College entrance examination voluntary reporting recommendation method and system

Abstract

The invention relates to the field of application filling auxiliary technologies, in particular to a college entrance examination application filling recommendation method, which specifically comprises the following steps of: taking a score, a rank and an examination subject as input parameters to obtain an application recommendation result; screening the voluntary recommendation results according to the intention as a condition; generating a screened recommended college as a first-level recommendation result; entering a first-level recommendation result to obtain a recommendation specialty as a second-level recommendation result; and after entering a second-level recommendation result, generating different plates of course categories, employment information and future development plans which need to be learned by the corresponding specialty, so as to present learning, working and future development trends which may be involved in the recommended specialty in the future, so that life after admission of the recommended specialty can have a preliminary cognition, fall is reduced, and the experience of the recommended specialty is improved. And the accuracy and effectiveness of volunteer filling recommendation are improved.

Description

Technical Field

[0001] The present application relates to the field of volunteer application assistance technology, and in particular, to a method and system for recommending college entrance examination volunteer application. Background Art

[0002] Filling out the college entrance examination application is a major event that determines a person's future development, so people attach great importance to it. Except for some top students or families engaged in related education industries, ordinary families do not know much about the application. It can even be said that they fill out the application blindly, which has a great impact on their children's future life. In order to reduce the blindness of most families when filling out the application, some recommendation systems used in the application have been designed for use to provide assistance for the application.

[0003] For example, a method and system for assisting in recommending college entrance examination volunteers, with publication number CN113407855A, obtains corresponding sprint scores, safe scores and bottom scores based on different algorithms according to the corresponding scores and rankings, and outputs colleges and majors with corresponding scores as results.

[0004] Regarding the above-mentioned related technologies, if only students' interest in majors is considered when filling out their applications, mismatches may easily occur when students do not understand the applied majors, making it difficult to achieve accurate and effective application recommendations. Summary of the invention

[0005] In order to make more accurate and effective volunteer application recommendations, this application provides a college entrance examination volunteer application recommendation method and system.

[0006] In the first aspect, the college entrance examination application recommendation method provided in this application adopts the following technical solution.

[0007] A method for recommending filling out college entrance examination volunteers includes the following steps.

[0008] S1. Use scores, rankings and elective subjects as input parameters to obtain volunteer recommendation results; S2. Screen the volunteer recommendation results based on intention as a condition; S3, generate the recommended institutions after screening as the first-level recommendation results; S4. Enter the first-level recommendation results to obtain the recommended majors as the second-level recommendation results; S5. After entering the secondary recommendation results, different sections are generated for the course categories, employment information and future development plans required for the corresponding major.

[0009] By adopting the above technical solution, when checking the majors and schools that you are interested in and correspond to the elective subjects, you can also analyze the course categories to be studied in the major, future employment and development after graduation, so as to minimize the sense of gap in study and work after admission to the recommended major, which helps to improve the accuracy and effectiveness of volunteer recommendations.

[0010] Optionally, the employment information in S5 includes a job position-number chart of graduates of the recommended majors in recent years after graduation, and a job position-number chart of corresponding graduates who have graduated for more than two years is displayed separately according to the two situations at the time of graduation and at present.

[0011] By adopting the above technical solution, the employment positions of students after graduation and the corresponding ratio of the number of people in employment positions two years after graduation can be displayed for reference by volunteers, so that they can better understand the jobs that they are likely to engage in in the future according to the applied major.

[0012] Optionally, the future development plan in S5 includes the certificates that can be obtained for the recommended major, a chart of the number of recruits-positions in recent years, and a chart of the number of companies in the industry corresponding to the recommended major-year.

[0013] By adopting the above technical solution, the development of the industry corresponding to the recommended major can be reflected through the number of companies and the number of recruits, so that volunteers can understand the development trend of the industry in which the corresponding major is located in recent years, and can make predictions about the development of the major in the next few years.

