KR101961144B1 - National competency standards based job matching system and method thereof - Google Patents

Abstract

Disclosed are a job fitness matching system based on a national competency standard and a method thereof. According to an embodiment of the present invention, the job fitness matching system based on a national competency standard performs machine learning by utilizing employment status public data and data related to a pre-employed user, processes data through classifying a specific job list and giving a weighting and processes individual information and educational career information of a user in accordance with a processed data value, thereby estimating and providing a job fitness matching. In addition, the job fitness matching system based on a national competency standard comprises a user terminal, a public data server, an employment information data server and a job fitness matching server.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a system and method for job matching,

More particularly, the present invention relates to a system and method for job matching based on national incompetency standards, and more particularly, to a system and method for job matching, The present invention relates to a system and method for matching a job based on a national incompetency standard that performs data processing and estimates and provides a job fit matching degree by processing personal information and educational history information of a user according to a data processing value performed.

Recently, in order to develop skilled human resources to build a core infrastructure, and to further improve national competitiveness, the government has enacted the national incompetence standard (NCS, National Competency Standards).

In other words, the national incompetence standard (NCS) is a system that classifies all industries into 24 categories, 24 categories, 77 categories, and 227 categories. 887 (up to the present). In addition, subdivided tasks are divided into sub-hierarchical structures such as ability units, capability unit elements, and performance criteria, so that specific companies can do what they can do, It provides a basic standard that details what an engineer can do.

In other words, the intention of the government to adopt the national incompetence standard (NCS) is to avoid the indiscriminate specification of students or employees, to evaluate people based on job ability, and to manage and manage human resources efficiently.

However, despite the introduction of the national incompetence standard (NCS), the recruitment and personnel management in the enterprise, vocational education and training in the training and training institutions, (NCS), it is inconsistent with the industrial field, and the human resources are inefficiently managed and operated.

Therefore, it is necessary to develop a system and method for matching job satisfaction based on the National Inability Standard (NCS), in particular, by managing the training and training, There is a growing need to do so.

Korean Patent Laid-Open Publication No. 10-2017-0057117 entitled " Automatic matching service method based on national incompetency standards and system using the same "(published on May 24, 2014) Korean Patent Registration No. 10-1656830 entitled " Network-based career search method and apparatus "(Announced 2016.12.12)

The present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to perform machine learning using public data of employment status data and user-related user data to perform data processing through classification of detailed job list and weighting, The present invention has been made to solve the above problems, and it is an object of the present invention to provide a system and method for matching a job based on the national incompetence standards by processing the user's personal information and educational history information according to the processed data values.

According to an embodiment of the present invention, a job matching system based on a national incompetency standard includes a user terminal for receiving personal information and educational history information of a user for job matching of a user, A job information data server for storing and updating data related to the employed user, and a personal information and education history information of the user received from the user terminal through the network, And the job matching server collects data on employment status public data and employed users through a public data server and a job information data server, Perform a detailed job list Classification and weighting, and processing the personal information and education history information of the user transmitted according to the data processing value, thereby estimating the job matching index.

Also, in the job matching system based on the national incompetency standards, the machine learning using the collected data to classify the detailed job list and processing the data by weighting can be performed by collecting data on the collected employment status data The career histories, and the aptitude test results of each of the employed workers are classified according to the detailed job list, and the occupation, career history, training history, aptitude The results of the test are assigned to the machine learning algorithm to estimate the job relevance of each employee and the difference between the estimated job relevance association and the average value is used to calculate the weight of each career, And the data can be processed.

In addition, in the job matching system based on national incompetence standards, the algorithm of machine learning is supported by support vector machine (SVM), artificial neural network (ANN), recurrent neural network (RNN) And a running algorithm.

According to an embodiment of the present invention, the job matching method based on the national incompetency standard includes a step of receiving a user's personal information and educational history information from a user terminal through a network, Collecting public data, collecting user-related data from the employment information data server, performing machine learning using the collected data, performing data processing through classification of detailed occupation lists and weighting, And processing the personal information and the education history information of the user transmitted according to the data processing value to estimate the job matching degree.

