KR20200105379A - Method and system for managing project results generated by a specialist based on bigdata stored in blockchain - Google Patents

Abstract

Provided are a method for managing project results generated by experts based on big data stored in a blockchain and a system thereof. According to an aspect of the present invention, in a big data-based management method performed by a server, the server includes the steps of: matching a first project with a first expert for performing the first project (S105); obtaining a first result according to the result of executing the matched first project (S110); extracting first data on the first result based on the first result (S115); and storing the extracted first data and the first result in a blockchain (S120).

Description

A method and system for managing project results generated by experts based on big data stored in the blockchain {METHOD AND SYSTEM FOR MANAGING PROJECT RESULTS GENERATED BY A SPECIALIST BASED ON BIGDATA STORED IN BLOCKCHAIN}

It relates to a method and system for managing project results generated by experts based on big data stored in a blockchain.

In recent years, research on methods of collecting various data to build big data and providing various services using the built big data is increasing. In order to build big data, it is becoming important to select and store what kind of data and how to use the stored data, and furthermore, the necessity of a method for safely storing the stored data is also emerging.

Block Chain refers to a data distribution processing technology that distributes and stores all data that are subject to management by all users participating in the network. It is also called'Distributed Ledger Technology (DLT)' or'Public Transaction Ledger' in that the ledger containing transaction information is not owned by the transaction entity or a specific institution, but is shared by all network participants. . Blockchain is a name given as a chain of blocks containing transaction details.

Blockchain is a core concept of decentralization, aiming for P2P (Peer to Peer) transactions, away from the existing financial system that secures and manages all transactions in financial institutions. P2P refers to a communication network that connects personal computers without a server or client, and each connected computer acts as a server and a client and shares information.

In the existing financial system, financial companies have kept transaction records on a central server, whereas in a block chain based on the P2P method, transaction information is stored in blocks and connected in sequence, which is shared by all participants.

Virtual currency, also known as electronic money or cryptocurrency, refers to currency that is traded online without real money such as bills or coins. Unlike ordinary currencies issued by governments or central banks in each country, virtual currency is valued according to rules established by the first invented person. In addition, due to the decentralization feature, the government or central bank does not manage transaction details and distributes based on blockchain technology, so specific institutions such as the government do not guarantee value or payment.

In the era of the 4th industrial revolution, it is expected that the depth, speed, and range will be greatly expanded based on hyperconnectivity and superintelligence. As a result, not only various industrial fields arise, but also numerous new fields such as the fusion of existing industrial fields and new industrial fields with each other, and it is expected that there will be no or very few experts in the field. Therefore, there is a need for a platform where experts in each field can be found and recruited, and experts in each field can cooperate.

In such an expert platform, there is a need for how to match various experts for various projects. In addition, there is a need for a project management method in order to efficiently use the results of project execution in the expert platform for new projects that occur later, without being consumed once.

Registered Patent Publication No. 10-0339055, 2002.05.21

The problem to be solved by the present invention is to provide a method and system for managing project results generated by experts according to the above-described necessity based on big data stored in a blockchain.

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

A big data-based management method performed by a server according to an aspect of the present invention for solving the above-described problem includes the step of matching, by the server, a first project and a first expert for performing the first project ( S105); Obtaining a first result according to the result of executing the matched first project (S110); Extracting first data on the first result based on the first result (S115); And storing the extracted first data and the first result in a blockchain (S120). Includes.

In this case, the step of matching the first expert (S105) may include collecting data on the first project (S210); Collecting structured information and unstructured information for each of a plurality of experts (S220); And matching the experts for the first project based on the data on the first project and the structured and unstructured information for each of the plurality of experts (S230). Including, the data on the first project, data related to the category of the first project, data on the task to be performed of the first project, data on the first project registration subject, the first project execution subject It may include at least one piece of information among the data for.

In this case, the management method may include obtaining a plurality of results according to a result of performing a plurality of projects for a plurality of projects (S125); Extracting a plurality of data for the plurality of results (S130); Storing a plurality of results according to the execution results of the plurality of projects and a plurality of data extracted for the plurality of results in the block chain (S135); Registering a new second project (S140); Collecting data on the second project (S145); Selecting a project associated with the second project from among the plurality of projects (S150); And when the selected project is the first project, acquiring (S155) 1-1 data corresponding to data for the second project from among the first data (S155), and the matching step ( S105), among the data on the second project, matching experts based on data excluding the 2-1 data (S240); It may include.

In this case, the step of extracting the first data (S115) may include dividing the first result according to a preset criterion (S310); The step of acquiring (S320) first data on the first result based on the divided data, and the step of matching experts to the project (S230) includes clustering information on a plurality of projects Obtaining a project cluster of (S410); Clustering data on a plurality of experts to obtain a plurality of expert clusters (S420); Obtaining a first project cluster to which the first project belongs from among the plurality of project clusters (S430); Determining a degree of association between the first project cluster and the plurality of expert clusters (S440); Obtaining (S450) a first expert cluster having a degree of association equal to or greater than a preset value among the plurality of expert clusters (S450); And matching the first project with an expert corresponding to the first expert cluster (S460). Including, the preset criterion, data on the execution subject, data on the category of the first result, data on the start time and end time of the first project, related to the format used for the first project It may be characterized in that it is a criterion related to data and data related to the summary of the first project.

In this case, the management method comprises the steps of classifying the first result into a common result and a general purpose result according to a type (S160); Setting a user who can access the first result according to the classified result (S165); It may include.

In this case, the step of storing the first result in the blockchain (S120) may include: if the first result is a public result, storing the first result in a private blockchain that only participants of the project can access (S510); And if the first result is a general purpose result, storing the first result in a public blockchain accessible to all users (S520). It may include.

