Data analysis method and system based on rural education resource evaluation

Abstract

The invention relates to the technical field of data analysis, and discloses a data analysis method and system based on rural education resource evaluation. The method comprises the steps of obtaining to-be-evaluated data of rural education resources; calculating the to-be-evaluated data to obtain standardized data and a scoring result; according to the standardized data and the scoring result, calculating to obtain a weight coefficient; according to the standardized data, calculating to obtain an incidence matrix; calculating a coefficient distribution matrix according to the weight coefficient and the incidence matrix; and according to the coefficient distribution matrix, generating and outputting a rural education resource evaluation data analysis result. The method has the following effects that the to-be-evaluated data is processed, the incidence matrix is established, the coefficient distribution matrix is established according to the incidence matrix and the calculated weight, and finally the evaluation result is generated according to the coefficient distribution matrix and output, so that the multi-dimensional data in the to-be-evaluated data is comprehensively considered, and the evaluation accuracy is improved. Therefore, the condition of the educational resources is comprehensively and accurately reflected, and the configuration of the educational resources is optimized.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a data analysis method and system based on rural education resource evaluation. Background Art

[0002] Rural education resources refer to the various conditions and elements required to carry out educational activities in rural areas. By evaluating rural education resources, we can gain an in-depth understanding of the operation of rural schools, teaching levels, and student learning outcomes, and provide a basis for the formulation of targeted education policies; at the same time, it helps to discover the imbalance in the allocation of rural education resources, thereby promoting the rational allocation of resources; and it can reveal the weak links and problems of rural education, and help to put forward suggestions for improvement. Therefore, evaluating rural education resources can not only understand the current situation and problems of rural education, but also provide strong support for optimizing resource allocation, improving education quality, and promoting balanced development of education.

[0003] However, the indicators used in some theoretical research and practical results are often limited to describing the current status of educational resources, without providing specific methods for allocating educational resources. At the same time, most actual studies only use the analytic hierarchy process, which is too subjective; and when studying the evaluation model, there is a lack of consideration of educational outcomes, so the model cannot fully reflect the status of educational resources.

[0004] In one prior art, a method for constructing a resource quality indicator system for a learning resource platform is disclosed, comprising the following steps: in the overall research design, determining the issues that need to be studied through literature research, and determining the research plan based on the issues that need to be studied; preliminarily determining the resource quality evaluation indicators and framework of the learning resource platform through literature research; determining existing problems and user needs through interviews, surveys and questionnaires; finally, using BP neural network to make a preliminary judgment on the indicator weights, and then using AHP hierarchical analysis method to determine the indicator weights; and using BP neural network for prediction.

[0005] However, the above technologies lack comprehensive consideration of all aspects of educational resources, such as basic school information, educational facilities, teaching staff, educational achievements, etc., which makes the collected information too subjective and unable to fully reflect the status of educational resources. At the same time, the technology does not further refine the analysis results to form a guiding method for resource allocation, and cannot effectively combine evaluation and allocation. Summary of the invention

[0006] The present invention provides a data analysis method, system, electronic device and storage medium based on rural education resource evaluation to solve the problems of incomplete analysis of the current status of education resources, excessive subjectivity and lack of integration with resource allocation.

[0007] In a first aspect, in order to solve the above technical problems, the present invention provides a data analysis method based on rural education resource evaluation, comprising: Obtain data on rural education resources to be evaluated; Calculating the data to be evaluated to obtain standardized data and scoring results; Calculate a weight coefficient according to the standardized data and the scoring result; According to the standardized data, a correlation matrix is ​​calculated; Calculate a coefficient allocation matrix according to the weight coefficients and the association matrix; According to the coefficient allocation matrix, the rural education resource evaluation data analysis results are generated and output.

[0008] In an optional implementation, the calculating the data to be evaluated to obtain standardized data and scoring results includes: Performing standardization processing on the data to be evaluated to obtain initial standardized data; Determining the positivity of the initial standardized data and calculating the standardized data; Scoring the standardized data to obtain a scoring result; The initial standardized data is obtained by calculating according to the following formula: in, Indicates The initial standardized data, Indicates The data to be evaluated, represents the average value of the data to be evaluated, represents the standard deviation of the data to be evaluated; The standardized data is calculated according to the following formula: in, Indicates The standardized data, Indicates The initial standardized data.

