Educational administration information management system and method based on intelligent middle desk

Abstract

The invention discloses an educational administration information management system and method based on an intelligent middle desk, relates to the technical field of educational administration information management, and solves the problems that the association rule of current textbooks and discipline majors is simple, and the textbooks of the discipline majors cannot be accurately selected according to course targets and actual demands. Comprising a specialty label acquisition module, a textbook analysis module, a specialty association module, a specialty history matching module and a textbook subject matching module, the textbook analysis module is used for analyzing textbook information of textbooks, the specialty association module is used for calculating association indexes of the textbooks and subjects and specialties, and the specialty history matching module is used for calculating history matching scores of the textbooks and the subjects and specialties. And the textbook and subject matching module is used for calculating a matching index between the textbook and the subject specialty. According to the invention, accurate matching between the subject specialty and the textbook is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of educational information management, and specifically is an educational information management system and method based on an intelligent middle station. Background Art

[0002] The Academic Affairs Information Management System is a comprehensive information platform for internal management of colleges and universities or educational institutions, mainly used to achieve efficient management and scheduling of teaching resources. The system includes functions such as course scheduling, teacher scheduling, student management, grade management, examination scheduling, and textbook management. It aims to simplify the management process of teaching and academic affairs through digital means and improve teaching quality and management efficiency. The Academic Affairs Information Management System supports various teaching activities within the school through automation and information technology, provides convenient services for teachers and students, and improves the management level of educational institutions.

[0003] In the prior art, the association rules between textbooks and disciplines are relatively simple, relying on manual input of textbooks used in a discipline, or matching textbooks with disciplines through simple rules. It is impossible to accurately select textbooks suitable for disciplines according to the course objectives and actual needs of the discipline, and it is impossible to adjust and optimize the selection of textbooks according to the historical usage of textbooks. To this end, the present invention proposes an educational information management system and method based on an intelligent middle platform. Summary of the invention

[0004] The purpose of the present invention is to propose an educational information management system and method based on an intelligent middle platform to solve the problems raised in the above-mentioned background technology.

[0005] The technical problems to be solved by the present invention are: How to achieve precise adaptation of subject majors and teaching materials based on an intelligent middle platform.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In the first aspect, the teaching information management system based on the intelligent middle station includes a data acquisition module, a professional label acquisition module, a teaching material analysis module, a professional association module, a professional history matching module, a database module, a teaching material subject matching module and a user terminal. The data acquisition module is used to acquire the subject professional information of the subject major and send the subject professional information to the professional label acquisition module; the professional label acquisition module is used to extract the subject field labels and technical field labels corresponding to the subject professional information of each subject major, and extract the subject field labels or technical field labels corresponding to the professional keywords of the subject major and send them to the professional association module; The data acquisition module is also used to acquire the teaching material information of the teaching material, and then send the teaching material information to the teaching material analysis module; the teaching material analysis module is used to analyze the teaching material information of the teaching material, and the teaching material keyword set obtained by the analysis is sent to the professional association module; the professional association module is used to calculate the correlation index between the teaching material and the subject major, and the first correlation index and the second correlation index between the teaching material and the subject major are calculated and sent to the teaching material subject matching module; The data acquisition module is also used to acquire the historical textbook data of all disciplines and send the historical textbook data to the discipline history matching module; the discipline history matching module is used to calculate the history matching score between the textbook and the discipline, and the calculated history matching score between the textbook and the discipline is sent to the textbook discipline matching module; The textbook subject matching module is used to calculate the matching index between the textbook and the subject major, and select the textbook suitable for the subject major according to the matching index, and send the subject textbook library of the subject major to the database module for storage. The user terminal is used to obtain the subject textbook library matching the subject major in the database module.