In addition, in the method of matching job adaptation based on the national incompetency standards, the machine learning using the collected data and classifying the detailed occupation list and processing the data by weighting can be performed by collecting data on the collected employment data The career histories, and the aptitude test results of each of the employed workers are classified according to the detailed job list, and the occupation, career history, training history, aptitude The results of the test are assigned to the machine learning algorithm to estimate the job relevance of each employee and the difference between the estimated job relevance association and the average value is used to calculate the weight of each career, And the data can be processed.

In addition, in the method of matching job adaptation based on the national incompetency standards, the algorithm of machine learning is supported by support vector machine (SVM), artificial neural network (ANN), recurrent neural network (RNN) And a running algorithm.

Meanwhile, as an embodiment of the present invention, a computer-readable recording medium on which a program for causing the computer to execute the above-described method may be provided.

According to the job matching system based on the national incompetency standard and the method according to the embodiment of the present invention, the user can confirm the job matching degree suitable for his / her personal information and educational history.

According to the job matching system and method according to the embodiment of the present invention, the user can identify the highest priority job group suited to her personal information and educational history, , And necessary courses in each group.

According to the job matching system based on the national incompetency standard and the method thereof according to the embodiment of the present invention, it is possible to provide a job adaptation matching diagram suitable for users, thereby enabling job-based human resource development and a core infrastructure It is possible to improve national competitiveness through building and employment and linking lifelong job skills development.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. It is to be understood, however, that the technical features of the present invention are not limited to the specific drawings, and the features disclosed in the drawings may be combined with each other to constitute a new embodiment.
1 is a block diagram of a job matching system 1000 based on a national incompetence standard according to an embodiment of the present invention.
FIG. 2 is a flow chart of a job matching method based on a national incompetency standard according to an exemplary embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

The terms used in this specification will be briefly described and the present invention will be described in detail.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments. Also, in certain cases, there may be a term selected arbitrarily by the applicant, in which case the meaning thereof will be described in detail in the description of the corresponding invention. Therefore, the term used in the present invention should be defined based on the meaning of the term, not on the name of a simple term, but on the entire contents of the present invention.

When an element is referred to as "including" an element throughout the specification, it is to be understood that the element may include other elements as well, without departing from the spirit or scope of the present invention. Also, the terms "part," " module, "and the like described in the specification mean units for processing at least one function or operation, which may be implemented in hardware or software or a combination of hardware and software . In addition, when a part is referred to as being "connected" to another part throughout the specification, it includes not only "directly connected" but also "connected with other part in between".

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of a job matching system 1000 based on a national incompetence standard according to an embodiment of the present invention.

Referring to FIG. 1, a job matching system 1000 based on a national incompetency standard according to an exemplary embodiment of the present invention includes a user terminal 100, a public data server 200, an employment information data server 300, A server 400, and the like.

The user terminal 100 according to an exemplary embodiment of the present invention may receive user's personal information and educational history information for job matching.

For example, the user can input personal information and educational history information of the user using the user terminal 100 to confirm his / her job matching. Here, the user's personal information and educational history information input through the user terminal 100 are transmitted to the job matching server 400 through the network.

That is, the user can input information such as personal information, sex, date of birth, age, and the like as his / her personal information through the user terminal 100. Further, the user can input information such as his / her education history information, And information such as the education program of the professional education institution taken.

In addition, the user can input his or her interest information, such as interests, hobbies and the like, and individual activity information such as a prize winning prize and an internship through the user terminal 100.

In addition, the user can transmit his / her personal information and educational history information to the job matching server 400 through the network using the server of the SNS or the related educational institution in cooperation with the job matching server 400.

The public data server 200 according to an embodiment of the present invention can store and update public data on the employment status.

For example, the public data server 200 collects public data from state agencies, local governments, public institutions, schools, and the like in order to collect public data on the employment status, You can build the data and update it at each time.

