CN118674242B - A student career planning method based on AI algorithm - Google Patents

Abstract

The embodiment of the present invention discloses a student career planning method based on AI algorithm, which is applied to the field of student management technology. The method includes: obtaining career planning request information of a target user, identifying content data of the career planning request information, and judging whether the career planning request information includes occupational data according to the content data; if the career planning request information includes occupational data, taking the occupation corresponding to the occupational data as the target occupation, and the occupational data includes at least one of occupational direction data and occupational name data; if the career planning request information does not include occupational data, generating recommended occupational data through basic information of the target user, sending the recommended occupational data to the target user, obtaining selection information of the target user for the recommended occupational data, and taking the occupation corresponding to the selection information as the target occupation; obtaining demand information of the target occupation, and generating the life planning of the target user based on the demand information.

Description

AI algorithm-based student career planning method

Technical Field

The invention relates to the technical field of student management, in particular to a student career planning method based on an AI algorithm.

Background

The student career planning can help students to learn knowledge more purposefully, but in practical application, when helping students to carry out career planning, the students often have no explicit purpose, for example, just-admitted students often only have a more fuzzy directional planning, and the existing career planning method is mainly aimed at students with explicit careers, ignoring students without explicit targets which need career planning, so before specific planning, some careers are often required to be recommended for the students, and all the careers related to the students are easily recommended to users (students) during recommendation, which is not beneficial to the selection of the users (students) and further influences the efficiency of career planning.

Disclosure of Invention

In view of the above, the invention provides a career planning method for students based on an AI algorithm, which is used for solving the problems that in the prior art, all occupations related to the profession of the students are recommended to users (students), so that the selection of the users (students) is not facilitated, and the efficiency of career planning is further affected.

In a first aspect, an embodiment of the present invention provides a method for career planning for students based on an AI algorithm, where the method includes:

Acquiring career planning request information of a target user, identifying content data of the career planning request information, and judging whether the career planning request information contains professional data according to the content data;

If the career planning request information contains professional data, selecting a professional corresponding to the professional data as a target professional in a preset professional database according to basic information of the target user, wherein the professional data comprises at least one of professional direction data and professional name data, and the basic information is information for representing an identity of the target user;

If the career planning request information does not contain professional data, generating recommended professional data through basic information of the target user based on an AI algorithm, sending the recommended professional data to the target user, acquiring selection information of the target user for the recommended professional data, and taking the profession corresponding to the selection information as a target profession, wherein the AI algorithm at least comprises a preset priori probability algorithm and a prediction algorithm;

and acquiring the demand information of the target occupation, and generating a career plan of the target user based on the demand information and the basic information.

Optionally, if the career planning request information includes occupation data, selecting, in a preset occupation database, an occupation corresponding to the occupation data as a target occupation according to basic information of the target user, where the occupation data includes at least one of occupation direction data and occupation name data, and the method includes:

If the career planning request information contains career direction data, selecting a career corresponding to the career direction data in a preset career database to obtain an initial career set, and acquiring skill demand data of each career in the initial career set;

acquiring course data of the target user based on the basic information;

and determining skill demand data matched with the course data as target demands, and screening preselected professions corresponding to the target demands according to preset selection rules to obtain target professions.

Optionally, the step of screening the pre-selected profession corresponding to the target requirement according to a preset selection rule to obtain the target profession includes:

Obtaining importance degree data of the target demand aiming at the preselected occupation;

And taking the preselected occupation with the importance degree data larger than a threshold value as the target occupation according to the preset selection rule.

Optionally, the method further comprises:

Updating the preset occupation database according to a preset period, and selecting an occupation corresponding to the job data from the updated preset occupation database according to the basic information of the target user as an updated occupation;

if the updated occupation is inconsistent with the target occupation, generating alarm information and acquiring feedback information of the user aiming at the alarm information;

When the feedback information is used for representing that the target occupation is replaced by the update occupation, acquiring update demand information of the update occupation based on the feedback information, and generating an update career plan of the target user based on the update demand information and the basic information;

When the feedback information is empty, acquiring first time data for generating the lifetime planning and second time data for generating alarm information, and calculating a time interval according to the first time data and the second time data;

And if the time interval is smaller than a preset value, acquiring the update demand information of the update occupation, and generating the update career plan of the target user based on the update demand information and the basic information.