[0007] Furthermore, the subject and specialty information includes the name of the subject and specialty's specialty field, the name of the specialty's branch field, the specialty's research direction, the technical methods used in the specialty, the research methods used in the specialty, and the specialty's application direction; The textbook information specifically includes the textbook title, textbook subtitle, textbook table of contents and textbook summary; The historical textbook usage data of academic disciplines include the total number of times all textbooks have been used by the academic discipline, the number of times any textbook has been used, and the number of times any textbook has been used each year.

[0008] Furthermore, the extraction process of the professional label acquisition module is as follows: Obtain subject and major information of each subject and major, and clean up the subject and major information of each subject and major; Perform word segmentation on the cleaned subject and major information to obtain professional keywords corresponding to the subject and major information, and group the professional keywords of the same subject and major into a corresponding professional keyword set; If any professional keyword in the professional keyword set corresponding to the subject major is one of the professional field name, professional branch field name or professional research direction, the professional keyword corresponding to the subject major is recorded as the subject field label; If any professional keyword in the professional keyword set corresponding to the subject major is one of the technical methods used in the major, the research methods used in the major, or the application direction of the major, the professional keyword corresponding to the subject major will be recorded as a technical field label.

[0009] Furthermore, the analysis process of the teaching material analysis module is specifically as follows: Obtain the textbook information of all textbooks and clean up the textbook information; The cleaned textbook information is segmented to obtain textbook keywords, and the textbook keywords of the same textbook are merged into a textbook keyword set.

[0010] Furthermore, the professional association module is used to calculate the association index between the teaching materials and the subject majors, and the calculation process is as follows: Calculate the first correlation index between the textbook and the subject; Then, the second cosine similarity between the technical field label of any subject and the textbook keyword set of the textbook is calculated, and the second cosine similarity is recorded as the second correlation index DEGi between the textbook and the subject.

[0011] Furthermore, the calculation process of the first correlation index is as follows: Obtain the subject field labels of the professional keywords corresponding to any subject major and the textbook keyword set of the textbook, merge the subject field labels and the textbook keyword set to obtain a keyword thesaurus; Count the number of times any professional keyword or textbook keyword appears in the keyword thesaurus, and count the total number of words in the keyword thesaurus. Divide the number of times any professional keyword or textbook keyword appears in the keyword thesaurus by the total number of words in the keyword thesaurus to obtain the frequency of occurrence TF (a) of any professional keyword or textbook keyword in the keyword thesaurus; where a is the professional keyword or textbook keyword; Similarly, the frequency of occurrence of all professional keywords or textbook keywords in the keyword database is calculated; The number of documents containing any professional keyword or textbook keyword, WDS, and the total number of documents, ZSL, are obtained, and the importance index, IDF(a), of any professional keyword or textbook keyword is calculated by the formula as follows: IDF(a)=log(ZSL / WDS).

[0012] Furthermore, the calculation process of the first correlation index further includes: The TF(a)-IDF(a) values ​​of all professional keywords or textbook keywords in the keyword vocabulary are calculated using the formula as follows: TF(a)-IDF(a)=TF(a)×IDF(a); Calculate the TF-IDF vector A of the subject field label and the TF-IDF vector B of the textbook keyword set respectively; The cosine similarity COS between the subject field label corresponding to any subject major and the textbook keyword set of the textbook is calculated by the cosine similarity formula. The specific formula is as follows: COS=(A·B) / (|A||B|); The first cosine similarity between the subject field label of any subject major and the textbook keyword set of the textbook is recorded as the first correlation index between the textbook and the subject major.

[0013] Furthermore, the calculation process of the professional history matching module is as follows: Obtain the total number of times all textbooks are used by the subject and the number of times any textbook is used in the subject. Divide the total number of times used by the subject by the number of textbooks to obtain the average number of times all textbooks are used by the subject. Then divide the historical number of times any textbook is used by the subject by the average number of times all textbooks are used by the subject to obtain the frequency of use of any textbook. Obtain the number of times any textbook is used each year, add up the number of times used each year and take the average to obtain the average number of times any textbook is used each year; Calculate the historical matching score between the textbook and the subject major.