[0009] In an optional implementation, the weight coefficient is calculated based on the standardized data and the scoring result, including: Calculating the standardized data and the scoring result to obtain an indicator entropy; Calculate according to the index entropy to obtain the entropy reduction; Calculate according to the entropy reduction number to obtain a weight coefficient; The index entropy is calculated according to the following formula: in, Indicates The entropy of the indicator is Indicates The standardized data, Indicates The scoring results are: represents the total number of entropies of the indicator; The entropy reduction number is calculated according to the following formula: in, Indicates The entropy reduction number, Indicates The entropy of the indicator.

[0010] In an optional implementation, the calculating the association matrix according to the standardized data includes: Calculate according to the standardized data to obtain the missing item rate and the missing item rate threshold; Assigning a value to the standardized data whose missing item rate is lower than the missing item rate threshold, and combining the value with the standardized data whose missing item rate is higher than the missing item rate threshold to obtain an indicator value; According to the index values, a correlation matrix is ​​established.

[0011] In an optional implementation, establishing a coefficient allocation matrix according to the weight coefficients and the association matrix includes: Normalizing the correlation matrix and calculating eigenvalues ​​and eigenvectors; Establishing a coefficient allocation matrix according to the eigenvalue, the eigenvector and the weight coefficient; Normalization is performed according to the following formula: in, The first Line The elements of the column, The first Line The elements of the column, n is the matrix dimension, represents the first Line Elements of a column.

[0012] In an optional implementation, scoring the standardized data to obtain a scoring result includes: The scoring result is calculated according to the following formula: in, Indicates The scoring results are: Indicates The standardized data, represents the standardized data, and Represent the minimum and maximum values ​​in the normalized data set, respectively.

[0013] In an optional implementation, the calculating according to the entropy reduction number to obtain the weight coefficient includes: The weight coefficient is calculated according to the following formula: in, Indicates the The weight coefficients, Indicates The entropy reduction number, Represents the total number of the entropy reduction numbers.

[0014] In an optional implementation, the calculating according to the standardized data to obtain the missing item rate and the missing item rate threshold includes: The missing item rate and the missing item rate threshold are calculated according to the following formula: in, Indicates The missing item rate, represents the missing item rate threshold, represents the total number of the standardized data, Indicates The standardized data Represents the total number of observations of the standardized data.

[0015] In an optional implementation, the establishing a coefficient allocation matrix according to the eigenvalue, the eigenvector and the weight coefficient includes: The coefficient allocation matrix is ​​established according to the following formula: in, represents the coefficient allocation matrix, Indicates The weight coefficients, represents the eigenvalue, Indicates The feature vectors.

[0016] In a second aspect, the present invention provides a data analysis system based on rural education resource evaluation, comprising: A data acquisition module is used to obtain the data to be evaluated of rural education resources; A standardization and scoring module, used to calculate the data to be evaluated to obtain standardized data and scoring results; A weight calculation module, used to calculate a weight coefficient according to the standardized data and the scoring result; A missing item rate and matrix calculation module, used to calculate the correlation matrix according to the standardized data; A coefficient allocation matrix calculation module, used for calculating a coefficient allocation matrix according to the weight coefficient and the association matrix; The result generation module is used to generate and output the rural education resource evaluation data analysis results according to the coefficient allocation matrix.

[0017] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned data analysis methods based on rural education resource assessment.

[0018] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned data analysis methods based on rural education resource assessment.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a data analysis method based on rural education resource evaluation, including obtaining data to be evaluated of rural education resources; calculating the data to be evaluated to obtain standardized data and scoring results; calculating a weight coefficient based on the standardized data and the scoring results; calculating an association matrix based on the standardized data; calculating a coefficient allocation matrix based on the weight coefficient and the association matrix; and generating and outputting rural education resource evaluation data analysis results based on the coefficient allocation matrix.

[0020] The method obtains the data to be evaluated of rural education resources, processes the data to be evaluated accordingly, and then uses the correlation matrix to quantify the correlation between different types of data. Then, a coefficient allocation matrix is ​​established according to the correlation matrix and the calculated weights. Finally, the analysis results of the rural education resource evaluation data are generated and output according to the coefficient allocation matrix. The method comprehensively considers multiple dimensions in the data to be evaluated, such as basic information of schools, educational facilities, teaching staff, educational achievements, etc., and performs evaluation and allocation optimization, thereby achieving a comprehensive and accurate reflection of the status of educational resources and the optimization of the configuration of educational resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a data analysis method based on rural education resource evaluation provided by the first embodiment of the present invention; Figure 2 It is a structural diagram of a data analysis system based on rural education resource evaluation provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Reference Figure 1 The first embodiment of the present invention provides a data analysis method based on rural education resource evaluation, comprising the following steps: S1, obtain the data to be evaluated on rural education resources; S2, calculating the data to be evaluated to obtain standardized data and scoring results; S3, calculating a weight coefficient according to the standardized data and the scoring result; S4, calculating a correlation matrix according to the standardized data; S5, calculating a coefficient allocation matrix according to the weight coefficients and the association matrix; S6. Generate and output rural education resource assessment data analysis results based on the coefficient allocation matrix.

