KR102112025B1 - Method, apparatus and program for controlling specialist platform - Google Patents

Abstract

Provided is a method for controlling an expert platform performed by a server. The method for controlling an expert platform comprises the steps of: registering an expert for solving the task to be solved of the present invention; obtaining expert information of the registered expert; calculating career information of the expert based on the obtained expert information; matching a project corresponding to the expert based on the career information of the expert; obtaining project data calculated according to a result of performing the matched project when the matched project is performed by the expert; and updating the career information of the expert based on the project data.

Description

Control method, device and program of expert platform {METHOD, APPARATUS AND PROGRAM FOR CONTROLLING SPECIALIST PLATFORM}

The present invention relates to an expert platform control method, apparatus and program.

In the era of the 4th Industrial Revolution, it is expected that the depth, speed and range will be greatly expanded based on hyper-connectivity and super-intelligence. This not only creates a variety of industries, but also a number of new sectors, such as the existing industry sectors and the new industry sectors being fused together, and it is expected that there will be no or very few experts in the field. Therefore, a platform is needed to discover and recruit experts in each field and collaborate with experts in each field.

In addition, due to the extended life expectancy in the future, the period of production activities will be extended, and it is necessary to enable each expert to make the field a lifelong “up” rather than just a job.

In addition, it is desirable to enable experts to be treated and treated based on their abilities in each field, but in the case of an organization such as an existing company, it is difficult to preserve the individuality of each expert because it seeks equity among employees and stability of the entire organization. However, treatment was not given according to abilities, and there was a problem such as being converted into a management position rather than using it as the career accumulated.

Therefore, it is necessary to ensure the autonomy of experts based on the Gig economy and to elicit maximum efficiency, while ensuring minimum stability and sustainability.

Meanwhile, Blockchain is also called a public transaction ledger and is a technology that prevents hacking that can occur when trading in virtual currency. In the case of existing financial companies, the transaction records are kept on a centralized server, while the blockchain sends a transaction history to all users participating in the transaction and decentralizes it by using a method to prevent data forgery by contrasting each transaction. Have

For example, blockchain is applied to Bitcoin, a representative online virtual currency. Bitcoin transparently records transaction history 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 various tokens are functioning as cryptocurrencies with the emergence of coins with platform functions.

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

Furthermore, various information such as detailed information of experts managed in the expert platform, work execution experience, specialization, and project execution experience exist, and there is a risk of hacking and forgery, and expert management when information leaked through hacking is used in the market The trust in the expert platform for is undermined and the degree of matching can be compromised.

Accordingly, there is a need to develop a technology capable of preventing forgery and alteration while maintaining the reliability of such information.

On the other hand, MICE is an abbreviated term in English, such as corporate meetings, incentive trips, conventions, exhibitions and events, and in a narrow sense, mainly international conferences and exhibitions. It means a promising industry, and in a broad sense, it means a convergence industry including participant-oriented reward tourism and mega events.

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

The problem to be solved by the present invention is to provide a control method, apparatus and program of an expert platform.

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

The method of controlling the expert platform according to an aspect of the present invention for solving the above-described problems includes registering an expert, obtaining expert information of the registered expert, and career of the expert based on the obtained expert information Calculating information, matching a project corresponding to the expert based on the expert's career information, and when the matched project is performed by the expert, project data calculated according to a result of the project And obtaining career information of the expert based on the acquiring step and the project data.

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

According to the disclosed embodiment, the expert platform operated by the server can verify the expert, calculate the career information of the expert to manage the expert's career, and manage project data generated from the expert to practically utilize it in the industrial field It is possible to informatize and data the project data.

In addition, according to the disclosed embodiment, a system capable of distributing profits according to the project execution of experts can be constructed to create a rational profit distribution model according to the project provision of the expert platform.

In addition, information about experts can be safely protected based on blockchain technology, which can contribute to securing the reliability of the expert platform and enhancing the matching degree.

The 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 view showing a blockchain system according to an embodiment of the present invention.
2 is a system diagram showing the 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 according to an embodiment of the present invention.
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 according to an embodiment of the present invention, and writing it to the blockchain.
5 is a flowchart illustrating a method of controlling an expert platform according to an embodiment of the present invention.
6 is a flowchart illustrating a method of calculating career information of an expert according to an embodiment of the present invention.
7 is a flowchart illustrating another method of calculating career information of an expert according to an embodiment of the present invention.
8 is a flowchart for explaining a method of updating a career information of an expert according to an embodiment of the present invention.
9 is a flowchart illustrating a method of providing a project to an expert according to an embodiment of the present invention.
10 is a flowchart illustrating an expert pool creation method and an expert allocation method according to an embodiment of the present invention.
11 is a flowchart illustrating a method of managing project data according to an embodiment of the present invention.
12 is an exemplary view for explaining detailed classification of a project according to an embodiment of the present invention.
13 is a flowchart illustrating a method of distributing profit according to an embodiment of the present invention.
14 is a configuration 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 clarified with reference to embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the embodiments allow the disclosure of the present invention to be complete, and those skilled in the art to which the present invention pertains. It is provided to fully inform the scope of the present invention to the present invention, and the present invention is only defined by the scope of the claims.

The terminology used herein is for describing the embodiments and is not intended to limit the present invention. In the present specification, the singular form also includes the plural form unless otherwise specified in the phrase. As used herein, “comprises” and / or “comprising” does not exclude the presence or addition of one or more other components other than the components mentioned. Throughout the specification, the same reference numerals refer to the same components, and “and / or” includes each and every combination of one or more of the components mentioned. Although "first", "second", etc. are used to describe various components, it goes without saying that these components are not limited by these terms. These terms are only used to distinguish one component from another component. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used as meanings commonly understood by those skilled in the art to which the present invention pertains. In addition, terms defined in the commonly used dictionary are not ideally or excessively interpreted unless explicitly defined.

The term "part" or "module" as used in the specification refers to a hardware component such as software, FPGA, or ASIC, and "part" or "module" performs 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 reproduce one or more processors. Thus, as an example, "part" or "module" means components, processes, functions, attributes, such as software components, object-oriented software components, class components and task components. Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays and variables. The functionality provided within components and "parts" or "modules" can be combined into a smaller number 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”, etc., are as shown in the figure. It can be used to easily describe a correlation between a component and other components. Spatially relative terms should be understood as terms including different directions of components in use or operation in addition to the directions shown in the drawings. For example, if a component shown in the drawing is turned over, a component described as "below" or "beneath" of another component will be placed "above" the other component. Can be. Accordingly, the exemplary term “below” can include both the directions below and above. The component can also be oriented in other directions, so that spatially relative terms can be interpreted according to the orientation.

In the present specification, the computer means all kinds of hardware devices including at least one processor, and may be understood as a meaning encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood as meaning including, but not limited to, a smart phone, a tablet PC, a desktop, a laptop, and a user client and application running on each device.

In the present specification, the business and the subject of the project may be understood to mean not only individual businesses or corporations, but also general individuals, and are not intended to limit specific objects. In one embodiment, the project, the project entity, and the performing entity may be a government, a local government, a public period, a narrow group, and the like.

In this specification, project or project information means government and local governments, public periods, partnerships, businesses and projects ordered by companies, business and project related business, domestic and international procurement bidding, and other professionals It can mean various projects and business-related tasks.

The type of project may also include various types such as provision of goods or services, and is not limited to a specific type. For example, the project may be for supplying a specific product, it may be for providing a research service or a professional service industry service, or a variety of tasks may be included in a complex manner.

In the present specification, an expert may be understood to mean any kind of person having a certain expertise in a specific field, and the standards are not limited. Any type of experienced person, educated person, or talented person capable of carrying out a specific field of work may be referred to as an expert, but preferably understood as a person having a certain experience in a specific field. Can be.

Accordingly, the expert according to the present disclosure may mean not only experts of existing conceptual, conventional, and general standards, but also experts meeting requirements of a specific group.

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

On the other hand, in this specification, MICE is an abbreviation for English letters such as corporate meetings, incentive trips, conventions, exhibitions and events, and in a narrow sense, international conferences and exhibitions. Means a promising industry with the main axis as the main concept, and a convergence industry that includes participant-oriented reward tourism and mega events. However, the meaning of MICE in this specification is not limited to the term itself, and may be used in various terms according to time and region.

For example, in Canada, Meeting, Convention and Incentive Travel is used as MC & IT, and in the United States, as ME & I or MEEC (Meetings, Expositions, Events and Conventions), in Singapore, Business Travel & MICE is reduced to use as BT MICE. As used, various terms are used according to the industrial structure and policy of each country.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

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

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

In the disclosed embodiment, each of the user terminals 30 records a transaction history based on a preset rule, and the recorded transaction history is recorded in each block and propagated to the blockchain holding servers 10, respectively 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, transaction information may mean transaction information based on a virtual currency, but may also mean information on various events performed based on a blockchain, and such transaction information may be transmitted to a blockchain. Is saved.

