KR102061625B1 - Method, apparatus and program for managing specialist career - Google Patents

Abstract

Provided is a method for managing a specialist career. The method for managing a specialist career includes: a step (S110) of registering a specialist; a step (S120) of obtaining specialist information on the registered specialist; a step (S130) of calculating absolute career information on the specialist based on the obtained specialist information; a step (S140) of matching a project corresponding to the specialist based on the absolute career information on the specialist; a step (S150) of obtaining project data calculated in accordance with an execution result of the project in a case where the matched project is executed by the specialist; a step (S160) of updating the absolute career information on the specialist based on the project data; and a step (S170) of obtaining relative career information based on the updated absolute career information and the project data.

Description

Professional career management methods, devices and programs {METHOD, APPARATUS AND PROGRAM FOR MANAGING SPECIALIST CAREER}

The present invention relates to a method, apparatus and program for professional career management.

In the era of the Fourth Industrial Revolution, the depth, speed and range are expected to expand greatly based on hyper-connectivity and super intelligence. This will not only create a variety of industries, but also a number of new fields will arise, such as fusion of existing and new industries, and it is expected that there will be few or no experts in the field. Therefore, it is necessary to find a platform for finding and recruiting experts in each field and collaborating with experts in each field.

In addition, prolonged life expectancy will prolong the production period, and it is necessary for each expert to make the field a lifelong “up” rather than just a profession.

In addition, it is desirable for professionals to be treated and treated based on their ability in each field.However, in the case of an organization such as an existing company, it is difficult to preserve the individuality of each expert because it pursues equity among employees and stability of the whole organization. In other words, they were not given treatment based on their abilities, and their careers were converted into managerial positions rather than using them.

On the other hand, in order for professionals to be treated based on their ability in each field, there is a need to calculate the exact professional career. In the past, however, it was often impossible to calculate the exact career of an expert. For example, when a plurality of experts have performed the A project, the reality is that each of the plurality of experts writes "Perform A project" in the career entry column. However, even if a plurality of experts performed the A project together, the work content and the quality of work performed by each expert will be different. It is unreasonable that the same careers of all the experts are the same. That is, in the related art, there is a problem in that there is no method for distinguishing the careers of the experts who performed only the subsidiary roles and the experts who performed the subsidiary roles. Therefore, there is a need for a career management method of an expert for solving the above problems.

Blockchain, also known as a book of public transactions, is a technology that prevents hacking that may occur when trading in virtual currency. Traditional financial firms keep transaction records on a centralized server, while blockchain sends a transaction history to all users participating in a transaction and compares it with each transaction to prevent data forgery. Have

For example, blockchain is applied to Bitcoin, a representative online virtual currency. Bitcoin records transaction details transparently in a book that anyone can read, and several computers using Bitcoin verify this record every 10 minutes to prevent hacking. Recently, various coins have been developed and provided to solve the shortcomings of bitcoin, and with the advent of coins having platform functions, various tokens function as virtual currency.

In addition, various decentralized applications (DAPPs) using the cryptocurrency platform are being developed and used, so the utilization of blockchain is increasing.

Furthermore, various information such as expert's detailed information, work experience, specialty, and project execution experience, project execution result and performance information managed on the expert platform are at risk of hacking and forgery. When used in the market, trust in the expert platform for expert management can be compromised and poorly matched.

Therefore, there is a demand for the development of a technology that can prevent forgery while maintaining the reliability of such information.

Meanwhile, MICE is a term that comes from English letters such as meetings, incentive trips, conventions, exhibitions, and events, and in a narrow sense, it focuses on international conferences and exhibitions. It means promising industry, and broad concept means convergence industry including participant-centered reward tourism and mega events.

Publication No. 10-2010-0026834, published March 10, 2010

The problem to be solved by the present invention is to provide a professional career management method, apparatus and program.

Problems to be solved by the present invention are not limited to the above-mentioned problems, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

Expert management method according to an aspect of the present invention for solving the above problems is the step of registering the expert (S110), obtaining the expert information of the registered expert (S120), based on the obtained expert information Calculating absolute career information of the expert (S130), matching a project corresponding to the expert based on the absolute career information of the expert (S140), when the matched project is performed by the expert, Acquiring project data calculated according to the execution result of the project (S150); Updating the absolute career information of the expert based on the project data (S160); and obtaining relative career information based on the updated absolute career information and the project data (S170).

Other specific details of the invention are included in the detailed description and drawings.

According to the disclosed embodiment, by determining the actual career information, not the formal career information for the expert, it is possible to expect the effect of treating the expert based on the true ability and career.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a diagram illustrating a blockchain system according to an embodiment of the present invention.
2 is a system diagram illustrating an operation of a blockchain system according to an embodiment of the present invention.
3 is a diagram illustrating a control system of an expert platform for expert career management according to an embodiment of the present invention.
FIG. 4 is a diagram for explaining a process of propagating valid information on a network of a blockchain to nodes on a network of a blockchain and recording the information on a blockchain according to an embodiment of the present invention.
5 is a flowchart illustrating an expert career management method performed in an expert platform according to an embodiment of the present invention.
6 is a flowchart illustrating a method of calculating absolute career information of an expert according to an embodiment of the present invention.
7 is a flowchart illustrating another method of calculating absolute career information of an expert according to an embodiment of the present invention.
8 is a flowchart illustrating a method of updating absolute career information of an expert according to an embodiment of the present invention.
9 is a flowchart illustrating a method of calculating relative career information of an expert according to an embodiment of the present invention.
10 is a flowchart illustrating a method of obtaining relative career information based on a first career score and a second career score according to an embodiment of the present invention.
11 is a flowchart illustrating a method of obtaining a career score based on a weight according to an embodiment of the present invention.
12 is a flowchart illustrating a method of calculating a career score according to whether a project data is a joint work or a combined work according to an embodiment of the present invention.
13 is a block diagram of an apparatus according to an embodiment of the present invention.

Advantages and features of the present invention and methods for achieving them will be apparent with reference to the embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but can be embodied in various different forms, and the present embodiments only make the disclosure of the present invention complete, and those of ordinary skill in the art to which the present invention belongs. It is provided to fully inform the skilled worker of the scope of the invention, which is defined only by the scope of the claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, "comprises" and / or "comprising" does not exclude the presence or addition of one or more other components in addition to the mentioned components. Like reference numerals refer to like elements throughout, and "and / or" includes each and all combinations of one or more of the mentioned components. Although "first", "second", etc. are used to describe various components, these components are of course not limited by these terms. These terms are only used to distinguish one component from another. Therefore, of course, the first component mentioned below may be a second component within the technical spirit of the present invention.

Unless otherwise defined, all terms used in the present specification (including technical and scientific terms) may be used in a sense that can be commonly understood by those skilled in the art. In addition, terms that are defined in a commonly used dictionary are not ideally or excessively interpreted unless they are specifically defined clearly.

As used herein, the term "part" or "module" refers to a hardware component such as software, FPGA, or ASIC, and the "part" or "module" plays certain roles. However, "part" or "module" is not meant to be limited to software or hardware. The “unit” or “module” may be configured to be in an addressable storage medium or may be configured to play one or more processors. Thus, as an example, a "part" or "module" may include components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, Procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Functions provided within components and "parts" or "modules" may be combined into smaller numbers of components and "parts" or "modules" or into additional components and "parts" or "modules". Can be further separated.

The spatially relative terms " below ", " beneath ", " lower ", " above ", " upper " It can be used to easily describe a component's correlation with other components. Spatially relative terms are to be understood as including terms in different directions of components in use or operation in addition to the directions shown in the figures. For example, when flipping a component shown in the drawing, a component described as "below" or "beneath" of another component may be placed "above" the other component. Can be. Thus, the exemplary term "below" can encompass both an orientation of above and below. Components may be oriented in other directions as well, so spatially relative terms may be interpreted according to orientation.

In the present specification, the computer refers to any kind of hardware device including at least one processor, and according to an embodiment, it may be understood as a meaning encompassing a software configuration that operates on the hardware device. For example, a computer may be understood as including, but not limited to, a smartphone, a tablet PC, a desktop, a notebook, and a user client and an application running on each device.

In the present specification, the subject of the business and the project may be understood in the sense encompassing not only a business such as a sole proprietor or a corporation, but also a general individual, and does not limit a specific subject. In one embodiment, the business, project subject and performer may be a government, a local government, a public period, a cooperative body, and the like.

In the present specification, the project or project information refers to a government, a local government, a public term, a cooperative organization, a business and project ordered by a company, a business and project related work, a domestic and international procurement tender, and other experts. It can mean various projects and business-related tasks.

