Individual dominance analysis-based vocational teaching planning method and system

Abstract

The invention belongs to the field of data analysis, particularly relates to a vocational teaching planning method and system based on individual dominance degree analysis, and aims to solve the problem that existing teaching course design cannot effectively meet actual post requirements. The method comprises the following steps: based on a recruitment database and a student learning database, counting post and personnel supply and demand, deviation between social recruitment posts and school recruitment posts, post and specialty matching and post supply and demand prediction values; calculating the student individual dominance degree of the student on each post; performing student occupational planning based on the individual advantage degree based on the post and personnel supply and demand quantity, the deviation between the social recruitment post and the school recruitment post, the post and specialty matching degree, the post supply and demand predicted value and the student individual advantage degree; and obtaining a post and professional course matching evaluation result, and carrying out course planning and teacher allocation at a department end. According to the method and the system, the learning content is associated with the post demand, so that the effect of updating course arrangement with the times is realized.

Description

Background Art

[0002] The current mainstream teaching curriculum design system is based on the completeness of the theoretical foundation, which is not highly related to job requirements. As a result, students’ academic skills cannot meet the practical job requirements when they graduate, increasing the cost of learning. On the other hand, the curriculum design model that is divorced from industry, academia, and research and ignores job requirements has caused outdated and time-sensitive courses to still exist in the curriculum system, so that there is still the problem that new courses cannot keep up with the times. Summary of the invention

[0003] In order to solve the above-mentioned problem in the prior art, that is, the current mainstream teaching course design system of the existing career planning and teaching planning methods is based on the completeness of the theoretical basis, and is not highly correlated with job requirements. As a result, the academic skills of students cannot meet the practical requirements of the job when they graduate, which increases the learning cost. The present invention provides a career teaching planning method based on individual advantage analysis, which includes: Step S1, obtaining historical recruitment data to build a recruitment database, and obtaining student learning data to build a student learning database; Step S2, based on the recruitment database and the student learning database, statistics are collected on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, and the forecast value of position supply and demand; Step S3, calculating the individual advantage of students in each position according to the student learning database; Step S4, based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, the predicted value of position supply and demand, and the individual advantage of students, career planning for students based on individual advantage; Step S5, obtaining the evaluation results of the matching between positions and professional courses, and conducting curriculum planning and teacher allocation at the department end.

[0004] Furthermore, the step S1 specifically includes: Building a recruitment database: according to time Job recruitment information Time Determine the first associated time interval as ; in, Indicates the constant of the time interval between adjacent campus recruitments; Get all the social recruitment information within the first associated time interval, use the TF_IDF algorithm to extract social recruitment keywords and campus recruitment keywords from the job responsibilities of social recruitment information and campus recruitment information obtained from the word segmentation of historical job recruitment information, and calculate the cosine similarity between social recruitment keywords and campus recruitment keywords, and extract social recruitment information with cosine similarity greater than the similarity threshold as the social recruitment extension information for the same position in the same period of the corresponding campus recruitment information ; in, express Expand the social recruitment information set for the same position during the relevant period; Expand all campus recruitment information to the same position of social recruitment data information and build time Job Recruitment Information Set for ;right The format of campus recruitment and social recruitment information from different sources is unified. After the format is unified time Position Recruitment Information ; in, express time A collection of job recruitment information numbers; The attributes include information source attributes , Match professional attributes and recruitment content attributes , recruitment content attributes Include job title 、Job Responsibilities , Technical requirements for the position Other job requirements ; In the above-mentioned format unification, The matching professional attribute value is determined by the corresponding history time Job recruitment information Matching assignment, when corresponding to history time If the corresponding attribute data is missing in the job recruitment information, the corresponding attribute value will be set to blank; The recruitment information set of the same position at different time points is constructed as the job position data element , ; Then build the employment database as ; in, express Position data element; Represents a set of job numbers. Indicates the school recruitment time collection; Build a student learning database According to history time Job recruitment information Time Determine the second associated time interval as ; Load the student information of the students who enrolled at the start of the second associated time interval in the second associated time interval, and construct the student information of each major according to the student's major: ; Summarize student information of each major at each time point and build a student learning database ; in, Respectively time major Students’ learning data in various subjects; Represents a professional number set, Indicates the constant of basic course duration, express time A collection of all student IDs for a major.