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 history is managed through a blockchain such as electronic money and cryptocurrency.

The virtual currency according to the disclosed embodiment may be a separate mainnet and a coin-type virtual currency managed accordingly, or a token-type virtual currency that utilizes other coin infrastructure 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 generated or issued and managed by the management server 20, and may be provided according to the transaction history of each of the user terminals 30 have.

For example, the management server 20 may perform an operation for generating a block storing a transaction history generated 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 exchange for this, and distribute it 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 can be generated and provided to the user terminals 30 according to a preset rule, and mining (mining) of the virtual currency ).

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 distributed consensus algorithms used to ensure the reliability of the distributed system.

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

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

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

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

Shown in Figure 2 is for an example of a configuration used in a general blockchain-based service, the form of the system utilizing the blockchain technology is not limited thereto.

In one embodiment, the user terminal 31 provides information to the management server 20, and the management server 20 provides information to the blockchain holding servers 10 when the corresponding information is information to be stored in the blockchain. You can provide that information. Blockchain holding servers 10 can store the information in the blockchain, and in this process, verification of the information based on the blockchain can be made. In this case, only information that has been successfully verified can be recorded on the blockchain.

In addition, the user terminal 31 may provide information to the management server 20 when there is information that requires verification. In this case, the management server 20 can verify this based on the information stored in the blockchain. In this process, the management server 20 obtains information for verifying the information from the blockchain holding servers 10 And, accordingly, verification can be performed. 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 the existing centralized server-based system, such a blockchain system provides a higher security by providing a decentralized distributed processing system, and has the 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 even higher.

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

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

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

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

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

In the disclosed embodiment, the server 100 may register experts according to registration requests of experts belonging to the plurality of expert groups 110 and collect expert information on the registered experts. In addition, the server 100 acquires project information that can be performed by experts belonging to the plurality of expert groups 110, distributes it to experts belonging to the plurality of expert groups 110, and manages and manages it for this. You can do your job.

In one embodiment, the server 100 may calculate a career information of an expert from expert information, and provide a project suitable for an expert based on the calculated career information. In addition, the server 100 may collect project data related to the result of the project performed by the expert, and manage the collected project data.

In another embodiment, the server 100 distributes the profits obtained as the project is performed, but can set the commission of the server 100 to limit and distribute the rest of the cost to each expert. The method of distributing profits to experts is not limited.

Meanwhile, experts may be freelance experts, but experts employed in a specific company may also belong to a plurality of expert groups 110 according to embodiments. Similarly, experts belonging to the plurality of expert groups 110 may be individuals, but a group (such as a company) including a plurality of experts may belong to the plurality of expert groups 110. In this case, the method of distributing fees and profits to experts belonging to a specific group may be applied in the same way as individual experts, but different distribution methods may be applied by viewing the group as one subject. In this case, at least one of the project work and the profit distribution method within the group may be determined by the group itself, not the server 100.

That is, the plurality of expert groups 110 may be operated as a member system, but this may include not only individual members but also group members (for example, corporate members), which is not limited.

In one embodiment, experts belonging to a plurality of expert groups 110 may be in an employment relationship with the server 100 or an operating entity of the server 100, or may be in a contract relationship other than an employment relationship, which is not limited. Does not.

In providing the service for managing the expert, the server 100 can improve the safety and security of information transaction for the expert based on the blockchain. Blockchain is a data distribution processing technology, and refers to a technology that distributes and stores data such as all transaction details by all users participating in the network. Blockchain is a distributed peer-to-peer (Peer to Peer) of a ledger that utilizes software elements composed of algorithms in which blocks connected in sequence negotiate service usage information using encryption and security technologies in order to secure and maintain integrity. ) Can mean a system. Here, the distributed P2P system may be a special form of the distributed system. In addition, in a P2P system, all nodes of a network can provide resources (processing power, storage space, data or network bandwidth, etc.) to each other without coordination of a central node. In addition, the blockchain may mean a distributed ledger technology in which nodes in the network are jointly recorded and managed by distributing the ledger recording the usage history information to a P2P network rather than a central server of a specific institution.

The blockchain network 120 may include nodes 121, 122, 123, 124, and 125. Nodes can mean components within the blockchain's network. Each of the nodes 121, 122, 123, 124, and 125 may be a server device of individuals participating in the system or the server 100 and participating. For example, each of the nodes 121, 122, 123, 124, and 125 is a special-purpose computer, a general-purpose computer, a supercomputer, and a mainframe computer. , Personal computer, smart phone, tablet PC, etc., but is not limited thereto.

In this way, the server 100 can improve the safety and security of information transaction for the expert based on the blockchain in providing a service for managing the expert.

Hereinafter, according to the disclosed embodiment, the server 100 manages a plurality of expert groups 110, acquires project information, and distributes the obtained project information to experts to manage and perform an expert project. How to manage the project data, which is the result of the project, and how to manage the profits accordingly will be described in detail.

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 the blockchain and recording it in the blockchain according to an embodiment.

The server 100 may store and store information necessary for expert management in the blockchain network 120. For example, expert management information includes expert background information, expert status information, expert career and history information, expert qualification information, expert area information, expert award information, expert achievement information, expert performance Information, expert's activities (advisory, judging, lectures, presentations, awards, SNS activities, reputation, etc.) information, professional project information, expert subscription information, expert contract information, expert orders, participation and performance Result information, data information related to the results of the expert's project execution (project), creations and copyrights from the expert's project data, expert's profit and payment information, expert's fee information, expert's evaluation and reputation information, expert's And information usage information.

According to an embodiment, the background information of the expert may include information necessary to determine the professional field and expertise of the expert.

According to one embodiment, the expert's background information may be divided into structured information and unstructured information. Formal information can mean information (or quantifiable information) that has already been quantitatively proven to be related to expertise in a specific field of expertise. For example, as described above, objective data that can prove an expert may correspond to structured information. For example, the formal information may include, but are not limited to, a history of working in a company or department in a specific field, a history of participating in a project or project in a specific specialty, education in a specific field, or thesis publication information.

Furthermore, the unstructured information does not belong to the structured information, but may mean all kinds of history information having a relationship with a specific specialized field. For example, as described above, historical information according to individual efforts may belong to unstructured information. For example, the unstructured information may include, but is not limited to, records of activities performed in online communities or clubs, personally performed projects or hobbies, and information on online education content.

In one embodiment, background information of an expert for a new specialty field in which the conventional structured information is not stored may be obtained. In this case, since there is no structured information that is a criterion for evaluating the relevant specialized field, background information of all experts can be evaluated as unstructured information.

For example, if there is no information that has been previously evaluated or stored for a specific field of expertise even if the information has been worked in a specific professional field or a department or a project or a task has been performed, it is evaluated as unstructured information for the relevant field. It can be used to evaluate the expertise of each expert.

In addition, advisory (committee) activities, review and evaluator activities, lecture and lecture activities, mentoring activity history, and outside history may be included in the expert's background information, which may belong to formal information or unstructured information. For example, the classification criterion may be whether or not the association with expertise in a specific field of expertise has been quantitatively proven and databased, but is not limited thereto.

In addition, data that can verify the professional's morality can also belong to the expert's background information.For example, various information such as the awareness and reputation of the activity in the industry, industry, SNS, etc., rebate status, criminal record, and fraud record are utilized. Even if all information is not available in reality, information that is publicly available and can be collected can be used as background information of experts classified as structured information or unstructured information.

On the other hand, some of the above-described structured information and unstructured information may be difficult to quantify with data. A case in which it is difficult to quantify data may mean a case in which data cannot be quantified because the standard is not prepared. In this case, it is needless to say that data that is difficult to be quantified can be directly input based on the surrounding evaluation and can be quantified through various types of algorithms.

According to one embodiment, the status information of experts is the amount of projects that each expert can currently perform or the number of projects each expert can participate in, such as belonging to each expert, whether or not he belongs, the current project, and other personal affairs. It may contain necessary information to evaluate the back.

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 meaningful results for the project. For example, a plurality of proposals calculated from a 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 an embodiment, the creation means data about a work obtained from project data. In this case, the creative work may mean data newly generated from a project execution subject among project data. However, the present invention is not limited thereto, and it is needless to say that the creation may mean data having creativity among newly generated data from a project execution entity. It is a matter of course that the creation is a different concept from the copyrighted work, and the creation can be a copyrighted work only if it is determined that it does not infringe on the copyright of others.

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

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

The server 100 may encrypt information and transmit the encrypted information to the node 121 on the blockchain network 120. The node 121 can 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 one embodiment, validation of information is obtained by decrypting a cipher text received using a first hash value obtained by applying a hash function to the received information and an operator's public key of the server 100 It can be performed by comparing hash values, but is not limited thereto.

As a result of verifying the validity of the information, if the information is valid, the node 121 may transmit the information to the node 122 on the blockchain network 120. Also, the node 121 may record information 1212 in a 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 for the candidate block. In addition, when the validity of information is also verified in the node 122, the node 122 may add information to the candidate block and perform proof of work for the candidate block to generate a valid block.

