Vocational Teaching Planning Method and System Based on Individual Dominance Analysis

Abstract

The present invention belongs to the field of data analysis, and particularly relates to a vocational teaching planning method and system based on individual dominance analysis, aiming to solve the problem that the current teaching curriculum design fails to effectively connect with the actual needs of positions. The present invention includes: based on the recruitment database and the student learning database, statistically analyzing the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching degree between positions and majors, and the predicted value of position supply and demand; calculating the individual dominance of students on each position; based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching degree between positions and majors, the predicted value of position supply and demand, and the individual dominance of students, conducting vocational planning for students based on individual dominance; obtaining the evaluation results of the matching between positions and professional courses, and conducting curriculum planning and teacher allocation at the department level. The present invention realizes the effect of updating the curriculum arrangement in a timely manner by associating the learning content with the position requirements.

Description

Background Art

[0002] Currently, the mainstream teaching curriculum design system is based on the completeness of theoretical basis and has a low relevance to job requirements. As a result, when students graduate, their academic skills cannot meet the practical job requirements, increasing the learning cost. On the other hand, the curriculum design mode of the separation of production, education and research and the neglect of job requirements makes the lagging courses with strong timeliness still exist in the curriculum system, so there is still a problem that new courses cannot keep up with the times and be updated. Summary of the Invention

[0003] To solve the above problems in the prior art, that is, the currently mainstream teaching curriculum design system in the existing career planning and teaching planning methods is based on the completeness of theoretical basis and has a low relevance to job requirements. As a result, when students graduate, their academic skills cannot meet the practical job requirements, increasing the learning cost, the present invention provides a vocational teaching planning method based on individual advantage degree analysis, and the method includes:

[0004] Step S1, obtaining historical recruitment data to construct a recruitment database, and obtaining students' learning data to construct a students' learning database;

[0005] Step S2, based on the recruitment database and the students' learning database, counting the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching degree between positions and majors, and the predicted value of position supply and demand;

[0006] Step S3, according to the students' learning database, calculating the individual advantage degree of students on each position;

[0007] Step S4, based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching degree between positions and majors, the predicted value of position supply and demand, and the individual advantage degree of students, making a career plan for students based on the individual advantage degree;

[0008] Step S5, obtaining the matching evaluation result between positions and professional courses, and making curriculum planning and teacher allocation at the department level.

[0009] Further, the step S1 specifically includes:

[0010] Constructing a recruitment database:

[0011] According to time campus recruitment information of positions in the determine the first associated time interval as ;

[0012] Wherein, represents the constant of the adjacent campus recruitment time interval;

[0013] Obtain 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 the social recruitment information obtained by word segmentation from historical job recruitment information and the job responsibilities of campus recruitment information respectively, and calculate the cosine similarity between the social recruitment keywords and the campus recruitment keywords. Extract the social recruitment information with a cosine similarity greater than the similarity threshold as the social recruitment expansion information for the same position and the same time period of the corresponding campus recruitment information ;

[0014] Among them, denotes the set of social recruitment expansion information for the same position and the same time period;

[0015] Perform social recruitment data information expansion for all campus recruitment employment information to construct time job recruitment information set as ; For in the campus recruitment and social recruitment information from different sources, perform format standardization, and denote as the time job's recruitment information ;

[0016] Among them, denotes time job recruitment information number set; 's attributes include information source attribute , matching major attribute and recruitment content attribute , and the recruitment content attribute includes job name , job responsibilities , job technical requirements and other job requirements ;

[0017] In the above-mentioned format standardization, 's matching major attribute value is assigned by matching the corresponding historical time campus recruitment information of the job . When the corresponding historical time campus recruitment information of the job lacks data for the corresponding attribute, the corresponding attribute value is set to null;

[0018] Construct the recruitment information sets at each time point for the same position into the data element of this employment position , ; Furthermore, construct the employment-side database as ;

[0019] Among them, represents job data elements; represents the set of job numbers, represents the set of school recruitment times;

[0020] Build a student learning database

[0021] According to historical time job school recruitment information time Determine that the second associated time interval is ;

[0022] Load the student information of the students who entered school at the start of the second associated time interval during the second associated time interval, and construct the student information of each major according to the major to which the students belong as ;

[0023] Summarize the student information at each time point for each major and construct it into a student learning database ;

[0024] Among them, respectively represent time major the learning data of each subject of the student; represents the set of major numbers, represents the basic class hour constant, represents time the set of all student numbers in the major.