[0005] Furthermore, the step S2 is specifically: Based on the recruitment database and student learning database, a job matching personnel database is constructed. : ; Build job demand : ; Constructing a sequence of successful campus recruitment positions : , ; in: ; Represents the set of The number of elements contained; Represents the set of The number of elements contained; The statistical deviation between social recruitment positions and campus recruitment positions is as follows: Construct the job technology set sequence as : , ; The sequence of job elimination technology sets is constructed as : ; The sequence of newly added technical sets for constructing positions is : ; in, ; Indicates the technical statistics time window, A set of statistical time points of technological change; , They represent the length of the technology statistics time window and the number of statistical time points for technology change respectively; The matching between statistical positions and majors is calculated as follows: Construct the professional-position matching set sequence as , ; in, Indicates the quantity threshold, Indicates the matching degree between major and position. ; Construct a job-professional matching set sequence to count the matching between job and major: , ; The job supply and demand forecast value is calculated as follows: The supply and demand forecast training data set is constructed as follows: , , ; Input the training data pair set into the LSTM time prediction model based on the neural network, complete the training and obtain the trained LSTM time prediction model based on the neural network; Load current campus recruitment time Number of students matching positions , job demand ,Will Input the trained LSTM time prediction model based on neural network, and obtain the output value of the prediction model as the success rate of each position in the current campus recruitment The predicted value is used as the job supply and demand forecast value.

[0006] Furthermore, the step S3 is specifically: Step S31: Loading the professional-position matching set sequence , build students of different majors in the same position into a competitive group ; Load the subject scores of each student in each competitive group , and sort them in descending order according to subject scores, and obtain the ranking of each student's subject scores in the competitive group ; in, express time Position competition group; express Student's Basic course results Ranking in the competitive group for the position; Step S32: Loading job skills set and basic courses for students of various majors in the competitive group; According to the matching degree between the job skill set and each basic course, each basic course is assigned a job weight value. ; Step S33, constructing a student individual advantage sequence as the student individual advantage.

[0007] Furthermore, the step S32 specifically includes: Step S321, calculate the matching degree between the basic course and each technology in the job technology set as: the ratio of the number of knowledge points covered by the basic course knowledge graph on the corresponding technology knowledge graph to the number of knowledge points covered by the corresponding technology, as the matching degree between the current basic course and the technology; Step S322: Building a job skills set The weight value of each technology is the frequency of occurrence of the corresponding technical knowledge point in the corresponding job recruitment information within the technical change statistical time window corresponding to the job technical change set; Step S323, constructing the position weight of the basic course as follows: ; in, Respectively The post technology set is the collection of all technology numbers; Basic Course Position weight; express Basic courses and The compatibility of technology; express Technical Position weight.

[0008] Furthermore, in step S33, the individual advantage sequence of students is constructed, specifically: Calculate the individual advantage of each student in each competitive group as: ; in, express Students in The individual advantages of the position, Represents the set of all basic course numbers; Find all the competitive groups that each student belongs to, and calculate the individual advantage of each student in each corresponding competitive group; sort the multiple individual advantage degrees from small to large according to the numerical value; use the sorted individual advantage degree sequence as the individual advantage degree of the student in the current position, and construct the individual advantage degree sequence of each student as follows: .

[0009] Furthermore, the step S4 is specifically: According to the student's major and the corresponding application time, extract all positions matching the student's major ; Load the supply and demand forecast for each matching position , sort them from high to low according to the numerical value, and record the ordered sequence of supply and demand of each position as ; Load the student's individual advantage sequence , record the student's position advantage number for exist Medium serial number; right For each position in , calculate the recommended priority of the position as ; Arrange the job recommendation priorities from small to large, and record the job recommendation priority sequence as ; Build The recommended job list from highest to lowest student recommendation is: , the best matching position for obtaining the student's individual advantage is ; in, Indicates seeking Corresponding job number ; According to the best matching position based on individual advantage, and according to the position elective weight of each professional course in the best matching position, the required number of professional elective courses are constructed.

