Intelligent recommendation system and method for college entrance examination volunteer filling based on artificial intelligence

Abstract

The invention discloses an intelligent recommendation system and method for college entrance examination application filling based on artificial intelligence, and belongs to the technical field of college entrance examination application filling, the system comprises a data preparation and integration module, the data preparation and integration module is in signal connection with a user information input module, and the user information input module is in signal connection with an intelligent recommendation engine module; and the intelligent recommendation engine module is in signal connection with the risk assessment module. According to the system, through the data preparation and integration module, score lines, enrollment plans, professional settings and employment trend information over the years are collected from authoritative channels such as the Ministry of Education, examination institutes of all provinces and official websites of colleges, so that data comprehensiveness and accuracy of filled information are ensured; the system can accurately match suitable colleges and majors according to scores, interests, occupational tendencies and family expectations of students, avoids the situation of'one score waste 'or'high scores and low scores', and improves the accuracy of application recommendation.

Description

Technical Field

[0001] The present invention belongs to the technical field of filling out college entrance examination volunteers, and specifically relates to an intelligent recommendation system and method for filling out college entrance examination volunteers based on artificial intelligence. Background Art

[0002] The existing college entrance examination application method and system have the following problems in actual application: First, students and parents often face incomplete information and scattered data when filling out their applications. Information such as admissions policies, historical scores, and professional settings is scattered across various channels (such as the Ministry of Education website and university websites), lacking a unified, authoritative integration platform. Second, traditional application relies heavily on experience or subjective judgment, lacking a systematic analysis of students' interests, subject strengths, and career preferences. Furthermore, most students have a vague understanding of their future career direction when filling out their applications and fail to effectively link their major choices with their career development. Furthermore, resources such as family economic conditions and social connections are not systematically considered in the application process, causing some students to miss out on more suitable development paths. Summary of the Invention

[0003] The purpose of the present invention is to provide an artificial intelligence-based intelligent recommendation system and method for filling out college entrance examination applications to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an artificial intelligence-based intelligent recommendation system and method for filling out college entrance examination volunteers, comprising a data preparation and integration module, wherein the data preparation and integration module is signal-connected to a user information input module, and the user information input module is signal-connected to an intelligent recommendation engine module, while the intelligent recommendation engine module is signal-connected to a risk assessment module, and the risk assessment module is signal-connected to a simulation filling and management module, and the simulation filling and management module is signal-connected to an information query and education guidance module, while the information query and education guidance module is signal-connected to a real-time update and policy update module, and the real-time update and policy update module is signal-connected to a feedback module;

[0005] As a further preferred embodiment of the present technical solution: the data preparation and integration module includes a data collection subunit, a data preprocessing unit, and a family resource assessment unit, and the data preparation and integration module are signal-connected to the data collection subunit, the data preprocessing unit, and the family resource assessment unit, and the data preparation and integration module integrates auxiliary information such as career trends and salary levels from third-party sources such as educational consulting agencies and industry reports;

[0006] As a further preferred embodiment of the present technical solution: the data preparation and integration module includes: S1: collecting information on scores, enrollment plans, major settings, and employment trends from authoritative channels such as the Ministry of Education, provincial and municipal examination authorities, and official websites of major universities, and removing erroneous values, duplicate records, and incomplete information from the original data to ensure data quality;

[0007] As a further preferred embodiment of the present technical solution: the intelligent recommendation engine module includes a demand analysis unit, a data matching and screening sub-unit, and a career development planning unit, and the intelligent recommendation engine module is signal-connected to the demand analysis unit, the data matching and screening sub-unit, and the career development planning unit. At the same time, the career development planning unit can provide guidance on learning paths and future career development directions during college, so that students can plan their studies and careers from the beginning to the end;

[0008] As a further preferred embodiment of the present technical solution: the user information input module includes: S2: inputting student and parent information, including but not limited to student's grades, interests, future career orientation, family expectations, and then transmitting the relevant information to the recommendation module;

[0009] As a further preferred embodiment of the present technical solution: the intelligent recommendation engine module includes: S3: focusing on student career development, helping students think about their future career direction and choose appropriate majors and schools accordingly;

[0010] As a further preferred embodiment of the present technical solution: the risk assessment module includes: S4: performing risk assessment on the recommendation results, identifying possible risk points, and giving corresponding avoidance suggestions, pointing out potential risk points, and giving optimization suggestions to reduce the possibility of slippage;