The same operation as in node 122 may be performed in node 123 and node 124.

When the process of generating a valid block in the node 121 and adding it to the blockchain is described, the node 121 may calculate the root of the Merkle tree for predetermined information. The node 121 may generate a hash reference pointing to the previous block header from the standpoint of the block to be added to the blockchain. The node 121 may obtain a difficulty required in the proof of work or constraint of the block to be added to the blockchain. The node 121 may check whether the value of the block hash that is generated by applying the hash function to the root of the Merkle tree, the hash reference pointing to the previous block header, the difficulty, the timestamp data, and the nonce satisfies the constraint. The node 121 may increase the nonce from 0 to 1 while acquiring a value of the nonce that satisfies the constraint and perform proof of work for the candidate block. The node 121 may add a candidate block as a valid block to the blockchain. Also, the node 121 can transmit a valid block to other nodes 122, 123, 124, etc. on the blockchain network.

Each node (122, 123, 124, etc.) on the network of the blockchain performs verification on a valid block received from the node 121, and then connects to the blockchain held by each node (122, 123, 124, etc.). You can add valid blocks.

Accordingly, the server 100 can securely secure 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 the blockchain.

In the disclosed embodiment, information about the expert, project information performed by the expert, and information on the result may all be stored in the blockchain.

For example, in the step of providing the above-mentioned project information to experts included in the expert pool, the server 100 may perform the step of providing the first project information to the first expert.

In addition, the server 100 may perform the step of storing the first project information in the first blockchain, but matching and storing information about the first expert.

In addition, the server 100 may perform the step of storing information about the first expert in the second blockchain, but matching and storing the first project information.

In addition, in the step of storing the above-described first project information in the first blockchain, the server 100 stores the information of hashing the node on the second blockchain where the first project information is stored in the first blockchain. You can follow the steps to save on.

In addition, in the step of storing the above-mentioned information on the first expert in the second blockchain, the server 100 may recall information on hashing the node on the first blockchain where the information on the first expert is stored. The step of 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 they act like blockchains connected to each other through one or more contact points and can verify each other's information. Through this, the two blockchains can verify each other, and even if one of the two blockchains has a problem, the other blockchain is not affected. Can provide.

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

In this case, the server 100 verifies the first project information based on the blockchain, but can double-verify the information by utilizing two blockchains related to each other.

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

Also, the server 100 may obtain a first hash value that hashs the node in which the first project information is stored.

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

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

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

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

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

The server 100 may return whether verification is successful, evaluate the expertise of the first project information and the first expert, or perform a process of acquiring career information according to the verification success, between the expert and the project information You can also use it for matching.

5 is a flowchart illustrating a method of controlling an expert platform according to an embodiment of the present invention.

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

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

In the present specification, the expert information may mean information related to the produced result when a specific person produces a specific result in a certain work field.

Accordingly, expert information may include all kinds of information for evaluating the professionalism of each expert or obtaining expert career information, and information for evaluating the professionalism of an expert may refer to a specific professional field of an expert. It may mean information having a relationship between professionalism and a predetermined reference value or higher, and career information of an expert may mean a degree of expertise acquired based on expert information about an expert, but is not limited thereto.

Furthermore, the expert information may mean information on the history of the expert, and depending on the embodiment, the history may include various information. For example, it may include information such as the workplace where each expert worked, the education of each expert, thesis published by each expert, the project each expert participated in, the awards and the qualifications held, and furthermore, each expert could be found in an online community or a club. It can be understood as a meaning that encompasses a variety of structured and unstructured information such as records of activities, personally executed projects or materials created as a hobby, SNS activities and reputation.

In one embodiment, the server 100 may acquire information input from the expert, and may also collect expert information by collecting crawled information about the expert.

For example, the server 100 may input an expert's resume or reference, or an expert based on a predetermined format, or obtain freely written information. The information entered by the expert may include information about the past project execution of the expert and various information that the expert wants to present in the relevant field.

In one embodiment, the server 100 may collect information related to a corresponding 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 input by the expert through crawling, but is not limited thereto. For example, the information entered by the expert may be verified, or more information may be obtained based on the information entered by the expert. Crawling may be performed to collect information.

For example, when the expert inputs information on a specific community site, the server 100 may crawl information on the expert on the corresponding community site, and in this case, according to an embodiment, the expert inputs the information Crawling may be performed using basic information including ID or nickname. This may be used to supplement the part that the expert cannot directly collect and submit all the data, or it may be used to verify the information entered by the expert.

In step S130, the server 100 may calculate the career information of the expert based on the obtained expert information.

In one embodiment, the server 100 may verify the acquired expert information and calculate career information of the expert based on the verified expert information. At this time, verification of expert information may refer to a task of checking whether information entered directly from an expert or information obtained through web crawling is authentic.

In one embodiment, when the expert information subject to verification is information on execution of a past project input by an expert, the server 100 obtains the source of the project and requests expert information from the acquired project source 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 information obtained by crawling, and to the acquired category among previously verified expert information. Expert information can be verified by comparing the corresponding expert information. For example, when the category information is education category information, the server 100 is assigned to the education category among information about the education category included in the information obtained by crawling (for example, graduating from A University) and previously verified expert information. Expert information can be verified by comparing the corresponding information (for example, graduation from University B). If University A and University B are different, information obtained by crawling can be excluded from expert information.

As another example, when the category information is the performance project category information, the server 100 performs information about the performance project category (for example, performing A project) included in information obtained by crawling and pre-verified expert information. Expert information can be verified by comparing information corresponding to the project category (for example, project B). When the A project and the B project are the same project, the server 100 may acquire information obtained by crawling as verified expert information. Of course, the server 100 may further determine whether the detailed project execution history is the same. If the A project and the B project are different projects, and the two projects cannot be performed at the same time, the server 100 may exclude information obtained by crawling from expert information. When the A project and the B project are different projects, and when both projects can be performed simultaneously, the server 100 may verify expert information by requesting to the expert proof of whether the information obtained by crawling is authentic.

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

Hereinafter, unless otherwise specified, the expert information utilized or described in the expert platform is verified expert information, and various embodiments are described.

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

In step S150, the server 100 may obtain project data calculated according to a result of performing the project.

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

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

In step S210, the server 100 may classify expert information into structured information and unstructured information.

As described above, in this specification, expert information may be divided into structured information and unstructured information.

In one embodiment, the stereotyped information may mean information (or information that can be quantified) quantitatively proved to be related to expertise in a specific specialized field. For example, as described above, objective data that can prove an expert may correspond to structured information.

For example, formal information includes a history of working in a company or department in a specific field, a history of participating in a project or assignment in a specific field, information on academic or thesis presentation in a specific field, information on a project involved, awards, and qualifications held. It can, but is not limited to.

In one embodiment, the unstructured information does not belong to the structured information, but may refer to all kinds of information having a relationship with a specific specialized field. For example, as described above, information according to individual efforts may belong to unstructured information.

For example, unstructured information may include records of activities performed in online communities or clubs, personally performed projects or materials created as a hobby, information taken online education content, SNS activity information, and reputation information, etc. It does not work.

In one embodiment, expert information may be obtained for a new specialized field in which the structured information is not previously stored. In this case, since there is no structured information that is the basis for calculating career information or evaluating expertise in the relevant specialized field, all expert information can be evaluated as unstructured information.

For example, if there is no information that has been previously evaluated or stored for a specific field of expertise even if the information has been worked in a specific professional field or a department or a project or a task has been performed, it is evaluated as unstructured information for the relevant field. It can be used to evaluate the professionalism of each expert or to obtain career information for each expert in the field.

In addition, expert information may include advisory (committee) activities, review and evaluator activities, lecture and lecture activities, mentoring activity experience, and outside careers, which may belong to structured information or unstructured information. For example, the classification criterion may be whether or not the association with expertise in a specific field of expertise has been quantitatively proven and databased, but is not limited thereto.

In addition, data that can verify the morality of experts can also belong to expert information. For example, various information such as the recognition and reputation of activities in the industry, industry, and SNS, rebate status, criminal records, and fraud records can be used. In addition, even if all information is not available in reality, information that is publicly available and can be collected can be used as expert information classified as structured information or unstructured information.

On the other hand, some of the above-described structured information and unstructured information may be difficult to quantify with data. A case in which it is difficult to quantify data may mean a case in which data cannot be quantified because the standard is not prepared. In this case, it is needless to say that data that is difficult to be quantified can be directly input based on the surrounding evaluation and can be quantified through various types of algorithms.

For example, the expert according to the disclosed embodiment and each expert information may be variously set, for example, an expert known by all, experts proved by objective data, and non-professionals who individually tried to become experts, experts There may be experts who have a degree of professionalism equivalent to or higher, experts who have proved to be objective data to some extent, or experts who have increased their expertise by adding individual efforts.