[0010] Furthermore, in step S5, the job and professional course matching assessment is obtained, specifically: The matching assessment between the position and the professional courses includes the elimination assessment of professional courses, the addition assessment of professional courses and the elective assessment of professional courses; The elimination evaluation method of the professional courses is as follows: Loading post elimination technology set , calculate the job weight value of each professional course, and call this job weight the job elimination weight of the professional course; load , calculate the elimination weight of professional courses as follows: ; in, Respectively mm Professional Courses k Position elimination weight, mm Elimination weights for professional courses; The new assessment method for professional courses is as follows: According to the newly added weight of each professional course, the class hours of each professional course are set in direct proportion, specifically: Load the newly added technical set of positions , calculate the job weight value of each professional course, and call this job weight the new job weight of the professional course.

[0011] load , calculate the new weight of professional courses as ; in, Respectively mm Professional Courses k New weight added to the position, mm Additional weights for professional courses; The method for elective assessment of professional courses is as follows: Loading job skills set , calculate the job weight value of each professional course, and call this job weight the job elective weight of the professional course; load , calculate the weight of professional elective courses as ; in, Respectively mm Professional Courses k Weight of post electives, mm The elective weight of professional courses.

[0012] Furthermore, in step S5, the job and professional course matching assessment is obtained and the course planning of the department is carried out, specifically: The professional course elimination mechanism is constructed as follows: according to the elimination evaluation of professional courses, according to the elimination weight of each professional course, the corresponding proportion of professional courses will be eliminated; The mechanism for setting hours for professional courses is as follows: according to the elective evaluation of professional courses and the elective weight of each professional course, the hours for each professional course are set in direct proportion; The mechanism for adding new professional courses is as follows: based on the evaluation of the newness of professional courses, add professional courses in corresponding proportion according to the new weight of each professional course; according to the new weight of each professional course, set the class hours of each professional course in direct proportion.

[0013] Another aspect of the present invention provides a career teaching planning system based on individual advantage analysis, the system comprising: Database construction module, which obtains historical recruitment data to build a recruitment database, and obtains student learning data to build a student learning database; A supply and demand assessment module, based on the recruitment database and the student learning database, counts the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, and the supply and demand forecast value of positions; Individual advantage analysis module, which calculates the individual advantage of students in various positions based on the student learning database; The individual planning module conducts career planning for students based on their individual strengths, based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, the predicted value of position supply and demand, and the individual strengths of students; The teaching deployment module obtains the evaluation results of the matching between positions and professional courses, and carries out curriculum planning and teacher deployment at the department end.

[0014] Beneficial effects of the present invention: (1) The present invention performs statistical and predictive evaluations on the job side and the student side, calculates individual advantage, and evaluates the matching of occupations and professional courses, and provides adjustment suggestions on the student side and the teaching side, respectively, so that both the teaching and learning sides can obtain targeted configurations and achieve the effect of keeping pace with the times.

[0015] (2) The present invention conducts career planning based on individual strengths on the student side, so that each student can obtain the best career position recommendation according to their personal ability, and then plan their professional course electives based on the career position; on the department side, it constructs a professional course elimination evaluation, a new course evaluation and an existing professional course elective evaluation, so that the professional course design of the department can adaptively increase or decrease professional courses and allocate class hours according to job requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 is a flow chart of a method for career teaching planning based on individual advantage analysis in an embodiment of the present invention; Figure 2 It is a structural block diagram of the vocational teaching planning method based on individual advantage analysis in an embodiment of the present invention integrated into the system. DETAILED DESCRIPTION

[0017] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0018] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0019] In order to more clearly illustrate the career teaching planning method based on individual advantage analysis of the present invention, the following is combined with Figure 1 Each step in the embodiment of the present invention is described in detail.