[0025] Furthermore, the step S2 is specifically:

[0026] Based on the recruitment database and the student learning database, construct the quantity of personnel matching the job :

[0027] ;

[0028] Construct the job demand quantity : ;

[0029] Construct the sequence of successful job school recruitment quantities :

[0030] , ;

[0031] Among them:

[0032] ; Denotes the number of elements in the set obtained ; Denotes the number of elements in the set obtained ;

[0033] Statistical deviation between social recruitment positions and campus recruitment positions, specifically:

[0034] Construct the sequence of job technical sets as :

[0035] , ;

[0036] Construct the sequence of eliminated job technical sets as : ;

[0037] Construct the sequence of newly added job technical sets as : ;

[0038] Among them, ; Denotes the technical statistics time window, Set of technical change statistical time points; 、 Denote the technical statistics time window length and the number of technical change statistical time points respectively;

[0039] Statistical job and major matching, calculation method:

[0040] Construct the sequence of major-to-job matching sets as , ;

[0041] Among them, Denotes the quantity threshold, Denotes the major and job matching degree, ;

[0042] Construct the sequence of job-to-major matching sets as the statistical job and major matching:

[0043] , ;

[0044] Job supply and demand prediction value, calculation method:

[0045] Construct the set of supply and demand prediction training data pairs as:

[0046] , , ;

[0047] Input the set of training data pairs into the neural network-based LSTM time prediction model, complete the training, and obtain the trained neural network-based LSTM time prediction model;

[0048] Load the current school recruitment time and the number of students matching the positions , the demand for positions , and input

[0049] into the trained neural network-based LSTM time prediction model, and obtain the predicted model output value as the success quantity of each position in the current school recruitment as the predicted value of the position supply and demand prediction.

[0050] Furthermore, step S3 is specifically as follows:

[0051] Step S31, load the sequence of major-to-position matching sets , and construct students of each major in the same position into a competition group ;

[0052] Load the scores of each subject of each student in each competition group , and sort them in descending order according to the subject scores from high to low to obtain the ranking of each student's scores in each subject within the competition group ;

[0053] Among them, represents time position competition group; represents the ranking of the basic course scores of the student in the position competition group;

[0054] Step S32, load the position technology set and the basic courses of students in each competition group;

[0055] According to the matching degree between the position technology set and each basic course, assign a position weight value to each basic course ;

[0056] Step S33, construct the student individual advantage degree sequence as the student individual advantage degree.

[0057] Furthermore, step S32 specifically includes:

[0058] Step S321, calculate the matching degree between the basic course and each technology in the position technology set as: the ratio of the number of knowledge points covered by the basic course knowledge graph appearing on the corresponding technology knowledge graph to the number of knowledge points covered by the corresponding technology, as the matching degree of the current basic course and this technology;

[0059] Step S322, construct the job technology set The weight value of each technology in is the occurrence frequency of the corresponding technical knowledge point in the recruitment information of the corresponding job within the technical change statistical time window corresponding to the job technical change set;

[0060] Step S323, the constructed job weight of the basic course is: ;

[0061] Among them, respectively represent the set of all technology numbers in the job technology set; of the basic course job weight; represents the matching degree between the basic course and technology; represents of the technology job weight.