In addition, the public data server 200 can estimate the employment information statistics by age, sexual employment information statistics, the employment information statistics by education level, the employment information statistics by field from the public data on the employment status that is constructed, And transmits the statistical information to the job matching server 400.

The employment information data server 300 according to an exemplary embodiment of the present invention can store and update user-related user data.

For example, the job information data server 300 is a server for acquiring related data for a previously employed user. The server includes personal information (e.g., personal information) of a previously employed user, job information, career information, Job performance evaluation information, training history information, and aptitude test result information can be stored and updated.

In addition, the job information data server 300 may include data on previously established users, for example, personal information (e.g., personal information) of an employed user, job information, career information, job satisfaction information, The training history information, and the aptitude test result information to the job matching server 400 through the network.

The job matching server 400 according to an exemplary embodiment of the present invention can receive user's personal information and education history information from the user terminal 100 through the network.

For example, when a user inputs personal information and educational history information via the user terminal 100, the user inputs personal information and educational history information such as information on the user's personal information, sex, date of birth, age, As information on the user education history information, information such as grades of the home school, the completion course, the receiving school, and the education program of the professional education institution taken may be transmitted to the job matching server 400 through the network.

In addition, the job matching server 400 collects the employment status public data and the user data related to the employed user through the public data server 200 and the job information data server 300 in order to estimate a weight according to job relevance association degree can do.

For example, the job matching server 400 collects data on employment status public data and employed users through the public data server 200 and the employment information data server 300, The machine learning using the data of the employed user can be performed to perform the data processing through classification of the detailed job list and weighting.

That is, the job matching server 400 can classify the collected employment status data from the public data and the employed user-related data into the detailed job list based on the national incompetency standards, in order to perform job matching on the basis of the national incompetency standards .

For example, the job matching server 400 classifies the occupations of each employed worker into a detailed job list according to the subdivisions classified as the national incompetence standard, using the collected employment data public data and the data on the employed user can do.

At this time, the classification of the detailed job list according to the classification according to the national incompetence standard can be performed through the classification method by machine learning, and the information of each worker such as the occupation, career, education history, Etc. (including personal information such as age and sex) can be classified according to the occupation group of the detailed occupation list.

That is, the job matching server 400 classifies the occupations, career histories, aptitude test results, and the like of each of the employed persons according to the detailed job list, and classifies the job, career, training history, Can be assigned to machine learning algorithms to estimate job relevance of each job.

For example, the job matching server 400 substitutes a job learning, a career history, a training history, and an aptitude test result of each of the workers classified according to the detailed job list into a machine learning algorithm, The career histories, the training history, and the aptitude test results are transformed into a vector form using, for example, a machine learning algorithm, and converted to a score obtained by a regression analysis from successive values, have.

For example, the algorithm of the machine learning used in the job matching server 400 may be a support vector machine (SVM), an artificial neural network (ANN), a recurrent neural network (RNN) And a deep learning algorithm.

In addition, the job matching server 400 can estimate job relevance relevance of each of the employed workers using the job, career, educational history, and aptitude test results of the classified workers using the data mining method.

For example, the job matching server 400 defines a data group based on the data characteristics from the career history, training history, and aptitude test results classified according to the detailed job classification of each of the employed persons by the cluster analysis method , And the relevance of job relevance can be estimated by finding representative points that can represent data groups and assigning each score.

The job matching server 400 according to an exemplary embodiment of the present invention estimates and adds data on the respective career histories, training histories, and aptitude test results in the estimated job fit associations, have.

For example, the job suitability matching server 400 can estimate the respective weights by calculating the difference from the average value in the job relevance relevance estimated using the machine learning.

In other words, the job suitability matching server 400 estimates job relevance relevance converted into a score through a regression analysis by converting into a vector form using a machine learning algorithm, , And the difference from the average value calculated in job relevance association can be the weight for each career, education history, and aptitude test results.

Here, the average value represents the average value of the data such as career history, educational history, and aptitude test result after classifying the user data related to the employed user into the detailed job list.