Therefore, in order to calculate career information of various experts, it is necessary to classify expert information according to its nature. Hereinafter, an embodiment of classifying it into structured information and unstructured information and calculating career information of experts accordingly will be described in detail.

In one embodiment, the stereotyped information may mean information that has already been quantitatively proved to be related to expertise in a specific field of expertise. For example, as described above, objective data that can prove the career information of an expert may correspond to stereotyped information.

For example, the formal information may include, but are not limited to, a history of working in a company or department in a specific field, a history of participating in a project or project in a specific specialty, education in a specific field, or thesis publication information.

In addition, expert information may include advisory (committee) activities, review and evaluator activities, lecture and lecture activities, mentoring activity history, and outside history, which may belong to structured information or unstructured information. For example, the classification criteria may be whether or not the association with expertise in a specific field of expertise has been quantitatively proven and databased, but is not limited thereto.

In addition, data that can verify the morality of experts can also belong to expert information. For example, various information such as the recognition and reputation of activities in the industry, industry, and SNS, rebate status, criminal records, and fraud records can be used. In fact, even if all information is not available in reality, information that is publicly available and can be collected can be used to calculate the career of experts.

In one embodiment, the unstructured information does not belong to the structured information, but may mean all kinds of information having a relationship with a specific field of expertise. For example, as described above, information according to individual efforts may belong to unstructured information.

For example, the unstructured information may include, but is not limited to, records of activities in an online community or club, personally executed projects or materials created as a hobby, and information taken online education content.

In one embodiment, expert information may be obtained for a new specialty field in which structured information is not previously stored. In this case, since there is no structured information that is the basis for calculating career information or evaluating professionalism in the relevant specialized field, all expert information can be evaluated as unstructured information.

For example, even in the case of information that has been worked in a specific professional field or a department, or information about a project or a task, if there is no information that has been previously evaluated or stored in the specialized field, it is evaluated as unstructured information for the relevant field. It can be used to evaluate the expertise of each expert.

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

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

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

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

In one embodiment, the server 100 may obtain career information of an expert based on evaluation results of structured information and unstructured information. However, the present invention is not limited thereto, and the server 100 may acquire expertise evaluation information based on expert information including structured information and unstructured information, and may acquire expert career information from the obtained expertise evaluation information. to be.

In one embodiment, the server 100 separately evaluates expert information including each structured information and unstructured information, and evaluates career information or expertise of experts by summing the individually evaluated results, or multiple It is possible to comprehensively evaluate the expert information of the company, to evaluate the professionalism of each group by grouping some expert information, and to evaluate the career information or expertise of the expert by summing up the results of the overall expertise evaluation. Does not work.

In step S240, the server 100 may classify expert information corresponding to historical information stored in the database as structured information.

In one embodiment, the database may store information about each specialized field and history information corresponding to each specialized field in advance.

Using the database, the server 100 may determine that information already stored in the database is structured information. In one embodiment, the server 100 may perform a process of matching information stored in the database with expert information. For example, since expert information input or crawled by an expert may not exactly match information stored in a database, it may be possible to perform a process of determining whether the information is structured by matching similar information. For example, it may be determined that information stored in the database and information showing a matching rate higher than a predetermined reference value is structured information.

In one embodiment, the server 100 may display the matching database information 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 step S250, the server 100 may classify expert information corresponding to information on a specialized field not stored in the database and history information not stored in the database as unstructured information.

In one embodiment, the server 100 may classify expert information as non-structured information except for structured information classified based on a database.

In one embodiment, a plurality of databases may be hierarchically configured. For example, information on each specialized field and history information corresponding to each specialized field may be stored in the first database, and expert information corresponding to information stored in the first database may be classified as structured information.

In addition, information on each specialized field and history information corresponding to each specialized field may be stored in the second database, but expert information corresponding to information stored in the corresponding database is classified as unstructured information. 2 Information that is not all stored in the database can be classified as unstructured information.

Also, according to an embodiment, expert information corresponding to information stored in the second database is classified as unstructured information, but information stored in the second database may be used for evaluation of the unstructured information.

That is, information stored in the second database may correspond to an intermediate step between unstructured information and structured information, but may prevent information stored in the second database from being evaluated as structured information. However, this is only an example, and the configuration of the database and the method of use thereof are not limited.

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

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

For example, the database contains information on how the specific history information relates to a specific field of expertise, the duration of the history information, and how much experience can be recognized based on the role played by experts in the history information. Can be stored in advance.

The pre-stored information may be input by experts in the 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 the relevant field, it can be used to build big data and use a learning method such as machine learning to determine the correlation between each field and history information. According to the detailed information, information capable of quantitatively evaluating expertise can be obtained.

Such information is stored in the database in advance, and can be used to evaluate expert information classified as structured information.

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

The trained model may be a model trained to derive an association between specific history information and expertise in each specialized field, and to estimate the expertise of the expert from unstructured information.

In one embodiment, the learned model may estimate the association between the unstructured information and the specialized field when new unstructured information is acquired for a specific specialized field pre-stored in the database.

In addition, the learned model can estimate the correlation between new specialized fields that are not stored in the database and new unstructured information in the specialized fields.

In one embodiment, the trained model may be trained based on information stored in the database. The server 100 may generate various learning data based on information stored in the database. For example, in order to estimate a correlation between a specific specialized field pre-stored in the database and new unstructured information, the server 100 Training data including history information pre-labeled for association may be generated, and a model may be trained based on this.

As another example, the correlation between the feature information extracted for a specific specialty field, the feature information extracted for the specific history information, and each feature information so as to estimate the association between the specific specialty field not stored in the database and the new unstructured information It is possible to generate training data pre-labeled with relations and train a model based on this.

That is, the server 100 estimates the career information of the expert by classifying the structured information and the unstructured information of the expert and evaluating expert information on each classified information using a database and a model learned based on the database. Can be.

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

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

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

For example, the time point may include information on the working period, the project participation period (including the starting point and the ending point), the online community activity period, the thesis publication time, the time of obtaining the certificate, and the time of the project execution, but is not limited thereto .

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

For example, the server 100 may assign weights so as to decrease the evaluation result as the old expert information. That is, in the case of old expert information, it may be forgotten over time, and the knowledge and sense of the expert may become dull, so the weight to reduce the evaluation result for the expert information can be assigned over time.

Similarly, the server 100 may assign a weight to increase the evaluation result as the duration of each career information is longer, and may assign a weight to increase the evaluation result as more recent expert information.

In step S340, the server 100 may calculate the career information of the expert based on the weighted evaluation result.

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

That is, the expert information may include information about blank periods as well as structured information and unstructured information, and one or more structured information and unstructured information may overlap with each other.

The blank period may mean a period without structured information or a period without both structured information and unstructured information. In addition, the unit of the blank period can be divided into various units such as a few hours, a long day, a week, a month, and a year.

In step S360, the server 100 may arrange the structured information, unstructured information, and blank periods included in the expert information in chronological order.

In step S370, the server 100 may assign weights to each sorted expert information.

For example, if there is trailing blank information in a particular expert information, the weight will be adjusted so that the evaluation result for the expert information falls during the blank period. On the other hand, even if the information has passed a long time, if the career of the same professional range is continuously registered thereafter, the 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, unstructured information corresponding to expert information obtained during the blank period of the structured information is evaluated. It is not regarded as an area, but can be evaluated by continuous career information.

In one embodiment, blank areas less than a preset period may be ignored.

Expert information that can demonstrate the expertise of a particular field can be recognized for higher value as it lasts longer, so the weight can also be adjusted to be reasonable. As the evaluation result for each expert information is higher, the weight for the previous expert information may also be adjusted in a direction to recognize the expertise, but is not limited thereto.

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

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

The type of feedback is not limited, but the feedback may include information on the difference between the career information estimated for the expert and the career information obtained based on the result of business processing.

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

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

In addition, the server 100 may provide a means for utilizing the expert's career information or enhancing the expert's career information when a change occurs in the expert's career information.

For example, depending on the career information of the expert, the type of tasks and projects assigned to the expert or the type of tasks in charge of the task and the project may change, and education information to increase the professionalism of the expert is provided. Can be provided.

In addition, when expertise estimation results based on unstructured information and actual estimated career information are different, the model may be updated based on the results.

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

For example, an expert's expertise can be estimated from unstructured information using a trained model as described above, and this can be evaluated based on the actual task performance of the expert.

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

The server 100 may update the trained model by performing learning by adding unstructured information of the corresponding expert and information including actual performance results to new learning data or adding to existing learning data.

Likewise, based on the estimation result and feedback therefor, the server 100 may update the model trained through reinforcement learning.

In step S440, when the association between the first unstructured information and the first specialized field exceeds a preset reference value, the first unstructured information may be stored in the database as the structured information for the first specialized field.

In step S450, if the information on the first specialization is not stored in the database, the information on the first specialization may be stored in the database.