[0062] Furthermore, in the said step S33, construct the individual superiority degree sequence of students, specifically:

[0063] Calculate the individual superiority degree of each student in each competition group as:

[0064] ;

[0065] Among them, represents the individual superiority degree of the student in the job, represents the set of all basic course numbers;

[0066] Find all the competition groups where each student is located, and calculate the individual superiority degree of each student in the corresponding competition groups respectively; arrange the multiple individual superiority degrees in ascending order according to the numerical value; use the sorted individual superiority degree sequence as the individual superiority degree of the student in the current job, and construct the individual superiority degree sequence of each student as .

[0067] Furthermore, the said step S4 is specifically:

[0068] Extract all the jobs that match the major where the student is located according to the major where the student is located and the corresponding application time ;

[0069] Load the supply and demand volume prediction of each matching job , sort them in descending order according to the numerical value, and record the ordered sequence of the supply and demand volume of each job as ;

[0070] Load the individual superiority degree sequence of the student Record the serial number of the student's position advantage degree as the serial number in ;

[0071] For each position in, calculate the position recommendation priority as ; Arrange the position recommendation priorities from small to large, and record the position recommendation priority sequence as ;

[0072] Construct the position recommendation list with the student's recommendation from high to low as , and obtain the best-matched position of the student's individual advantage degree as ;

[0073] Among them, means to obtain the corresponding position number ;

[0074] According to the best-matched position of the individual advantage degree, construct the required number of professional elective courses according to the position selection weights of each professional course in the best-matched position.

[0075] Furthermore, in step S5, obtain the matching evaluation of the position and the professional course, specifically:

[0076] The said obtaining the matching evaluation of the position and the professional course includes the elimination evaluation of the professional course, the addition evaluation of the professional course, and the selection evaluation of the professional course;

[0077] The method of the elimination evaluation of the professional course is:

[0078] Load the position elimination technology set , calculate the position weight value of each professional course, and call this position weight the position elimination weight of the professional course;

[0079] Load , calculate the elimination weight of the professional course as: ;

[0080] Among them, respectively represent mm the professional course k position elimination weight, mm the elimination weight of the professional course;

[0081] The method of the addition evaluation of the professional course is:

[0082] Set the class hours of each professional course proportionally according to the addition weight of each professional course, specifically: Load the position addition technology set , calculate the job weight values of each specialized course, and refer to this job weight as the newly added job weight of the specialized course.

[0083] Load , calculate the newly added weight of the specialized course as ;

[0084] Among them, respectively represent mm specialized course k the newly added job weight, mm the newly added weight of the specialized course;

[0085] The elective evaluation of the specialized course, the method is:

[0086] Load the job technology set , calculate the job weight values of each specialized course, and refer to this job weight as the job elective weight of the specialized course;

[0087] Load , calculate the elective weight of the specialized course as ;

[0088] Among them, respectively represent mm specialized course k the job elective weight, mm the elective weight of the specialized course.

[0089] Furthermore, in step S5, obtain the matching evaluation between the job and the professional courses and conduct curriculum planning at the department level, specifically:

[0090] Construct a mechanism for eliminating specialized courses as follows: According to the elimination evaluation of the specialized courses, eliminate the corresponding proportion of specialized courses according to the elimination weights of each specialized course;

[0091] Construct a mechanism for setting class hours of specialized courses as follows: According to the elective evaluation of the specialized courses, set the class hours of each specialized course proportionally according to the elective weights of each specialized course;

[0092] Construct a mechanism for adding specialized courses as follows: According to the addition evaluation of the specialized courses, add the corresponding proportion of specialized courses according to the newly added weights of each specialized course; Set the class hours of each specialized course proportionally according to the newly added weights of each specialized course.

[0093] On the other hand, the present invention proposes a vocational teaching planning system based on individual dominance analysis, and the system includes:

[0094] A database construction module, which obtains historical recruitment data to construct a recruitment database and obtains student learning data to construct a student learning database;

[0095] Supply and demand assessment module, based on the recruitment database and the student learning database, to count the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching degree between positions and majors, and the predicted value of position supply and demand;

[0096] Individual advantage degree analysis module, according to the student learning database, to calculate the individual advantage degree of students in each position;

[0097] Individual planning module, based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching degree between positions and majors, the predicted value of position supply and demand, and the individual advantage degree of students, to conduct career planning for students based on the individual advantage degree;

[0098] Teaching deployment module, to obtain the evaluation result of the matching between positions and professional courses, and conduct curriculum planning and teacher deployment at the department level.

