, if the score distribution range of students in a certain area is 345 to 678 points, then the average deviation of the score distribution range of students in this area is (345+678) / 2=511.5.

[0111] In a preferred embodiment of the present invention, the types and quantities of educational resource requirements in different regions are analyzed according to the subject group set and the preset threshold data set to obtain an educational resource requirement data set, including:

[0112] Determine whether there is a demand for teacher resources in different disciplines based on the preset teacher staffing ratio and the preset ratio of excellent teachers;

[0113] When there is a demand for teacher resources, the number of teacher shortages and the proportion of excellent teachers in different disciplines are identified based on the teacher staffing rate and the proportion of excellent teachers, and the demand for teacher resources is obtained;

[0114] According to the total number of computers and laboratory equipment, screen out disciplines with low hardware equipment configuration to determine whether they have hardware equipment resource requirements;

[0115] When there is a demand for hardware equipment resources, the demand for computers and laboratory equipment of the discipline is calculated according to the preset hardware equipment configuration ratio to obtain the hardware equipment resource demand.

[0116] In an embodiment of the present invention, based on a preset teacher staffing ratio and a preset ratio of excellent teachers, it is determined whether there is a demand for teacher resources in different disciplines. Through refined management of subject group sets and data analysis, the resource gap of each discipline can be accurately identified; when there is a demand for teacher resources, the number of teacher shortages and the ratio of excellent teacher shortages in different disciplines are identified based on the teacher staffing ratio and the ratio of excellent teachers, and the demand for teacher resources is obtained, thereby accurately predicting and optimizing the teacher resources required for each discipline; based on the total number of computers and laboratory equipment, disciplines with a low hardware equipment configuration ratio are screened out to determine whether they have a demand for hardware equipment resources; when there is a demand for hardware equipment resources, the demand for computers and laboratory equipment by the discipline is calculated based on the preset hardware equipment configuration ratio to obtain the demand for hardware equipment resources. The demand for hardware equipment is accurately calculated to ensure that the teaching equipment of each discipline can meet the actual teaching needs.

[0117] Among them, according to the preset teacher staffing ratio and the preset excellent teacher ratio, determine whether there is a demand for teacher resources in different disciplines, including:

[0118] According to the preset teacher staffing ratio, the teacher staffing situation of each subject in the current area is counted. When the number of teachers per 100 students is less than the prescribed standard, the number of teacher shortages in the subject needs to be calculated. For example, when the teacher staffing ratio of the mathematics subject is lower than the preset teacher staffing ratio of 0.8 teachers / 100 students, the number of teacher resource shortages in the subject is calculated proportionally.

[0119] By further analyzing the proportion of excellent teachers in each subject, calculating the proportion of excellent teachers in each subject in the current region, and judging whether the preset proportion of excellent teachers is reached, when the proportion of excellent teachers in a certain subject is lower than the preset proportion of excellent teachers, for example, the proportion of excellent teachers in mathematics subjects in a certain region is lower than the preset proportion of excellent teachers, then it is necessary to calculate the shortage of excellent teachers.

[0120] By combining the data on teacher staffing rate and the proportion of excellent teachers, we can determine the number of teachers and excellent teachers required for the subject, and then calculate the demand for teacher resources. For example, if the teacher staffing rate for mathematics is low, the number of teachers needs to be increased; if the proportion of excellent teachers is insufficient, the number of excellent teachers needs to be increased.

[0121] The preset threshold data set includes the preset teacher allocation ratio, the preset excellent teacher ratio and the preset hardware equipment allocation ratio. Specifically:

[0122] The preset teacher staffing ratio is calculated by analyzing the relationship between teacher staffing and student numbers in different regions in the past, and the reasonable teacher staffing ratios in different regions at different educational stages. For example, through regression analysis of historical education data, the number of teachers that should be allocated for every 100 students while ensuring the quality of education can be calculated. Quantitative analysis can be conducted based on factors such as educational resources, number of students, and teaching tasks in different regions to obtain a standard value.

[0123] The proportion of excellent teachers is preset by evaluating the teaching effectiveness of teachers in the region. A set of standards is set to evaluate the level of teachers based on multi-dimensional indicators such as teaching quality, student feedback, and test scores. Through regular assessment of teachers, a number of teachers with excellent academic performance and significant teaching results are selected to determine the proportion of excellent teachers.

[0124] The preset hardware equipment configuration ratio determines the hardware resources required for each student based on the subject characteristics and educational needs of students in the region. For example, science laboratory equipment is particularly important for middle school students, and computer resources are also crucial for students of information technology courses. Based on these needs, the hardware resource requirements for different subjects and educational stages are determined, and then a reasonable hardware equipment configuration ratio is derived.