[0014] Optionally, the step of obtaining volunteer recommendation results in S1 includes: S11. Divide the number of applicants for the college entrance examination this year by the number of applicants for the college entrance examination in the corresponding year to obtain the conversion ratio for the corresponding year, and multiply the minimum admission score for each school and major in the corresponding year by the conversion ratio to obtain the preliminary score threshold for each school and major in the corresponding year; S12. Multiply the lowest admission ranking of each school and each major in the corresponding year by the conversion ratio to obtain the preliminary ranking threshold of each school and each major in the corresponding year; S13, averaging the preliminary score thresholds of the same school and the same major in each year to obtain the recommended score threshold, and averaging the preliminary ranking thresholds of the same school and the same major in each year to obtain the recommended ranking threshold; S14. Schools and majors whose recommended score threshold and recommended ranking threshold are both lower than the entered score and ranking are safe options, schools and majors whose recommended ranking threshold is higher than the entered ranking are not recommended, and schools and majors whose recommended score threshold is higher than the entered score and whose recommended ranking threshold is lower than the entered ranking are risky options; S15. Sort safe options and risky options from small to large according to the difference between the recommended ranking threshold and the input ranking as the voluntary recommendation result.

[0015] By adopting the above technical solution, the admission rankings and scores of previous years are converted into corresponding proportions according to the different number of applicants each year to obtain corresponding safe options and risky options, so as to output the schools and corresponding majors that can be admitted as accurately as possible for reference.

[0016] Optionally, in S15, the safe options and risky options for which the difference between the recommended ranking threshold and the input ranking is greater than the minimum value are used as minimum recommended options, and the minimum value ranges from 1000 to 1500.

[0017] By adopting the above technical solution, schools and corresponding majors with a high probability of being admitted are recommended to volunteers, thereby providing volunteers with more options.

[0018] Optionally, the intention in S2 includes the location of the desired school, personal preferences, and whether there are graduate and doctoral training centers.

[0019] By adopting the above technical solution, the volunteer recommendation results can be screened and the schools and majors that the applicants are not interested in can be removed to reduce the time required for volunteer application.

[0020] Optionally, the intention acquisition step includes: S21, inputting text related to the intention; S22, segmenting the text into words or phrases; S23, obtaining the word frequency corresponding to each word or phrase by counting; S24, filter out words or phrases with a frequency higher than a threshold as intended keywords and output them; S25. Evaluate the intended keywords. If the evaluation is positive, use the intended keywords as screening conditions for volunteer recommendation results; otherwise, return to S23 and change the statistical method until the intended keywords obtain a positive evaluation.

[0021] By adopting the above technical solution, volunteers only need to input relevant intentions in the text language organized by themselves to generate intention keywords that meet the standard screening conditions, so that volunteers can obtain appropriate screening conditions.

[0022] Optionally, when the intended keywords output after all statistical methods are replaced in S25 have not yet obtained a positive evaluation, the intended keywords in S24 are generated into new intended keywords by using a machine learning model and output until the intended keywords obtain a positive evaluation.

[0023] By adopting the above technical solution, it is possible to generate intended keywords that meet the requirements as much as possible.

[0024] Optionally, a database of data corresponding to each school, each major and different sections is established in S5, with each major as the first label, and each school as the second label included in the first label. Employment information and future development plans are matched with the first label, and course categories are matched with the second label. At the same time, the database crawls network data at a preset time to update the database.

[0025] By adopting the above technical solution, the database can be updated in a timely manner to provide a real-time recommendation result to the volunteers every year, and the major is used as the first label so that all schools with the same major can be matched with the same employment information and future development plan. The two corresponding data can reduce the amount of data in the database and reduce the time for data retrieval.

[0026] On the second aspect, the college entrance examination volunteer application recommendation system provided in this application adopts the following technical solution.