[0014] Furthermore, the working process of the teaching material subject matching module is as follows: Obtain the first correlation index, second correlation index and historical matching score between textbooks and subject majors; Calculate the matching index between textbooks and subject majors; When the matching index between the textbook and the subject is greater than the index threshold, the corresponding textbook is determined to match the subject, and the textbooks matching the subject are packaged into a subject textbook library according to the matching index from large to small. When the matching index between the textbook and the subject is less than or equal to the numerical threshold, it is determined that the corresponding textbook and the subject do not match.

[0015] The second aspect is the teaching information management method based on the intelligent middle platform, which includes: Step S1, obtaining the subject information of the subject, and then extracting the subject field labels and technical field labels corresponding to the subject information of each subject, to obtain the subject field labels or technical field labels corresponding to the professional keywords of the subject; Step S2, obtaining teaching material information of the teaching material, and analyzing the teaching material information of the teaching material to obtain a teaching material keyword set of the teaching material; Step S3, calculating the correlation index between the textbook and the subject, and obtaining a first correlation index and a second correlation index between the textbook and the subject; Step S4, obtaining the historical textbook data of all disciplines and majors, and calculating the historical matching scores between the textbooks and the disciplines and majors based on the historical textbook data; Step S5, calculating the matching index between the teaching material and the subject major according to the historical matching score, the first correlation index and the second correlation index, thereby obtaining the subject teaching material library of the subject major.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: On the one hand, the present invention extracts the subject field labels and technical field labels corresponding to the subject field information of each subject major, obtains the subject field labels or technical field labels of the subject field keywords corresponding to the subject major, and then analyzes the teaching material information of the textbook to obtain the teaching material keyword set of the textbook, combines the subject field labels, technical field labels and teaching material keyword set, calculates the correlation index between the textbook and the subject major, and obtains the first correlation index and the second correlation index between the textbook and the subject major; on the other hand, calculates the historical matching score of the textbook and the subject major based on the historical use of the teaching material data, and finally calculates the matching index of the textbook and the subject major based on the historical matching score, the first correlation index and the second correlation index, thereby obtaining the subject teaching material library of the subject major, and the present invention realizes the precise adaptation of the subject major and the teaching material. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0018] Figure 1 is the overall system block diagram of the present invention; Figure 2 is a diagram of the calculation process of the matching index in the present invention; Figure 3 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0019] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0020] Example 1: Please refer to Figure 1-Figure 2 As shown, the technical solution provided by the present invention is: an educational information management system based on an intelligent middle platform, which is mainly used to analyze the matching situation between teaching materials and subject majors, so as to select teaching materials suitable for subject majors, including a data acquisition module, a professional label acquisition module, a teaching material analysis module, a professional association module, a professional history matching module, a database module, a teaching material subject matching module and a user terminal; In this embodiment, the data acquisition module is used to acquire subject professional information of subject majors and send the subject professional information to the professional label acquisition module; Among them, subject and major information includes the name of the professional field of the subject, the name of the professional branch field, the professional research direction, the technical methods used in the major, the research methods used in the major and the application direction of the major.