The type of project may also include a variety of types, such as the provision of goods or services, and is not limited to any particular type. For example, a project may be to deliver a specific product, to provide a research service, a professional service industry, or a combination of various tasks.

In this specification, an expert may be understood to mean any kind of person having a certain expertise in a particular field, and the criteria are not limited. Any kind of experienced person, trained person, or talented person who can perform a specific field of work can be called an expert, but it is understood that it means a person who has a predetermined career in a specific field. Can be.

Thus, the expert according to the present disclosure may mean experts who satisfy the requirements of a specific group as well as those of existing conceptual, conventional, and general criteria.

Meanwhile, an expert in the present disclosure means an individual single expert, but is not limited thereto. In other words, the group itself may be viewed as an expert.

On the other hand, in the present specification, MICE is a term that is taken from the English letters such as meeting, incentive trip, convention, exhibition and event (Exhibition & Event), in a narrow sense international conferences and exhibitions It is a promising industry with a focus on it, and a broad concept means a convergence industry that includes participant-centered reward tourism and mega events. However, the meaning of MICE herein is not limited to the term itself, and may be used in various terms according to time and region.

For example, in Canada, shorten meetings, conventions and incentive travel, and use it as MC ⁢ in the US, use it as ME & I or MEEC (Meetings, Expositions, Events and Conventions); in Singapore, reduce Business Travel & MICE to use BT MICE As you can see, different terms are used depending on the industrial structure and policies of each country.

Hereinafter, with reference to the accompanying drawings will be described an embodiment of the present invention;

Each step described herein is described as being performed by a computer or a server, but the subject of each step is not limited thereto, and at least some of the steps may be performed by different devices according to embodiments.

Referring to FIG. 1, blockchain holding servers 10, a management server 20, and user terminals 30 are shown.

In the disclosed embodiment, each of the user terminals 30 records the transaction history based on a predetermined rule, the recorded transaction history is recorded in each block and propagated to the blockchain holding servers 10, It is stored and managed on the server.

In the disclosed embodiment, the blockchain holding servers 10 refer to servers equipped with a blockchain that authenticates and records transaction information. In one embodiment, the transaction information may mean transaction information based on virtual currency, but may also mean information on various events performed based on the blockchain, and such transaction information may be applied to the blockchain. Stored.

The virtual currency according to the disclosed embodiment is understood as a concept that collectively refers to all kinds of non-real money whose transaction details are managed through a blockchain such as electronic money and cryptocurrency.

The cryptocurrency according to the disclosed embodiment may be a coin-type virtual currency managed by a separate mainnet and thus managed, or may be a token-type virtual currency utilizing an infrastructure of another coin, such as an Ethereum network. It is not limited.

In one embodiment, the virtual currency provided to each of the user terminals 30 is already created or issued and managed by the management server 20, and may be provided according to a transaction history of each of the user terminals 30. have.

For example, the management server 20 may perform an operation for generating a block for storing a transaction history occurring between the user terminals 30, and may generate a block through proof of work. According to an embodiment, the management server 20 may receive a predetermined virtual currency generated in return and distribute the same to the user terminals 30 according to the transaction history of the user terminals 30.

In one embodiment, as the transaction history is recorded by each of the user terminals 30, a virtual currency may be generated according to a predetermined rule and provided to the user terminals 30, which may be mined (mined). )

In general, mining of cryptocurrency is performed by one of Proof Of Work (POW), Proof Of Stake (POS) and Proof Of Importance (POI). The above methods are one of the algorithms of variance sum used to ensure the reliability of distributed systems.

In the disclosed embodiment, a transaction between the user terminals 30 and a method for providing virtual currency are performed by a smart contract.

2 is a system diagram illustrating an operation of a blockchain system according to an embodiment of the present invention.

Referring to FIG. 2, operations of the blockchain holding servers 10, the management server 20, and the user terminal 31 illustrated in FIG. 1 are illustrated.

The management server 20 shown in FIG. 2 is a server of a telecommunication company via which the user terminal 31 communicates with the blockchain holding servers 10 or a local server connected to the user terminal 31 or the user terminal 31. ) May mean a server managing a blockchain-based service, but the management server 20 may be omitted, and the user terminal 31 directly communicates with the blockchain holding servers 10 to completely decentralize it. You can also build a system.

2 illustrates an example of a configuration used in a general blockchain-based service, and a form of a system using blockchain technology is not limited thereto.

In one embodiment, the user terminal 31 provides information to the management server 20, the management server 20 to the blockchain holding servers 10, if the information is information to be stored in the blockchain This information can be provided. The blockchain holding servers 10 may store the corresponding information in the blockchain, and in this process, verification of information based on the blockchain may be performed. In this case, only successful information can be recorded in the blockchain.

In addition, when there is information that needs to be verified, the user terminal 31 may provide the information to the management server 20. In this case, the management server 20 may verify this based on the information stored in the blockchain. In this process, the management server 20 obtains information for verifying the corresponding information from the blockchain holding servers 10. And verification can be performed accordingly. The management server 20 may provide a verification result to the user terminal 31 or provide a service to the user terminal 31 based on the verification result.

Unlike existing server-based systems that are centralized, such a blockchain system provides a higher security by providing a decentralized distributed processing system, and has an advantage of providing reliability that cannot be manipulated by a specific subject.

In addition, since it functions as a platform that can provide various blockchain-based applications in recent years, its utilization is expected to be further increased.

Each step described herein is described as being performed by a server, but the subject of each step is not limited thereto, and at least some of the steps may be performed in different devices according to embodiments. For example, each step described herein may be performed by a computer, a cloud, or the like.

3 is a diagram illustrating a control system of an expert platform for expert career management according to an embodiment of the present invention.

Referring to FIG. 3, the system may include a server 100, a plurality of expert groups 110, and a blockchain network 120 for performing operations of an expert platform for expert career management.

For convenience of description, referring to FIG. 3, the plurality of expert groups 110 are described as including user terminals of experts included in the plurality of expert groups 110. In addition, the definition of the server 100 is as described above, but for the convenience of description, the server 100 will be described as corresponding to the computer or the cloud.

In one embodiment, the server 100 may refer to a computer used to perform a task according to one or more services provided in the offline expert platform, and manage the online expert platform when the server 100 is an online expert platform. The server may mean, but is not limited thereto.

In the disclosed embodiment, the server 100 may register an expert according to a registration request of experts belonging to the plurality of expert groups 110 and collect expert information about the registered expert. In addition, the server 100 obtains project information that can be performed by experts belonging to a plurality of expert groups 110, distributes it to experts belonging to a plurality of expert groups 110, and manages and manages all of them. You can do your work.

In an embodiment, the server 100 may calculate the career information of the expert from the expert information and provide a suitable project for the expert based on the calculated career information. The professional's career information may include absolute career information and relative career information. In this specification, the absolute career information may refer to career information irrelevant to a specific criterion. That is, the absolute career information may refer to career information or professional evaluation results for all kinds of information that the expert has performed from the past to the present. In addition, relative career information may refer to career information on a specific standard. That is, the relative career information may mean, for example, career information on a project category performed by an expert, information on whether or not an expert performs a project category, or career information on a job title of a project performed by an expert. In this case, the relative career information may be obtained based on the absolute career information. On the other hand, the project position described in this specification may mean a position for distinguishing the position, role and task scope, scope of responsibility and evaluation scope of the expert who performs the project. In other words, the project position described in this specification includes information on the position, role and scope of work, scope of responsibility and evaluation scope of the expert who carries out the project, or the position, role and scope of work of the expert who carries out the project. And the evaluation range itself.

Meanwhile, absolute career information and relative career information may be provided through various methods. For example, the absolute career information and the relative career information may be provided as the information about the expert itself. Alternatively, the absolute career information and the relative career information may be provided in the form of a score calculated based on the information on the expert. In this case, the scores for the absolute career information and the relative career information are obtained by obtaining career scores from each of the information on the experts, giving weights or relevance to the obtained career scores, Can be obtained in the form of a grand total.

In another embodiment, the server 100 may collect project data related to the output of the project performed by the expert, and manage the collected project data.

In providing the service for managing the professional career, the server 100 may improve the safety and security of the information transaction for the expert based on the blockchain. Blockchain is a data distribution processing technology, which refers to a technology in which all users participating in the network distribute and store data such as all transaction details. Blockchain is a distributed peer-to-peer (P2P) of a ledger that utilizes a software component consisting of algorithms in which blocks connected in order negotiate usage history information of services using encryption and security techniques to ensure and maintain integrity. Can mean a system. Here, the distributed P2P system may be a special type of distributed system. In addition, the P2P system allows all nodes in the network to provide resources (processing capacity, storage space, data or network bandwidth, etc.) to each other without coordination of the central node. In addition, the blockchain may refer to a distributed ledger technology in which the ledger recording the usage history information is distributed to a P2P network, not a central server of a specific institution, so that nodes in the network jointly record and manage the ledger.