[0024] It should be noted that in some existing methods, the reference indicators are often limited to the description of the current status of educational resources, without providing specific methods for the allocation of educational resources. At the same time, actual research lacks comprehensive consideration of all aspects of educational resources, such as basic school information, educational facilities, teaching staff, educational achievements, etc., which makes the collected information too subjective and unable to fully reflect the status of educational resources. In addition, some technologies do not further refine the analysis results to form a guiding method for resource allocation, and cannot effectively combine evaluation and allocation.

[0025] In the present invention, the main steps include processing the data to be evaluated accordingly, using the correlation matrix to quantify the correlation relationship, and establishing a coefficient allocation matrix based on the correlation matrix and the calculated weights, and finally generating and outputting the rural education resource evaluation data analysis results based on the coefficient allocation matrix. Through the above steps, multiple dimensional data in the data to be evaluated are comprehensively considered and evaluated; at the same time, through the coefficient allocation matrix, the configuration of educational resources can also be optimized, thereby achieving a comprehensive and accurate reflection of the status of educational resources and the optimization of the configuration of educational resources.

[0026] In step S1, firstly, the data to be evaluated of rural education resources is obtained from relevant units.

[0027] Specifically, the data to be evaluated include four indicators: basic information of the school, educational facilities, teaching staff and educational achievements. More specifically, the basic information indicators of the school include school size (such as the number of students, the number of classes, etc.), full-time teachers, school area, school building area, equipment compliance rate, environmental compliance rate, etc.; educational facilities indicators include the number of classrooms, the number of teacher rooms, the number of student rooms (such as libraries, laboratories, etc.), the number of living rooms (such as dormitories, canteens, etc.), the number of cultural and sports rooms (such as sports fields, art rooms, etc.) and the number of logistics rooms, etc.; the teaching staff indicators include the number of teachers at all levels, the professionalism of school (kindergarten) principals, the proportion of teachers with different academic qualifications, the continuing education of full-time teachers, the ratio of teachers to students, etc.; educational achievement indicators include the proportion of students with bachelor's degrees and above, the proportion of students with high school degrees and below, the rate of students' admission to higher education and the employment rate after graduation, etc.

[0028] It should be noted that in the process of collecting data, in order to ensure the accuracy and reliability of the data, it is necessary to pre-process the basic information data, including data cleaning, missing value processing, outlier processing, etc. The purpose of data cleaning is to find and correct errors and duplicate values ​​in the data, so as to prevent erroneous or duplicate data from affecting the final results. Among them, the missing values ​​can be processed by the mean filling and median filling methods, while the outliers can be directly deleted to avoid their interference with the overall evaluation results.

[0029] In step S2, corresponding calculations are performed on the acquired data to be evaluated to obtain standardized data and scoring results.

[0030] In one embodiment, calculating the data to be evaluated to obtain standardized data and scoring results includes the following steps: S21, performing standardization processing on the data to be evaluated to obtain initial standardized data; S22, judging the positivity of the initial standardized data, and calculating to obtain standardized data; S23, scoring the standardized data to obtain a scoring result.

[0031] In step S21, the acquired data to be evaluated is standardized to obtain initial standardized data. The initial standardized data is obtained by calculating according to the following formula: in, Indicates The initial standardized data, Indicates The data to be evaluated, represents the average value of the data to be evaluated, Represents the standard deviation of the data to be evaluated.

[0032] It should be noted that when performing standardization, an appropriate standardization method can be selected according to the actual situation for subsequent analysis and evaluation. For example, if the data needs to be linearly mapped to the range of [0,1], the minimum-maximum standardization can be selected, which is more convenient for intuitive comparison and analysis; if the data needs to be converted to a standard normal distribution, then Z-score standardization is selected so that the data has the same standard deviation; for situations where the data range needs to be flexibly adjusted, decimal calibration standardization is selected so that the data is more in line with special analysis requirements. Exemplarily, Z-score standardization is used in the present invention. Of course, different methods can be selected according to different application scenarios and user needs, and the present invention is not limited to this.