In other words, even if the atypical information is sufficiently accumulated, references related to the atypical information are sufficiently accumulated to check the association with a specific specialty or more than a predetermined reference value. By adding the atypical information and information on the corresponding specialty to the database, The unstructured information can be registered as structured information.

For example, a specific online community activity may initially be treated as unstructured information, but when the professionalism of experts actively engaged in the online community is proved, activity information of the online community may be registered as structured information.

In addition, even in the case of a specialized field that is not already stored in the database, when securing at least one unstructured information and a predetermined reference value or higher, information about the specialized field and the unstructured information corresponding thereto are registered in the database as structured information. It is possible.

9 is a flowchart illustrating a method of providing a project to an expert according to an embodiment of the present invention.

In step S510, the server 100 may acquire project information.

At this time, the project information may be information on various configurations included in the project. In one embodiment, the project information includes data on a registered entity of a project, category information of a project, request for proposal (RFP) and task details, information about when a project is started or completed, It can mean information about the subject of the project, information about the project's work, and information about the outcome of the project.

In step S520, the server 100 may extract career information required to perform the project from the acquired project information.

In one embodiment, the server 100 extracts information about a specialized field from category information of a project among project information, request for proposal (RFP) of a project, and details of a task, and performs tasks of the project Career information necessary for project execution can be extracted from information about.

In step S530, the server 100 may match the expert including the extracted career information.

In one embodiment, if the career information required to perform the project is career information requiring that the position of the project manager (Project Manager, PM) is performed more than a preset number of times, the server 100 performs the PM more than the preset number of times You can match experts with career information to your project. Or, if the career information required to perform the project is career information that requires a project manager (Project Manager, PM) for a project having a size that satisfies a preset condition more than a predetermined number of times, the server 100 may set a preset It is possible to match an expert who has experience with performing PM for a project of a size that satisfies the conditions more than a predetermined number of times to the project. At this time, the size of the project can be various, such as the scale according to the scope of the budget, the scale according to the scope of the task, the scale according to the range (number) of experts and the regional scope.

According to various embodiments of the present disclosure, the server 100 may match a project and an expert based on various information as well as career information. For example, the server 100 matches an expert who can perform work in the period based on the project execution period with the project, or an expert who can perform the corresponding job based on the project's work level with the project. Of course it can. Alternatively, when there are two or more experts required for the project, the server 100 may first match a main expert suitable for the project, and a sub expert who can exhibit the best synergy with the main expert to the project. According to various embodiments, the main expert and the sub expert may be experts belonging to the same expert pool. Alternatively, the sub-expert may be determined as an expert capable of covering all of the career information except the career information corresponding to the career information of the main expert among the career information extracted from the project information. However, the present invention is not limited to this, and the server 100 can match experts and projects based on various information as needed.

 10 is a flowchart illustrating an expert pool creation method and an expert allocation method according to an embodiment of the present invention.

In step S610, the server 100 may acquire expert information for each of a plurality of experts.

In step S620, the server 100 may generate a plurality of expert pools based on the obtained plurality of expert information.

Specifically, the server 100 may generate a pool of experts by classifying a plurality of expert information according to a preset classification criterion. In one embodiment, the classification criterion may be a classification criterion obtained based on formal information included in expert information. At this time, even if it is structured information, the importance of information may be different. When a weight is obtained in the structured information, the server 100 may classify information about an expert based on the structured information in which the weight is greater than or equal to a preset value.

For example, when the structured information for classification is an expert's specialized field, the server 100 may classify the expert information by obtaining structured and unstructured information associated with the specialized field. That is, the server 100 obtains a category (for example, a legal category, an accounting category, etc.) for a specialized field, and classifies the expert information by determining which category of the collected expert information is related to the acquired category can do.

In one embodiment, when there is more than two structured information for classification, the server 100 may classify expert information in various ways. When the formal information for classification is specialized field and career information, the server 100 may classify expert information in consideration of both the specialized field and career. In this case, it may be possible to classify more finely than when there is only one formal information for classification. For example, if the formal information for classification is one of the specialized fields, the category is limited to the categories by specialty fields such as the legal category and the accounting category, but when the formal information for classification is the specialized field and career information, the category is the legal field Expert information can be classified according to subdivided categories such as experts under 10 years, experts over 10 years in the field of law, experts under 10 years in the field of accounting, experts over 10 years in the field of accounting.

That is, the server 100 may classify the expert pool according to various criteria as needed.

In one embodiment, if the expert pool according to the present invention is an expert pool for classifying MICE industry experts, the server 100 may include a corporate meeting, incentive trip, convention, exhibition fair and event ( Exhibition & Event), you can classify a pool of experts based on each industry.

As another embodiment, the server 100 may classify a pool of experts for each functional field such as planning, directing, production, operation, sales, and administration.

As another embodiment, the server 100 may classify a pool of experts according to the degree of experience possessed by experts according to the type of project. Specifically, even an expert with the same experience in the same field may have a difference in project execution experience (Know-how). For example, even if it is a planning or directing expert of the same year, experience in domestic or overseas projects and experience in performing projects by host or orderer such as government, public institutions, local governments, companies, etc. 100 million, 1 billion, 30 Depending on the experience of project execution by size, such as billion, etc., it is possible to have different degrees of performance (Know-how). Therefore, the server 100 may classify the expert pool based on the degree of retention of the expert's experience.

In another embodiment, the server 100 may classify the expert pool according to the degree of reputation of the expert. Expert information on the reputation of experts can be obtained through various information such as the recognition of activities in the industry, industry, SNS, rebate status, criminal records, and fraud records, or through recommendations and supporting documents for the experts.

It is needless to say that the various embodiments described above may not be individually used for classification of expert pools, but may be used in combination. For example, the server 100 may, of course, classify the expert pool using at least two criteria among the expert's field, function, experience and retention level, and reputation.

On the other hand, according to various embodiments of the present invention, the server 100 may of course generate an expert pool using an artificial intelligence model.

Specifically, the server 100 may cluster the collected expert information based on an artificial intelligence model. At this time, clustering may be achieved through various methods. According to various embodiments, various techniques, such as K-mean clustering, Mean-Shift clustering, Density-Based Spatial clustering, Gaussian Mixture Model clustering, and Agglomerative Hierarchical clustering, may be applied to clustering according to the present disclosure.

In one embodiment, the server 100 may cluster expert information using a K-mean clustering algorithm. Specifically, 1) When a set of expert information (D) and a number of clusters (k) are input, the server 100 randomly extracts k objects from the set of expert information D, and extracts the extracted k expert information, respectively. Can be set as the centroid of the cluster. 2) The server 100 may acquire k clusters based on the center point of the cluster. Specifically, the server 100 obtains a distance for each expert information included in the expert information set (D) and each of the extracted k expert information, and determines which center point has the most similarity with each expert information , To obtain a cluster. 3) The server 100 may reset the center point of the cluster based on expert information included in the obtained cluster, and reset k clusters based on the reset center point. That is, the server 100 acquires a cluster from an arbitrary center point (k center points), recalculates the center point of the obtained cluster, determines a new center point, and acquires a new cluster from a new center point (k center points). The process can be repeated until the center point of the cluster does not change. Through the above method, the server 100 may acquire a cluster for input data. Meanwhile, in the above-described embodiment, a method of obtaining a cluster through a K-mean clustering algorithm is described, but it is needless to say that the cluster can be acquired by various other methods such as the GMM algorithm.

The server 100 may acquire one or more clusters based on the clustering result.

In one embodiment, clusters having similar characteristics may be located closer to each other. Therefore, the server 100 may determine the degree of association between each cluster based on the distance. Specifically, the server 100 may obtain association information between clusters by analyzing a distance between center points of each cluster.

The server 100 may include acquiring characteristics of the acquired one or more clusters.

Specifically, the server 100 may label the obtained cluster. That is, the cluster generated by the above-described clustering process is only a set of data that collects data having similar properties, and there is no information about what specialization is the expert pool. Accordingly, the server 100 may analyze the cluster generated by clustering to perform a labeling process corresponding to the expert pool.

In one embodiment, the server 100 may determine characteristics such as a professional field, a career, and background information of an expert corresponding to each cluster.

The server 100 may generate an expert pool corresponding to each of the one or more clusters based on the acquired characteristics. Unlike the traditional method of classifying experts by specialty or career, the expert pools created therefrom include expert pools classified based on various parameters based on artificial intelligence models, in which case one expert is assigned to multiple expert pools. When there are parts that may belong to the same time and can be fused with each other in different specialized fields, one expert pool including all of them may be generated, or an expert pool having overlapping parts (intersection) may be generated.

Therefore, information on experts belonging to different technical fields but mutually common or collaborate can be easily obtained based on a pool of experts to enable convergence between different technical fields according to purposes, and matching accordingly By doing this, it is possible to mix and match various experts who can cope with the era of the 4th Industrial Revolution.

In step S630, the server 100 may obtain the expert's specialty and expertise evaluation results based on expert information.