The blockchain network 120 may include nodes 121, 122, 123, 124, 125. A node may mean a component within a network of blockchains. Each of the nodes 121, 122, 123, 124, and 125 may be a server device of individuals who are joined to and participate in the system or server 100. For example, each of the nodes 121, 122, 123, 124, 125 may be a special-purpose computer, a general-purpose computer, a supercomputer, a mainframe computer. , A personal computer, a smartphone, a tablet PC, and the like, but is not limited thereto.

Thus, in providing a service for managing the professional career, the server 100 may improve the safety and security of the information transaction for the expert based on the blockchain.

Hereinafter, according to the disclosed embodiment, the server 100 manages a plurality of expert groups 110, receives expert information from the plurality of expert groups 110, obtains absolute career information for each expert, and project information. Obtains the project information, distributes the acquired project information to experts, manages the expert to carry out the project, manages project data that is the result of the executed project, updates absolute career information based on the project data, and provides relative career information. It will be described in detail how to obtain.

FIG. 4 is a diagram for describing a process of propagating valid information on a network of a blockchain to nodes on a network of a blockchain and recording the information on a blockchain according to an embodiment.

The server 100 may distribute and store expert information necessary for expert management in the blockchain network 120. In the present specification, the expert information may refer to the produced result and information related to the result when a specific person produces a specific result in a certain work area. Therefore, the expert information may include all kinds of information for evaluating the professionalism of each expert or obtaining the absolute and relative career information of the expert, and the information for evaluating the expert's professionalism is the expert It may refer to information that is related to the expertise of a specific specialty of the above or more than a predetermined reference value, and the absolute career information of the expert may mean the degree of expertise acquired based on the expert information on the expert, but is not limited thereto. It doesn't happen.

For example, the expert information includes expert background information, expert status information, expert career and history information, expert qualification information, expert professional area information, expert award information, professional achievement information, and professional performance information. Information about the professional's activities (advising, judging, lectures, presentations, awards, SNS activities, etc.), project information that the expert can perform, expert signing-up information, expert contract information, expert ordering, participation and performance results, Project data information related to the project's execution results, creations, works and copyrights generated from the expert's project data, expert's revenue and payment information, expert's fee information, expert's evaluation and reputation information, and expert's information usage information. It may include.

According to an embodiment of the present disclosure, the expert's background information may include information necessary for determining the expert's specialty and expertise.

According to an embodiment of the present disclosure, the expert's background information may be divided into formal and atypical information. Formal information may refer to information (or quantifiable information) that has already been quantitatively associated with a particular area of expertise. For example, as described above, objective data capable of demonstrating an expert may correspond to formal information. For example, the formal information may include, but is not limited to, a history of working in a company or a department in a specific field, a history of participating in a project or a task in a specific specialty, or educational background or publication information in a specific field.

In addition, the unstructured information does not belong to the formal information, but may mean all kinds of historical information having a connection with a specific specialty. For example, as described above, the history information according to individual efforts may belong to the atypical information. For example, the atypical information may include, but is not limited to, records of activities in online communities or communities, materials created by projects or hobbies performed by individuals, and information on online education contents.

In one embodiment, the background information of the expert in the new specialty field in which the conventional information is not stored may be obtained. In this case, since no formal information, which is a criterion for evaluating the specialized field, has been stored at all, background information of all experts may be evaluated as unstructured information.

For example, if there is no pre-evaluated or stored information about the specialty, even if the information was performed in a workplace or department of a particular specialty, or when the project or task was performed, the information is evaluated as an informal information. It can be used to assess the expertise of each expert.

In addition, advisory (committee), auditor and evaluator activities, lectures and lectures, mentoring activities, and outside histories may be included in the expert's background information, which may belong to formal or informal information. For example, the classification criterion may be, but is not limited to, whether the association with expertise in a particular specialty has been quantitatively proven and databased.

In addition, data that can verify the morality of experts can also belong to the background information of experts, for example, a variety of information, such as the recognition and reputation of activities in the industry, industry, SNS, rebate, criminal records, fraud records, etc. Even if all the information is not available in reality, information that can be publicly collected and collected outside can be used as the background information of experts classified into standard or unstructured information.

Meanwhile, some of the above-described structured information and atypical information may be difficult to digitize with data. When it is difficult to digitize data, it may mean that data is not digitized because no standard is provided. In this case, data that is difficult to quantify may not only be input directly based on the surrounding evaluation but also may be quantified through various types of algorithms.

According to an embodiment of the present disclosure, the expert's status information may include the number of projects each expert can perform or the number of projects each expert can participate in, such as the affiliation of each expert, existence of affiliation, current project, and other personal matters. And the necessary information to evaluate the etc.

According to an embodiment, the project data related to the project execution result may mean all data acquired in the project process, and does not mean only a meaningful result of the project. For example, the plurality of proposals calculated from the Request For Proposal (RFP) presented in the project bidding process may be project data according to the present invention. Thus, not only the adopted proposal but also the unadopted proposal may be project data according to the present invention.

According to one embodiment, the creation refers to data on the work obtained from the project data. In this case, the creation may refer to data newly generated from the project execution subject among the project data. However, the present invention is not limited thereto, and the creation may mean data having creativity in the data newly generated from the project execution subject. A work is a different concept from a copyrighted work, and a work may be a copyrighted work only when it is determined that it does not infringe the copyright of another person.

However, the expert management information that can be stored on the blockchain is not limited thereto, and may include any of the types of information necessary for the above-described information and other expert management.

According to an embodiment of the present disclosure, the server 100 may transmit information to the node 121 of the blockchain network 120.

The server 100 may encrypt the information and transmit the encrypted information to the node 121 on the blockchain network 120. The node 121 may decrypt the encrypted information and verify whether the information is valid based on the decrypted result. As a result of validating the information, if the information is not valid, the node 121 may discard the information. According to an embodiment, the validity of the information may be obtained by decrypting the received cipher text using the first hash value obtained by applying a hash function to the received information and the public key of the operator of the server 100. This may be performed by comparing hash values, but is not limited thereto.

As a result of validating the information, if the information is valid, the node 121 may transmit the information to the node 122 on the blockchain network 120. In addition, the node 121 may record 1212 information in the candidate block. When predetermined transaction information is recorded in the candidate block, the node 121 may generate a valid block by performing proof of work on the candidate block. In addition, if the node 122 also validates the information, the node 122 may add the information to the candidate block and perform a proof of operation on the candidate block to generate a valid block.

The same operations may be performed at the nodes 123 and 124 as at the node 122.

Referring to the process of generating a valid block in the node 121 and adding it to the blockchain, the node 121 may calculate the root of the Merkle tree for predetermined information. The node 121 may generate a hash reference that points to the previous block header in terms of the block to be added to the blockchain. The node 121 may acquire a difficulty level required for proof of work or constraint of a block to be added to the blockchain. The node 121 may check whether the value of the block hash generated by applying the hash function to the root of the Merkle tree, a hash reference indicating the previous block header, the difficulty, the data and the nonce of the timestamp satisfies the constraint. The node 121 may perform proof of work on the candidate block by acquiring a value of the nonce that satisfies the constraint while increasing the nonce by 0 to 1. The node 121 may add the candidate block as a valid block to the blockchain. In addition, the node 121 may transmit a valid block to other nodes 122, 123, 124, etc. on the network of the blockchain.

Each node 122, 123, 124, etc. on the network of the blockchain performs validation on valid blocks received from the node 121, and the nodes are retained in the blockchain held by each node 122, 123, 124, etc.). You can add valid blocks.

Accordingly, the server 100 may secure the information based on the blockchain technology, thereby securing the security and reliability of the expert platform.

In addition, various embodiments may be provided based on a blockchain.

In the disclosed embodiment, the expert information, the absolute career information, the relative career information, the project information and the project data performed by the expert may all be stored in the blockchain.

For example, in providing the aforementioned project information to the experts, the server 100 may perform providing the first project information to the first expert.

In addition, the server 100 may store the first project information in the first blockchain, but store the first project information by matching the information with respect to the first expert.

In addition, the server 100 may store the information about the first expert in the second blockchain, but may match and store the first project information.

In the storing of the first project information described above in the first blockchain, the server 100 may hash the node on the second blockchain in which the first project information is stored. Can be stored in the.

In addition, in the storing of the above-mentioned information about the first expert in the second blockchain, the server 100 stores the information hashing the node on the first blockchain in which the information about the first expert is stored. The storing in the second blockchain may be performed.