[0125] In a preferred embodiment of the present invention, the resource allocation weight of each region is determined by combining the education quality score data set and the education resource demand data set of all regions to obtain a weight data set, including:

[0126] Based on the education quality score dataset, calculate the education quality score weight of each region;

[0127] According to the education resource demand data set, calculate the education resource demand weight of each region;

[0128] The resource allocation weight of each region is calculated by combining the education quality score weight with the education resource demand weight. The resource allocation weight calculation formula is: ,

[0129] in, For the The resource allocation weight of each region, is the index of the region, For the The weight of the education quality score of each region, , For the The education quality score of each region, For the The education quality score of each region, For the The educational resource demand weight of each region, , For the The total demand for educational resources in a region For the The total demand for educational resources in a region For the The weight of the education quality score of each region, For the The educational resource demand weight of each region, is the index of the region, is the total number of regions, is the coefficient;

[0130] By normalizing the resource allocation weights to ensure that the sum of the resource allocation weights of all regions is 1, a weighted data set is obtained.

[0131] In an embodiment of the present invention, by combining the two key factors of education quality and resource demand, a comprehensive resource allocation weight is generated, which takes into account both the current educational situation in the region and the actual needs of future development, providing a more comprehensive decision-making basis for resource allocation. The normalization process ensures the overall proportionality and consistency of resource allocation, and avoids the problem of weight accumulation exceeding the total available resources. The normalized weight value is easy to understand and can be directly used in resource allocation calculations.

[0132] in, For the The total demand for educational resources in a region. Specifically, a region has multiple different types of educational resource demands. Each type of resource demand has a corresponding weight, which reflects the importance of the demand type. The total demand for educational resources can be obtained by summing the weighted demands of all demand types. For example, a region has two types of educational resource demands:

[0133] The teacher resource requirement is 500 teachers, and the hardware resource requirement is 1,000 computers;

[0134] The weight of teacher resources is 0.7, and the weight of hardware resources is 0.3, which means that teacher resources are relatively more important, which is determined based on historical data;

[0135] The total demand for educational resources = 0.7×500+0.3×1000=650.

[0136] in, For the The weight of the education quality score of each region, , the weight of the education quality score is inversely proportional to the education quality score, ensuring that areas with lower education levels receive more weight.

[0137] in, For the The educational resource demand weight of each region, , the weight of educational resource demand is directly proportional to the amount of resource demand, ensuring that areas with greater resource demand receive higher weights.

[0138] In a preferred embodiment of the present invention, the resource allocation amount to be allocated to each area is calculated according to the weight data set, and a resource allocation scheme is generated, including:

[0139] By summarizing all the unallocated educational resource data, we get the total educational resource data set. The types of educational resources include teacher resources and hardware equipment resources.

[0140] Determine the allocation ratio of each type of educational resources based on the proportion of educational resource types in the total educational resource data set;

[0141] By combining the resource allocation weight of each region and the allocation ratio of the educational resource type, the resource allocation amount of each educational resource type in each region is calculated to obtain the resource allocation data set;

[0142] The resource allocation dataset is adjusted according to the education resource demand dataset to ensure that the minimum education resource demand of each region is met and a resource allocation plan is generated.

[0143] In an embodiment of the present invention, by aggregating all unallocated educational resource data, a total educational resource data set is obtained, and the educational resource types include teacher resources and hardware equipment resources, thereby ensuring that the educational resource data of all regions can be centrally stored and organized; according to the proportion of educational resource types in the total educational resource data set, the allocation ratio of each educational resource type is determined, and by reasonably calculating and determining the allocation ratio of each type of educational resources, it can be ensured that resource allocation is more in line with the actual needs of the region; by combining the resource allocation weight of each region and the allocation ratio of the educational resource type, the resource allocation amount of each educational resource type in each region is calculated to obtain a resource allocation data set, and by accurately calculating the amount of each resource that should be allocated to each region, it is ensured that each region can be allocated on demand within a limited total amount of resources, and the needs of different regions can be reasonably met; the resource allocation data set is adjusted according to the educational resource demand data set to ensure that the minimum educational resource demand of each region is met, and a resource allocation plan is generated to ensure the fairness of educational resource allocation and avoid the failure of basic resources in some regions to meet the demand due to errors in the calculation process or resource shortages.

[0144] Among them, the allocation ratio of each type of educational resources is determined according to the proportion of educational resource types in the total educational resource data set, including:

[0145] Based on the total amount of educational resources data set, the demand for different types of educational resources is analyzed to determine the allocation ratio of each resource, such as teachers, computers, laboratory equipment, etc. The demand intensity of different resources is calculated through the regional education quality score, resource demand weights and differences in regional education quality. According to the different resource demand intensities, the weighted average method is used to determine the allocation ratio of each resource type. Based on these ratios, the proportion of each resource type in each region can be determined.