In step S640, the server 100 may allocate an expert to at least one expert pool among a plurality of expert pools based on the result of expertise evaluation.

Specifically, in the server 100, the step of allocating experts to the expert pool includes: obtaining an expert pool having the highest association with the expert, and determining whether to allocate the expert according to the degree of expertise set in the acquired expert pool It may include.

In one embodiment, the server 100 may allocate the expert as the expert pool having the highest association by comparing expert information on the expert and a plurality of expert pools.

On the other hand, in the method of allocating experts, the server 100 evaluates the expertise of each expert and can allocate the expert to the expert pool only when the evaluated expertise is higher than a certain level. For example, even if you are an expert in the field of law, some experts may have completed only a degree in law (the results of professional evaluation will be low), and others may have various practical experience (the results of professional evaluation will be high). . Accordingly, the server 100 may determine whether to allocate a specific expert to the expert pool based on the evaluated expertise.

At this time, the server 100 may match a plurality of experts assigned to each expert pool with the project.

In step S650, the server 100 may allocate the expert to the expert pool corresponding to the expert evaluation result when the expert evaluation result is greater than or equal to a preset value.

Specifically, when the expertise of an expert is evaluated, the server 100 may determine the degree of expertise set in the expert pool. In other words, even if a specific person has information on a specific field, if he does not have expertise, he should not enter the expert pool. For example, law students should not be assigned to a pool of experts, as they do not have a certain level of expertise. Therefore, the server 100 may evaluate the professionalism of the expert and allocate the expert to the expert pool only when the professionalism is greater than or equal to a preset value.

At this time, the degree of expertise (altitude) that the expert pool should have can be determined through various methods.

In one embodiment, the degree of expertise can be directly entered by the user. However, since the scale of professionalism is often quantified and not intuitively understandable, the server 100 is based on the expertise of a plurality of experts associated with a specific pool of experts, leading experts, advanced experts, and general experts. As such, the expertise of experts can be classified and provided. For example, assuming that 100 experts are associated with the first expert pool, the server 100 has the top 5% experts as the top experts, the top 15% experts as the advanced experts, and the top 80%. You can set your expert as a general expert. In this case, the remaining 20% of experts may not be assigned to the expert pool.

In another embodiment, the degree of expertise may be determined in relation to the difficulty of the task to be performed. That is, the experts assigned to the expert pool are not fixed, but can be flexibly configured according to individual tasks. Considering that the purpose of building the expert pool is to match experts, it is necessary to provide an expert pool suitable for tasks for tasks with a clear direction and purpose. Therefore, the server 100 is provided with information of a task to be provided (for example, a work field, an order amount, work content, a level of expertise required, previous work results and evaluation information, etc.), and an expert based on the degree of work provided You can determine the degree of expertise to be applied to the pool.

The degree of expertise described above can be directly set by the expert platform, but is not limited thereto. In other words, the degree of expertise can be directly set by the users of the platform that require experts (e.g., government, local governments, public organizations, narrow groups, companies, etc., experts who need experts for collaboration). Of course.

In step S660, when a plurality of expert evaluation results corresponding to a plurality of expert pools is greater than or equal to a preset value, the server 100 may allocate experts to the plurality of expert pools according to the priority of the plurality of expert evaluation results. That is, it is needless to say that one expert may not belong to one expert pool, but may belong to multiple expert pools.

Specifically, the server 100 may allocate experts to a plurality of expert pools by comparing the expert evaluation results of the expert evaluation result for a specific expert and the generated expert information.

Specifically, when a plurality of expert evaluation results corresponding to a plurality of expert pools is greater than or equal to a preset value, the server 100 may allocate experts to the plurality of expert pools according to the priority of the plurality of expert evaluation results.

That is, the expert may have both the first professionalism evaluation result for the first expert pool and the second professionalism evaluation result for the second expert pool more than a preset value. In this case, experts can be assigned to both the first expert pool and the second expert pool. However, the server 100 may determine the priority by comparing the first professional evaluation result with the second professional evaluation result, and allocate the expert to the expert pool according to the priority. That is, even when the experts are assigned to a plurality of expert pools, the server 100 may preferentially assign experts to the expert pool with higher expertise.

Meanwhile, the server 100 not only performs a function of matching a project with an expert, but also manages the project data calculated by the expert performing the matched project. The server 100 uses the project data to update the expert's career information and professionalism evaluation information to increase the accuracy of the next project matching, or when the project data satisfies a preset condition, grants rights such as copyright to the expert, or In addition, you can help the experts work to use some of the generated project data for the next project.

11 is a flowchart illustrating a method of registering and managing project data by copyright according to an embodiment of the present invention. For convenience of description, an expert who has calculated a project data by performing a project in the embodiment of FIG. 11 may be named as a creator.

In step S710, the server 100 may acquire at least one creation for registration as a work among the acquired project data.

In one embodiment, the server 100 determines data that satisfies the requirements of the work among the project data and obtains data satisfying the requirements as a creation. Specifically, the server 100 may acquire data, in which creativity is recognized by including human thoughts or emotions, as a creation.

In one embodiment of the method for determining the creation, the server 100 may acquire data generated by experts among project data. For example, the server 100 includes project-related data (for example, documents, images, videos, drawings, designs, graphics, etc., project-related data, project-related forms, and project data), which are provided from the registration entity of the project. Data, excluding related background data, etc.).

As another embodiment, the server 100 may determine the degree of creativity by analyzing data generated by experts. 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 words related to emotions in the text data and the connection relationship between words related to emotions. For example, the server 100 may store words related to emotions in a database, and may determine a ratio of words related to emotions stored in a database among text data generated by experts. When the text data generated by the expert includes words related to emotions in a predetermined ratio or more, the server 100 may determine that the data is a creation.

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

As another embodiment, when 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.

In step S720, the server 100 may determine the creator for each of the at least one creation.

In step S730, the server 100 may determine whether at least one creation is the same as a previously registered work.

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

In step S740, when the at least one creation is not the same as the previously registered work, the server 100 may copyright-register the at least one work.

On the other hand, in the case of a work, it can be divided into individual works created by an individual alone, joint works that cannot be separated because a plurality of people jointly create them, and combined works that can be separated by a plurality of people. Accordingly, the server 100 may determine whether a creative work is a work, and set a share ratio or contribution of a plurality of copyright holders according to the determined type of work, so that the respective copyright holders can be compensated properly.

In step S750, when the creator is a single creator, the server 100 may determine the creation created by the single creator as the creation of the single creator.

In step S760, when the creator is a plurality of creators, the server 100 may determine whether the creations generated by the plurality of creators are a joint creation or a combined creation.

In step S761, the server 100 may acquire the work content of the project corresponding to the creation.

At this time, as shown in FIG. 12, the project 1 may first be divided into one or more business units 200, 300, and 400. For example, if the project (1) is for a specific event, the business units (200, 300 and 400) may include programs, food and beverage, space creation, operation, production, contracts, etc., but are not limited thereto. It is not.

In addition, each business unit (200, 300 and 400) may be further divided into one or more business items (210 and 220). For example, work items for the program work unit may include, but are not limited to, time tables, cue sheets, scenarios, moderators, performers, BGM, and other checks.

In addition, each work item 210 and 220 may be further divided into one or more detailed items 212 and 214. For example, performer-related work items can be classified into details such as securing contact point of a performer, checking clothes, props and personnel, waiting space, preparing other props, and planning a rehearsal, but are not limited thereto. At this time, one detail item may be divided into one category. That is, the data obtained by performing one detail item is composed of one work, and the detail items may be classified so as not to be composed of a joint work or a combined work. That is, the work obtained by performing one detail item may be one work. However, the present invention is not limited thereto, and the work obtained by performing detailed items according to various embodiments of the present invention may be a joint work or a combined work.

In one embodiment, each of the divided details is stored in a different blockchain node, and it can be managed to prevent forgery of at least some of the separated details through interconnection.

That is, each of the blocks shown in FIG. 12 is stored in different nodes (blocks) on the blockchain, and if some of them are forged, this can be verified through the entire blockchain to verify the integrity of the entire information. Can be.

Furthermore, of course, the results of project execution on detailed items can also be stored in different blockchain nodes.

The server 100 may determine a creator (specifically, a person in charge or an expert) to perform each of the divided detailed items. In one embodiment, the server 100 may determine the creator for each detail item. However, one creator may perform a plurality of detailed items related to each other according to an embodiment. The task assigned to the creator may be assigned in units of detailed items, may be assigned in units of work items, or may be assigned according to tasks, but is not limited.

One creator may perform part of one work item, or all or part of a plurality of work items. Accordingly, the server 100 may create a group of performing tasks including a plurality of detailed items. The performance task group may be created as a group including details related to a plurality of mutually related or single creators regardless of the classification unit.

The server 100 may determine the creator to perform each performance task group.

Depending on the embodiment, a plurality of creators may perform one detail item. Therefore, depending on the embodiment, there may be detail items overlapping each other between different performance task groups.