[0020] The vocational teaching planning method based on individual advantage analysis of the first embodiment of the present invention includes steps S1 to S5, each of which is described in detail as follows: Step S1, obtaining historical recruitment data to build a recruitment database, and obtaining student learning data to build a student learning database; In this embodiment, the step S1 specifically includes: Building a recruitment database: according to time Job recruitment information Time Determine the first associated time interval as ; in, Indicates the constant of the time interval between adjacent campus recruitments; Get all the social recruitment information within the first associated time interval, use the TF_IDF algorithm to extract social recruitment keywords and campus recruitment keywords from the job responsibilities of social recruitment information and campus recruitment information obtained from the word segmentation of historical job recruitment information, and calculate the cosine similarity between social recruitment keywords and campus recruitment keywords, and extract social recruitment information with cosine similarity greater than the similarity threshold as the social recruitment extension information for the same position in the same period of the corresponding campus recruitment information ; in, express Expand the social recruitment information set for the same position during the relevant period; Expand all campus recruitment information to the same position of social recruitment data information and build time Job Recruitment Information Set for ;right The format of campus recruitment and social recruitment information from different sources is unified. After the format is unified time Position Recruitment Information ; in, express time A collection of job recruitment information numbers; The attributes include information source attributes , Match professional attributes and recruitment content attributes , recruitment content attributes Include job title 、Job Responsibilities , Technical requirements for the position Other technical requirements of the position ; In the above-mentioned format unification, The matching professional attribute value is determined by the corresponding history time Job recruitment information Matching assignment, when corresponding to history time If the corresponding attribute data is missing in the job recruitment information, the corresponding attribute value will be set to blank; The recruitment information set of the same position at each time point is constructed as a job position data element , ; Then build the employment database as ; in, express Position data element; Represents a set of job numbers. Indicates the school recruitment time collection; Build a student learning database According to history time Job recruitment information Time Determine the second associated time interval as ; According to the students' majors, the student information of each major is constructed as follows: ; Summarize student information of each major at each time point and build a student learning database ; in, Respectively time major Students’ learning data in various subjects; Represents a professional number set, Indicates the constant of basic course duration, express time A collection of all student IDs for a major.

[0021] Step S2, based on the recruitment database and the student learning database, statistics are collected on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, and the forecast value of position supply and demand; In this embodiment, based on the recruitment database and the student learning database, a job matching personnel database is constructed. : ; Build job demand : ; Constructing a sequence of successful campus recruitment positions : , ; in: ; Represents the set of The number of elements contained; Represents the set of The number of elements contained; The statistical deviation between social recruitment positions and campus recruitment positions is as follows: Construct the job technology set sequence as : , ; The sequence of job elimination technology sets is constructed as : ; The sequence of newly added technical sets for constructing positions is : ; in, ; Indicates the technical statistics time window, A set of statistical time points of technological change; , They represent the length of the technology statistics time window and the number of statistical time points for technology change respectively; The matching between statistical positions and majors is calculated as follows: Construct the professional-position matching set sequence as , ; in, Indicates the quantity threshold, Indicates the matching degree between major and position. ; Construct a job-professional matching set sequence to count the matching between job and major: , ; The job supply and demand forecast value is calculated as follows: The supply and demand forecast training data set is constructed as follows: , , ; Input the training data pair set into the LSTM time prediction model based on the neural network, complete the training and obtain the trained LSTM time prediction model based on the neural network; Load current campus recruitment time Number of students matching positions , job demand ,Will Input the trained LSTM time prediction model based on neural network, and obtain the output value of the prediction model as the success rate of each position in the current campus recruitment The predicted value is used as the job supply and demand forecast value.

[0022] Step S3, calculating the individual advantage of students in each position according to the student learning database; In this embodiment, the step S3 is specifically as follows: Step S31: Loading the job-to-professional matching set sequence , build students of different majors in the same position into a competitive group ; Load the subject scores of each student in each competitive group , and sort them in descending order according to subject scores, and obtain the ranking of each student's subject scores in the competitive group ; in, express time Position competition group; express Student's Basic course results Ranking in the competitive group for the position; Step S32: Loading job skills set and basic courses for students of various majors in the competitive group; According to the matching degree between the job skill set and each basic course, each basic course is assigned a job weight value. , obtain the individual advantage of each student in each position.