[0099] Advantages of the present invention:

[0100] (1) By separately conducting statistical and predictive evaluations at the position end and the student end, calculating the individual advantage degree, and evaluating the matching between occupations and professional courses, and giving adjustment suggestions at the student end and the teaching end respectively, the present invention can enable both the teaching and learning sides to obtain targeted configurations and achieve the effect of keeping up with the times.

[0101] (2) By conducting career planning based on the individual advantage degree at the student end, each student can obtain the best career position recommendation according to personal ability, and then conduct professional course elective planning according to the career position; at the department level, a professional course elimination evaluation, a new course evaluation, and a current professional course elective evaluation are constructed, so that the professional course design of the department can adaptively increase or decrease professional courses and configure class hours according to the position requirements. Description of the drawings

[0102] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, purposes, and advantages of the present application will become more obvious:

[0103] Figure 1 is a schematic flowchart of the vocational teaching planning method based on individual advantage degree analysis in an embodiment of the present invention;

[0104] Figure 2 is a structural block diagram of integrating the vocational teaching planning method based on individual advantage degree analysis in an embodiment of the present invention into a system. Detailed implementation manners

[0105] The following further describes the present application in detail with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention, rather than limiting the invention. Additionally, it should be noted that for the sake of description, only parts related to the relevant invention are shown in the drawings.

[0106] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments 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.

[0107] To more clearly illustrate the vocational teaching planning method based on individual dominance analysis of the present invention, the following combines Figure 1 to elaborate on each step in the embodiments of the present invention.

[0108] The vocational teaching planning method based on individual dominance analysis according to the first embodiment of the present invention includes steps S1 - S5, and each step is described in detail as follows:

[0109] Step S1, obtain historical recruitment data to construct a recruitment database, and obtain student learning data to construct a student learning database;

[0110] In this embodiment, step S1 specifically includes:

[0111] Construct a recruitment database:

[0112] According to time in the on - campus recruitment information of the position to determine the first associated time interval as ; ;

[0113] Wherein, represents the constant of the adjacent on - campus recruitment time interval;

[0114] Obtain all the off - campus recruitment information within the first associated time interval, and use the TF - IDF algorithm to extract off - campus recruitment keywords and on - campus recruitment keywords from the job responsibilities of the off - campus recruitment information and the job responsibilities of the on - campus recruitment information obtained by word - segmenting the historical position recruitment information respectively, and calculate the cosine similarity between the off - campus recruitment keywords and the on - campus recruitment keywords, and extract the off - campus recruitment information with a cosine similarity greater than the similarity threshold as the off - campus recruitment expansion information of the same position and the same time period corresponding to the on - campus recruitment information ;

[0115] Wherein, represents the set of off - campus recruitment expansion information of the same position in the associated time period;

[0116] Expand the off - campus recruitment data information of the same position for all the on - campus recruitment employment information, and construct time the set of position recruitment information as ; For in the on - campus and off - campus recruitment information from different sources, perform format standardization, and denote as after format standardization Time for the job recruitment information

[0117] Among them, represents time set of job recruitment information numbers; The attributes of include information source attribute and matching major attribute and recruitment content attribute The recruitment content attribute includes job name job responsibilities job technical requirements and other technical requirements for the job

[0118] In the above-mentioned format standardization, the value of the matching major attribute is assigned by the corresponding historical time job campus recruitment information When there is no data for the corresponding attribute in the corresponding historical time job campus recruitment information, the corresponding attribute value is set to null;

[0119] The recruitment information sets at each time point for the same job are concentrated to construct job data elements , ; Furthermore, the employment-side database is constructed as ;

[0120] Among them, represents job data elements; represents the set of job numbers, represents the set of campus recruitment times;

[0121] Construct the student learning database

[0122] According to the historical time job campus recruitment information the time to determine that the second associated time interval is ;

[0123] Construct the student information for each major according to the major to which the student belongs as ;

[0124] Summarize the student information at each time point for each major and construct it into the student learning database ;

[0125] Among them, respectively represent time major the learning data of each subject of students; represents the set of major numbers, represents the basic class hour constant, represents time the set of all student numbers of the major.