[0033] In step S22, the positive and negative of the initial standardized data is judged, and corresponding calculations are performed according to the positive and negative, to obtain standardized data. The standardized data is obtained by calculating according to the following formula: in, represents the standardized data, represents the initial normalized data.

[0034] It should be noted that in some special scenarios, negative numbers represent some kind of missing, reduced or reverse change, which to some extent limits the interpretation of the data and may have a bad impact on subsequent calculations. By converting negative numbers to positive numbers, we can more accurately describe the scenario that conforms to reality.

[0035] In step S23, first, it is determined that the scoring range is 0 to 20 points, and then the standardized data is scored to obtain a scoring result. The scoring result is calculated according to the following formula: in, Indicates The scoring results are: Indicates The standardized data, represents the standardized data, and Represent the minimum and maximum values ​​in the normalized data set, respectively.

[0036] It should be noted that in the above formula, in order to simplify calculations and improve processing efficiency, the scoring results are set to 0 to 20 points. If faced with more complex and sophisticated evaluation needs, the 20 in the above formula can be replaced by other larger numbers to expand the range of scoring results. By replacing a larger value, a more detailed and accurate scoring result can be obtained. This not only helps to more accurately evaluate small differences between objects or factors, but also improves the accuracy and reliability of the evaluation. At the same time, when expanding the range of scoring results, it is necessary to pay attention to maintaining the consistency and comparability of the evaluation, that is, when adjusting the values ​​in the formula, it is necessary to ensure that the new range of values ​​can still reflect the relative position and relationship between objects or factors, so as to avoid confusion and misleading evaluation results.

[0037] In step S3, a weight coefficient is calculated based on the calculated standardized data and the scoring result.

[0038] In one embodiment, the calculating the weight coefficient according to the standardized data and the scoring result comprises the following steps: S31, calculating the standardized data and the scoring result to obtain an index entropy; S32, calculating according to the indicator entropy to obtain an entropy reduction number; S33, calculating according to the entropy reduction number to obtain a weight coefficient.

[0039] In step S31, according to the principle of entropy, the standardized data and the scoring result are calculated to obtain the index entropy. The index entropy is obtained by calculating according to the following formula: in, Indicates The entropy of the indicator is Indicates The standardized data, Indicates The scoring results are: Indicates the total entropy of the indicator. The smaller the entropy of the indicator, the greater the difference between samples of the indicator, and the more effective information it contains. Therefore, the weight of the indicator should be greater. Conversely, the larger the entropy of the indicator, the smaller the difference and the smaller the weight.

[0040] It should be noted that information entropy can measure the uncertainty of information. The smaller the information entropy of an indicator, the greater the difference in the values ​​of the indicator between different samples, that is, the greater the amount of information contained in the indicator, and the greater the contribution to the evaluation results. By calculating the information entropy of each indicator, the degree of difference of the indicator in different samples can be measured, thereby determining its weight. This method avoids the subjectivity and arbitrariness of artificial weighting. Through the calculation of entropy, the effectiveness of each indicator in the evaluation can be accurately reflected.

[0041] In step S32, the entropy reduction number is calculated based on the obtained index entropy. The entropy reduction number is calculated based on the following formula: in, Indicates The entropy reduction number, Indicates The entropy of the indicator.

[0042] It should be noted that, since the value of the index entropy is generally a negative number in this calculation case, by subtracting the index entropy from 1, the entropy reduction number can be made greater than 0, thereby facilitating subsequent observation and judging whether the intermediate process is correct. In addition, if the entropy reduction number is a number greater than 0, it is also more convenient to perform mathematical operations and statistical analysis, thereby improving the efficiency and accuracy of the entire calculation process.

[0043] In step S33, according to the entropy reduction number, the proportion of each entropy reduction number to the total number is calculated to obtain a weight coefficient. The weight coefficient is obtained by calculating according to the following formula: in, Indicates the The weight coefficients, Indicates The entropy reduction number, Represents the total number of the entropy reduction numbers.

[0044] Specifically, first, according to the given entropy reduction number set, the proportion of each entropy reduction number in the total number is calculated one by one, and then these proportion values ​​are recorded and stored as weight coefficients.

[0045] It should be noted that the above weight coefficient is a relative value and does not directly represent the absolute size or quantity of each entropy reduction number, but reflects the importance or influence of each entropy reduction number in the whole. In addition, the above formula uses division operation to calculate the weight coefficient, but in practical applications, other calculation methods can be selected according to the characteristics of the data and the needs of the application. For example, when the data has obvious uneven distribution or outliers, the weighted average method can be used to calculate the weight coefficient to better reflect the true situation of the data; the use of normalization processing can convert the original data into dimensionless values, which is convenient for subsequent analysis and comparison.