[0021] In this embodiment, the professional label acquisition module is used to extract the discipline field labels and technology field labels corresponding to the discipline professional information of each discipline. The extraction process is as follows: Obtain the discipline professional information of each discipline, and perform a cleaning operation on the discipline professional information of the discipline; among them, the cleaning operation is intended to clean up the irrelevant information in the discipline professional information; Perform word segmentation on the cleaned discipline professional information, and segment it to obtain the professional keywords corresponding to the discipline professional information of the discipline. Combine the professional keywords of the same discipline into the corresponding professional keyword set; Among them, the irrelevant information includes punctuation marks, meaningless words, stop words, etc. in the discipline professional information of the discipline, such as "of", "in", or "already", etc.; It should be specifically noted that the cleaned discipline professional information is segmented through a word segmentation tool. The word segmentation tools include Jieba Segmentation, THULAC, and LTP, etc. In this embodiment, Jieba Segmentation is preferably used as the word segmentation tool. Jieba Segmentation supports multiple segmentation modes and is an open-source Chinese word segmentation tool; Exemplarily, when the professional information of computer science and technology is "Computer science and technology is a discipline that studies computers and their applications, covering directions such as artificial intelligence, data structure, and operating system", the professional keyword set corresponding to computer science and technology obtained by word segmentation is "computer science", "technology", "research", "computer", "application", "discipline", "artificial intelligence", "data structure", and "operating system"; If any professional keyword in the professional keyword set corresponding to the discipline is one of the professional field name, professional branch field name, or professional research direction, then the professional keyword corresponding to the discipline is recorded as the discipline field label; If any professional keyword in the professional keyword set corresponding to the discipline is one of the technical methods used in the profession, research methods used in the profession, or professional application directions, then the professional keyword corresponding to the discipline is recorded as the technology field label; In this embodiment, the discipline field labels include professional field names, professional branch field names, professional research directions, etc., and the technology field labels include technical methods used in the profession, research methods used in the profession, professional application directions, etc.; The professional label acquisition module sends the discipline field label or technology field label of the professional keyword corresponding to the discipline to the professional association module.

[0022] Furthermore, the data acquisition module is also used to obtain the textbook information of the textbook, and then send the textbook information to the textbook analysis module; among them, the textbook information specifically includes the textbook title, textbook subtitle, textbook table of contents, and textbook abstract, etc.; In this embodiment, the teaching material analysis module is used to analyze the teaching material information of the teaching material, and the analysis process is specifically as follows: Obtaining textbook information of all textbooks, and performing a cleaning operation on the textbook information; wherein the cleaning operation is intended to clean irrelevant information in the textbook information; Perform word segmentation on the cleaned textbook information to obtain textbook keywords, and merge textbook keywords of the same textbook into a textbook keyword set; The teaching material analysis module sends the teaching material keyword set of the teaching material to the professional association module.

[0023] In this embodiment, the professional association module is used to calculate the association index between the teaching material and the subject major. The calculation process is as follows: Calculate the first correlation index between textbooks and subject majors. The calculation process is as follows: Obtain the subject field labels of the professional keywords corresponding to any subject major and the textbook keyword set of the textbook, merge the subject field labels and the textbook keyword set to obtain a keyword thesaurus; Count the number of times any professional keyword or textbook keyword appears in the keyword thesaurus, and count the total number of words in the keyword thesaurus. Divide the number of times any professional keyword or textbook keyword appears in the keyword thesaurus by the total number of words in the keyword thesaurus to obtain the frequency of occurrence TF (a) of any professional keyword or textbook keyword in the keyword thesaurus; where a is the professional keyword or textbook keyword; Similarly, the frequency of occurrence of all professional keywords or textbook keywords in the keyword database is calculated; The number of documents containing any professional keyword or textbook keyword, WDS, and the total number of documents, ZSL, are obtained, and the importance index, IDF(a), of any professional keyword or textbook keyword is calculated by the formula. The specific formula is as follows: IDF(a)=log(ZSL / WDS); The TF(a)-IDF(a) values ​​of all professional keywords or textbook keywords in the keyword vocabulary are calculated using the formula as follows: TF(a)-IDF(a)=TF(a)×IDF(a); Calculate the TF-IDF vector A of the subject field label and the TF-IDF vector B of the textbook keyword set respectively; For example, A=[TF("operating system")-IDF("operating system"), TF("artificial intelligence")-IDF("artificial intelligence"), TF("database")-IDF("database"), TF("computer network")-IDF("computer network"), TF("network protocol")-IDF("network protocol")], and A=[0, 0, 0.0753, 0, 0] is calculated. B = [TF-IDF ("computer network"), TF-IDF ("operating system"), TF-IDF ("network protocol"), TF-IDF ("artificial intelligence")], and we get B = [0, 0, 0.0753, 0, 0.0753], where A and B are both vectors; The cosine similarity COS between the subject field label corresponding to any subject major and the textbook keyword set of the textbook is calculated by the cosine similarity formula. The specific formula is as follows: COS=(A·B) / (|A||B|); The first cosine similarity between the subject field label of any subject major and the textbook keyword set of the textbook is recorded as the first correlation index DYGi between the textbook and the subject major, where i is the number of the textbook, i=1, 2, ..., n, and n is a positive integer; Through the above steps, the second cosine similarity between the technical field label of any subject major and the textbook keyword set of the textbook is calculated, and the second cosine similarity is recorded as the second correlation index DEGi between the textbook and the subject major; The professional association module sends the first association index and the second association index between the teaching material and the subject major to the teaching material subject matching module.