Optionally, the step of generating recommended occupation data based on the AI algorithm through the basic information of the target user and sending the recommended occupation data to the target user further includes:

Acquiring professional data of the target user according to the basic information;

acquiring historical employment information corresponding to the professional data based on the professional data, wherein the historical employment information at least comprises different professions and priori data of the different professions;

Determining prior probability distribution data of different occupations according to the prior data and a preset prior probability algorithm in the AI-based algorithm;

defining a likelihood function based on preset observation data and the prior data;

According to the Bayesian theorem, combining prior distribution and likelihood functions to obtain posterior distribution functions, constructing a prediction algorithm in an AI algorithm according to the posterior distribution functions, and predicting professional characteristic parameters of different professions based on the prediction algorithm;

And taking the job characteristic parameters as recommended job data, and sending the recommended job data to the target user.

Optionally, the step of using the job characteristic parameter as recommended job data and sending the recommended job data to the target user includes:

Acquiring preset information of the target user, and assigning values for each occupational characteristic parameter according to a preset assignment rule to obtain a characterization value of each occupational characteristic parameter;

calculating the score of the occupation corresponding to the recommended occupation data based on the characterization value;

and sequencing different professions according to the order of the scores from high to low to obtain the recommended sequence of the profession corresponding to the recommended professional data, and sending the recommended professional data to the target user according to the recommended sequence.

Optionally, the step of acquiring the requirement information of the target occupation and generating the lifetime planning of the target user based on the requirement information and the basic information includes:

Acquiring the demand information of the target occupation, and carrying out quantitative processing on the demand information based on a preset evaluation standard to obtain quantitative standard of the target occupation;

Performing quantitative processing on the basic information based on a preset evaluation standard to obtain the capability score of the target user;

and comparing the quantitative standard with the capability score to obtain comparison information, and generating a career plan of the target user according to the comparison information.

Optionally, the step of generating a lifetime plan of the target user according to the comparison information includes:

determining data to be lifted of the target user based on the comparison information;

determining first content in the lifetime planning according to the data to be lifted and the course data;

acquiring campus activity data of the target user based on the basic information;

determining second content in the lifetime planning according to the data to be lifted and the campus activity data;

And generating a career plan of the target user according to the first content and the second content.

Optionally, the method further comprises:

Acquiring learning information of the target user;

Selecting a occupation corresponding to the learning information from a preset occupation database based on the learning information as an alternative occupation;

when the alternative occupation is different from the target occupation, generating replacement information, and acquiring control information of the user for the replacement information;

When the control information is used for representing that the target occupation is replaced by the alternative occupation, alternative demand information of the alternative occupation is obtained based on the control information, and an alternative life planning of the target user is generated based on the alternative demand information and the basic information.

In another aspect, the present application provides an AI algorithm-based student lifetime planning apparatus, the apparatus comprising:

The system comprises a data acquisition module, a user management module and a user management module, wherein the data acquisition module is used for acquiring career planning request information of a target user, identifying content data of the career planning request information and judging whether the career planning request information contains professional data according to the content data;

The first recommending module is used for selecting a career corresponding to the career data as a target career in a preset career database according to basic information of the target user if the career planning request information contains career data, wherein the career data comprises at least one of career direction data and career name data, and the basic information is information used for representing the identity of the target user;

The second recommending module is used for generating recommended occupation data through basic information of the target user based on an AI algorithm if the career planning request information does not contain occupation data, sending the recommended occupation data to the target user, acquiring selection information of the target user for the recommended occupation data, and taking occupation corresponding to the selection information as a target occupation, wherein the AI algorithm at least comprises a preset priori probability algorithm and a prediction algorithm;

and the planning module is used for acquiring the demand information of the target occupation and generating a lifetime plan of the target user based on the demand information and the basic information.

According to the technical scheme, through obtaining career planning request information of a target user, content data of the career planning request information are identified, whether the career planning request information contains professional data or not is judged according to the content data, if the career planning request information contains the professional data, a professional corresponding to the career data is selected in a preset professional database according to basic information of the target user to serve as a target professional, the career data comprises at least one of professional direction data and professional name data, the basic information is information used for representing identity identification of the target user, if the career planning request information does not contain the professional data, recommended professional data are generated through the basic information of the target user based on an AI algorithm, the recommended professional data are sent to the target user, selection information of the target user for the recommended professional data is obtained, the professional corresponding to the selection information is taken as a target, the AI algorithm at least comprises a preset probability algorithm and a prediction algorithm, the requirement information of the target is obtained, and the prior requirement information of the target user is generated based on the requirement information of the target user and the basic career planning request information. When facing a target user (student) with an explicit purpose (professional direction data and professional name data), the method can select the profession corresponding to the professional data in a preset professional database as a target profession according to basic information of the target user, further complete career planning, when facing a target user (student) without the explicit purpose, the method can generate recommended professional data through the basic information of the target user based on an AI algorithm, and send the recommended professional data to the target user for selection of the target user (student), meanwhile, the method can accurately recommend possible professions for the target user (student) based on the AI algorithm and the basic information, avoid data redundancy caused by invalid recommendation, facilitate selection of the target user (student), and improve efficiency.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Wherein:

Fig. 1 is a schematic flow chart of a student career planning method based on an AI algorithm in one embodiment;

fig. 2 is a schematic structural diagram of a career planning device based on an AI algorithm in an embodiment;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

Fig. 4 is a schematic diagram of a computer readable storage structure according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In an embodiment, the present invention provides an AI algorithm-based student career planning method, which may be executed by an AI algorithm-based student career planning apparatus. The AI algorithm-based student career planning apparatus may be implemented by software and/or hardware.

As shown in fig. 1, the student career planning method based on the AI algorithm according to the embodiment of the present invention specifically includes the following steps:

s110, acquiring career planning request information of a target user, identifying content data of the career planning request information, and judging whether the career planning request information contains professional data or not according to the content data;

The method includes the steps that when an execution subject is a client, the client receives career planning request information of a target user, specifically, the career planning request information of the target user can be received through modes of voice input, text input and the like of the user, and when the execution subject is a server, the server receives career planning request information of the target user through user equipment (mobile phone, computer and other electronic equipment).

Specifically, the execution body may be selected according to the calculated force pressure.

S120, if the career planning request information contains professional data, selecting a professional corresponding to the professional data as a target professional in a preset professional database according to basic information of the target user, wherein the professional data comprises at least one of professional direction data and professional name data, and the basic information is information for representing an identity of the target user;

The job data includes at least one of job direction data, such as a design direction, a manufacturing direction, and a corresponding job of a clothing designer, a software designer, a garden planner, etc., and job name data, such as names of specific jobs of patent agents, lawyers, etc.

For example, the basic information may be academic information of the target user, and course data, school information, professional data, campus activity data, etc. of the target user may be determined based on the academic information.

S130, if the career planning request information does not contain professional data, generating recommended professional data through basic information of the target user based on an AI algorithm, sending the recommended professional data to the target user, acquiring selection information of the target user for the recommended professional data, and taking the professional corresponding to the selection information as a target professional, wherein the AI algorithm at least comprises a preset priori probability algorithm and a prediction algorithm;

and S140, acquiring the demand information of the target occupation, and generating a career plan of the target user based on the demand information and the basic information.

The method comprises the steps of obtaining career planning request information of a target user, identifying content data of the career planning request information, judging whether the career planning request information comprises professional data according to the content data, selecting a professional corresponding to the career planning request information as a target professional in a preset professional database according to basic information of the target user if the career planning request information comprises the professional data, wherein the career planning request information comprises at least one of professional direction data and professional name data, the basic information is information used for representing identity identification of the target user, generating recommended professional data based on AI algorithm through the basic information of the target user if the career planning request information does not comprise the professional data, sending the recommended professional data to the target user, obtaining selection information of the target user aiming at the recommended professional data, taking the professional corresponding to the selection information as the target professional, obtaining requirement information of the target professional according to basic information, and generating the target planning information of the target user based on the requirement information and the target basic information. When facing a target user (student) with an explicit purpose (professional direction data and professional name data), the method can select the profession corresponding to the professional data in a preset professional database as a target profession according to basic information of the target user, further complete career planning, when facing a target user (student) without the explicit purpose, the method can generate recommended professional data through the basic information of the target user based on an AI algorithm, and send the recommended professional data to the target user for selection of the target user (student), meanwhile, the method can accurately recommend possible professions for the target user (student) based on the AI algorithm and the basic information, avoid data redundancy caused by invalid recommendation, facilitate selection of the target user (student), and improve efficiency.

In one possible implementation manner, if the career planning request information includes professional data, selecting, as a target professional, a professional corresponding to the career data in a preset professional database according to basic information of the target user, where the career data includes at least one of professional direction data and professional name data, and the method includes:

If the career planning request information contains career direction data, selecting a career corresponding to the career direction data in a preset career database to obtain an initial career set, and acquiring skill demand data of each career in the initial career set;

acquiring course data of the target user based on the basic information;

and determining skill demand data matched with the course data as target demands, and screening preselected professions corresponding to the target demands according to preset selection rules to obtain target professions.