Through this, the first blockchain and the second blockchain are linked to each other, and they are different blockchains, but operate like blockchains connected to each other through one or more contacts and verify information of each other. This allows the two blockchains to verify each other, and even if one of the blockchains fails, the other one will not be affected. Can provide.

In an embodiment, the server 100 may receive a verification request for the first project information.

In this case, the server 100 may verify the first project information based on the blockchain, but may verify the information twice by utilizing two blockchains associated with each other.

In addition, the server 100 may verify the first project information based on the first project information stored in the second blockchain.

In addition, the server 100 may obtain a first hash value that hashes a node in which the first project information is stored.

In addition, the server 100 may obtain information about the first expert who performed the first project information.

In addition, the server 100 may verify the first hash value based on the node on the first blockchain in which the information about the first expert is stored.

In addition, the server 100 may receive a verification request for the first expert.

In addition, the server 100 may perform verification on the first expert based on the information about the first expert stored in the first blockchain.

In addition, the server 100 may obtain a second hash value that hashes a node in which the information about the first expert is stored.

In addition, the server 100 may obtain the first project information performed by the first expert.

In addition, the server 100 may verify the second hash value based on a node on the second blockchain in which the first project information is stored.

The server 100 may return whether the verification is successful, and may perform a process of evaluating the expertise of the first project information and the first expert or obtaining absolute career information and relative career information according to the verification success, It can also be used for matching between expert and project information.

5 is a flowchart illustrating an expert career management method performed in an expert platform according to an embodiment of the present invention.

In operation S110, the server 100 may register an expert.

In operation S120, the server 100 may obtain expert information of a registered expert.

In an embodiment, the server 100 may obtain information input from the expert, and collect expert information by collecting crawled information about the expert.

For example, the server 100 may obtain a resume or reference of an expert or information input or freely written by an expert based on a preset form. The information entered by the expert may include information about the expert's past projects and various information that the expert wants to present in the specialty.

In an embodiment, the server 100 may collect information related to the expert through web crawling. For example, the server 100 may collect information about the expert based on the personal information of the expert.

The server 100 may obtain information not inputted by the expert through crawling, but is not limited thereto. For example, the server 100 may verify information input by the expert, or more information based on the information input by the expert. Crawl may also be performed to collect information.

For example, if the expert inputs information on an activity on a specific community site, the server 100 may crawl the information about the expert on the community site. In this case, according to an embodiment, Crawl can also be performed using basic information, including ID and nickname. This can be used to supplement the areas where the expert cannot collect and submit all the data directly, or it can be used to verify the information entered by the expert.

In operation S130, the server 100 may calculate absolute career information of the expert based on the acquired expert information.

In an embodiment, the server 100 may verify the acquired expert information and calculate the absolute career information of the expert based on the verified expert information. In this case, the verification of the expert information may refer to a task of confirming the authenticity of information obtained directly from the expert and information obtained through web crawling.

In one embodiment, when the expert information subject to verification is information on past project execution input by the expert, the server 100 obtains a source of the project and requests expert information from the obtained project source. The expert information can be verified by determining whether the expert information entered by the expert and the expert information received from the project source are the same.

In another embodiment, when the expert information to be verified is information obtained by crawling, the server 100 obtains category information from the information obtained by crawling, and the category information is obtained from categories obtained from the verified expert information. The expert information can be verified by comparing the corresponding expert information. For example, if the category information is education category information, the server 100 may include information on the education category included in the information obtained by the crawl (for example, A university graduate) and the education category among the verified professional information. Expert information can be verified by comparing the corresponding information (for example, graduating from B University). If A and B are different universities, the information obtained by crawling can be excluded from expert information.

As another example, when the category information is the execution project category information, the server 100 executes among the information about the execution project category included in the information obtained by the crawl (for example, the execution of the A project) and the verified expert information. Expert information can be verified by comparing the information corresponding to the project category (for example, performing project B). When the A project and the B project is the same project, the server 100 may obtain the information obtained by the crawl as verified expert information. The server 100 may further determine whether the execution details of the detailed project are the same. If the A project and the B project is a different project, and both projects cannot be performed at the same time, the server 100 may exclude the information obtained by the crawl from the expert information. If the A project and the B project is a different project, and both projects can be performed at the same time, the server 100 may verify the expert information by asking the expert for proof of authenticity of the information obtained by the crawl.

In addition to the above-described embodiments, the server 100 may extract various category information from the expert information and verify the expert information corresponding to the extracted category information.

Hereinafter, unless otherwise stated, the expert information used or described in the expert platform is verified expert information and various embodiments will be described.

In operation S140, the server 100 may match a project corresponding to the expert based on the absolute career information of the expert.

In operation S150, the server 100 may obtain project data calculated according to the execution result of the project.

In operation S160, the server 100 may update the absolute career information of the expert based on the project data.

In operation S170, the server 100 may obtain relative career information based on the updated absolute career information and project data.

6 is a flowchart illustrating a method of calculating absolute career information of an expert according to an embodiment of the present invention.

In operation S210, the server 100 may classify the expert information into formal information and unstructured information.

As described above, the expert information in the present specification may be divided into formal information and atypical information.

In one embodiment, the formal information may refer to information (or information that can be quantified) that has already been quantitatively proved to be related to the specialty of a specific specialty. For example, as described above, objective data capable of demonstrating an expert may correspond to formal information.

For example, formal information may include history of work in a company or department in a particular field, history of participation in a project or assignment in a particular field of expertise, information on academic background or publication in a particular field, project participation, awards, or qualifications held. May be, but is not limited thereto.

In one embodiment, the atypical information does not belong to the formal information, but may refer to all kinds of information having an association with a specific specialty. For example, as described above, information according to individual efforts may belong to unstructured information.

For example, unstructured information may include records of activities in online communities or communities, personally created projects or hobbies, information about online education content, SNS activity information, reputation information, and the like. It doesn't happen.

In one embodiment, expert information for a new specialty field in which no conventional information is stored may be obtained. In this case, since no formal information, which is a standard for calculating absolute career information or evaluating professionalism, has been stored in the specialized field, all expert information can be evaluated as unstructured information.

For example, if there is no pre-evaluated or stored information about the specialty, even if the information was performed in a workplace or department of a particular specialty, or when the project or task was performed, the information is evaluated as an informal information. It can be used to evaluate the expertise of each expert or to obtain absolute career information of each expert in the area of expertise.

In addition, expert information may include advisory (committee), auditor and evaluator activities, lectures and lectures, mentoring experiences, and outside career experience, which may belong to formal or informal information. For example, the classification criterion may be, but is not limited to, whether the association with expertise in a particular specialty has been quantitatively proven and databased.

In addition, data that can verify the morality of experts can also belong to expert information, for example, a variety of information, such as the recognition and reputation of activities in the industry, industry, SNS, rebate, criminal records, fraud records, etc. In addition, even if all the information is not available in reality, information that can be publicly collected and collected outside can be used as expert information divided into formal information or unstructured information.

Meanwhile, some of the above-described structured information and atypical information may be difficult to digitize with data. When it is difficult to digitize data, it may mean that data is not digitized because no standard is provided. In this case, data that is difficult to quantify may not only be input directly based on the surrounding evaluation but also may be quantified through various types of algorithms.

For example, the expert and each expert information according to the disclosed embodiment may be set in various ways, for example, as an expert who is known to everyone, experts who are proven by objective data, and non-specialists who have individually tried to become experts. There may be specialists who meet or exceed professional expertise, to some extent experts who are proven to be objective data, or experts who have increased their expertise by adding individual efforts.

Therefore, in order to calculate the absolute career information of these various experts, it is necessary to classify the expert information according to its properties. Hereinafter, an embodiment of classifying the information into atypical information and atypical information, and calculating the absolute career information of the expert accordingly, will be described in detail.

In one embodiment, the formal information may refer to information that has already been quantitatively proved to be related to the specialty of a specific specialty. For example, as described above, objective data that can prove the absolute career information of the expert may correspond to the formal information.

For example, the formal information may include, but is not limited to, a history of working in a company or a department in a particular field, a history of participating in a project or a task in a specific specialty, or educational background or publication information in a specific field.

In addition, expert information may include advisory (committee), auditor and evaluator activities, lectures and lectures, mentoring activities, and external histories, which may belong to formal or informal information. For example, the classification criteria may be, but are not limited to, whether the association with expertise in a particular area of expertise has been quantitatively proven and databased.

In addition, data that can verify the morality of experts can also belong to expert information, for example, a variety of information, such as the recognition and reputation of activities in the industry, industry, SNS, rebate, criminal records, fraud records, etc. Even if all the information is not available in reality, information that can be publicly collected and collected outside can be used to calculate the career of an expert as well.