[0146] Among them, the resource allocation dataset is adjusted according to the education resource demand dataset to ensure that the minimum education resource demand of each region is met and generate a resource allocation plan, which specifically includes:

[0147] Compare the actual allocation of each area with the preset minimum resource requirement standards, such as the minimum number of teachers and hardware equipment in each area;

[0148] For areas that do not meet the minimum demand standards, priority will be given to supplementing from the remaining resources. For example, if a certain area is insufficiently equipped with computers but the education quality score of the area is high, supplementation will be made based on the remaining resources;

[0149] Ensure that each region's basic educational resource needs, such as teachers and hardware equipment, are met before allocating additional resources;

[0150] The resource allocations for each region are sorted by resource type and subject group to form a detailed resource allocation plan.

[0151] An embodiment of the present invention further provides a system for processing regional education data, the system comprising:

[0152] The data collection module is used to collect educational data from multiple regions, including educational resource data and student performance data;

[0153] The preprocessing module is used to preprocess the education data, remove redundant data, unify the data format, and divide it by region to obtain a regional data set;

[0154] The resource feature module is used to calculate the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers and the utilization rate of hardware equipment in the region based on the educational resource data in the regional data set, and obtain the resource feature data set;

[0155] The score feature module is used to calculate the average score, score distribution range, score volatility and subject bias of students in the region based on the student score data in the regional data set, and obtain the score feature data set;

[0156] The education quality module is used to calculate the education quality scores of different regions based on the resource feature dataset and the achievement feature dataset to obtain the education quality score dataset;

[0157] The subject division module is used to divide the regional data set into different sets according to the subject type to obtain the subject group set;

[0158] The resource demand module is used to analyze the types and quantities of educational resource demands in different regions according to the subject group set and the preset threshold data set, and obtain the educational resource demand data set. The preset threshold data set includes: preset teacher allocation ratio, preset excellent teacher ratio and preset hardware equipment allocation ratio;

[0159] An allocation weight module is used to determine the resource allocation weight of each region by combining the education quality score data set and the education resource demand data set of all regions to obtain a weight data set;

[0160] The allocation plan module is used to calculate the resource allocation amount that should be allocated to each area based on the weight data set and generate a resource allocation plan.

[0161] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0162] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0163] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above method. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0164] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for processing regional education data, characterized in that: The method comprises: Collect education data from multiple regions, including education resource data and student performance data; By preprocessing the education data, removing redundant data, unifying the data format, and dividing it by region, we get the regional data set; According to the educational resource data in the regional dataset, the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers and the utilization rate of hardware equipment in the region are calculated to obtain the resource feature dataset; According to the student performance data in the regional data set, the average performance of students in the region, the performance distribution range, performance volatility and subject bias are calculated to obtain the performance feature data set; According to the resource feature dataset and the achievement feature dataset, the education quality scores of different regions are calculated to obtain the education quality score dataset; According to the subject type, the regional data set is divided into different sets to obtain the subject group set; According to the subject group set and the preset threshold data set, the types and quantities of educational resource demands in different regions are analyzed to obtain the educational resource demand data set. The preset threshold data set includes the preset teacher allocation ratio, the preset excellent teacher ratio and the preset hardware equipment allocation ratio. By combining the education quality score dataset and the education resource demand dataset of all regions, the resource allocation weight of each region is determined to obtain the weight dataset; According to the weight data set, the amount of resources that should be allocated to each region is calculated and a resource allocation plan is generated.

2. A method for processing regional education data according to claim 1, characterized in that: Based on the educational resource data in the regional dataset, the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers and the utilization rate of hardware equipment in the region are calculated to obtain the resource feature dataset, including: By counting the total number of teachers and students in the region, the number of teachers per 100 students is calculated to determine the teacher staffing ratio; By counting the number of excellent teachers in the region and determining the proportion of excellent teachers based on the total number of teachers; According to the teachers’ years of employment, the teachers’ years of teaching experience are determined, and the average years of teaching experience of the teachers is obtained; Calculate hardware equipment utilization by recording the total number and daily usage of computers and laboratory equipment in the area.

3. A method for processing regional education data according to claim 2, characterized in that: Based on the student score data in the regional dataset, the average score, score distribution range, score volatility and subject bias of students in the region are calculated to obtain the score feature dataset, including: According to the total number of students in the area and the total score of each student, the average score of students in the area is calculated to obtain the average score; Determine the distribution range of students' scores in the region based on the lowest and highest total scores of students; According to the historical transcripts of students in the region, the historical average scores of students are calculated, and the standard deviation of students' scores is calculated based on it to obtain the volatility of scores; By calculating the average of all students' scores in different subjects, we get the subject mean; By calculating the average of different students' scores in all subjects, we can get the individual mean and compare it with the subject mean to determine whether there is any subject bias; According to the subject mean and individual mean, the performance of different students in different subjects is evaluated to obtain the degree of subject bias. The calculation formula for the degree of subject bias is: ， in, The degree of students' partiality in one subject. Index for students, is the total number of students in the region, Index of subjects, is the total number of subjects, For the The subject mean of each subject, For the The individual mean of the students, For the The weight of each subject, For the The standard deviation of the scores of each subject, For student performance volatility, is the coefficient, is a constant.