[0027] A college entrance examination volunteer application recommendation system operates according to the above-mentioned college entrance examination volunteer application recommendation method, including a user terminal for inputting scores and rankings and displaying primary recommendation results, secondary recommendation results, various sections and intention keywords, and a server for processing the input scores and rankings to output volunteer recommendation results. The server includes an analysis and extraction module for analyzing and processing the intention text input into the user terminal to obtain corresponding keywords, a volunteer recommendation module for processing the input scores and rankings to obtain volunteer recommendation results, a screening module for screening volunteer recommendation results according to positively evaluated keywords to obtain primary recommendation results and secondary recommendation results, and a storage module for establishing a database.

[0028] By adopting the above technical solution, volunteers can operate on each user terminal that can access the server to obtain recommended colleges and majors and analyze the course categories to be studied in the recommended majors, future employment and development after graduation.

[0029] In summary, the present application includes at least the following beneficial effects: Minimizing the sense of gap between study and work after admission to the recommended major will help improve the accuracy and effectiveness of volunteer recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flowchart of the steps of a recommended method for filling out college entrance examination volunteers in this application; Figure 2 It is a flowchart of the steps to obtain volunteer recommendation results; Figure 3 It is the system block diagram of the database; Figure 4 This is a module block diagram of a college entrance examination volunteer application recommendation system in this application. DETAILED DESCRIPTION

[0031] The present application is further described in detail below in conjunction with the accompanying drawings.

[0032] This application embodiment discloses a method for recommending college entrance examination volunteers, referring to Figure 1 , specifically including the following steps.

[0033] S1. Use scores, rankings and elective subjects as input parameters to obtain volunteer recommendation results; S2. Screen the volunteer recommendation results based on intention as a condition; S3, generate the recommended institutions after screening as the first-level recommendation results; S4. Enter the first-level recommendation results to obtain the recommended majors as the second-level recommendation results; S5. After entering the secondary recommendation results, different sections are generated for the course categories, employment information and future development plans required for the corresponding major. Each section can be entered to display detailed information.

[0034] Reference Figure 2 , the specific steps for obtaining volunteer recommendation results in S1 are: S11. Divide the number of applicants for the college entrance examination this year by the number of applicants for the college entrance examination in the corresponding year to obtain the conversion ratio for the corresponding year, and multiply the minimum admission score for each school and major in the corresponding year by the conversion ratio to obtain the preliminary score threshold for each school and major in the corresponding year; S12. Multiply the lowest admission ranking of each school and each major in the corresponding year by the conversion ratio to obtain the preliminary ranking threshold of each school and each major in the corresponding year; S13, averaging the preliminary score thresholds of the same school and the same major in each year to obtain the recommended score threshold, and averaging the preliminary ranking thresholds of the same school and the same major in each year to obtain the recommended ranking threshold; S14. Schools and majors whose recommended score threshold and recommended ranking threshold are both lower than the entered score and ranking are safe options, schools and majors whose recommended ranking threshold is higher than the entered ranking are not recommended, and schools and majors whose recommended score threshold is higher than the entered score and whose recommended ranking threshold is lower than the entered ranking are risky options; S15. Sort safe options and risky options from small to large according to the difference between the recommended ranking threshold and the input ranking as the voluntary recommendation result.

[0035] In addition, for the safe options and risky options whose difference between the recommended ranking threshold and the input ranking is greater than the minimum value, the minimum value is 1000 to 2000. When filling in the application form, there are two risky options, two to three safe options, and one to two minimum recommended options. The order of each selected school major in the application form is obtained in the order of risky options, safe options, and minimum options.