In one embodiment, the atypical information does not belong to the formal information, but may refer to all kinds of information having an association with a specific specialty. For example, as described above, information according to individual efforts may belong to unstructured information.

For example, the atypical information may include, but is not limited to, records of activities in online communities or communities, materials created by personally executed projects or hobbies, information on taking online education contents, and the like.

In one embodiment, expert information for a new specialty field in which no conventional information is stored may be obtained. In this case, since no formal information, which is a standard for calculating absolute career information or evaluating expertise, has been stored in the specialty field, all expert information can be evaluated as unstructured information.

For example, if there is no pre-evaluated or stored information about the specialty, even if it is information, work or department work in a particular specialty, it is evaluated as unstructured information for that specialty. It can be used to assess the expertise of each expert.

In operation S220, the server 100 may evaluate the structured information and the unstructured information.

In one embodiment, the server 100 may evaluate each of the structured information individually, or may comprehensively evaluate a plurality of the structured information, the method is not limited.

In one embodiment, the server 100 may individually evaluate each unstructured information, may comprehensively evaluate a plurality of unstructured information, the method is not limited.

In operation S230, the server 100 may calculate absolute career information of the expert based on the evaluation result.

In one embodiment, the server 100 may obtain the absolute career information of the expert based on the evaluation results of the structured information and atypical information. However, the present invention is not limited thereto, and the server 100 may acquire professional evaluation information based on expert information including formal information and atypical information, and may obtain absolute career information of the expert from the acquired professional evaluation information. Of course.

In an embodiment, the server 100 may evaluate the expert's absolute career information or expertise in a manner of individually evaluating the expert information including each of the formal information and the atypical information, and summing the individually evaluated results. It is possible to comprehensively evaluate a plurality of expert information, and to evaluate the professionalism of each group by grouping some expert information, and to evaluate the absolute career information or professionalism of the expert by summing up the overall professional evaluation result. Is not limited.

In operation S240, the server 100 may classify the expert information corresponding to the history information stored in the database into formal information.

In an embodiment, the database may store information about each specialty and history information corresponding to each specialty in advance.

The server 100 may determine that the information already stored in the database is formal information using a database. In one embodiment, the server 100 may perform a process of matching the information stored in the database with expert information. For example, since expert information input or crawled by an expert may not exactly match information previously stored in a database, a process of determining whether or not formal information is performed by matching similar information may be performed. For example, it may be determined that the information stored in the database and the information showing a matching rate equal to or greater than a predetermined reference value are standard information.

In an embodiment, the server 100 may display the database information matched in the input step by the expert and filter the structured information in the input step by allowing the expert to directly match the corresponding database information.

In operation S250, the server 100 may classify the expert information corresponding to the information on the specialized field not stored in the database and the history information not stored in the database into unstructured information.

In one embodiment, the server 100 may classify expert information as unstructured information, except for the standardized information classified based on the database.

In one embodiment, multiple databases may be hierarchically organized. For example, the first database may store information about each specialty and history information corresponding to each specialty, and the expert information corresponding to the information stored in the first database may be classified as formal information.

In addition, although information about each specialty and history information corresponding to each specialty may be stored in the second database, expert information corresponding to the information stored in the corresponding database may be classified as unstructured information. 2 Information not all stored in the database can be classified as unstructured information as well.

In addition, according to an embodiment, the expert information corresponding to the information stored in the second database may be classified as unstructured information, and the information stored in the second database may be used to evaluate the atypical information.

That is, the information stored in the second database may correspond to an intermediate stage between the unstructured information and the structured information, but the information stored in the second database may not be evaluated as the structured information. However, this is only an embodiment, and the configuration of the database and its usage are not limited.

In operation S260, the server 100 may evaluate the formal information based on the evaluation information stored in the database.

For example, evaluation information on expert information corresponding to each formal information may already be stored in the database, and the server 100 quantitatively calculates a score corresponding to each formal information based on the information stored in the database. can do.

For example, the database may contain information on how certain history information is related to a particular field of expertise, how long the history of the history information can be, and how much experience can be recognized based on the role played by the expert in that history. Can be stored in advance.

Information stored in advance may be input by an expert in the relevant field, or may be determined based on information calculated based on pre-collected big data. For example, if there is a lot of reference data for a relevant field, it can be used to construct big data, and use the learning methods such as machine learning to determine the correlation between each field and the historical information. According to the detailed information, information that can quantitatively evaluate the expertise can be obtained.

Such information may be stored in a database in advance and used to evaluate expert information classified as formal information.

In operation S270, the server 100 may estimate absolute career information of the expert's specialty from the atypical information using a model trained to derive a correlation between the history information and the expertise of each specialty.

The learned model may be a model trained to derive a relationship between specific historical information and expertise in each specialty field, and to estimate the expertise of the corresponding expert from unstructured information.

In an embodiment of the present disclosure, when new unstructured information is obtained for a specific specialty previously stored in a database, the learned model may estimate a correlation between the atypical information and the specialty.

In addition, the trained model can estimate the correlation between new specialties that are not stored in the database and new unstructured information of the specialties.

In one embodiment, the trained model may be trained based on information stored in a database. The server 100 may generate various learning data based on the information stored in the database. For example, the server 100 may generate an association between a specific specialty previously stored in the database and new unstructured information. The training data may include training data including the pre-labeled history information and train the model based on the training data.

As another example, the feature information extracted for the specific specialty, the feature information extracted for the specific history information, and the correlation between each feature information can be estimated to estimate the association between the specific specialty not previously stored in the database and the new unstructured information. You can create training data with pre-labeled relationships and train the model based on them.

That is, the server 100 estimates the absolute career information of the expert by classifying the professional and atypical information of the expert and evaluating the expert information for each classified information by using the model trained on the database and the database. can do.

7 is a flowchart illustrating another method of calculating absolute career information of an expert according to an embodiment of the present invention.

In operation S310, the server 100 may calculate an evaluation result for each expert information.

In operation S320, the server 100 may obtain a time point corresponding to each expert information.

For example, the time point may include information about working period, project participation period (including start and end points), online community activity period, thesis publication time, certification time, project execution time, etc., but is not limited thereto. .

In operation S330, the server 100 may assign a first weight to the evaluation result calculated based on the period from the time point corresponding to each expert information to the present time.

For example, the server 100 may assign a first weight to reduce the evaluation result as the old expert information is included. That is, the old expert information may be forgotten over time, and the knowledge and sensation of the expert may become obsolete, and thus, the first weight may be given to reduce the evaluation result of the expert information over time. have.

Similarly, the server 100 may assign a first weight to increase the evaluation result as the period of each absolute career information is longer, or may assign a first weight to increase the evaluation result as the latest expert information.

In operation S340, the server 100 may calculate absolute career information of the expert based on the evaluation result to which the first weight is assigned.

In operation S350, the server 100 may obtain information about a blank period included in expert information.

That is, the expert information may include not only the structured information and the unstructured information, but also information on the blank period, and the one or more structured information and the unstructured information may overlap each other.

The blank period may mean a period without structured information or a period without both structured and unstructured information. In addition, the unit of the blank period may be divided into various units such as days, weeks, months, and years from as few hours as long.

In operation S360, the server 100 may sort the structured information, the unstructured information, and the blank period included in the expert information in chronological order.

In operation S370, the server 100 may assign a first weight to each of the sorted expert information.

For example, if there is a trailing blank information in a specific expert information, the first weight will be adjusted so that the evaluation result for the expert information is decreased during the blank period. On the other hand, even if the information has passed for a long time, if a career of the same professional scope is continuously registered thereafter, the first weight may be set so that the evaluation result does not decrease or increases depending on the embodiment.

In addition, unstructured information may be arranged in the blank period of the structured information. In this case, the unstructured information corresponding to the expert information obtained in the blank period of the structured information is evaluated. Instead of being regarded as an area, it can be evaluated by continuous absolute career information. In an embodiment, the blank area below the preset period may be ignored.

Meanwhile, expert information capable of demonstrating expertise in a particular field may be recognized for a higher value as it lasts longer, so that the first weight may also be adjusted accordingly. As the evaluation result of each expert information is higher, the first weight of the previous expert information may also be adjusted in a manner of acknowledging the expertise, but is not limited thereto.

8 is a flowchart illustrating a method of updating absolute career information of an expert according to an embodiment of the present invention.

In operation S410, the server 100 may generate feedback on the absolute career information of the expert based on the project data.

Although the type of feedback is not limited, the feedback may include information about the difference between the absolute career information estimated for the expert and the absolute career information obtained based on the business process result.

In addition, the server 100 may perform the step of updating the expertise of the expert based on the feedback.

That is, the server 100 may maintain, increase, or decrease the absolute career information of the expert based on the feedback.