[0024] In this embodiment, the data acquisition module is also used to acquire the historical teaching material data used in all subject majors, and send the historical teaching material data used in all subject majors to the professional history matching module; Specifically, the historical textbook usage data for disciplines and majors include the total number of times all textbooks have been used by the discipline and major, the number of times any textbook has been used in history, and the number of times any textbook has been used each year; The professional history matching module is used to calculate the historical matching score between the textbook and the subject major. The calculation process is as follows: Obtain the total number of times all textbooks are used by the subject and the number of times any textbook is used in history. Divide the total number of times used in history by the number of textbooks to obtain the average number of times all textbooks are used by the subject. Then divide the historical number of times any textbook is used by the average number of times all textbooks are used by the subject to obtain the frequency of use SYPi of any textbook. Obtain the number of times any textbook is used each year, add up the number of times used each year and take the average to obtain the annual average number of times any textbook is used SCSi; After removing the dimension, the historical matching score PPFi between the textbook and the subject major is calculated by the formula PPFi=u1×SYPi+u2×SCSi, where u1 and u2 are weight coefficients, and u1>u2>0. The frequency of use and the average annual number of uses are both proportional to the historical matching score, that is, the greater the value of the frequency of use and the average annual number of uses, the greater the value of the historical matching score. The historical matching score is used to reflect the degree of use of the textbook; The professional history matching module sends the history matching scores between the textbook and the subject major to the textbook subject matching module.

[0025] The textbook subject matching module is used to calculate the matching index between the textbook and the subject, and select the textbook suitable for the subject according to the matching index. The specific working process is as follows: Obtain the first correlation index DYGi, the second correlation index DEGi and the historical matching score PPFi between the textbook and the subject, and calculate the matching index PPZi between the textbook and the subject through the formula. The specific formula is as follows: PPZi=v1×DYGi+v2×DEGi+v3×PPFi, where v1, v2 and v3 are weight coefficients, and v1>v2>v3; When the matching index between the textbook and the subject is greater than the index threshold, the corresponding textbook is determined to match the subject, and the textbooks matching the subject are packaged into a subject textbook library according to the matching index from large to small. When the matching index between the textbook and the subject is less than or equal to the numerical threshold, it is determined that the corresponding textbook and the subject do not match; The teaching material subject matching module sends the subject teaching material library of the subject major to the database module for storage, and the user terminal is used to obtain the subject teaching material library matching the subject major in the database module.

[0026] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0027] Example 2: Figure 3 As shown, based on another concept of the same invention, a teaching information management method based on an intelligent middle station is proposed, comprising the following steps: Step S1, obtaining the subject information of the subject, and then extracting the subject field labels and technical field labels corresponding to the subject information of each subject, to obtain the subject field labels or technical field labels corresponding to the professional keywords of the subject; Step S2, obtaining teaching material information of the teaching material, and analyzing the teaching material information of the teaching material to obtain a teaching material keyword set of the teaching material; Step S3, calculating the correlation index between the textbook and the subject, and obtaining a first correlation index and a second correlation index between the textbook and the subject; Step S4, obtaining the historical textbook data of all disciplines and majors, and calculating the historical matching scores between the textbooks and the disciplines and majors based on the historical textbook data; Step S5, calculating the matching index between the teaching material and the subject major according to the historical matching score, the first correlation index and the second correlation index, thereby obtaining the subject teaching material library of the subject major.