[0036] The intention in S2 includes the region of the school you want to attend, personal preferences, whether there are graduate and doctoral training centers, etc. In addition, the specific steps for obtaining the intention are: S21, inputting text related to the intention; S22, segmenting the text into words or phrases; S23, obtaining the word frequency corresponding to each word or phrase by statistics, and the statistical method may be TF-IDF and NLP, etc.; S24, filter out words or phrases with a frequency higher than a threshold as intended keywords and output them; S25. Evaluate the intended keywords. If they are positively evaluated, use the intended keywords as screening conditions for volunteer recommendation results; otherwise, return to S23 and change the statistical method until the intended keywords obtain a positive evaluation. When the intended keywords output after changing all statistical methods have not obtained a positive evaluation, use the machine learning model to generate new intended keywords for output from the intended keywords in S24 until the intended keywords obtain a positive evaluation. The machine learning model can be a Seq2Seq model, Pointer-Generator Network, UniLM, T5, BART, PEGASUS, a multimodal generation model, and a non-autoregressive sequence generation model, etc.

[0037] The course categories in S5 include the names of the main courses that need to be studied during school for the corresponding schools and majors. Employment information includes a chart of the positions and the number of people employed by graduates of the recommended majors in recent years. Specifically, it can be a bar chart with the horizontal axis being the position and the vertical axis being the number of people. The position-number chart of the corresponding graduates who have graduated for more than two years is displayed according to the two situations at the time of graduation and today. The future development plan includes a chart of the number of recruits-positions in recent years and a chart of the number of companies in the industry corresponding to the recommended majors-years. The chart of the number of recruits-positions can be a bar chart with the horizontal axis being the position and the vertical axis being the number of recruits, and each position corresponds to the number of recruits in different years in recent years. The chart of the number of companies-years can be a bar chart with the horizontal axis being the year and the vertical axis being the number of companies.

[0038] Reference Figure 3, establish a database consisting of data corresponding to each school, major and different sections, with each major as the first label, each school as the second label included in the first label, employment information and future development plans matching the first label, and course categories matching the second label. In addition, the database crawls network data at a preset time to update the database. For example, the database is updated every day starting from two months before the volunteer application, and the crawling is performed by data crawling software.

[0039] The implementation principle of a method for recommending college entrance examination volunteers in an embodiment of the present application is as follows: after inputting scores, rankings and elective subjects, the first-level recommendation results of the corresponding schools are obtained, and the first-level recommendation results of interest are selected to view the second-level recommendation results of the corresponding majors. After selecting the corresponding major, different sections of course categories, employment information and future development plans can be viewed to better understand the relevant content of the relevant majors in the volunteer application.

[0040] The present application also discloses a college entrance examination volunteer application recommendation system. Figure 4 , including a user terminal for inputting scores and rankings and displaying the first-level recommendation results, second-level recommendation results, various sections and intended keywords, and a server for processing the input scores and rankings to output volunteer recommendation results. The server includes an analysis and extraction module for analyzing and processing the intended text input into the user terminal to obtain corresponding keywords, a volunteer recommendation module for processing the input scores and rankings to obtain volunteer recommendation results, a screening module for screening the volunteer recommendation results according to positively evaluated keywords to obtain first-level recommendation results and second-level recommendation results, and a storage module for establishing a database.

[0041] The implementation principle of the college entrance examination volunteer application recommendation system in the first embodiment of the present application is: select the corresponding major to view the different sections of course categories, employment information and future development plans, so as to better understand the relevant content of the volunteer application related major.

[0042] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for recommending college entrance examination volunteers, characterized in that: The specific steps include: S1. Use scores, rankings and elective subjects as input parameters to obtain volunteer recommendation results; S2. Screen the volunteer recommendation results based on intention as a condition; S3, generate the recommended institutions after screening as the first-level recommendation results; S4. Enter the first-level recommendation results to obtain the recommended majors as the second-level recommendation results; S5. After entering the secondary recommendation results, different sections are generated for the course categories, employment information and future development plans required for the corresponding major.

2. A method for recommending college entrance examination volunteers according to claim 1, characterized in that: The employment information in S5 includes a job position-number chart of graduates of the recommended majors in recent years after graduation, and a job position-number chart of corresponding graduates who have graduated for more than two years is displayed according to the two situations at the time of graduation and the current situation.