In addition, when a change occurs in the absolute career information of the expert, the server 100 may use the absolute career information of the expert or provide a means for improving the absolute career information of the expert.

For example, the type of assignments and projects assigned to the expert or the types of tasks undertaken by the assignment and the project may be changed according to the expert's absolute career information, and the educational information to increase the expert's professionalism. May be provided.

In addition, when the professional estimation result based on the unstructured information and the actual estimated absolute career information are different, the model may be updated based on this.

In operation S420, the server 100 may evaluate the estimation result of the trained model based on the feedback.

For example, the expert's expertise can be estimated from the unstructured information using the model learned as in the above-described method, which can be evaluated based on the actual work performance of the expert.

In operation S430, the server 100 may update the trained model based on the evaluation information about the estimation result.

The server 100 may update the learned model by performing the learning by adding the atypical information of the corresponding expert and the information including the actual work result as new learning data or by adding the existing learning data.

Similarly, based on the estimation result and the feedback thereto, the server 100 may update the model learned through reinforcement learning.

In operation S440, when the association between the first atypical information and the first specialty exceeds a preset reference value, the first atypical information may be stored in the database as standardized information for the first specialty.

In operation S450, when the information about the first specialty is not stored in the database, the information about the first specialty may be stored in the database.

That is, even if the unstructured information can be confirmed that the reference information associated with the atypical information is accumulated enough to determine the association with the specific specialty more than the predetermined reference value, by adding the information about the atypical information and the corresponding specialty to the database, The unstructured information can be registered as structured information.

For example, a particular online community activity may initially be treated as unstructured information. However, when the expertise of professionals actively active in the online community is demonstrated, activity information of the online community may be registered as formal information.

In addition, even in a specialty field not previously stored in the database, when associating one or more unstructured information with a predetermined reference value or more, information about the specialty field and corresponding informal information is registered in the database as formal information. It is possible.

9 is a flowchart illustrating a method of calculating relative career information of an expert according to an embodiment of the present invention.

In operation S510, the server 100 may acquire a project position of an expert based on data on work division of a matched project.

In this case, the data on the division of work is included in the project information, it may mean a table that divides the work broken down by project. In one embodiment, the server 100 may divide the project into work units according to the data on the work division, divide the work unit into work items, and divide the work items into detailed items. For example, if a project is intended to carry out a particular event, a business unit may include programs, food and beverage, space creation, operations, invitations, registrations, contracts, etc., and business items may include program composition, cue sheets, scenarios, moderators. , Performers, BGM, and other checks may be included.Details may be divided into securing contact points for performers, checking clothes, props and personnel, checking the waiting space, preparing other props, and rehearsing plans. It is not. In this case, one sub-item may be divided into one category. That is, the data obtained by performing one subitem may be composed of one work, and the subitems may be divided so as not to be a co-work or a combined work. That is, a work obtained by performing one detail item may be a work. However, the present invention is not limited thereto, and the work obtained by performing the detailed items according to various embodiments of the present disclosure may be a joint work or a combined work.

Furthermore, the data on the work division may include a plurality of project positions, and the server 100 may classify work units, work items, or sub-items performed by each of the plurality of project positions included in the data on the work division. Can be. For example, a first project position is a position to perform a first business unit, a first task item, or a first sub-item, and a second project position is a second business unit, a second task item, or a second sub-item. It may be a position to perform.

In operation S520, the server 100 may obtain an association degree of each expert information with a project position based on the project data.

Specifically, the server 100 may determine the degree of association between the project position and the expert information obtained from the expert when the expert is registered. The expert information acquired at the time of expert registration does not contain information about the project position of the expert. Therefore, it is necessary to determine the relationship between each expert information and the project position.

For example, when an expert performs a project according to a first project position to obtain first project data, the server 100 may determine that the first project data is project data that the first project position should generate. have. Accordingly, the server 100 compares the first project data with the expert information acquired at the time of registering the expert, and the expert information similar to the first project data among the expert information has a high correlation, and the information different from the first project data has a low association. Can be given.

In operation S530, the server 100 may obtain relative career information on the project position based on the degree of association of each of the expert information and the expert information.

10 is a flowchart illustrating a method of obtaining relative career information based on a first career score and a second career score according to an embodiment of the present invention.

In operation S605, the server 100 may obtain a plurality of project positions based on data on work divisions of the plurality of projects performed by the server 100.

In operation S610, the server 100 may match project data obtained by a plurality of experts having a plurality of project positions and a plurality of project positions.

That is, the server 100 may obtain a plurality of project positions for a plurality of projects based on projects performed by all experts registered in the expert platform, and match and store project data performed in each project position. Thus, project data pairs performed by specific project positions can be obtained.

In operation S615, the server 100 may cluster a plurality of matched project positions to obtain a plurality of position clusters.

In one embodiment, the server 100 may obtain a plurality of job clusters by clustering a plurality of project data matched with each of the plurality of project job titles. Since clusters generated by clustering project data are groups of similar projects, the server 100 may determine that a plurality of project positions corresponding to similar projects are similar project positions.

In another embodiment, the server 100 may cluster a plurality of project positions themselves to obtain a plurality of position clusters. Since the project positions performing similar tasks are likely to include similar words, the server 100 may cluster the project positions themselves to obtain a position cluster.

In another embodiment, the server 100 clusters a plurality of project positions themselves to obtain a plurality of first clusters, and obtains a plurality of second clusters by clustering a plurality of project data matched to each of the plurality of project positions. After that, the position cluster may be acquired based on the obtained plurality of first clusters and the plurality of second clusters. Specifically, the server 100 obtains a center point for each of the plurality of first clusters, obtains a center point for each of the plurality of second clusters, and matches each of the plurality of center points for the plurality of first clusters. Determining a center point for the plurality of second clusters, determining a plurality of center points for the plurality of job clusters based on the matched two center points, and performing clustering based on the plurality of center points for the plurality of job clusters By acquiring a plurality of job title clusters, a plurality of job title clusters may be obtained.

In operation S620, the server 100 may obtain a first center point that is a center point of the first job cluster and a second center point that is a center point of the second job cluster among the plurality of job clusters.

In operation S625, the server 100 may obtain a first degree of association of the first position cluster with respect to the second position cluster based on the distance between the first center point and the second center point.

In operation S630, the server 100 may obtain a position cluster corresponding to a project position of an expert among the plurality of position clusters.

That is, the server 100 may determine that the correlation between the two job clusters is low when the distance between the job clusters is far, and may determine that the correlation between the two job clusters is high when the distance between the job clusters is close. . The server 100 may determine a value of the first degree of association in proportion to the distance between the first center point and the second center point. In this case, the first association degree may be 0 or more and 1 or less.

In one embodiment, the server 100 may obtain two position clusters with the farthest distance between the center points among the plurality of position clusters, and change the distance between the two clusters to a unit distance (for example, 1). . The server 100 may obtain the distance between any two position clusters among the plurality of position clusters based on the unit distance. The server 100 may obtain an association value based on the distance between any two position clusters among the plurality of position clusters obtained based on the unit distance.

For example, when the distance between the first center point and the second center point is obtained as 0.3 based on the unit distance, the server 100 may determine the first degree of association as 0.7 (= 1-0.3). That is, since the degree of association between the two position clusters with the farthest distance between the center points among the plurality of position clusters should be lower than that between the two other position clusters, the server 100 has a unit distance of 1, the first center point, and the second position. The difference value of the distance between the center points may be determined as the first association degree.

In operation S635, the server 100 may obtain at least one position cluster corresponding to expert information among the plurality of position clusters. In detail, the server 100 may determine whether each of the plurality of expert information registered by the expert who performs the project corresponds to at least one position cluster among the plurality of position clusters. For example, the first expert information in the expert information may correspond to the first job cluster, and the second expert information in the expert information may correspond to the second job cluster.

In operation S640, when the position cluster corresponding to the expert's project position is the first position cluster, and the at least one position cluster corresponding to the expert information is the first position cluster, the server 100 is included in the first position cluster. The first career score can be obtained based on the expert information.

In operation S645, when the position cluster corresponding to the expert's project position is the first position cluster and the at least one position cluster corresponding to the expert information is the second position cluster, the server 100 is included in the second position cluster. The second career score may be obtained based on the expert information and the first relevance.

Specifically, when the project position of the expert who carries out the project is a project position included in the first position cluster, the server 100 scores the first career score based on the first expert information included in the first position cluster among the expert information. Can be obtained. In addition, the server 100 based on the second expert information included in the second job cluster of the expert information and the second career score based on the first degree of association (association between the first project job and the second project job) previously obtained. Can be obtained.