[0046] In step S4, a correlation matrix is ​​calculated based on the obtained standardized data.

[0047] In one embodiment, calculating the association matrix according to the standardized data comprises the following steps: S41, performing calculation according to the standardized data to obtain a missing item rate and a missing item rate threshold; S42, assigning a value to the standardized data whose missing item rate is lower than the missing item rate threshold, and combining the value with the standardized data whose missing item rate is higher than the missing item rate threshold to obtain an indicator value; S43, establishing a correlation matrix according to the index values.

[0048] In step S41, the missing ratio of each data is calculated according to the standardized data to obtain the missing item ratio and the missing item ratio threshold. The missing item ratio and the missing item ratio threshold are calculated according to the following formula: in, Indicates The missing item rate, represents the missing item rate threshold, represents the total number of the standardized data, Indicates The standardized data Represents the total number of observations of the standardized data.

[0049] It should be noted that all the standardized data need to be reviewed first to determine whether each data item is complete. Indicates The standardized data is also The actual number of observations for each data point; Represents the total number of observations of the standardized data, which can be obtained by By checking each data point one by one and recording the missing data items, the missing ratio of each data is calculated to obtain the missing item rate. Subsequently, these missing item rates are aggregated and averaged to obtain the missing item rate threshold. This missing item rate threshold will be used as a reference for evaluating data quality, thereby identifying which standardized data requires additional processing.

[0050] It should be noted that a reasonable missing item rate threshold should be able to accurately reflect the overall quality of the data while avoiding over-processing or missing important data. Therefore, when determining the missing item rate threshold, it is necessary to fully consider multiple factors such as the distribution characteristics of the data, the nature of the missing data, and the needs of subsequent analysis.

[0051] In step S42, the missing item rate and the missing item rate threshold of each standardized data are determined, a value is assigned to the standardized data whose missing item rate is lower than the missing item rate threshold, and the data is combined with the standardized data whose missing item rate is higher than the missing item rate threshold to obtain an indicator value.

[0052] It should be noted that the missing item rate is used to quantify the degree of missing data in the standardized data in a specific category. The missing item rate directly reflects the integrity and availability of the data. In order to ensure that there is sufficient and reliable data available for use in subsequent steps, it is necessary to calculate the missing item rate threshold and perform an assignment operation on the standardized data whose missing item rate is higher than the missing item rate threshold. When performing the assignment operation, generally, the higher the degree of missing items, the greater the value assigned to it. At the same time, to ensure the uniformity and comparability of the assignment, the assignment value is generally between 0 and 1.

[0053] In step S43, a correlation matrix is ​​established according to the combined index values.

[0054] Specifically, the correlation of the index values ​​is calculated using a preset calculation method. Among them, the calculation method of the correlation is selected according to the specific application scenario. For example, the correlation coefficient can be used to calculate the linear correlation between the two index values. This method is simple, intuitive, and applicable to most cases; covariance is also a commonly used correlation calculation method, which can reflect the synergy of the two index values ​​in the process of change; mutual information, as a measurement method in information theory, can also effectively measure the correlation between the two index values, especially suitable for processing data with nonlinear relationships. Then the calculated correlation is sorted, and it is arranged as the rows and columns of the matrix according to certain rules to establish the correlation matrix. Exemplarily, the correlation coefficient is used in the present invention to calculate the linear correlation between the two index values.

[0055] It should be noted that the correlation matrix can intuitively show the degree of correlation and interaction between different indicator values. Each element of the correlation matrix represents the degree of correlation between two indicator values. These correlations can be positive (an increase in one indicator value is accompanied by an increase in another indicator value), negative (an increase in one indicator value is accompanied by a decrease in another indicator value) or no correlation (there is no clear correlation between the two indicator values). By using the correlation matrix for calculation and analysis, it is possible to quickly and accurately identify which indicator values ​​have strong correlations, as well as the positive and negative nature of these correlations, thereby obtaining a quantitative relationship between the data.

[0056] In step S5, a coefficient allocation matrix is ​​calculated based on the weight coefficients and the association matrix.

[0057] In one embodiment, the step of establishing a coefficient allocation matrix according to the weight coefficients and the association matrix comprises the following steps: S51, normalizing the correlation matrix and calculating eigenvalues ​​and eigenvectors; S52: Establish a coefficient allocation matrix according to the eigenvalue, the eigenvector and the weight coefficient.