For example, when the target user (student) has only a blurred target (only a professional direction and no specific profession), the professional corresponding to the professional direction data is selected in the preset professional database according to the blurred target (professional direction data) of the target user (student), and a large number of professions corresponding to the professional direction data are searched out according to the blurred target (professional direction data), for example, when the professional direction data is a design direction (designer), a professional belonging to the design direction such as a "software engineer", "clothing designer", "building designer" is searched out, if all professions belonging to the design direction such as a "software engineer", "clothing designer", "building designer" are recommended to the target user (student), professional selection is not facilitated for the target user (student), and professional planning efficiency is reduced.

In a possible implementation manner, the step of screening the pre-selected profession corresponding to the target requirement according to a preset selection rule to obtain the target profession includes:

Obtaining importance degree data of the target demand aiming at the preselected occupation;

And taking the preselected occupation with the importance degree data larger than a threshold value as the target occupation according to the preset selection rule.

For example, the importance degree data may be determined by the frequency of use of professional software, for example, the programming software is used every day in the work of the a-profession, and the importance degree data of the programming skills corresponding to the programming software in the a-profession may be understood to be important.

Specifically, the preset selection rule may be an assignment method, for example, the programming software is used daily in the work of the a-profession, the importance degree data of the programming skills corresponding to the programming software in the a-profession may be assigned to 5, the importance degree data of the programming skills corresponding to the programming software in the B-profession may be assigned to 3 when the programming software is used once per week in the work of the B-profession, the importance degree data of the programming skills corresponding to the programming software in the C-profession may be assigned to 2 when the programming software is used once per month in the work of the C-profession, the threshold value is set to 1, and the preselected profession with a lower association degree may be deleted to reduce to the profession recommended by the target user.

In one possible embodiment, the method further comprises:

Updating the preset occupation database according to a preset period, and selecting an occupation corresponding to the job data from the updated preset occupation database according to the basic information of the target user as an updated occupation;

if the updated occupation is inconsistent with the target occupation, generating alarm information and acquiring feedback information of the user aiming at the alarm information;

When the feedback information is used for representing that the target occupation is replaced by the update occupation, acquiring update demand information of the update occupation based on the feedback information, and generating an update career plan of the target user based on the update demand information and the basic information;

When the feedback information is empty, acquiring first time data for generating the lifetime planning and second time data for generating alarm information, and calculating a time interval according to the first time data and the second time data;

And if the time interval is smaller than a preset value, acquiring the update demand information of the update occupation, and generating the update career plan of the target user based on the update demand information and the basic information.

For example, as society develops, new professions will continuously emerge, so the preset profession database is updated according to a preset period (for example, three months), so as to ensure that professions are recommended to target users following the society development, avoid hysteresis of lifetime planning, and ensure reliability of lifetime planning.

In one possible implementation manner, the step of generating recommended occupation data based on the AI algorithm through the basic information of the target user and sending the recommended occupation data to the target user further includes:

Acquiring professional data of the target user according to the basic information;

acquiring historical employment information corresponding to the professional data based on the professional data, wherein the historical employment information at least comprises different professions and priori data of the different professions;

Determining prior probability distribution data of different occupations according to the prior data and a preset prior probability algorithm in the AI-based algorithm;

defining a likelihood function based on preset observation data and the prior data;

According to the Bayesian theorem, combining prior distribution and likelihood functions to obtain posterior distribution functions, constructing a prediction algorithm in an AI algorithm according to the posterior distribution functions, and predicting professional characteristic parameters of different professions based on the prediction algorithm;

And taking the job characteristic parameters as recommended job data, and sending the recommended job data to the target user.

Illustratively, the prior data may be understood as average income, employment rate, work satisfaction, skill requirements, etc. of the profession, and the process of obtaining historical employment information corresponding to the professional data based on the professional data may be implemented by obtaining employment data of the same professional graduate.

The process of determining the prior probability distribution data of different professions according to the prior data and the preset prior probability algorithm based on the AI algorithm is exemplified, for example, the prior probability distribution data of different professions is calculated through a normal distribution, binomial distribution, beta distribution and other prior probability algorithms (preset prior probability algorithm), for example, the prior probability distribution data of the professional characteristics M in the prior data is calculated by using binomial distribution, and in particular, the prior probability distribution data is calculated by using different algorithms according to different professional characteristics.

In one possible implementation manner, the step of using the job characteristic parameter as recommended job data and sending the recommended job data to the target user includes:

Acquiring preset information of the target user, and assigning values for each occupational characteristic parameter according to a preset assignment rule to obtain a characterization value of each occupational characteristic parameter;

calculating the score of the occupation corresponding to the recommended occupation data based on the characterization value;

and sequencing different professions according to the order of the scores from high to low to obtain the recommended sequence of the profession corresponding to the recommended professional data, and sending the recommended professional data to the target user according to the recommended sequence.