[0126] Step S2, based on the recruitment database and the student learning database, count the supply and demand of positions and personnel, the deviation between the positions for social recruitment and campus recruitment, the matching degree between positions and majors, and the predicted value of position supply and demand;

[0127] In this embodiment, based on the recruitment database and the student learning database, construct the quantity of personnel matching the position :

[0128] ;

[0129] Construct the position demand quantity : ;

[0130] Construct the sequence of the successful quantity of campus recruitment for positions :

[0131] , ;

[0132] Among them:

[0133] ; represents obtaining the number of elements contained in the set ; represents obtaining the number of elements contained in the set ;

[0134] Count the deviation between the positions for social recruitment and campus recruitment, specifically:

[0135] Construct the sequence of position technology sets as :

[0136] , ;

[0137] Construct the sequence of position elimination technology sets as : ;

[0138] Construct the sequence of position new technology sets as : ;

[0139] Among them, ; represents the technical statistics time window, a set of technical change statistical time points; , respectively represent the technical statistics time window length and the number of technical change statistical time points;

[0140] The matching degree between the statistical position and the major is calculated as follows:

[0141] Construct a sequence of major-to-position matching sets as , ;

[0142] Among them, represents the quantity threshold, represents the matching degree between the major and the position, ;

[0143] Construct a sequence of position-to-major matching sets as the matching degree between the statistical position and the major:

[0144] , ;

[0145] The predicted value of position supply and demand is calculated as follows:

[0146] Construct a set of supply and demand prediction training data pairs as:

[0147] , , ;

[0148] Input the set of training data pairs into the LSTM time prediction model based on neural network, and complete the training to obtain the trained LSTM time prediction model based on neural network;

[0149] Load the current school recruitment time and the number of students matching the position , the position demand , and input

[0150] into the trained LSTM time prediction model based on neural network, and obtain the output value of the prediction model as the predicted value of the success quantity of each position in the current school recruitment as the predicted value of the position supply and demand.

[0151] Step S3: Calculate the individual advantages of students in each position according to the student learning database;

[0152] In this embodiment, the step S3 is specifically:

[0153] Step S31, load the professional matching set sequence for the position , and construct each group of students with the same major for the same position into a competition group ;

[0154] Load the scores of each subject of each student in each competition group , and sort them in descending order according to the subject scores from high to low to obtain the ranking of each student's scores in each subject within the competition group ;

[0155] Among them, represents time the position competition group; represents of the student basic course score in the ranking in the position competition group;

[0156] Step S32, load the position technology set and the basic courses of each major student in the competition group;

[0157] According to the matching degree between the position technology set and each basic course, assign a position weight value to each basic course , and obtain the individual advantage degree of each student on each position.

[0158] In this embodiment, the step S32 specifically includes:

[0159] Step S321, calculate the matching degree between the basic course and each technology in the position technology set as: the ratio of the number of knowledge points covered by the basic course knowledge graph appearing on the corresponding technology knowledge graph to the number of knowledge points covered by the corresponding technology, as the matching degree of the current basic course and this technology;

[0160] Step S322, construct the weight value of each technology in the position technology set as the occurrence frequency of the corresponding technology knowledge points in the position recruitment information corresponding to the position technology change set within the corresponding technology change statistical time window;

[0161] Step S323, construct the position weight of the basic course as: ;

[0162] Among them, respectively represent the set of all technology numbers in the position technology set; of the basic course position weight; represents the matching degree between the basic course and technology; represents of the technology Job weight.