3. A method for recommending college entrance examination volunteers according to claim 1, characterized in that: The future development plan in S5 includes a chart of the number of recruits in recent years versus positions and a chart of the number of companies in the industry corresponding to the recommended majors versus years.

4. A method for recommending college entrance examination volunteers according to claim 1, characterized in that: The step of obtaining the volunteer recommendation result in S1 includes: S11. Divide the number of applicants for the college entrance examination this year by the number of applicants for the college entrance examination in the corresponding year to obtain the conversion ratio for the corresponding year, and multiply the minimum admission score for each school and major in the corresponding year by the conversion ratio to obtain the preliminary score threshold for each school and major in the corresponding year; S12. Multiply the lowest admission ranking of each school and each major in the corresponding year by the conversion ratio to obtain the preliminary ranking threshold of each school and each major in the corresponding year; S13, averaging the preliminary score thresholds of the same school and the same major in each year to obtain the recommended score threshold, and averaging the preliminary ranking thresholds of the same school and the same major in each year to obtain the recommended ranking threshold; S14. Schools and majors whose recommended score threshold and recommended ranking threshold are both lower than the entered score and ranking are safe options, schools and majors whose recommended ranking threshold is higher than the entered ranking are not recommended, and schools and majors whose recommended score threshold is higher than the entered score and whose recommended ranking threshold is lower than the entered ranking are risky options; S15. Sort safe options and risky options from small to large according to the difference between the recommended ranking threshold and the input ranking as the voluntary recommendation result.

5. A method for recommending college entrance examination volunteers according to claim 4, characterized in that: In S15, the safe options and risky options whose difference between the recommended ranking threshold and the input ranking is greater than the minimum value are used as minimum recommended options, and the minimum value ranges from 1000 to 1500.

6. A method for recommending college entrance examination volunteers according to claim 1, characterized in that: The intention in S2 includes the location of the desired school, personal preferences, and whether there are graduate and doctoral training centers.

7. A method for recommending college entrance examination volunteers according to claim 6, characterized in that: The intention acquisition step includes: S21, inputting text related to the intention; S22, segmenting the text into words or phrases; S23, obtaining the word frequency corresponding to each word or phrase by counting; S24, filter out words or phrases with a frequency higher than a threshold as intended keywords and output them; S25. Evaluate the intended keywords. If the evaluation is positive, use the intended keywords as screening conditions for volunteer recommendation results; otherwise, return to S23 and change the statistical method until the intended keywords obtain a positive evaluation.

8. A method for recommending college entrance examination volunteers according to claim 7, characterized in that: When the intended keywords output after all statistical methods are replaced in S25 have not obtained a positive evaluation, the intended keywords in S24 are generated into new intended keywords by using the machine learning model and output until the intended keywords obtain a positive evaluation.

9. A method for recommending college entrance examination volunteers according to claim 1, characterized in that: In the S5, a database of data corresponding to each school, each major and different sections is established. Each major is used as the first label, and each school is included in the first label as the second label. Employment information and future development plans are matched with the first label, and course categories are matched with the second label. At the same time, the database crawls network data at a preset time to update the database.

10. A college entrance examination volunteer application recommendation system, operating according to a college entrance examination volunteer application recommendation method according to any one of claims 1 to 9, characterized in that: It includes a user terminal for inputting scores and rankings and displaying primary recommendation results, secondary recommendation results, various sections and intended keywords, and a server for processing the input scores and rankings to output volunteer recommendation results. The server includes an analysis and extraction module for analyzing and processing the intended text input into the user terminal to obtain corresponding keywords, a volunteer recommendation module for processing the input scores and rankings to obtain volunteer recommendation results, a screening module for screening the volunteer recommendation results according to positively evaluated keywords to obtain primary recommendation results and secondary recommendation results, and a storage module for establishing a database.

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