For example, because the information of interest of different target users is different, for example, the promotion prospect of the occupation interest of the target user E, the average income of the occupation interest of the target user F is the same, so that the assignment is performed for each occupation characteristic parameter according to the preset information of the target user and the preset assignment rule, specifically, the most interesting occupation characteristic parameter of the target user is assigned in a percentage system, the second interesting occupation characteristic parameter of the target user is assigned in a ten-way system, the score of the occupation is obtained, the different occupation is ordered according to the order of the score from high to low, the recommendation order of the occupation corresponding to the recommended occupation data is obtained, and the recommended occupation data is sent to the target user according to the recommendation order.

In one possible embodiment, the step of obtaining the requirement information of the target occupation and generating the lifetime plan of the target user based on the requirement information and the basic information includes:

Acquiring the demand information of the target occupation, and carrying out quantitative processing on the demand information based on a preset evaluation standard to obtain quantitative standard of the target occupation;

Performing quantitative processing on the basic information based on a preset evaluation standard to obtain the capability score of the target user;

and comparing the quantitative standard with the capability score to obtain comparison information, and generating a career plan of the target user according to the comparison information.

The requirements (each requirement information) of each occupation are quantitatively processed to obtain quantitative standards of the target occupation, a weight is set for different requirement information, and finally a final score is obtained by multiplying the weight according to the quantitative standards of each requirement information, so that the requirements of the target occupation on the capability score can be met by calculating the total score. The Java software engineer can meet the requirement of 50 total scores, the skill of developing language Java is weighted 80, foreign language 10 and mathematics 10, the ability of the last student is divided into 0, mathematics are divided into 0, java is divided into 70, and the final score can reach 56, so that the requirement can be met.

By means of the recruitment information acquisition method, the recruitment information acquisition device and the recruitment information acquisition system, the recruitment information acquisition device can be used for acquiring the recruitment information according to the recruitment information, and the recruitment information acquisition device can be used for acquiring the recruitment information according to the recruitment information.

In one possible embodiment, the step of generating the lifetime plan of the target user according to the comparison information includes:

determining data to be lifted of the target user based on the comparison information;

determining first content in the lifetime planning according to the data to be lifted and the course data;

acquiring campus activity data of the target user based on the basic information;

determining second content in the lifetime planning according to the data to be lifted and the campus activity data;

And generating a career plan of the target user according to the first content and the second content.

By way of example, more and more professions may require a practitioner to have some capability, such as social capability, not available from a lesson, and thus, when the target user's data to be promoted includes social capability and professional capability, a first content in the career plan is determined according to the professional capability in the data to be promoted and the lesson data, the first content user is used to promote the professional capability of the target user, and a second content in the career plan is determined according to the social capability in the data to be promoted and the campus activity data, the second content is used to promote the social capability of the target user.

In one possible embodiment, the method further comprises:

Acquiring learning information of the target user;

Selecting a occupation corresponding to the learning information from a preset occupation database based on the learning information as an alternative occupation;

when the alternative occupation is different from the target occupation, generating replacement information, and acquiring control information of the user for the replacement information;

When the control information is used for representing that the target occupation is replaced by the alternative occupation, alternative demand information of the alternative occupation is obtained based on the control information, and an alternative life planning of the target user is generated based on the alternative demand information and the basic information.

For example, in the learning process of the target user, the target user often learns some abilities (learning information) other than the special skills (fields) due to interests, for example, when medical student learning software programs, and when planning for students, the interests of the students should be considered, that is, the profession corresponding to the learning information is selected in a preset professional database based on the learning information as an alternative profession, so that the reasonability and the comprehensiveness of career planning are ensured.

In a specific student career planning process, one possible implementation is:

collecting basic information (professional, grade) of students, learning achievements, selected courses including non-professional courses, extracurricular activities, hobbies and the like, quantifying the interest intensity and preference direction of students through questionnaire adjustment modes, online generation analysis and the like, constructing a data set containing wide occupation types, responsibilities, required skills, market demands and development trends based on stored priori data and student basic information, associating a series of skill requirements and potential interest labels with each occupation, filling missing values, removing noise data and standardized processing by using a data cleaning algorithm in the process, ensuring the accuracy and consistency of the data, converting unstructured or semi-structured data into a vectorization form which can be processed by a machine learning algorithm, and converting file information into numerical representation (quantitative processing) by adopting word embedding, independent heat coding and label coding.