[0163] In this embodiment, the individual superiority degree of the students in the current job is specifically:

[0164] Calculate the individual superiority degree of each student in each competition group as:

[0165] ;

[0166] Among them, represents the individual superiority degree of the student in the job, represents the set of all basic course numbers;

[0167] Find all the competition groups where each student is located, and calculate the individual superiority degree of each student in the corresponding competition groups respectively; arrange the multiple individual superiority degrees in ascending order according to the numerical value; use the sorted individual superiority degree sequence as the individual superiority degree of the students in the current job .

[0168] Step S4: Based on the job and the supply and demand of personnel, the deviation between the social recruitment job and the campus recruitment job, the matching degree between the job and the major, the job supply and demand prediction value, and the individual superiority degree of the students, conduct career planning for the students based on the individual superiority degree and construct the required number of professional elective courses;

[0169] In this embodiment, the step S4 is specifically:

[0170] Extract all the jobs that match the major where the student is located according to the major where the student is located and the corresponding application time ;

[0171] Load the prediction of the supply and demand of each matching job , sort them in descending order according to the numerical value, and record the ordered sequence of the supply and demand of each job as ;

[0172] Load the individual superiority degree sequence of the students , and record the serial number of the job superiority degree of the students as the serial number in ;

[0173] For each job in , calculate the job recommendation priority as ; arrange the job recommendation priorities in ascending order, and record the job recommendation priority sequence as ;

[0174] Construct a job recommendation list with the students recommended from high to low as , the best-matched position obtained for the individual advantage degree of the student is ;

[0175] Among them, represents obtaining the corresponding position number ;

[0176] According to the best-matched position of the individual advantage degree, construct the required number of professional elective courses according to the position selection weights of each professional course in the best-matched position.

[0177] Step S5, obtain the matching evaluation result of the position and the professional course, and conduct curriculum planning and teacher allocation at the department level.

[0178] In this embodiment, in step S5, obtaining the matching evaluation of the position and the professional course is specifically:

[0179] The obtaining of the matching evaluation of the position and the professional course includes the elimination evaluation of the professional course, the addition evaluation of the professional course, and the selection evaluation of the professional course;

[0180] The method of the elimination evaluation of the professional course is:

[0181] Load the position elimination technology set , calculate the position weight value of each professional course, and call this position weight the position elimination weight of the professional course;

[0182] Load , calculate the elimination weight of the professional course as: ;

[0183] Among them, respectively represent mm professional course k position elimination weight, mm the elimination weight of the professional course;

[0184] The method of the addition evaluation of the professional course is:

[0185] Set the class hours of each professional course proportionally according to the addition weight of each professional course. Specifically: Load the position addition technology set , calculate the position weight value of each professional course, and call this position weight the position addition weight of the professional course.

[0186] Load , calculate the addition weight of the professional course as ;

[0187] Among them, respectively represent mm professional course k position addition weight, mm the addition weight of the professional course;

[0188] For the elective evaluation of professional courses, the method is as follows:

[0189] Load the job technical set , calculate the job weight value of each professional course, and call this job weight the job elective weight of the professional course;

[0190] Load , calculate that the elective weight of the professional course is ;

[0191] Among them, respectively represent mm professional course k job elective weight, mm elective weight of the professional course.

[0192] In step S5, obtain the job and professional course matching evaluation and perform curriculum planning at the department level, specifically:

[0193] Construct a professional course elimination mechanism as follows: According to the elimination evaluation of professional courses, eliminate the corresponding proportion of professional courses according to the elimination weight of each professional course;

[0194] Construct a professional course class hour setting mechanism as follows: According to the elective evaluation of professional courses, set the class hours of each professional course proportionally according to the elective weight of each professional course;

[0195] Construct a professional course addition mechanism as follows: According to the addition evaluation of professional courses, add the corresponding proportion of professional courses according to the addition weight of each professional course; Set the class hours of each professional course proportionally according to the addition weight of each professional course.

[0196] Although the above steps are described in the above order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of this embodiment, different steps do not have to be executed in such an order, and they can be executed simultaneously (in parallel) or in a reversed order, and these simple changes are all within the protection scope of the present invention.