[0011] As a further preferred embodiment of the present technical solution: the information query and education guidance module includes: S5: allowing candidates to try different volunteer combinations, save different plans, and compare the advantages and disadvantages of different plans;

[0012] As a further preferred embodiment of the present technical solution: the simulation application and management module includes: S6: providing detailed introduction information of colleges and majors, including the score lines and employment prospects of previous years;

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] 1. In the present invention, through the data preparation and integration module, the score lines, enrollment plans, major settings and employment trend information of previous years are collected from authoritative channels such as the Ministry of Education, provincial examination institutes, and official websites of universities, ensuring the comprehensiveness and accuracy of the data filled in. At the same time, through the intelligent recommendation engine module, the system can accurately match suitable colleges and majors according to students' grades, interests, career inclinations and family expectations, avoiding the situation of "wasting a point" or "high scores but getting a low job", and improving the accuracy of application recommendations.

[0015] 2. In the present invention, through the career development planning unit, it can help students think about their future career direction and choose majors and colleges with the end in mind, avoiding confusion and waste of time during college due to unclear direction. Secondly, it can analyze the correlation between high school subject interests and university majors, help students choose majors that suit their own subject strengths, and reduce the risk of "not knowing what to learn" or "not using what to learn".

[0016] 3. In the present invention, through the simulation filling and management module, students can try different volunteer combinations, compare the advantages and disadvantages of the plans, and reduce the possibility of filling errors. At the same time, the system can comprehensively consider family resources (such as economic conditions and social relationships) and recommend volunteer plans for students that can both meet the needs of further study and utilize family advantages. Secondly, through the demand analysis unit, the system balances student interests, family expectations and social needs to generate the optimal volunteer list.

[0017] 4. In the present invention, the real-time update and policy update modules can ensure that the system data is synchronized with the latest admissions policies, avoiding filling errors caused by policy changes. Secondly, it helps students choose majors with good future employment prospects. At the same time, the information query and education guidance modules can provide detailed introductions to majors and careers to help students broaden their horizons, clarify their future career paths, and avoid blindly following trends. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a process of the present invention's intelligent recommendation system and method for filling out college entrance examination applications based on artificial intelligence Figure 1 ;

[0019] Figure 2 This is the principle process of the present invention's intelligent recommendation system and method for filling out college entrance examination applications based on artificial intelligence Figure 1 ;

[0020] Figure 3 The principle of the present invention is an intelligent recommendation system and method for filling out college entrance examination volunteers based on artificial intelligence Figure 2 ;

[0021] Figure 4 This is a partial process of the present invention's intelligent recommendation system and method for filling out college entrance examination applications based on artificial intelligence Figure 1 ;

[0022] Figure 5 This is a partial process of the present invention's intelligent recommendation system and method for filling out college entrance examination applications based on artificial intelligence Figure 2 ;

[0023] Figure 6 This is a process of the present invention's intelligent recommendation system and method for filling out college entrance examination applications based on artificial intelligence Figure 2 . DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0025] Example

[0026] See also Figures 1-6 As shown, the present invention provides a technical solution: an artificial intelligence-based intelligent recommendation system and method for filling out college entrance examination volunteers, including a data preparation and integration module, the data preparation and integration module signal is connected to the user information input module, and the user information input module signal is connected to the intelligent recommendation engine module, while the intelligent recommendation engine module signal is connected to the risk assessment module, the risk assessment module signal is connected to the simulation filling and management module, and the simulation filling and management module signal is connected to the information query and education guidance module, while the information query and education guidance module signal is connected to the real-time update and policy update module, and the real-time update and policy update module signal is connected to the feedback module;

[0027] In this embodiment, specifically: the data preparation and integration module includes a data collection subunit, a data preprocessing unit, and a family resource assessment unit, and the data preparation and integration module are all signal-connected to the data collection subunit, the data preprocessing unit, and the family resource assessment unit. At the same time, the data preparation and integration module can capture relevant data such as professional settings, curriculum systems, and graduate employment status from the official websites of major universities;

[0028] In this embodiment, specifically: the data preparation and integration module includes: S1: collecting information on historical scores, enrollment plans, major settings, and employment trends from authoritative channels such as the Ministry of Education, provincial and municipal examination authorities, and official websites of major universities, as well as information on family background, regional preferences, and family resources;