[0023] In this embodiment, the step S32 specifically includes: Step S321, calculate the matching degree between the basic course and each technology in the job technology set as: the ratio of the number of knowledge points covered by the basic course knowledge graph on the corresponding technology knowledge graph to the number of knowledge points covered by the corresponding technology, as the matching degree between the current basic course and the technology; Step S322: Building a job skills set The weight value of each technology is the frequency of occurrence of the corresponding technical knowledge point in the corresponding job recruitment information within the technical change statistical time window corresponding to the job technical change set; Step S323, constructing the position weight of the basic course as follows: ; in, Respectively The post technology set is the collection of all technology numbers; Basic Course Position weight; express Basic courses and The compatibility of technology; express Technical Position weight.

[0024] In this embodiment, the individual advantage of the student in the current position is specifically: Calculate the individual advantage of each student in each competitive group as: ; in, express Students in The individual advantages of the position, Represents the set of all basic course numbers; Find all the competitive groups that each student belongs to, and calculate the individual advantage of each student in each corresponding competitive group; sort the multiple individual advantage degrees from small to large according to the numerical value; use the sorted individual advantage degree sequence as the individual advantage degree of the student in the current position .

[0025] Step S4, based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, the predicted value of position supply and demand, and the individual advantages of students, conduct career planning for students based on individual advantages and construct the required number of professional elective courses; In this embodiment, the step S4 is specifically as follows: According to the student's major and the corresponding application time, extract all positions matching the student's major ; Load the supply and demand forecast for each matching position , sort them from high to low according to the numerical value, and record the ordered sequence of supply and demand of each position as ; Load the student's individual advantage sequence , record the student's position advantage number for exist Medium serial number; right For each position in , calculate the recommended priority of the position as ; Arrange the job recommendation priorities from small to large, and record the job recommendation priority sequence as ; Build The recommended job list from highest to lowest student recommendation is: , the best matching position for obtaining the student's individual advantage is ; in, Indicates seeking Corresponding job number ; According to the best matching position based on individual advantage, and according to the position elective weight of each professional course in the best matching position, the required number of professional elective courses are constructed.

[0026] Step S5, obtaining the evaluation results of the matching between positions and professional courses, and conducting curriculum planning and teacher allocation at the department end.

[0027] In this embodiment, in step S5, obtaining the job and professional course matching assessment is specifically as follows: The matching assessment between the position and the professional courses includes the elimination assessment of professional courses, the addition assessment of professional courses and the elective assessment of professional courses; The elimination evaluation method of the professional courses is as follows: Loading post elimination technology set , calculate the job weight value of each professional course, and call this job weight the job elimination weight of the professional course; load , calculate the elimination weight of professional courses as follows: ; in, Respectively mm Professional Courses k Position elimination weight, mm Elimination weights for professional courses; The new assessment method for professional courses is as follows: According to the newly added weight of each professional course, the class hours of each professional course are set in direct proportion, specifically: Load the newly added technical set of positions , calculate the job weight value of each professional course, and call this job weight the new job weight of the professional course.

[0028] load , calculate the new weight of professional courses as ; in, Respectively mm Professional Courses k New weight added to the position, mm Additional weights for professional courses; The method for elective assessment of professional courses is as follows: Loading job skills set , calculate the job weight value of each professional course, and call this job weight the job elective weight of the professional course; load , calculate the weight of professional elective courses as ; in, Respectively mm Professional Courses k Weight of post electives, mm The elective weight of professional courses.

[0029] In step S5, the job and professional course matching assessment is obtained and the course planning of the department is carried out, specifically: The professional course elimination mechanism is constructed as follows: according to the elimination evaluation of professional courses, according to the elimination weight of each professional course, the corresponding proportion of professional courses will be eliminated; The mechanism for setting hours for professional courses is as follows: according to the elective evaluation of professional courses and the elective weight of each professional course, the hours for each professional course are set in direct proportion; The mechanism for adding new professional courses is as follows: based on the evaluation of the newness of professional courses, add professional courses in corresponding proportion according to the new weight of each professional course; according to the new weight of each professional course, set the class hours of each professional course in direct proportion.