4. A method for processing regional education data according to claim 3, characterized in that: Based on the resource feature dataset and the achievement feature dataset, the education quality scores of different regions are calculated to obtain the education quality score dataset, including: The calculation formula for the education quality score is: ， in, For the The education quality score of each region, is the index of the region, The proportion of excellent teachers, is the average teaching experience of teachers, is the average score of the students, For student performance volatility, is the average deviation of the distribution range of students’ scores, is the hardware device utilization rate, The degree of students' partiality in one subject. For teacher staffing ratio, is the coefficient.

5. A method for processing regional education data according to claim 4, characterized in that: According to the subject group set and the preset threshold data set, the types and quantities of educational resource demands in different regions are analyzed to obtain the educational resource demand data set, including: Determine whether there is a demand for teacher resources in different disciplines based on the preset teacher staffing ratio and the preset ratio of excellent teachers; When there is a demand for teacher resources, the number of teacher shortages and the proportion of excellent teachers in different disciplines are identified based on the teacher staffing rate and the proportion of excellent teachers, and the demand for teacher resources is obtained; According to the total number of computers and laboratory equipment, select the subjects with low hardware equipment ratio to determine whether they have hardware equipment resource needs; When there is a demand for hardware equipment resources, the demand for computers and laboratory equipment of the discipline is calculated according to the preset hardware equipment configuration ratio to obtain the hardware equipment resource demand.

6. A method for processing regional education data according to claim 5, characterized in that: By combining the education quality score dataset and education resource demand dataset of all regions, the resource allocation weight of each region is determined, and the weight dataset is obtained, including: Based on the education quality score dataset, calculate the education quality score weight of each region; According to the education resource demand data set, calculate the education resource demand weight of each region; The resource allocation weight of each region is calculated by combining the education quality score weight with the education resource demand weight. The calculation formula for the resource allocation weight is: ， in, For the The resource allocation weight of each region, is the index of the region, For the The weight of the education quality score of each region, , For the The education quality score of each region, For the The education quality score of each region, For the The educational resource demand weight of each region, , For the The total demand for educational resources in a region For the The total demand for educational resources in a region For the The weight of the education quality score of each region, For the The educational resource demand weight of each region, is the index of the region, is the total number of regions, is the coefficient; By normalizing the resource allocation weights to ensure that the sum of the resource allocation weights of all regions is 1, a weighted data set is obtained.

7. A method for processing regional education data according to claim 6, characterized in that: According to the weight data set, the resource allocation amount that should be allocated to each region is calculated, and a resource allocation plan is generated, including: By summarizing all the unallocated educational resource data, we get the total educational resource data set. The types of educational resources include teacher resources and hardware equipment resources. Determine the allocation ratio of each type of educational resources based on the proportion of educational resource types in the total educational resource data set; By combining the resource allocation weight of each region and the allocation ratio of the educational resource type, the resource allocation amount of each educational resource type in each region is calculated to obtain the resource allocation data set; The resource allocation dataset is adjusted according to the education resource demand dataset to ensure that the minimum education resource demand of each region is met and a resource allocation plan is generated.

8. A system for processing regional education data, characterized in that: The system comprises: The data collection module is used to collect educational data from multiple regions, including educational resource data and student performance data; The preprocessing module is used to preprocess the education data, remove redundant data, unify the data format, and divide it by region to obtain a regional data set; The resource feature module is used to calculate the teacher staffing rate, the proportion of excellent teachers, the average teaching years of teachers and the utilization rate of hardware equipment in the region based on the educational resource data in the regional data set, and obtain the resource feature data set; The score feature module is used to calculate the average score, score distribution range, score volatility and subject bias of students in the region based on the student score data in the regional data set, and obtain the score feature data set; The education quality module is used to calculate the education quality scores of different regions based on the resource feature dataset and the achievement feature dataset to obtain the education quality score dataset; The subject division module is used to divide the regional data set into different sets according to the subject type to obtain the subject group set; The resource demand module is used to analyze the types and quantities of educational resource demands in different regions according to the subject group set and the preset threshold data set, and obtain the educational resource demand data set. The preset threshold data set includes: preset teacher allocation ratio, preset excellent teacher ratio and preset hardware equipment allocation ratio; An allocation weight module is used to determine the resource allocation weight of each region by combining the education quality score data set and the education resource demand data set of all regions to obtain a weight data set; The allocation plan module is used to calculate the resource allocation amount that should be allocated to each area based on the weight data set and generate a resource allocation plan.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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