The method comprises the steps of matching basic information of students with technical requirements in preset professional data sets, particularly non-professional skills, identifying skill sets mastered or learned by the students, analyzing interest labels of the students and interest correlations in the professional data sets by means of a machine learning algorithm (one of AI algorithms), establishing a mapping relation between the interests and the professions, and analyzing future development potential and market demands of each profession by means of an external data source (industry report and recruitment trend). Extracting key information in a text by using NLP, screening important features by using a statistical method (correlation analysis and Principal Component Analysis (PCA)), selecting features by using a filtering method (based on variance selection and chi-square test) and a wrapping method (recursive feature elimination) in the extracted feature set, performing classified regression prediction by using supervised learning (logistic regression algorithm, decision tree algorithm and random forest algorithm in AI algorithm), finding potential interest groups by using clustering (K-Means), and updating interest labels of students;

Based on skill matching degree, interest correlation and market demand prediction, a target occupation list (recommended occupation data) which is most suitable for students is screened out, further target users (students) select target occupation, detailed career planning paths are generated for each target occupation, the detailed career planning paths comprise further learned courses, training experiences, technical lifting suggestions and possible career stages, and according to student feedback and actual condition changes, a recommendation algorithm and a planning path are continuously optimized, so that continuous effectiveness and adaptability of career planning are ensured. Based on the technology matching degree, interest correlation and market demand prediction of students, collaborative filtering (User-based and Item-based), content-based recommendation (Content-based) and Hybrid recommendation (Hybrid) are adopted to conduct personalized recommendation, and the most suitable professional path is recommended for the students.

In one possible embodiment, the present application provides an AI algorithm-based student career planning apparatus, the apparatus comprising:

The data acquisition module 201 is configured to acquire career planning request information of a target user, identify content data of the career planning request information, and determine whether the career planning request information includes professional data according to the content data;

A first recommending module 202, configured to select, if the career planning request information includes professional data, a professional corresponding to the professional data as a target professional in a preset professional database according to basic information of the target user, where the professional data includes at least one of professional direction data and professional name data, and the basic information is information for characterizing an identity of the target user;

A second recommending module 203, configured to generate recommended occupation data according to basic information of the target user based on an AI algorithm if the career planning request information does not include occupation data, and send the recommended occupation data to the target user, obtain selection information of the target user for the recommended occupation data, and use an occupation corresponding to the selection information as a target occupation, where the AI algorithm at least includes a preset priori probability algorithm and a prediction algorithm;

The planning module 204 is configured to obtain requirement information of the target occupation, and generate a lifetime plan of the target user based on the requirement information and the basic information.

It should be noted that the above-mentioned devices include the respective modules that are only divided according to the functional logic, but not limited to the above-mentioned division, as long as the corresponding functions can be implemented, and the specific names of the respective functional modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiments of the present invention.

In another embodiment of the invention, an electronic device is also provided. Fig. 3 shows a block diagram of an exemplary electronic device 50 suitable for use in implementing the embodiments of the present invention. The electronic device 50 shown in fig. 3 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

As shown in fig. 3, the electronic device 50 is embodied in the form of a general purpose computing device. The components of electronic device 50 may include, but are not limited to, one or more processors or processing units 501, a system memory 502, and a bus 503 that connects the various system components (including system memory 502 and processing units 501).

Bus 503 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 50 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by electronic device 50 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 502 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 504 and/or cache memory 505. Electronic device 50 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 506 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, commonly referred to as a "hard disk drive"). Although not shown in fig. 3, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 503 through one or more data medium interfaces. Memory 502 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.

A program/utility 508 having a set (at least one) of program modules 507 may be stored, for example, in memory 502, such program modules 507 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 507 typically perform the functions and/or methods of the described embodiments of the invention.

The electronic device 50 may also communicate with one or more external devices 509 (e.g., keyboard, pointing device, display 510, etc.), one or more devices that enable a user to interact with the electronic device 50, and/or any device (e.g., network card, modem, etc.) that enables the electronic device 50 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 511. Also, the electronic device 50 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through a network adapter 512. As shown, the network adapter 512 communicates with other modules of the electronic device 50 over the bus 503. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in connection with electronic device 50, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

The processing unit 501 executes various functional applications and data processing by running a program stored in the system memory 502, for example, implementing the AI algorithm-based student career planning method provided by the embodiment of the present invention.