[0030] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art can understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0031] The second embodiment of the present invention provides a scheme for integrating the career teaching planning method based on individual advantage analysis in the first embodiment into the system, referring to Figure 2 ,include: Recruitment database, student learning database, prediction system, career and major matching assessment system, advantage analysis system and planning system; The recruitment database stores historical recruitment data and communicates with the career and major matching assessment system; the historical recruitment data includes historical employment data and historical social recruitment data; The student learning database stores student learning data and communicates with the career and major matching assessment system, prediction system and advantage analysis system; Through the career and major matching evaluation system, the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, and the supply and demand forecast of positions will be counted based on the data stored in the recruitment database and the student learning database; According to the student learning database, the prediction system is used to conduct industry and student prediction assessments, and the advantage degree analysis system is used to obtain the individual advantage degree of students in various positions; The career and major matching assessment system calculates the matching degree between the existing job skill sets and each basic course and gives the skill sets to be eliminated or added.

[0032] Finally, the planning system is used to eliminate old professional courses and prepare new majors, and career course planning is carried out for students.

[0033] The third embodiment of the present invention is a vocational teaching planning system based on individual advantage analysis, the system comprising: Database construction module, which obtains historical recruitment data to build a recruitment database, and obtains student learning data to build a student learning database; A supply and demand assessment module, based on the recruitment database and the student learning database, counts the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, and the supply and demand forecast value of positions; Individual advantage analysis module, which calculates the individual advantage of students in various positions based on the student learning database; The individual planning module conducts career planning for students based on their individual strengths, based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, the predicted value of position supply and demand, and the individual strengths of students; The teaching deployment module obtains the evaluation results of the matching between positions and professional courses, and carries out curriculum planning and teacher deployment at the department end.

[0034] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0035] It should be noted that the career teaching planning system based on individual advantage analysis provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functional distribution can be completed by different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiment can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not regarded as improper limitations of the present invention.

[0036] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the storage device and processing device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0037] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0038] The terms "first", "second", etc. are used to distinguish similar objects rather than to describe or indicate a particular order or sequence.

[0039] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus / device.

[0040] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A vocational teaching planning method based on individual advantage analysis, characterized in that: The method comprises: Step S1, obtaining historical recruitment data to build a recruitment database, and obtaining student learning data to build a student learning database; Step S2, based on the recruitment database and the student learning database, statistics are collected on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, and the forecast value of position supply and demand; Step S3, calculating the individual advantage of students in each position according to the student learning database; Step S4, based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, the predicted value of position supply and demand, and the individual advantage of students, career planning for students based on individual advantage; Step S5, obtaining the evaluation results of the matching between positions and professional courses, and conducting curriculum planning and teacher allocation at the department end.