In this case, the career score may be obtained through various methods. In one embodiment, the career score may be obtained by acquiring each score corresponding to each of the expert information, and summing each acquired score. In this case, the first career score is determined by the sum of the respective scores corresponding to each expert information included in the first job cluster, and the second career score is each corresponding to each expert information included in the second job cluster. The sum of the scores may be determined by multiplying the first degree of association.

In operation S650, the server 100 may obtain relative career information based on at least one career score of the first career score and the second career score. For example, the relative career information may be a total score obtained by adding up a first career score and a second career score.

11 is a flowchart illustrating a method of obtaining a career score based on a weight according to an embodiment of the present invention.

In operation S710, the server 100 may determine whether a work corresponding to the project data corresponding to the first job cluster may be registered as a work.

In operation S720, when the creation corresponding to the project data may be registered as an asset, the server 100 may obtain a second weight for the project data.

In operation S730, the server 100 may obtain a first career score based on expert information included in the first job cluster and the second weight.

In operation S740, the server 100 may determine whether a work corresponding to the project data corresponding to the second job cluster may be registered as a work.

In operation S750, when the creation corresponding to the project data may be registered as an asset, the server 100 may obtain a third weight for the project data.

In operation S760, the server 100 may obtain a second career score based on the expert information included in the second job cluster and the third weight. At this time, the second weight and the third weight may be greater than one.

In detail, the server 100 may determine whether a work obtained from project data is a work that can be registered as a copyright to calculate a career score. When the copyright corresponding to the creation data corresponding to the project data is recognized, the server 100 determines that more effort is put into the creation without the copyright recognition, and obtains a second weight and a third weight to increase the career score. It can be reflected in obtaining a career score.

 12 is a flowchart illustrating a method of calculating a career score according to whether a project data is a joint work or a combined work according to an embodiment of the present invention.

Specifically, a work may be classified into an individual work that is individually created by an individual, a joint work that cannot be separated by a plurality of people created jointly, and a combined work that is created by a plurality of people but which can be separated. Accordingly, the server 100 may determine whether a work obtained from the project data is a copyrightable work, and further, determine whether a work that can be registered by copyright is a joint work or a combined work. In addition, the server 100 may assign different weights to the combined work and the joint work in consideration of the fact that the combined work is a work produced solely and the joint work is a joint work.

In detail, in operation S810, when the project data is data generated by a plurality of experts, the server 100 may determine whether a creation corresponding to the project data is a joint work or a combined work.

In operation S820, the server 100 may obtain a 2-1 weight on the project data when the creation is a collaborative work, and obtain a 2-2 weight on the project data when the creation is a combined work. .

In operation S830, when the project data is data generated by a plurality of experts, the server 100 may determine whether a creation corresponding to the project data is a joint work or a combined work.

In operation S840, the server 100 may obtain a 3-1 weight on the project data when the creation is a joint work, and obtain a 3-2 weight on the project data when the creation is a combined work. . At this time, the 2-1 weight may be greater than the 2-2 weight and the 3-1 weight may be greater than the 3-2 weight.

That is, in the case of a collaborative work, the server 100 reflects that the efforts of a plurality of creators are inseparably combined and a new one is obtained, so that the necessity of collaboration between each other is inevitably larger than in the case of the collaborative work. In this case, the career score can be obtained by giving the weight of the joint work higher than the weight of the combined work.

However, the present invention is not limited thereto, and in some cases, the server 100 may obtain a career score by assigning a weight of the combined work higher than the weight of the joint work.

On the other hand, in order to assign different weights to the combined work and the joint work, the process of determining whether the creation is a combined work or a co-work should be made in advance. Hereinafter, a method of determining whether a work has a copyright and a method of determining whether a work is a combined work or a co-work in case of having a copyright will be described.

First, the server 100 may obtain at least one creation for registering as a work from the obtained project data.

In one embodiment, the server 100 may determine the data satisfying the requirements of the work of the project data to obtain the data satisfying the requirements as a creation. In detail, the server 100 may acquire, as a creation, data in which creativity is recognized by including human thoughts or emotions.

In an embodiment of the method of determining a creation, the server 100 may obtain data generated by an expert among project data. For example, the server 100 may include data related to the project, including documents, images, videos, drawings, plans, graphics, etc., provided from the project's registrant, among the project data, forms associated with the project, projects and the like. Data can be obtained except for relevant background materials.

In another embodiment, the server 100 may determine the degree of creativity by analyzing data generated by an expert. For example, when the data generated by the expert is text data, the server 100 may determine the degree of creativity based on the weight of the word associated with the emotion among the text data and the connection relationship between the words related to the emotion. For example, the server 100 may store a word related to an emotion in a database, and may determine a ratio including a word related to an emotion stored in a database among text data generated by an expert. When the text data generated by the expert includes more than a predetermined ratio of words related to emotion, the server 100 may determine that the data is a creation.

In another embodiment, the server 100 may obtain all sentences included in the text data, and determine emotion information about each of the obtained plurality of sentences. For example, the server 100 may quantify the emotion information of the sentence based on the connection relationship between the word for the emotion included in the sentence and the word for the emotion. The server 100 may determine the text data as a creation when the sentence in which the emotion information is numerically equal to or greater than a preset value.

In another embodiment, if the data generated by the expert is image data or image data, the server 100 may determine the data as a creation. In general, images or videos will often express human thoughts or emotions, which are the requirements of the work.

When the creation is obtained, the server 100 may determine a creator for each of the at least one creation, and determine whether the at least one creation is the same as a previously registered work.

That is, since the obtained creations may not all be registered as copyrights in relation to other works, the server 100 may determine whether the obtained works are the same as the previously registered works.

If the at least one creation is not the same as a previously registered work, the server 100 may copyright-register at least one work.

When the creator is a single creator, the server 100 judges the creation created by a single creator as the creation of a single creator, and when the creator is a plurality of creators, the combined creation whether the creation created by the plurality of creators is a co-creation Can be determined.

Hereinafter, a method for judging a joint creation and a combined creation will be described in detail.

First, the server 100 may obtain the work content of the project corresponding to the creation. The server 100 may obtain a plurality of category information on the work content performed by each of the plurality of creators.

At this time, since the subitems may be configured into one category, the server 100 may obtain a plurality of category information on the work content performed by each of the creators who performed the work unit or the work item.

The server 100 may obtain an association degree between the obtained plurality of categories. When the acquired degree of association is equal to or greater than a predetermined value, the server 100 may determine the creation as a co-author. When the acquired degree of association is less than a predetermined value, the server 100 may determine the creation as a combined work.

In other words, the more closely the plurality of categories are related to each other, the higher the probability that the creation is a collaborative work.

Accordingly, when the degree of association is greater than or equal to a preset value, the server 100 determines that the creation is inseparable, and may determine the creation as a joint work. When the degree of association is less than the preset value, the server 100 determines that the creation It can be judged as separable, and the creation can be judged as a combined work.

Meanwhile, the degree of association between a plurality of categories may be obtained through various methods.

According to an embodiment, the server 100 may obtain a plurality of result data about work content performed by each of the plurality of creators. In this case, the plurality of result data may refer to data performed by a plurality of creators (or persons in charge, experts) according to the assigned work content. That is, the result data generated by one creator may constitute one work, a plurality of works, or may be part of one work.

The server 100 may cluster a plurality of creations to obtain a plurality of clusters. That is, the server 100 may group a plurality of creations to group creations of a similar category. In this case, the server 100 may cluster not only a creation but also a work generated from the work and a sub work for the work together.

The server 100 may label a plurality of category information on each of the plurality of clusters. That is, the server 100 may determine the characteristics of each of the plurality of clusters to label the clusters with categories. At this time, the category may be based on the expression of the creation, such as image, audio, text category, language, music, theater, art, architecture, photography, video, graphics, computer program categories and the above-mentioned language, music, theater, It may also be in the form of a work, such as a category of secondary works and editorial works produced from art, architecture, photography, video, graphics, computer programs.

The server 100 may determine which cluster among the plurality of clusters corresponding to the plurality of category information, each of the plurality of result data. That is, the server 100 may determine which cluster the result data belongs to by determining which cluster the result data is included in.

The server 100 may obtain an association degree based on the distance between the plurality of clusters corresponding to each of the plurality of result data. For example, when the first result data is included in the first cluster and the second result data is included in the second cluster, the server 100 obtains a distance between the first cluster and the second cluster, and obtains the distance. According to the association can be determined. If the distance is far, the association is low, and if the distance is close, the association will be high. Meanwhile, in one embodiment, the server 100 may determine the degree of association of the plurality of result data using not only the distance between clusters but also the result data. In another embodiment, when the first result data and the second result data exist in the same cluster, the server 100 may determine the degree of correlation between the first result data and the second result data as the maximum value that can be determined. In this case, the copyrighted work on the first result data and the second result data may be determined to be a joint work.