In another embodiment of the present invention, as shown in fig. 4, there is also provided a storage medium 400 containing a computer program 411, which when executed by a computer processor, is configured to perform a student career planning method based on an AI algorithm, the method comprising:

Acquiring career planning request information of a target user, identifying content data of the career planning request information, and judging whether the career planning request information contains professional data according to the content data;

If the career planning request information contains professional data, selecting a professional corresponding to the professional data as a target professional in a preset professional database according to basic information of the target user, wherein the professional data comprises at least one of professional direction data and professional name data, and the basic information is information for representing an identity of the target user;

If the career planning request information does not contain professional data, generating recommended professional data through basic information of the target user based on an AI algorithm, sending the recommended professional data to the target user, acquiring selection information of the target user for the recommended professional data, and taking the profession corresponding to the selection information as a target profession, wherein the AI algorithm at least comprises a preset priori probability algorithm and a prediction algorithm;

and acquiring the demand information of the target occupation, and generating a career plan of the target user based on the demand information and the basic information.

The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for embodiments of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The foregoing disclosure is illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.

Claims (7)

1. The utility model provides a student career planning method based on AI algorithm which characterized in that, the method includes:

Acquiring career planning request information of a target user, identifying content data of the career planning request information, and judging whether the career planning request information contains professional data according to the content data;

If the career planning request information contains professional data, selecting a professional corresponding to the professional data as a target professional in a preset professional database according to basic information of the target user, wherein the professional data comprises at least one of professional direction data and professional name data, and the basic information is information for representing an identity of the target user;

If the career planning request information does not contain professional data, generating recommended professional data through basic information of the target user based on an AI algorithm, sending the recommended professional data to the target user, acquiring selection information of the target user for the recommended professional data, and taking the profession corresponding to the selection information as a target profession, wherein the AI algorithm at least comprises a preset priori probability algorithm and a prediction algorithm;

The step of generating recommended occupation data through the basic information of the target user based on an AI algorithm and sending the recommended occupation data to the target user further comprises the following steps:

Acquiring professional data of the target user according to the basic information;

acquiring historical employment information corresponding to the professional data based on the professional data, wherein the historical employment information at least comprises different professions and priori data of the different professions;

Determining prior probability distribution data of different occupations according to the prior data and a preset prior probability algorithm in the AI-based algorithm;

defining a likelihood function based on preset observation data and the prior data;

According to the Bayesian theorem, combining prior distribution and likelihood functions to obtain posterior distribution functions, constructing a prediction algorithm in an AI algorithm according to the posterior distribution functions, and predicting professional characteristic parameters of different professions based on the prediction algorithm;

Taking the job characteristic parameters as recommended job data, and sending the recommended job data to the target user;

Acquiring the demand information of the target occupation, and generating a career plan of the target user based on the demand information and the basic information;

The step of obtaining the requirement information of the target occupation and generating the career plan of the target user based on the requirement information and the basic information comprises the following steps:

Acquiring the demand information of the target occupation, and carrying out quantitative processing on the demand information based on a preset evaluation standard to obtain quantitative standard of the target occupation;

Performing quantitative processing on the basic information based on a preset evaluation standard to obtain the capability score of the target user;

Comparing the quantitative standard with the capability score to obtain comparison information, and generating a career plan of the target user according to the comparison information;

The step of generating the lifetime planning of the target user according to the comparison information includes:

determining data to be lifted of the target user based on the comparison information;

Determining first content in the lifetime planning according to the data to be lifted and the course data, wherein the first content user is used for improving the professional ability of the target user;

acquiring campus activity data of the target user based on the basic information;

determining second content in the lifetime planning according to the data to be lifted and the campus activity data, wherein the second content is used for improving social capacity of a target user;

And generating a career plan of the target user according to the first content and the second content.

2. The method according to claim 1, wherein if the career planning request information includes career data, selecting a career corresponding to the career data as a target career in a preset career database according to basic information of the target user, the career data including at least one of career direction data and career name data, comprising:

If the career planning request information contains career direction data, selecting a career corresponding to the career direction data in a preset career database to obtain an initial career set, and acquiring skill demand data of each career in the initial career set;

acquiring course data of the target user based on the basic information;

and determining skill demand data matched with the course data as target demands, and screening preselected professions corresponding to the target demands according to preset selection rules to obtain target professions.

3. The method according to claim 2, wherein the step of screening the pre-selected profession corresponding to the target demand according to a preset selection rule to obtain the target profession includes:

Obtaining importance degree data of the target demand aiming at the preselected occupation;

And taking the preselected occupation with the importance degree data larger than a threshold value as the target occupation according to the preset selection rule.