2. The vocational teaching planning method based on individual advantage analysis according to claim 1 is characterized in that: The step S1 specifically includes: Building a recruitment database: according to time Job recruitment information Time Determine the first associated time interval as ; in, Indicates the constant of the time interval between adjacent campus recruitments; Get all the social recruitment information within the first associated time interval, use the TF_IDF algorithm to extract social recruitment keywords and campus recruitment keywords from the job responsibilities of social recruitment information and campus recruitment information obtained from the word segmentation of historical job recruitment information, and calculate the cosine similarity between social recruitment keywords and campus recruitment keywords, and extract social recruitment information with cosine similarity greater than the similarity threshold as the social recruitment extension information for the same position in the same period of the corresponding campus recruitment information ; in, express Expand the social recruitment information set for the same position during the relevant period; Expand all campus recruitment information to the same position of social recruitment data information and build time Job Recruitment Information Set for ;right The format of campus recruitment and social recruitment information from different sources is unified. After the format is unified time Position Recruitment Information ; in, express time A collection of job recruitment information numbers; The attributes include information source attributes , Match professional attributes and recruitment content attributes , recruitment content attributes Include job title 、Job Responsibilities , Technical requirements for the position Other job requirements ; In the above-mentioned format unification, The matching professional attribute value is determined by the corresponding history time Job recruitment information Matching assignment, when corresponding to history time If the corresponding attribute data is missing in the job recruitment information, the corresponding attribute value will be left blank; Construct the recruitment information set of the same position at different time points into a job position data element , ; Then build the employment database as ; in, express Position data element; Represents a set of job numbers. Indicates the school recruitment time collection; Build a student learning database According to history time Job recruitment information Time Determine the second associated time interval as ; Load the student information of the students who enrolled at the start of the second associated time interval in the second associated time interval, and construct the student information of each major according to the student's major: ; Summarize student information of each major at each time point and build a student learning database ; in, Respectively time major Students’ learning data in various subjects; Represents a professional number set, Indicates the constant of basic course duration, express time A collection of all student IDs for a major.

3. The vocational teaching planning method based on individual advantage analysis according to claim 2 is characterized in that: The step S2 is specifically: Based on the recruitment database and student learning database, a job matching personnel database is constructed. : ； Build job demand : ; Constructing a sequence of successful campus recruitment : ， ； in: ; Represents the set of The number of elements contained; Represents the set of The number of elements contained; The statistical deviation between social recruitment positions and campus recruitment positions is as follows: Construct the job technology set sequence as : ， ； The sequence of job elimination technology sets is constructed as : ; The sequence of newly added technical sets for constructing positions is : ; in, ; Indicates the technical statistics time window, A set of statistical time points of technological change; , They represent the length of the technology statistics time window and the number of statistical time points for technology change respectively; The matching between statistical positions and majors is calculated as follows: Construct the professional-position matching set sequence as , ; in, Indicates the quantity threshold, Indicates the matching degree between major and position. ; Construct a job-professional matching set sequence to count the matching between job and major: ， ； The job supply and demand forecast value is calculated as follows: The supply and demand forecast training data set is constructed as follows: ， ， ； Input the training data pair set into the LSTM time prediction model based on the neural network, complete the training and obtain the trained LSTM time prediction model based on the neural network; Load current campus recruitment time Number of students matching positions , job demand ,Will Input the trained LSTM time prediction model based on neural network, and obtain the output value of the prediction model as the success rate of each position in the current campus recruitment The predicted value is used as the job supply and demand forecast value.

4. The vocational teaching planning method based on individual advantage analysis according to claim 3 is characterized in that: The step S3 is specifically: Step S31: Loading the professional-position matching set sequence , build students of different majors in the same position into a competitive group ; Load the subject scores of each student in each competitive group , and sort them in descending order according to subject scores, and obtain the ranking of each student's subject scores in the competitive group ; in, express time Position competition group; express Student's Basic course results Ranking in the competitive group for the position; Step S32: Loading job skills set and basic courses for students of various majors in the competitive group; According to the matching degree between the job skill set and each basic course, each basic course is assigned a job weight value. ; Step S33, constructing a student individual advantage sequence as the student individual advantage.

5. The vocational teaching planning method based on individual advantage analysis according to claim 4 is characterized in that: The step S32 specifically includes: Step S321, calculate the matching degree between the basic course and each technology in the job technology set as: the ratio of the number of knowledge points covered by the basic course knowledge graph on the corresponding technology knowledge graph to the number of knowledge points covered by the corresponding technology, as the matching degree between the current basic course and the technology; Step S322: Building a job skills set The weight value of each technology is the frequency of occurrence of the corresponding technical knowledge point in the corresponding job recruitment information within the technical change statistical time window corresponding to the job technical change set; Step S323, constructing the position weight of the basic course as follows: ; in, Respectively The post technology set is the collection of all technology numbers; Basic Course Position weight; express Basic courses and The compatibility of technology; express Technical Position weight.