[0028] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The educational information management system based on the intelligent middle platform is characterized by: It includes a data acquisition module, a professional label acquisition module, a textbook analysis module, a professional association module, a professional history matching module, a database module, a textbook subject matching module and a user terminal. The data acquisition module is used to acquire the subject professional information of the subject major and send the subject professional information to the professional label acquisition module; the professional label acquisition module is used to extract the subject field labels and technical field labels corresponding to the subject professional information of each subject major, and extract the subject field labels or technical field labels corresponding to the professional keywords of the subject major and send them to the professional association module; The data acquisition module is also used to acquire the teaching material information of the teaching material, and then send the teaching material information to the teaching material analysis module; the teaching material analysis module is used to analyze the teaching material information of the teaching material, and the teaching material keyword set obtained by the analysis is sent to the professional association module; the professional association module is used to calculate the correlation index between the teaching material and the subject major, and the first correlation index and the second correlation index between the teaching material and the subject major are calculated and sent to the teaching material subject matching module; The data acquisition module is also used to acquire the historical textbook data of all disciplines and send the historical textbook data to the discipline history matching module; the discipline history matching module is used to calculate the history matching score between the textbook and the discipline, and the calculated history matching score between the textbook and the discipline is sent to the textbook discipline matching module; The textbook subject matching module is used to calculate the matching index between the textbook and the subject major, and select the textbook suitable for the subject major according to the matching index, and send the subject textbook library of the subject major to the database module for storage. The user terminal is used to obtain the subject textbook library matching the subject major in the database module.

2. According to the teaching information management system based on the intelligent middle platform according to claim 1, it is characterized in that: Discipline and major information includes the name of the discipline and major’s professional field, the name of the professional branch field, the professional research direction, the technical methods used in the major, the research methods used in the major, and the professional application direction; The textbook information specifically includes the textbook title, textbook subtitle, textbook table of contents and textbook summary; The historical textbook usage data of academic disciplines include the total number of times all textbooks have been used by the academic discipline, the number of times any textbook has been used, and the number of times any textbook has been used each year.

3. The teaching information management system based on the intelligent middle station according to claim 2 is characterized in that: The extraction process of the professional label acquisition module is as follows: Obtain subject and major information of each subject and major, and clean up the subject and major information of each subject and major; Perform word segmentation on the cleaned subject and major information to obtain professional keywords corresponding to the subject and major information, and group the professional keywords of the same subject and major into a corresponding professional keyword set; If any professional keyword in the professional keyword set corresponding to the subject major is one of the professional field name, professional branch field name or professional research direction, the professional keyword corresponding to the subject major is recorded as the subject field label; If any professional keyword in the professional keyword set corresponding to the subject major is one of the technical methods used in the major, the research methods used in the major, or the application direction of the major, the professional keyword corresponding to the subject major will be recorded as a technical field label.

4. The teaching information management system based on the intelligent middle station according to claim 3 is characterized in that: The analysis process of the teaching material analysis module is as follows: Obtain the textbook information of all textbooks and clean up the textbook information; The cleaned textbook information is segmented to obtain textbook keywords, and the textbook keywords of the same textbook are merged into a textbook keyword set.

5. The teaching information management system based on the intelligent middle station according to claim 4 is characterized in that: The professional association module is used to calculate the association index between the textbook and the subject major. The calculation process is as follows: Calculate the first correlation index between the textbook and the subject; Then, the second cosine similarity between the technical field label of any subject and the textbook keyword set of the textbook is calculated, and the second cosine similarity is recorded as the second correlation index between the textbook and the subject.