[0058] In step S51, the correlation matrix is ​​normalized and the eigenvalue and eigenvector are calculated. The normalization is performed according to the following formula: in, The first Line The elements of the column, The first Line The elements of the column, n is the matrix dimension, represents the first Line Elements of a column.

[0059] Specifically, when calculating eigenvalues, first construct a characteristic equation related to the eigenvalue to be solved, then calculate the characteristic polynomial, and finally solve the characteristic polynomial to obtain several eigenvalues. When calculating eigenvectors, first substitute the obtained eigenvalues ​​into the characteristic equation, then use elimination, matrix decomposition and other methods to solve the linear equations, and finally solve the eigenvector.

[0060] It should be noted that the eigenvalues ​​obtained may contain duplicate values. When they are substituted into the eigenvector, the eigenvector will not be unique and duplicate values ​​will be generated. In practical applications, duplicate eigenvectors may bring unnecessary redundancy and complexity. Therefore, for duplicate eigenvalues ​​and eigenvectors, it is necessary to consider whether to perform duplicate removal operations based on computing requirements.

[0061] It should be noted that since the elements of the correlation matrix may come from different dimensions or have different value ranges, this may cause the influence of some elements in the matrix to be over-amplified, while the influence of other elements is relatively ignored. In order to overcome this limitation, the correlation matrix needs to be normalized to eliminate the influence of different dimensions or value ranges on the results, so as to more accurately reflect the degree of association between different variables. At the same time, the eigenvalue represents the importance or strength of different association patterns, and the eigenvector represents the specific form or direction of these association patterns. By calculating the eigenvalues ​​and eigenvectors, it is possible to identify which association patterns dominate the matrix and how these association patterns are distributed among different variables.

[0062] In step S52, a coefficient allocation matrix is ​​established according to the eigenvalue, the eigenvector and the weight coefficient. The coefficient allocation matrix is ​​established according to the following formula: in, represents the coefficient allocation matrix, Indicates The weight coefficients, represents the eigenvalue, Indicates The feature vectors.

[0063] It should be noted that the weight coefficient is used to measure the contribution of different eigenvalues ​​and eigenvectors to the overall system, and its value will directly affect the accuracy and reliability of the final result. When determining the weight coefficient, it is necessary to comprehensively consider the information of the eigenvalues ​​and eigenvectors and their relationship with each other. At the same time, the corresponding weight allocation is carried out in combination with specific application scenarios and business needs, because different application scenarios and business needs attach different importance to eigenvalues ​​and eigenvectors. In addition, in order to avoid excessive concentration or dispersion of the weight coefficient, it is also necessary to perform appropriate normalization processing on it, by converting data with different dimensions and different value ranges into data with the same dimension and value range, so as to facilitate subsequent analysis and comparison.

[0064] In step S6, based on the coefficient allocation matrix, rural education resource assessment data analysis results are generated and output.

[0065] Specifically, the coefficient allocation matrix quantitatively scores the key indicators of rural education resources (such as teaching staff, teaching facilities, curriculum, capital investment, etc.), and performs weighted summation according to their respective weight coefficients, thereby obtaining the comprehensive evaluation score of each rural education resource. Then, in the process of generating data analysis results, statistical analysis methods and data mining techniques are used to clean, preprocess and deeply mine the original data. Finally, through various forms such as charts and reports, the distribution characteristics, existing problems and improvement suggestions of rural education resources are intuitively displayed, and a rural education resource evaluation report with guiding significance and practical value is formed.

[0066] It should be noted that the interpretation and presentation of data analysis results should be clear, accurate and easy to understand. In the report, concise and clear language and charts should be used, and overly professional or complex terms and expressions should be avoided. At the same time, during data processing and analysis, privacy protection principles should be strictly followed to ensure that personal privacy and sensitive information are not leaked or abused. For data involving personal privacy, appropriate encryption and desensitization measures should be taken to protect it.

[0067] To facilitate understanding of the present invention, the working process of the present invention is described below using a relatively common scenario as an example.

[0068] In this embodiment, it is planned to establish a data analysis system based on rural education resource evaluation. The system aims to conduct a comprehensive and accurate evaluation of the current status of rural education resources based on the data to be evaluated, and propose an optimization plan that can be combined with resource allocation.

[0069] Step 1: The system obtains the data to be evaluated of rural education resources from relevant units.

[0070] Step 2: The system performs corresponding calculations on the data to be evaluated to obtain standardized data and scoring results.