In addition, the average value can be replaced with the standard score based on the national incompetence standard to calculate the weight, and each weight calculation can be weighted by normalizing based on the national incompetence standard.

Also, even when the job matching server 400 estimates job relevance using the data mining method, it calculates the difference between the estimated job relevance association and the average value, and calculates the career history, training history, And the like can be estimated.

That is, the job matching server 400 calculates the difference between the estimated job relevance association and the average value, estimates the respective weights for each career, the training history, and the aptitude test results, And data processing can be performed by storing the data as the data processing value in each detailed job.

In addition, the job matching server 400 collects individual activity information, interest information, and the like from the collected information of the employed users, estimates job relevance relevance by applying a machine learning algorithm or a data mining technique, Information, or information of interest, and also can perform data processing.

In addition, the job matching server 400 assigns a predetermined weight to individual activity information, interest information, and the like from the collected information of the employed users. For example, the individual activity information has a high weight, The data processing can be performed in such a manner that a predetermined weight is given by setting the weight to a low value.

The job matching server 400 according to an exemplary embodiment of the present invention can estimate the job matching index by processing personal information and education history information of the user transmitted according to the data processing value.

For example, the job matching server 400 uses the transmitted personal user information and the educational history information to calculate data processing values stored in information such as age, sex, subject, The user's personal information and education history information can be processed by assigning the weight according to each detailed job to the age, sex, and educational history information of the user, thereby estimating the job matching degree for each detailed job.

That is, the job suitability matching server 400 assigns weights according to data processing values to information such as age, gender, subject, and learning point input by the user, estimates the job relevance matching degree for each detailed job, .

At this time, the Job Matching Diagram can be provided in order from the job group matching the highest priority, and the score of the user corresponding to each job group, the required course in the job group, and the like are displayed, can confirm.

The user can retrieve a job group suitable for his / her own by using the provided job matching index. Also, the user can search not only the job matching information on the personal information and the education history information but also the individual activity information The matching degree can also be confirmed.

In addition, the estimated job matching score can be provided to the personnel manager of each company as needed so that the job customized personnel can be employed.

FIG. 2 is a flow chart of a job matching method based on a national incompetency standard according to an exemplary embodiment of the present invention.

Referring to FIG. 2, in step S10, the job matching server 400 may receive user's personal information and education history information from the user terminal 100 through a network.

For example, the user can input personal information and educational history information of the user using the user terminal 100 to confirm his / her job matching, The information and education history information is transmitted to the job matching server 400. The job matching server 400 can receive personal information and education history information of the user from the user terminal 100 via the network.

In step S20, the job matching server 400 collects the public data of the employment status from the public data server 200, and in step S30, the job matching server 400 searches the job information data server 300 User-related data can be collected.

That is, the job matching server 400 can collect public data of the current employment status and user-related user data through the public data server 200 and the job information data server 300, and the job matching server 400 May be performed prior to inputting personal information of a user through the user terminal 100 and educational history information.

In step S40, the job matching server 400 may perform machine learning using the collected data to perform data processing through detailed job list sorting and weighting.

For example, the job matching server 400 classifies the occupations of the respective occupants into a detailed occupational list based on the collected employment data, public data, and user data related to the occupation. Based on the detailed occupational list, (Such as age, gender, etc.), and classifies the occupations, career histories, aptitude test results, and aptitude test results of each of the classified workers into the machine learning algorithm, The data of the job can be processed by estimating and adding the respective weights of the career histories, the training history data, and the aptitude test results, etc., by estimating the job fit relevance and using the difference between the estimated job fit association and the average value .

In step S50, the job matching server 400 can estimate the job matching degree by processing personal information and education history information of the user according to the data processing value.

For example, the job matching server 400 assigns weights according to data processing values to information such as age, gender, subject, and learning point inputted by the user, estimates the job matching degree per each detailed job, And can provide the estimated job matching index to the user.