6. The teaching information management system based on the intelligent middle station according to claim 5 is characterized in that: The calculation process of the first correlation index is as follows: Obtain the subject field labels of the professional keywords corresponding to any subject major and the textbook keyword set of the textbook, merge the subject field labels and the textbook keyword set to obtain a keyword thesaurus; Count the number of times any professional keyword or textbook keyword appears in the keyword thesaurus, and count the total number of words in the keyword thesaurus. Divide the number of times any professional keyword or textbook keyword appears in the keyword thesaurus by the total number of words in the keyword thesaurus to obtain the frequency of occurrence TF (a) of any professional keyword or textbook keyword in the keyword thesaurus; where a is the professional keyword or textbook keyword; Similarly, the frequency of occurrence of all professional keywords or textbook keywords in the keyword database is calculated; The number of documents containing any professional keyword or textbook keyword, WDS, and the total number of documents, ZSL, are obtained, and the importance index, IDF(a), of any professional keyword or textbook keyword is calculated by the formula as follows: IDF(a)=log(ZSL / WDS).

7. The teaching information management system based on the intelligent middle station according to claim 6 is characterized in that: The calculation process of the first correlation index also includes: The TF(a)-IDF(a) values ​​of all professional keywords or textbook keywords in the keyword vocabulary are calculated using the formula as follows: TF(a)-IDF(a)=TF(a)×IDF(a); Calculate the TF-IDF vector A of the subject field label and the TF-IDF vector B of the textbook keyword set respectively; The cosine similarity COS between the subject field label corresponding to any subject major and the textbook keyword set of the textbook is calculated by the cosine similarity formula. The specific formula is as follows: COS=(A·B) / (|A||B|); The first cosine similarity between the subject field label of any subject major and the textbook keyword set of the textbook is recorded as the first correlation index between the textbook and the subject major.

8. The teaching information management system based on intelligent middle platform according to claim 5 is characterized in that: The calculation process of the professional history matching module is as follows: Obtain the total number of times all textbooks are used by the subject and the number of times any textbook is used in the subject. Divide the total number of times used by the subject by the number of textbooks to obtain the average number of times all textbooks are used by the subject. Then divide the historical number of times any textbook is used by the subject by the average number of times all textbooks are used by the subject to obtain the frequency of use of any textbook. Obtain the number of times any textbook is used each year, add up the number of times used each year and take the average to obtain the average number of times any textbook is used each year; Calculate the historical matching score between the textbook and the subject major.

9. The teaching information management system based on the intelligent middle platform according to claim 8 is characterized in that: The working process of the textbook subject matching module is as follows: Obtain the first correlation index, second correlation index and historical matching score between textbooks and subject majors; Calculate the matching index between textbooks and subject majors; When the matching index between the textbook and the subject is greater than the index threshold, the corresponding textbook is determined to match the subject, and the textbooks matching the subject are packaged into a subject textbook library according to the matching index from large to small. When the matching index between the textbook and the subject is less than or equal to the numerical threshold, it is determined that the corresponding textbook and the subject do not match.

10. The teaching information management method based on the intelligent middle platform is characterized by: In combination with the intelligent middle station-based educational information management system according to any one of claims 1 to 9, the method includes: Step S1, obtaining subject information of subject majors, and then extracting subject field labels and technical field labels corresponding to the subject information of each subject major, to obtain subject field labels or technical field labels corresponding to professional keywords of the subject majors; Step S2, obtaining teaching material information of the teaching material, and analyzing the teaching material information of the teaching material to obtain a teaching material keyword set of the teaching material; Step S3, calculating the correlation index between the textbook and the subject, and obtaining a first correlation index and a second correlation index between the textbook and the subject; Step S4, obtaining the historical textbook data of all disciplines and majors, and calculating the historical matching scores between the textbooks and the disciplines and majors based on the historical textbook data; Step S5, calculating the matching index between the teaching material and the subject major according to the historical matching score, the first correlation index and the second correlation index, thereby obtaining the subject teaching material library of the subject major.

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