[0071] Step 3: The system calculates the weight coefficient using a preset formula based on the standardized data and the scoring results.

[0072] Step 4: The system calculates the correlation matrix based on the standardized data.

[0073] Step 5: The system establishes a coefficient allocation matrix using a preset method based on the weight coefficients and the association matrix.

[0074] Step 6: The system generates and outputs rural education resource assessment data analysis results based on the coefficient allocation matrix.

[0075] Through the above steps, the data analysis system based on rural education resource evaluation can conduct a comprehensive and accurate evaluation of the current status of rural education resources according to the data to be evaluated, reduce reliance on subjective views, and propose corresponding optimization plans.

[0076] In summary, the present invention provides a data analysis method based on rural education resource evaluation, including obtaining data to be evaluated of rural education resources; calculating the data to be evaluated to obtain standardized data and scoring results; calculating the weight coefficient according to the standardized data and the scoring results; calculating the association matrix according to the standardized data; calculating the coefficient allocation matrix according to the weight coefficient and the association matrix; generating and outputting the rural education resource evaluation data analysis results according to the coefficient allocation matrix. The method obtains the data to be evaluated of rural education resources, processes the data to be evaluated accordingly, and then uses the association matrix to quantify the association relationship between different types of data, then establishes the coefficient allocation matrix according to the association matrix and the calculated weight, and finally generates and outputs the rural education resource evaluation data analysis results according to the coefficient allocation matrix. The method comprehensively considers multiple dimensions in the data to be evaluated, such as basic information of the school, educational facilities, teaching staff, educational achievements, etc., and performs evaluation and allocation optimization, thereby realizing a comprehensive and accurate reflection of the status of educational resources and the optimization of the configuration of educational resources.

[0077] Reference Figure 2 The second embodiment of the present invention provides a data analysis system based on rural education resource evaluation, including: A data acquisition module is used to obtain the data to be evaluated of rural education resources; A standardization and scoring module, used to calculate the data to be evaluated to obtain standardized data and scoring results; The standardization and scoring module includes an initial standardization module, which is used to standardize the data to be evaluated to obtain initial standardized data; a standardization module, which is used to judge the positivity of the initial standardized data and calculate the standardized data; and a scoring module, which is used to score the standardized data to obtain a scoring result. A weight calculation module, used to calculate a weight coefficient according to the standardized data and the scoring result; The weight calculation module includes an index entropy calculation module, which is used to calculate the standardized data and the scoring result to obtain the index entropy; an entropy reduction number calculation module, which is used to calculate according to the index entropy to obtain the entropy reduction number; and a weight coefficient calculation module, which is used to calculate according to the entropy reduction number to obtain the weight coefficient. A matrix calculation module, used for calculating a correlation matrix according to the standardized data; The matrix calculation module includes a missing item rate calculation module, which is used to calculate according to the standardized data to obtain the missing item rate and the missing item rate threshold; an index value combination module, which is used to assign values ​​to the standardized data whose missing item rate is lower than the missing item rate threshold, and combine them with the standardized data whose missing item rate is higher than the missing item rate threshold to obtain the index value; and an association matrix establishment module, which is used to establish an association matrix according to the index value; A coefficient allocation matrix calculation module, used for calculating a coefficient allocation matrix according to the weight coefficient and the association matrix; The coefficient allocation matrix calculation module includes a characteristic calculation module, which is used to normalize the correlation matrix and calculate the eigenvalue and eigenvector; a coefficient allocation matrix establishment module, which is used to establish the coefficient allocation matrix according to the eigenvalue, the eigenvector and the weight coefficient; The result generation module is used to generate and output the rural education resource evaluation data analysis results according to the coefficient allocation matrix.

[0078] It should be noted that the data analysis system based on rural education resource assessment provided in an embodiment of the present invention is used to execute all the process steps of the data analysis method based on rural education resource assessment in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, and therefore will not be repeated here.

[0079] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data analysis program based on rural education resource evaluation. When the processor executes the computer program, the steps in the above-mentioned data analysis method based on rural education resource evaluation are implemented, such as Figure 1Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned system embodiments are realized, such as the coefficient allocation matrix calculation module.

[0080] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0081] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0082] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0083] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0084] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0085] It should be noted that the system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the system embodiment provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0086] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data analysis method based on rural education resource evaluation, characterized in that: include: Obtain data on rural education resources to be evaluated; Calculating the data to be evaluated to obtain standardized data and scoring results; Calculate a weight coefficient according to the standardized data and the scoring result; According to the standardized data, a correlation matrix is ​​calculated; Calculate a coefficient allocation matrix according to the weight coefficients and the association matrix; According to the coefficient allocation matrix, the rural education resource evaluation data analysis results are generated and output.