Similarly, a plurality of creators may perform one group of performing tasks. The method of creating a performance group is not limited, and a set of intersections between different performance groups may or may not exist, or may be identical or inclusive.

The server 100 may determine a creator to perform a task according to each performance task group based on the nature of each performance task group.

In one embodiment, the server 100 may perform the steps of setting the work content, budget, and authority corresponding to each of the one or more groups of performed tasks.

In addition, the server 100 may perform the step of allocating the set task content, budget, and authority for each of the one or more creators to perform each of the one or more performing task groups.

In addition, the server 100 may perform a step of providing information corresponding to the assigned work content, budget, and authority for each of the one or more creators.

In one embodiment, a blockchain in which data about a project is stored and a blockchain in which information about an creator is stored may be provided.

The server 100 stores the project data in the project blockchain and information about the creator in the creator blockchain, and exchanges and stores information about the project and creators matched with each other in nodes corresponding to each other.

Accordingly, the matching information of the project and the creator is stored on the blockchain, and the information of the matched creator and the project is stored on the blockchain of each other, so that the two blockchains interact, verify the integrity of the information, and safely store it. It has the advantage of being able to do so.

In one embodiment, the server 100 may determine whether the creative work according to the work contents performed by the individual creators or the plurality of creators is a work unit, a work item, or a detail item.

At this time, the creation is 1) if the individual creator is data obtained by performing a business unit or business item, 2) if the individual creator is data obtained by performing a detailed item, 3) multiple creators of the business unit or business item In the case of data obtained by performing, 4) multiple creators may be classified into four cases of data obtained by performing detailed items.

1) When the individual creator is data obtained by performing a work unit or a work item, the server 100 may determine that the copyright of the creative is the copyright of the individual creator. However, in this case, the server 100 may classify the creative work into a plurality of works using the methods of steps S762 to S765, which will be described later.

2) When the individual creators are data obtained by performing detailed items, the server 100 may determine that the creations for the detailed items are the copyrights of the individual creators.

3) When a plurality of creators are data obtained by performing a business unit or a business item, the server 100 may determine whether the creative work is a joint work or a combined work using the method of steps S762 to S765 described later. have.

4) When a plurality of creators are data obtained by performing detailed items, the server 100 may determine that the creations are joint works of multiple creators. That is, even in steps S762 to S765 described later, since the creation for a detailed item is a creation for one category or an associated category, the server 100 may determine that the creation is a co-production of a plurality of creators.

Hereinafter, using steps S762 to S765, 3) when multiple creators are data obtained by performing a work unit or a work item, a method of determining whether the work is a joint work or an individual work will be described.

In step S762, the server 100 may obtain a plurality of category information about the work contents performed by each of the plurality of creators.

At this time, since the detailed items may be composed of one category, the server 100 may acquire a plurality of category information on the work content performed by each of a plurality of creators who have performed a work unit or a work item.

In one embodiment, as illustrated in FIG. 12, when a plurality of creators perform the business unit 200, the server 100 corresponds to a category corresponding to the detailed items 212,214 of the business unit 200. Information may be obtained as a plurality of category information.

In step S763, the server 100 may determine the degree of association between the obtained plurality of categories.

In step S764, the server 100 may determine the creation as a co-production if the determined degree of association is greater than or equal to a preset value.

In step S765, the server 100 may determine the creation as a combined work when the obtained association is less than a preset value.

That is, the more closely a plurality of categories are closely related to each other, the higher the probability that the creation is a co-production.

Therefore, when the correlation is greater than or equal to a preset value, the server 100 determines that the creation is inseparable, so that the creation can be determined as a co-production, and when the association is less than a preset value, the server 100 determines that the creation is It can be judged to be separable, and the creation can be judged as a combined work.

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

In one embodiment, the server 100 may obtain a plurality of result data for the work contents performed by each of the plurality of creators. In this case, the plurality of result data may refer to data performed according to the work contents assigned by the plurality of creators. That is, the result data generated by one creator may constitute one work, constitute a plurality of works, or may be part of one work.

The server 100 may obtain a plurality of clusters by clustering a plurality of creations. That is, the server 100 may group a plurality of creations to group the creations of similar categories. In this case, the server 100 can cluster not only the creation, but also the work created from the creation, and sub-works for the work.

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

The server 100 may determine which cluster among the plurality of clusters each of the plurality of result data corresponds to the plurality of category information. That is, the server 100 may determine which category 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 a distance between a plurality of clusters corresponding to each of a 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 acquires a distance between the first cluster and the second cluster, and the obtained distance Depending on the degree of association can be determined. If the distance is far, the association will be low, and if the distance is close, the association will be high. On the other hand, in one embodiment, the server 100 may use a distance between the clusters as well as the distances between the result data to determine the degree of association between a plurality of result data. As another embodiment, when the first result data and the second result data exist in the same cluster, the server 100 may determine a correlation between the first result data and the second result data as a maximum value that can be determined. In this case, the works for the first result data and the second result data may be determined as a joint work.

13 is a flowchart illustrating a method of distributing profit according to an embodiment of the present invention.

In step S810, the server 100 may obtain fee information corresponding to an expert.

At this time, the fee may include a prepayment fee, a fee for project execution revenue, and a reject fee. In particular, the prepayment fee is a fee provided to the expert platform from the expert before the profit distribution stage, periodically or at least once. For example, the prepayment fee may mean an enrollment fee, an annual fee, or provide in advance by an expert in order to bid for a specific project or participate in the project in the planning stage according to an embodiment. It could mean some sort of participation fee.

In step S820, the server 100 may obtain revenue for the project.

Specifically, the server 100 may acquire project data for a project performed by an expert, subtract fees from the revenue generated therefrom, and distribute the remaining profits to the expert. In addition, the server 100 may receive a commission from an expert and use it as funds for acquiring and managing project information such as basic administrative tasks, sales tasks, risk management tasks, etc., and when a profit is generated according to the project, a part of the revenue is used as revenue. Can be distributed. Experts can be allocated profits according to the type and manner of participation in the project, role, contribution, input period, and performance.

In step S830, the server 100 may calculate the commission of the expert platform among the profits for the project.

In step S840, the server 100 may distribute the amount of the project excluding commissions to experts.

 In step S850, the server 100 may calculate the contribution of the expert platform including the administrative contribution, sales contribution, and risk management contribution of the expert platform.

In one embodiment, the server 100 performs not only administrative tasks, but also business operations such as collecting business and project information or attracting investments, and also plays a role in taking risks due to the occurrence of sunk costs, job blanks, accidents, etc. .

Accordingly, in calculating the fees of the expert platform, administrative contributions according to the administrative tasks of the expert platform, sales contributions corresponding to the expenses required for the sales tasks, and risk management contributions according to risk management may be considered.

In one embodiment, the risk management contribution may be calculated based on the expected value of the loss calculated based on the expected loss according to the risk and the probability that the loss will occur, but is not limited thereto. For example, the fee according to the risk management contribution can also be understood as a kind of preliminary cost concept for risk. At this time, the fee according to the risk management contribution may be weighted based on the assets possessed by the expert platform and the expected return based on sales or orders. For example, if there are sufficient assets on the expert platform and the risk of operating expenses is not largely burdened, the fee due to the risk contribution may be set relatively low, and the expected value may be calculated based on expected returns from sales or orders. After calculation, a fee may be calculated by comparing the two expected values.

In step S860, the server 100 may calculate an expert's contribution, including an expert's sales contribution and business contribution.

In the case of experts participating in the project, contributions can be calculated according to the division of duties, and in some cases, experts can also directly conduct sales or help with sales. For example, you can attract investments through a network of experts, help experts create proposals, provide information about projects and businesses, or introduce experts to other experts. Therefore, in calculating the fees of the expert platform, the administrative contribution, sales contribution and risk management contribution of the expert platform are considered to distinguish the share of the expert platform from the share of the expert, and the expert's sales contribution is also used to distinguish the share of the expert. And work contribution can be considered.

In step S870, the server 100 may obtain a preliminary contract of the expert platform and the expert.

In step S880, the server 100 may calculate the commission of the expert platform based on the contribution of the expert platform, the expert's contribution, and the prior contract.

The server 100 adds and adjusts weights according to prior contracts, in addition to the expert platform's contribution, including the expert platform's administrative contribution, sales contribution, and risk management contribution, and the expert's sales contribution and business contribution. Through this, the commission rate of the expert platform can be finally calculated.

On the other hand, if there are a plurality of experts who have performed the project, the server 100 can, of course, distribute profits among a plurality of experts through various methods.

Specifically, in the process of calculating the commission of the expert platform, the share of the expert platform and the share of the expert are divided based on the sales contribution and contribution of the entire expert, and then the share of the expert is contributed to the sales contribution and work contribution of each expert. Based on this, it can be distributed to each expert.

In addition, the server 100 may perform a step of calculating individual contributions of each expert who has performed the project based on sales contribution and business contribution of each expert who has performed the project.