6. The method for vocational teaching planning based on individual advantage analysis according to claim 5 is characterized in that: In step S33, the individual advantage sequence of students is constructed, specifically: Calculate the individual advantage of each student in each competitive group as: ； in, express Students in The individual advantages of the position, Represents the set of all basic course numbers; Find all the competitive groups that each student belongs to, and calculate the individual advantage of each student in each corresponding competitive group; sort the multiple individual advantage degrees from small to large according to the numerical value; use the sorted individual advantage degree sequence as the individual advantage degree of the student in the current position, and construct the individual advantage degree sequence of each student as follows: .

7. The vocational teaching planning method based on individual advantage analysis according to claim 1 is characterized in that: The step S4 is specifically: According to the student's major and the corresponding application time, extract all positions matching the student's major ; Load the supply and demand forecast for each matching position , sort them from high to low according to the numerical value, and record the ordered sequence of supply and demand of each position as ; Load the student's individual advantage sequence , record the student's position advantage number for exist Medium serial number; right For each position in , calculate the recommended priority of the position as ; Arrange the job recommendation priorities from small to large, and record the job recommendation priority sequence as ; Build The recommended job list from highest to lowest student recommendation is: , the best matching position for obtaining the student's individual advantage is ; in, Indicates seeking Corresponding job number ; According to the best matching position based on individual advantage, and according to the position elective weight of each professional course in the best matching position, the required number of professional elective courses are constructed.

8. The vocational teaching planning method based on individual advantage analysis according to claim 7 is characterized in that: In step S5, the job and professional course matching assessment is obtained, specifically: The matching assessment between the position and the professional courses includes the elimination assessment of professional courses, the addition assessment of professional courses and the elective assessment of professional courses; The elimination evaluation method of the professional courses is as follows: Loading post elimination technology set , calculate the job weight value of each professional course, and call this job weight the job elimination weight of the professional course; load , calculate the elimination weight of professional courses as follows: ; in, Respectively mm Professional Courses k Position elimination weight, mm Elimination weights for professional courses; The new assessment method for professional courses is as follows: According to the newly added weight of each professional course, the class hours of each professional course are set in direct proportion, specifically: Load the newly added technical set of positions , calculate the job weight value of each professional course, and call this job weight the new job weight of the professional course; load , calculate the new weight of professional courses as ; in, Respectively mm Professional Courses k New weight added to the position, mm Additional weights for professional courses; The method for elective assessment of professional courses is as follows: Loading job skills set , calculate the job weight value of each professional course, and call this job weight the job elective weight of the professional course; load , calculate the weight of professional elective courses as ; in, Respectively mm Professional Courses k Weight of post electives, mm The elective weight of professional courses.

9. The method for career teaching planning based on individual advantage analysis according to claim 8 is characterized in that: In step S5, the job and professional course matching assessment is obtained and the course planning of the department is carried out, specifically: The professional course elimination mechanism is constructed as follows: according to the elimination evaluation of professional courses, according to the elimination weight of each professional course, the corresponding proportion of professional courses will be eliminated; The mechanism for setting hours for professional courses is as follows: according to the elective evaluation of professional courses and the elective weight of each professional course, the hours for each professional course are set in direct proportion; The mechanism for adding new professional courses is as follows: based on the evaluation of the newness of professional courses, add professional courses in corresponding proportion according to the new weight of each professional course; according to the new weight of each professional course, set the class hours of each professional course in direct proportion.

10. A vocational teaching planning system based on individual advantage analysis, characterized in that: The system comprises: Database construction module, which obtains historical recruitment data to build a recruitment database, and obtains student learning data to build a student learning database; A supply and demand assessment module, based on the recruitment database and the student learning database, counts the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, and the supply and demand forecast value of positions; Individual advantage analysis module, which calculates the individual advantage of students in various positions based on the student learning database; The individual planning module conducts career planning for students based on their individual strengths, based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching between positions and majors, the predicted value of position supply and demand, and the individual strengths of students; The teaching deployment module obtains the evaluation results of the matching between positions and professional courses, and carries out curriculum planning and teacher deployment at the department end.

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