2. The data analysis method based on rural education resource evaluation according to claim 1 is characterized in that: The step of calculating the data to be evaluated to obtain standardized data and scoring results includes: Performing standardization processing on the data to be evaluated to obtain initial standardized data; Determining the positivity of the initial standardized data and calculating the standardized data; Scoring the standardized data to obtain a scoring result; The initial standardized data is obtained by calculating according to the following formula: in, Indicates The initial standardized data, Indicates The data to be evaluated, represents the average value of the data to be evaluated, represents the standard deviation of the data to be evaluated; The standardized data is calculated according to the following formula: in, Indicates The standardized data, Indicates The initial standardized data.

3. The data analysis method based on rural education resource evaluation according to claim 1 is characterized in that: The calculating a weight coefficient according to the standardized data and the scoring result includes: Calculating the standardized data and the scoring result to obtain an indicator entropy; Calculate according to the index entropy to obtain the entropy reduction; Calculate according to the entropy reduction number to obtain a weight coefficient; The index entropy is calculated according to the following formula: in, Indicates The entropy of the indicator is Indicates The standardized data, Indicates The scoring results are: represents the total number of entropies of the indicator; The entropy reduction number is calculated according to the following formula: in, Indicates The entropy reduction number, Indicates The entropy of the indicator.

4. The data analysis method based on rural education resource evaluation according to claim 1 is characterized in that: The step of calculating the association matrix according to the standardized data includes: Calculate according to the standardized data to obtain the missing item rate and the missing item rate threshold; Assigning a value to the standardized data whose missing item rate is lower than the missing item rate threshold, and combining the value with the standardized data whose missing item rate is higher than the missing item rate threshold to obtain an indicator value; According to the index values, a correlation matrix is ​​established.

5. The data analysis method based on rural education resource evaluation according to claim 1 is characterized in that: The step of establishing a coefficient allocation matrix according to the weight coefficients and the association matrix comprises: Normalizing the correlation matrix and calculating eigenvalues ​​and eigenvectors; Establishing a coefficient allocation matrix according to the eigenvalue, the eigenvector and the weight coefficient; Normalization is performed according to the following formula: in, The first Line Elements of the column, The first Line Elements of the column, n is the matrix dimension, represents the first Line Elements of a column.

6. The data analysis method based on rural education resource evaluation according to claim 2 is characterized in that: The step of scoring the standardized data to obtain a scoring result includes: The scoring result is calculated according to the following formula: in, Indicates The scoring results are: Indicates The standardized data, represents the standardized data, and Represent the minimum and maximum values ​​in the normalized data set, respectively.

7. The data analysis method based on rural education resource evaluation according to claim 3 is characterized in that: The calculation according to the entropy reduction number to obtain the weight coefficient includes: The weight coefficient is calculated according to the following formula: in, Indicates the The weight coefficients, Indicates The entropy reduction number, Represents the total number of the entropy reduction numbers.

8. The data analysis method based on rural education resource evaluation according to claim 4 is characterized in that: The step of calculating according to the standardized data to obtain the missing item rate and the missing item rate threshold comprises: The missing item rate and the missing item rate threshold are calculated according to the following formula: in, Indicates The missing item rate, represents the missing item rate threshold, represents the total number of the standardized data, Indicates The standardized data, Represents the total number of observations of the standardized data.

9. The data analysis method based on rural education resource evaluation according to claim 5 is characterized in that: The step of establishing a coefficient allocation matrix according to the eigenvalue, the eigenvector and the weight coefficient comprises: The coefficient allocation matrix is ​​established according to the following formula: in, represents the coefficient allocation matrix, Indicates The weight coefficients, represents the eigenvalue, Indicates The feature vectors.

10. A data analysis system based on rural education resource evaluation, characterized in that: include: A data acquisition module is used to obtain the data to be evaluated of rural education resources; A standardization and scoring module, used to calculate the data to be evaluated to obtain standardized data and scoring results; A weight calculation module, used to calculate a weight coefficient according to the standardized data and the scoring result; A missing item rate and matrix calculation module, used to calculate the correlation matrix according to the standardized data; A coefficient allocation matrix calculation module, used for calculating a coefficient allocation matrix according to the weight coefficient and the association matrix; The result generation module is used to generate and output the rural education resource evaluation data analysis results according to the coefficient allocation matrix.

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