In other words, the server 100 can calculate each expert's individual contribution based on each expert's sales contribution and business contribution, and the evaluation criteria for sales contribution and business contribution can be performed according to predetermined criteria. Weights assigned to work contributions may be set differently based on specific gravity and difficulty.

In addition, the server 100 may perform a step of assigning weights to the individual contributions based on the career and history of each expert who has performed the project.

In one embodiment, the server 100 may guarantee a minimum income to experts with little experience by assigning a relatively high weight to the contribution of experts who are relatively difficult to independently perform the project.

In another embodiment, the server 100 may assign a relatively low weight to the work contribution, considering that the work performed by the experienced expert has been assisted by the less experienced expert with respect to the experienced expert. .

In another embodiment, a relatively high weight may be assigned to the work contribution of an experienced expert, considering that there is guidance and review of an experienced expert for the work contribution of the less experienced expert The way is not limited.

In one embodiment, that is, the server 100 may obtain a weight for work contribution based on career information of each expert. Specifically, the server 100 may extract data related to project execution among data included in the career information, and obtain association information between the extracted data and the project to obtain a weight for each expert.

In addition, the server 100 may perform a step of distributing profits among experts who have performed the project based on the weighted individual contribution. Needless to say, the weight setting can be implemented through various methods.

14 is a configuration diagram of an apparatus according to an embodiment of the present invention.

The processor 102 may include one or more cores (not shown) and a connection passage (for example, a bus) for transmitting and receiving signals to and from a graphic processing unit (not shown) and / or other components. .

The processor 102 according to an embodiment performs the method described in connection with FIGS. 1 to 13 by executing one or more instructions stored in the memory 104.

For example, the processor 102 acquires new learning data by executing one or more instructions stored in memory, and uses the trained model to perform tests on the acquired new learning data, and the test results and labeling. Extracts the first learning data obtained by the obtained information with an accuracy equal to or greater than a predetermined first reference value, deletes the extracted first learning data from the new learning data, and extracts the new learning data from which the extracted learning data is deleted. By using it, the trained model can be trained again.

On the other hand, the processor 102 is a RAM (Random Access Memory, not shown) and a ROM (Read-Only Memory) that temporarily and / or permanently stores signals (or data) processed inside the processor 102. , Not shown). Further, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, RAM, and ROM.

Programs (one or more instructions) for processing and controlling the processor 102 may be stored in the memory 104. 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, a software module executed by hardware, or a combination thereof. The 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 on any type of computer readable recording medium well known in the art.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a hardware computer, and stored in a medium. The components of the present invention can be implemented in software programming or software components, and similarly, embodiments include C, C ++, including various algorithms implemented in a combination of data structures, processes, routines or other programming components. , Can be implemented in programming or scripting languages such as Java, assembler, etc. Functional aspects can be implemented with algorithms running on one or more processors.

The embodiments of the present invention have been described above with reference to the accompanying drawings, but a person skilled in the art to which the present invention pertains may implement the present invention in other specific forms without changing its technical spirit or essential features. You will understand. Therefore, the embodiments described above are illustrative in all respects, and should be understood as non-limiting.

100: server
110: multiple expert group

Claims (10)

In the control method of the expert platform performed by the server,
Registering an expert (S110);
Obtaining expert information of the registered expert (S120);
Calculating career information of the expert based on the acquired expert information (S130);
Matching a project corresponding to the expert based on the expert's career information (S140);
If the matched project is performed by the expert, obtaining project data calculated according to a result of the project (S150); And
Updating career information of the expert based on the project data (S160); Including,
The step of registering the expert (S110),
Acquiring the fee information corresponding to the expert (S810),
The control method,
Obtaining a profit for the project (S820);
Calculating a commission of the expert platform among revenues for the project (S830); And
Distributing the amount of the profit for the project excluding the fee to the expert (S840); Including,
The step of calculating the fee of the expert platform (S830),
Calculating a contribution level of the expert platform including administrative contribution, sales contribution and risk management contribution of the expert platform (S850);
Calculating a contribution of the expert including the expert's sales contribution and work contribution (S860);
Obtaining a preliminary contract of the expert platform and the expert (S870); And
Calculating a commission of the expert platform based on the contribution level of the expert platform, the contribution level of the expert, and the prior agreement (S880); Including,
The step of calculating the career information of the expert (S130),
Classifying the expert information into structured information and unstructured information (S210);
Evaluating the structured information and the unstructured information (S220); And
Calculating career information of the expert based on the evaluation result (S230); Including,
The step of classifying the expert information into structured information and unstructured information (S210),
Classifying expert information corresponding to historical information stored in the database into structured information based on a previously stored database (S240); And
Classifying the expert information corresponding to information on a specialized field not stored in the database and history information not stored in the database into the unstructured information (S250); Including,
Evaluating the structured information and the unstructured information (S220),
Evaluating the structured information based on the evaluation information stored in the database (S260); And
Including the step of estimating career information for the professional field of the expert from the unstructured information using a model trained to derive a relationship between the history information and the expertise of each specialty field (S270);
The step of calculating the career information of the expert (S130),
Calculating an evaluation result for each of the expert information (S310);
Obtaining a time point corresponding to each of the expert information (S320);
Assigning a weight to the calculated evaluation result based on a period from the time corresponding to each of the expert information to the present (S330); And
Calculating career information of the expert based on the weighted evaluation result (S340); Including,
In the step of assigning the weight (S330),
Obtaining information on the blank period included in the expert information (S350);
Sorting structured information, unstructured information, and blank periods included in the expert information in chronological order (S360); And
Assigning weights to each of the sorted expert information, wherein the weights are adjusted according to evaluation results of one or more expert information located in time with respect to each of the expert information (S370); Including,
In the adjusting step (S370), if there is a trailing blank period in any one of the sorted specialist information, the weight is adjusted so that the evaluation result decreases according to the trailing blank period,
The blank period is at least one of periods without formal information in the period or periods without formal information and unstructured information in the period,
The control method,
Obtaining at least one creation for registration as a work among the acquired project data (S710);
Determining a creator for each of the at least one creation (S720);
Determining whether the at least one creation is the same as a previously registered work (S730);
Including, if the at least one creation is not the same as the previously registered work, copyright registration of the at least one creation (S740);
The step of registering the copyright of the at least one creative work (S740),
If the creator is a single creator, determining a creation created by the single creator as the work of the single creator (S750);
If the creator is a plurality of creators, determining whether a creation created by the plurality of creators is a joint or combined work (S760); Including,
The step (S760) of determining whether the created creation is a joint work or a combined work,
Obtaining the business content of the project corresponding to the creation (S761);
Acquiring a plurality of category information about the work contents performed by each of the plurality of creators (S762);
Determining a degree of association between the obtained plurality of categories (S763);
If the degree of association is greater than or equal to a predetermined value, determining the creation as a co-production (S764); And
If the degree of association is less than a predetermined value, determining the creative work as a combined work (S765); Including,
The work content includes at least one work unit, the work unit includes at least one work item, and the work item comprises at least one detail item. delete delete According to claim 1,
Step of updating the career information of the expert based on the project data (S160),
Generating feedback on the career information of the expert based on the project data (S410);
Evaluating the estimated result of the trained model based on the feedback (S420); And
Updating the trained model based on evaluation information on the estimation result (S430); Further comprising,
The step of updating the trained model (S430),
When the association between the first unstructured information and the first specialized field exceeds a preset reference value according to a result of updating the learned model, the first unstructured information is stored in the database as the structured information for the first specialized field Step (S440); And
If the information on the first specialization is not stored in the database, storing the information on the first specialization in the database (S450); Control method characterized in that it further comprises. According to claim 1,
Matching the project corresponding to the expert (S140),
Obtaining project information (S510);
Extracting career information necessary for project execution from the acquired project information (S520);
Matching the expert including the extracted career information (S530); Control method comprising a. ◈ Claim 6 was abandoned when payment of the set registration fee was made.◈ The method of claim 5,
The control method,
Obtaining expert information for each of a plurality of experts (S610);
Generating a plurality of expert pools based on the obtained plurality of expert information (S620);
Obtaining a result of professional evaluation of the expert based on the expert information (S630);
Assigning the expert to at least one expert pool among the plurality of expert pools based on the expertise evaluation result (S640); Including,
The step of allocating (S640),
If the professionalism evaluation result is greater than or equal to a preset value, allocating the expert to a professional pool corresponding to the professionalism evaluation result (S650); And
If the plurality of expert evaluation results corresponding to a plurality of expert pools is greater than or equal to a preset value, allocating the experts to the plurality of expert pools according to the priority of the plurality of expert evaluation results (S660). Way. delete delete A memory for storing one or more instructions; And
And a processor executing the one or more instructions stored in the memory.
The processor executes the one or more instructions,
An apparatus for performing the method of claim 1. A computer program stored in a recording medium readable by a computer to perform the method of claim 1 in combination with a computer that is hardware.

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