4. The method according to claim 1, characterized in that the method further comprises:

Updating the preset occupation database according to a preset period, and selecting an occupation corresponding to the job data from the updated preset occupation database according to the basic information of the target user as an updated occupation;

if the updated occupation is inconsistent with the target occupation, generating alarm information and acquiring feedback information of the user aiming at the alarm information;

When the feedback information is used for representing that the target occupation is replaced by the update occupation, acquiring update demand information of the update occupation based on the feedback information, and generating an update career plan of the target user based on the update demand information and the basic information;

When the feedback information is empty, acquiring first time data for generating the lifetime planning and second time data for generating alarm information, and calculating a time interval according to the first time data and the second time data;

And if the time interval is smaller than a preset value, acquiring the update demand information of the update occupation, and generating the update career plan of the target user based on the update demand information and the basic information.

5. The method according to claim 1, wherein the step of taking the job characteristic parameter as recommended job data and transmitting the recommended job data to the target user comprises:

Acquiring preset information of the target user, and assigning values for each occupational characteristic parameter according to a preset assignment rule to obtain a characterization value of each occupational characteristic parameter;

calculating the score of the occupation corresponding to the recommended occupation data based on the characterization value;

and sequencing different professions according to the order of the scores from high to low to obtain the recommended sequence of the profession corresponding to the recommended professional data, and sending the recommended professional data to the target user according to the recommended sequence.

6. The method according to claim 1, characterized in that the method further comprises:

Acquiring learning information of the target user;

Selecting a occupation corresponding to the learning information from a preset occupation database based on the learning information as an alternative occupation;

when the alternative occupation is different from the target occupation, generating replacement information, and acquiring control information of the user for the replacement information;

When the control information is used for representing that the target occupation is replaced by the alternative occupation, alternative demand information of the alternative occupation is obtained based on the control information, and an alternative life planning of the target user is generated based on the alternative demand information and the basic information.

7. A career planning apparatus based on AI algorithm, the apparatus comprising:

The system comprises a data acquisition module, a user management module and a user management module, wherein the data acquisition module is used for acquiring career planning request information of a target user, identifying content data of the career planning request information and judging whether the career planning request information contains professional data according to the content data;

The first recommending module is used for selecting a career corresponding to the career data as a target career in a preset career database according to basic information of the target user if the career planning request information contains career data, wherein the career data comprises at least one of career direction data and career name data, and the basic information is information used for representing the identity of the target user;

The second recommending module is used for generating recommended occupation data through basic information of the target user based on an AI algorithm if the career planning request information does not contain occupation data, sending the recommended occupation data to the target user, acquiring selection information of the target user for the recommended occupation data, and taking occupation corresponding to the selection information as a target occupation, wherein the AI algorithm at least comprises a preset priori probability algorithm and a prediction algorithm;

The step of generating recommended occupation data through the basic information of the target user based on an AI algorithm and sending the recommended occupation data to the target user further comprises the following steps:

Acquiring professional data of the target user according to the basic information;

acquiring historical employment information corresponding to the professional data based on the professional data, wherein the historical employment information at least comprises different professions and priori data of the different professions;

Determining prior probability distribution data of different occupations according to the prior data and a preset prior probability algorithm in the AI-based algorithm;

defining a likelihood function based on preset observation data and the prior data;

According to the Bayesian theorem, combining prior distribution and likelihood functions to obtain posterior distribution functions, constructing a prediction algorithm in an AI algorithm according to the posterior distribution functions, and predicting professional characteristic parameters of different professions based on the prediction algorithm;

Taking the job characteristic parameters as recommended job data, and sending the recommended job data to the target user;

the planning module is used for acquiring the demand information of the target occupation and generating a lifetime plan of the target user based on the demand information and the basic information;

The step of obtaining the requirement information of the target occupation and generating the career plan of the target user based on the requirement information and the basic information comprises the following steps:

Acquiring the demand information of the target occupation, and carrying out quantitative processing on the demand information based on a preset evaluation standard to obtain quantitative standard of the target occupation;

Performing quantitative processing on the basic information based on a preset evaluation standard to obtain the capability score of the target user;

Comparing the quantitative standard with the capability score to obtain comparison information, and generating a career plan of the target user according to the comparison information;

The step of generating the lifetime planning of the target user according to the comparison information includes:

determining data to be lifted of the target user based on the comparison information;

Determining first content in the lifetime planning according to the data to be lifted and the course data, wherein the first content user is used for improving the professional ability of the target user;

acquiring campus activity data of the target user based on the basic information;

determining second content in the lifetime planning according to the data to be lifted and the campus activity data, wherein the second content is used for improving social capacity of a target user;

And generating a career plan of the target user according to the first content and the second content.