[0197] The second embodiment of the present invention provides a solution for integrating the vocational teaching planning method based on individual dominance analysis in the first embodiment into a system. Refer to Figure 2 , including:

[0198] Recruitment database, student learning database, prediction system, occupation and major matching evaluation system, dominance analysis system and planning system;

[0199] The recruitment database stores historical recruitment data and is communicatively connected to the occupation and major matching evaluation system; The historical recruitment data includes historical employment data and historical social recruitment data;

[0200] The student learning database stores students' learning data and is communicatively connected to the career and major matching evaluation system, prediction system, and dominance analysis system;

[0201] Through the career and major matching evaluation system, based on the data stored in the recruitment database and the student learning database, the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching degree between positions and majors, and the predicted value of position supply and demand will be statistically calculated;

[0202] According to the student learning database, through the prediction system, industry and student prediction evaluations are respectively carried out, and through the dominance analysis system, the individual dominance of students in each position is obtained;

[0203] The career and major matching evaluation system gives the technology set for course elimination or addition by calculating the matching degree between the existing position technology set and each basic course.

[0204] Finally, through the planning system, the old specialized courses are eliminated and the preparation of new majors is carried out, and career course planning is carried out for students.

[0205] The vocational teaching planning system based on individual dominance analysis according to the third embodiment of the present invention, the system includes:

[0206] The database construction module obtains historical recruitment data to construct a recruitment database and obtains students' learning data to construct a student learning database;

[0207] The supply and demand evaluation module, based on the recruitment database and the student learning database, statistically calculates the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching degree between positions and majors, and the predicted value of position supply and demand;

[0208] The individual dominance analysis module calculates the individual dominance of students in each position according to the student learning database;

[0209] The individual planning module conducts career planning for students based on individual dominance based on the supply and demand of positions and personnel, the deviation between social recruitment positions and campus recruitment positions, the matching degree between positions and majors, the predicted value of position supply and demand, and the individual dominance of students;

[0210] The teaching deployment module obtains the matching evaluation results of positions and professional courses and conducts curriculum planning and teacher deployment at the department level.

[0211] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and related descriptions of the above-described system can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0212] It should be noted that the vocational teaching planning system based on individual dominance analysis provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be combined into one module or further split 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 used to distinguish each module or step and are not regarded as an improper limitation of the present invention.

[0213] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes and related descriptions of the above-described storage device and processing device can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0214] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in the form of 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.

[0215] The terms "first", "second", etc. are used to distinguish similar objects and are not used to describe or represent a specific order or sequence.

[0216] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, so that a process, method, article, or device / equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent in these processes, methods, articles, or devices / equipment.

[0217] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art 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 all 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 advantages of students, career planning for students based on individual advantages; Step S5, obtaining the evaluation results of the matching between positions and professional courses, and conducting course planning and teacher allocation at the department end; Obtain the results of the job and professional course matching assessment, specifically: The evaluation results of the matching between positions and professional courses include the elimination evaluation of professional courses, the addition evaluation of professional courses and the elective evaluation 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 The elimination weight of professional courses, for t Time and students’ j All positions matching professional requirements; 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; And obtain the evaluation results of the matching between positions and professional courses and carry out course planning on the department side, 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.

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 set to 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 social recruitment 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, , Represents 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 forecast value is used as the forecast value of job supply and demand.

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 method for career teaching planning 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 3 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 forecast values ​​of supply and demand for each position, sort them from high to low according to the value, and record the ordered sequence of supply and demand for 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. 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 conducts course planning and teacher deployment at the department end; Obtain the results of the job and professional course matching assessment, specifically: The evaluation results of the matching between positions and professional courses include the elimination evaluation of professional courses, the addition evaluation of professional courses and the elective evaluation 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 The elimination weight of professional courses, for t Time and students’ j All positions matching professional requirements; 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; And obtain the evaluation results of the matching between positions and professional courses and carry out course planning on the department side, 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.

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