13 is a block diagram of an apparatus according to an embodiment.

The processor 102 may include a connection passage (eg, a bus, etc.) that transmits and receives signals with one or more cores (not shown) and graphics processor (not shown) and / or other components. .

The processor 102 according to one embodiment executes one or more instructions stored in the memory 104 to perform the method described in connection with FIGS. 1-12.

For example, the processor 102 obtains new training data by executing one or more instructions stored in a memory, performs a test on the acquired new training data using a trained model, and performs the test result and labeling. Extracting the first learning data for which the obtained information is obtained with an accuracy equal to or greater than a predetermined first reference value, deleting the extracted first learning data from the new learning data, and extracting the new learning data from which the extracted learning data is deleted. Can be used to retrain the learned model.

Meanwhile, the processor 102 may include random access memory (RAM) and read-only memory (ROM) for temporarily and / or permanently storing a signal (or data) processed in the processor 102. , Not shown) may be further included. In addition, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processor, a RAM, and a ROM.

The memory 104 may store programs (one or more instructions) for processing and controlling the processor 102. Programs stored in the memory 104 may be divided into a plurality of modules according to functions.

The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, in a software module executed by hardware, or by a combination thereof. Software modules may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art.

The components of the present invention may be embodied as a program (or an application) and stored in a medium for execution in combination with a computer which is hardware. The components of the present invention may be implemented as software programming or software elements, and likewise, embodiments may include various algorithms implemented in combinations of data structures, processes, routines or other programming constructs, such as C, C ++. It may be implemented in a programming or scripting language such as Java, an assembler, or the like. The functional aspects may be implemented with an algorithm running on one or more processors.

In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those skilled in the art to which the present invention pertains may realize the present invention in other specific forms without changing the technical spirit or essential features thereof. I can understand that. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive.

100: server
110: Multiple Expert Groups

Claims (10)

In the professional career management method performed by the server,
Registering an expert (S110);
Obtaining expert information of the registered expert (S120);
Calculating absolute career information of the expert based on the acquired expert information (S130);
Matching a project corresponding to the expert based on the absolute career information of the expert (S140);
When the matched project is performed by the expert, acquiring project data calculated according to the execution result of the project (S150);
Updating absolute career information of the expert based on the project data (S160); And
Acquiring relative career information based on the updated absolute career information and the project data (S170); Including,
Acquiring the relative career information (S170),
Acquiring a project position of the expert on the basis of data on the work division of the matched project in step S160 (S510);
Based on the project data, acquiring an association degree for each of the expert information and the project position (S520); And
And obtaining relative career information on the project position based on the degree of association of the expert information and each of the expert information (S530).
Obtaining the degree of association (S520),
Acquiring a plurality of project positions based on data on work divisions of a plurality of projects performed by the server (S605);
Matching project data acquired by a plurality of experts with the plurality of project positions and the plurality of project positions (S610);
Clustering the matched plurality of project positions to obtain a plurality of position clusters (S615);
Obtaining a first center point, which is a center point of a first position cluster, and a second center point, which is a center point of a second position cluster, among the plurality of position clusters (S620); And
Acquiring (S625) a first degree of association of the first position cluster with respect to the second position cluster based on the distance between the first center point and the second center point; Including,
Acquiring the relative career information (S530),
Acquiring a position cluster corresponding to a project position of the expert among the plurality of position clusters (S630);
Acquiring at least one position cluster corresponding to the expert information among the plurality of position clusters (S635);
If the position cluster corresponding to the project position of the expert is a first position cluster, and at least one position cluster corresponding to the expert information is the first position cluster, based on the expert information included in the first position cluster Acquiring a first career score (S640);
If the position cluster corresponding to the project position of the expert is a first position cluster, and at least one position cluster corresponding to the expert information is the second position cluster, the expert information and the first information included in the second position cluster Obtaining a second career score based on the first degree of association (S645); And
Acquiring the relative career information based on at least one career score of the first career score and the second career score (S650); Including,
The first degree of association is characterized in that more than 0 or less 1
Acquiring the first career score (S640),
Determining whether a work corresponding to the project data can be registered as a work (S710);
When the creation corresponding to the project data can be registered as a work, obtaining a second weight for the project data (S720); And
Obtaining the first career score based on the expert information and the second weight included in the first job cluster (S730); Including,
Acquiring the second career score (S645),
Determining whether a work corresponding to the project data can be registered as a work (S740);
Obtaining a third weight for the project data when the creation corresponding to the project data can be registered as a work (S750); And
Acquiring the second career score based on the expert information included in the second job cluster and the third weight value (S760); Including,
The second weight and the third weight is characterized in that the value greater than one,
In operation S710, it may be determined whether a work corresponding to the project data can be registered as a work.
Acquiring work contents of the project;
Acquiring a plurality of category information on work contents performed by each of the plurality of creators;
Obtaining an association degree between the obtained plurality of categories;
Determining the creation as a collaborative work when the association degree is greater than or equal to a preset value; And
Determining the creation as a combined work when the degree of association is less than a preset value;
The absolute career information means career information irrelevant to a specific criterion, the relative career information means career information on the specific criterion, and the specific criterion is a criterion related to a project category performed by the expert. And at least one of a criterion for whether or not a category is performed by the expert and a criterion for a job title of the project. Claim 2 has been abandoned upon payment of a set-up fee. The method of claim 1,
Calculating the absolute career information of the expert (S130),
Classifying the expert information into structured information and atypical information (S210);
Evaluating the atypical information and the atypical information (S220); And
Calculating absolute career information of the expert based on the evaluation result (S230); Including,
In step S210, the expert information is classified into structured information and atypical information.
Classifying the expert information corresponding to the history information stored in the database into formal information based on a previously stored database (S240); And
Classifying the expert information corresponding to the information on the specialized field not stored in the database and the history information not stored in the database as the unstructured information (S250); Including,
Evaluating the structured information and the atypical information (S220),
Evaluating the formal information based on the evaluation information stored in the database (S260); And
Estimating absolute career information from the atypical information using a model trained to derive a correlation between history information and expertise in each specialty (S270). Claim 3 has been abandoned upon payment of a set-up fee. The method of claim 2,
Calculating the absolute career information of the expert (S130),
Calculating an evaluation result for each expert information (S310);
Obtaining a time point corresponding to each of the expert information (S320);
Assigning a first weight to the calculated evaluation result based on a period from a time point corresponding to each of the expert information to the present time (S330); And
Calculating absolute career information of the expert based on the first weighted evaluation result (S340); Including,
The step of giving the first weight (S330),
Acquiring information on a blank period included in the expert information (S350);
Sorting the structured information, the atypical information, and the blank period included in the expert information in chronological order (S360); And
Assigning a first weight to each of the sorted expert informations, wherein the first weights are adjusted according to an evaluation result of one or more expert informations located later in time with respect to each of the expert informations (S370); Professional career management method comprising a. Claim 4 has been abandoned upon payment of a setup registration fee. The method of claim 2,
Updating the absolute career information of the expert based on the project data (S160),
Generating feedback on the absolute career information of the expert based on the project data (S410);
Evaluating an estimated result of the learned model based on the feedback (S420); And
Updating the learned model based on the evaluation information on the estimation result (S430); More,
Updating the learned model (S430),
If the association between the first atypical information and the first specialty exceeds a preset reference value according to a result of updating the learned model, the first atypical information is stored in the database as standardized information for the first specialty. Step (S440); And
If information on the first specialty is not stored in the database, storing information on the first specialty in the database (S450); Professional career management method characterized in that it further comprises. delete delete delete Claim 8 has been abandoned upon payment of a set-up fee. The method of claim 1,
Obtaining the second weight (S720),
If the project data is data generated by a plurality of experts, determining whether a work corresponding to the project data is a joint work or a combined work (S810); And
If the creation is a collaborative work, acquiring a 2-1 weight on the project data; and when the creation is a combined work, acquiring a 2-2 weight on the project data (S820). and,
Obtaining the third weight (S750),
When the project data is data generated by a plurality of experts, determining whether a work corresponding to the project data is a joint work or a combined work (S830); And
When the creation is a collaborative work, acquiring a 3-1 weight on the project data; when the creation is a combined work, acquiring a 3-2 weight on the project data (S840); Including,
And the second-1 weight is greater than the second-2 weight, and the third-1 weight is greater than the third-2 weight. Memory for storing one or more instructions; And
A processor for executing the one or more instructions stored in the memory;
The processor executes the one or more instructions,
An apparatus for carrying out the method of claim 1. A computer program, coupled to a computer, which is hardware, stored on a recording medium readable by a computer to perform the method of claim 1.

Images