In this case, the Job Satisfaction Score can be provided in order from the job group matching the highest priority, and the score of the user corresponding to each job group and the required subject for each job group are displayed, You can check the course.

In the case of the job matching method based on the national incompetency standard according to an embodiment of the present invention, the above-described job incompatibility standard based job matching system 1000 may be applied. Accordingly, the description of the same content as the above-described national incompetence standards-based job matching system 1000 is omitted for the job matching method based on the national incompetency standards.

One embodiment of the present invention may also be embodied in the form of a recording medium including instructions executable by a computer, such as program modules, being executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer readable medium may include both computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: User terminal
200: public data server
300: job information data server
400: Job matching server
1000: national incompetence standards-based job matching system

Claims (7)

In the national incompetence standards-based job matching system,
A user terminal for receiving personal information and educational history information of a user for job matching;
Public data server for storing and updating public data and estimating employment information statistics by age, employment information statistics by gender, employment information statistics by field of study and employment information statistics by field from the public data;
A job information data server for storing and updating data on the employed user; And
And a job matching server for providing a job matching degree to the user using personal information and educational history information of the user received from the user terminal through a network,
The job matching server
Collecting the employment status public data and user-related user data via the public data server and the employment information data server, and collecting the occupations of the respective occupants from the collected employment status public data and the user data related to the employed user, Career histories and aptitude test results according to the list of detailed occupations according to the list of occupation, career, education history and aptitude test results of each of the occupants classified by the machine learning algorithm , And converting the continuous value to a score obtained by regression analysis to estimate a job relevance association of each of the above mentioned workers or a data analysis method using a cluster analysis method as a data mining method, A representative point of the data group is found, Estimating and adding the respective weights of the career histories, the educational histories, and the aptitude test results by using the difference between the estimated job fit associations and the average values, And processing the personal information and the educational history information of the transmitted user according to the data processing value to estimate the job matching degree,
The machine learning algorithm includes at least one of a support vector machine (SVM), an artificial neural network (ANN), a recurrent neural network (RNN), and a deep learning algorithm,
Wherein the average value is a data average value of the career history, the education history and the aptitude test result for each occupation after the classified user-related data is classified into the detailed occupation list, or a standard score based on the national incompetence standard National inefficiency standards based job matching system.
delete delete In the national incompetence standards-based job matching method,
Receiving a user's personal information and education history information from a user terminal through a network;
The job matching server collecting employment status public data from a public data server;
Collecting user-related data from the job information data server by the job matching server;
Performing the machine learning using the collected data to classify the detailed job list and perform data processing through weighting;
And the job matching server compares personal information and education history information of the transmitted user according to the data processing value to estimate the job matching degree,
Wherein the job matching server performs the machine learning using the collected data to perform the data classification through the detailed job list classification and the weighting,
Classifies the occupations of the respective occupations into the detailed occupation list from the collected data of the employment status public data and the data of the occupation employers, classifies the occupation, the career history, the education history and the aptitude test results of each of the occupants according to the detailed occupation list , Converting the occupation, career, education history and aptitude test results of each of the classified workers into a vector form using a machine learning algorithm and converting the result into a score obtained by a regression analysis from successive values, Estimating a job suitability association by estimating a data group using a cluster analysis method that is a data mining method and finding a representative point of the data group and assigning a score, Using the difference between associations and mean values, History information, and estimated by adding the respective weights for the aptitude test results and perform the data processing,
The machine learning algorithm includes at least one of a support vector machine (SVM), an artificial neural network (ANN), a recurrent neural network (RNN), and a deep learning algorithm,
The public data server estimates employment information statistics by age, employment information statistics by sex, employment information statistics by education level, and job information statistics by field from the public data,
Wherein the average value is a data average value of the career history, the education history and the aptitude test result for each occupation after the classified user-related data is classified into the detailed occupation list, or a standard score based on the national incompetence standard Based national incompetence standards based job matching method.
delete delete A computer-readable recording medium on which a program for implementing the method of claim 4 is recorded.

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