[0029] In this embodiment, specifically: the intelligent recommendation engine module includes a demand analysis unit, a data matching and screening sub-unit, and a career development planning unit, and the intelligent recommendation engine module is signal-connected to the demand analysis unit, the data matching and screening sub-unit, and the career development planning unit. Secondly, the demand analysis unit can clarify the user's priority, such as score priority, major priority, region priority, or school level priority, and the data matching and screening sub-unit can quickly screen out a list of schools and majors that meet the conditions based on the user's score and other basic information;

[0030] In this embodiment, specifically: the user information input module includes: S2: student and parent information input, including but not limited to student's grades, interests, future career orientation, and family expectations;

[0031] In this embodiment, specifically: the intelligent recommendation engine module includes: S3: focusing on student career development, helping students think about their future career direction and choose suitable majors and schools accordingly, and using machine learning algorithms to analyze the information input by students and combine it with data in the database to provide personalized school and major recommendations;

[0032] In this embodiment, specifically, the risk assessment module includes: S4: performing risk assessment on the recommendation results, identifying possible risk points, and providing corresponding avoidance suggestions, and using the calculation formula: Suppose the standard deviation of a school's admission score over the years is σ, the candidate's college entrance examination score is G, and the school's admission score last year is L, then the admission risk is R.

[0033] In this embodiment, specifically: the information query and education guidance module includes: S5: allowing candidates to try different volunteer combinations, save different plans, and compare the advantages and disadvantages of different plans. At the same time, through the college entrance examination volunteer application process, it stimulates students' active thinking ability, broadens their horizons, and enhances their understanding of future planning.

[0034] In this embodiment, specifically: the simulation application and management module includes: S6: providing detailed introduction information of colleges and majors, including the score lines of previous years and employment prospects, and being able to provide a real application environment, allowing users to choose schools and majors just like formal application, and supporting users to save multiple different application plans for easy viewing and comparison in the future.

[0035] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based intelligent recommendation system and method for filling out college entrance examination applications, including a data preparation and integration module, characterized by: The data preparation and integration module signal is connected to the user information input module, and the user information input module signal is connected to the intelligent recommendation engine module. At the same time, the intelligent recommendation engine module signal is connected to the risk assessment module. The risk assessment module signal is connected to the simulation reporting and management module, and the simulation reporting and management module signal is connected to the information query and education guidance module. At the same time, the information query and education guidance module signal is connected to the real-time update and policy update module, and the real-time update and policy update module signal is connected to the feedback module.

2. The AI-based intelligent recommendation system and method for filling out college entrance examination applications according to claim 1 is characterized by: The data preparation and integration module includes a data collection subunit, a data preprocessing unit, and a family resource evaluation unit, and the data preparation and integration module are signal-connected to the data collection subunit, the data preprocessing unit, and the family resource evaluation unit.

3. The AI-based intelligent recommendation system and method for filling out college entrance examination applications according to claim 2 is characterized by: The data preparation and integration module includes: S1: collecting information on previous years' score lines, enrollment plans, major settings, and employment trends from authoritative channels such as the Ministry of Education, provincial, municipal and district examination institutes, and official websites of major universities.

4. The AI-based intelligent recommendation system and method for filling out college entrance examination applications according to claim 3 is characterized by: The intelligent recommendation engine module includes a demand analysis unit, a data matching and screening sub-unit, and a career development planning unit, and the intelligent recommendation engine module is signal-connected to the demand analysis unit, the data matching and screening sub-unit, and the career development planning unit.

5. The AI-based intelligent recommendation system and method for filling out college entrance examination applications according to claim 4 is characterized by: The user information input module includes: S2: student and parent information input, including but not limited to student's grades, interests and hobbies, future career orientation, and family expectations.

6. The AI-based intelligent recommendation system and method for filling out college entrance examination applications according to claim 5 is characterized by: The intelligent recommendation engine module includes: S3: focusing on student career development, helping students think about their future career direction and choose suitable majors and schools accordingly.

7. The AI-based intelligent recommendation system and method for filling out college entrance examination applications according to claim 6 is characterized by: The risk assessment module includes: S4: performing risk assessment on the recommendation results, identifying possible risk points, and giving corresponding avoidance suggestions.

8. The artificial intelligence-based intelligent recommendation system and method for filling out college entrance examination applications according to claim 7, characterized in that: The information inquiry and education guidance module includes: S5: allowing candidates to try different volunteer combinations, save different plans, and compare the advantages and disadvantages of different plans.

9. The AI-based intelligent recommendation system and method for filling out college entrance examination applications according to claim 8, characterized in that: The simulation application and management module includes: S6: providing detailed introduction information of colleges and majors, including the score lines in previous years and employment prospects.

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