In this case, the management method may include providing a virtual currency to a participant of the first project when the first result is used by a user other than a participant of the first project (S170); It may include.

In this case, the step of storing the block chain (S120) may include encrypting the first data and the first result (S610); Storing the encrypted data in the block chain (S620); It may include.

According to various embodiments of the present invention described above, the project management system has an effect of safely managing the executed project and efficiently using it for a new project.

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 block diagram illustrating a system for managing project results generated by experts based on big data according to an embodiment of the present invention.
2 is a flowchart schematically illustrating a method of managing a project according to an embodiment of the present invention.
3 is a flowchart illustrating a method of matching experts on a project according to an embodiment of the present invention.
4 is a flowchart illustrating a method of matching experts for a new project using data on a plurality of projects according to an embodiment of the present invention.
5 is a flowchart illustrating a method of matching experts on a project using clustering according to an embodiment of the present invention.
6 is a flowchart illustrating a project management method according to a type of project result according to an embodiment of the present invention.
7 is a flowchart illustrating a method of storing a project according to a type of a result of a project according to an embodiment of the present invention.
8 is a flowchart illustrating a method of encrypting and storing a project result in a block chain according to an embodiment of the present invention.
9 is a block diagram of an apparatus according to an embodiment of the present invention.

Advantages and features of the present invention, and a method of achieving them will become apparent with reference to the 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 a variety of different forms, only the present embodiments are intended to complete the disclosure of the present invention, It is provided to fully inform the technician of the scope of the present invention, and the present invention is only defined by the scope of the claims.

The terms used in the present specification are for describing exemplary embodiments and are not intended to limit the present invention. In this specification, the singular form also includes the plural form unless specifically stated in the phrase. As used in the specification, “comprises” and/or “comprising” do not exclude the presence or addition of one or more other elements other than the mentioned elements. Throughout the specification, the same reference numerals refer to the same elements, and “and/or” includes each and all combinations of one or more of the mentioned elements. Although "first", "second", and the like are used to describe various elements, it goes without saying that these elements 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 idea of the present invention.

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

The term "unit" or "module" used in the specification refers to a hardware component such as software, FPGA or ASIC, and the "unit" or "module" performs certain roles. However, "unit" 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, "sub" or "module" refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, It includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays and variables. Components and functions provided within "sub" or "module" may be combined into a smaller number of components and "sub" or "modules" or into additional components and "sub" or "modules". Can be further separated.

Spatially relative terms "below", "beneath", "lower", "above", "upper", etc., as shown in the figure It can be used to easily describe the correlation between a component and other components. Spatially relative terms should be understood as terms including different directions of components during use or operation in addition to the directions shown in the drawings. For example, if a component shown in a drawing is turned over, a component described as "below" or "beneath" of another component will be placed "above" the other component. I can. Accordingly, the exemplary term “below” may include both directions below and above. Components may be oriented in other directions, and thus spatially relative terms may be interpreted according to orientation.

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

Meanwhile, before describing various embodiments according to the present disclosure, information or terms used in the present disclosure are summarized below.

In this specification, an expert may be understood to mean all kinds of people with a predetermined expertise in a specific field, and the criteria are not limited. All kinds of experienced persons, educated persons, or talented persons capable of performing work in a specific field can all be referred to as experts, but preferably understood to mean persons with a predetermined experience in a specific field. Can be.

Accordingly, experts according to the present disclosure may refer to experts who meet the requirements required by a specific group as well as experts of the existing conceptual, common, and general criteria.

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

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

Therefore, expert data can include all kinds of information that can evaluate the expertise of each expert, and information that can evaluate expert expertise means the relationship between the expert's expertise in a specific area of expertise and a predetermined reference value or more. It may mean information having, but is not limited thereto.

Furthermore, expert data may mean information on the career of a corresponding expert, and the career may include various information according to embodiments. For example, information such as the workplace where each expert worked, the academic background of each expert, the thesis published by each expert, and the project each expert participated in may be included. Furthermore, the record of each expert's activities in online communities or clubs, etc. It can be understood to mean that it encompasses all various formal and unstructured information, such as projects performed as a hobby or materials created as a hobby.

Meanwhile, in the present specification, expert data may be divided into structured information and unstructured information.

In an embodiment, the formal information may refer to information (or information that can be quantified) that has already been quantitatively proven to have a relationship with the expertise of a specific specialized field. For example, as described above, objective data that can prove an expert may correspond to formal information.

For example, the formal information may include, but is not limited to, a career in a company or department in a specific field, a career in a project or task in a specific field, academic background in a specific field, or thesis presentation information.

In an embodiment, the unstructured information does not belong to the structured information, but may mean all types of career information having a relationship with a specific specialized field. For example, as described above, career 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 online communities or clubs, materials created for personal projects or hobbies, and information on taking online educational contents.

In an embodiment, expert data for a new specialty field in which no formal information is previously stored may be obtained. In this case, since no formal information, which is a criterion for evaluating the relevant specialized field, is stored at all, all expert data can be evaluated as unstructured information.

For example, if there is no pre-evaluated or stored information for the specific field, even if information about the job or department worked in a specific field of expertise, or information about performing a project or task, it is evaluated like unstructured information and It can be used to assess the expertise of each expert.

In addition, expert data may include advisory (committee) activities, screening and evaluator activities, lectures and lectures, mentoring activities, and outside experience, etc., which may belong to either structured or unstructured information. For example, the classification criterion may be whether or not a database has been established by quantitatively verifying the relationship with the expertise of a specific specialized field, but is not limited thereto.

In addition, data that can verify the morality of experts may also belong to expert data.For example, various information such as awareness and reputation of activities in the relevant industry or industry, SNS, etc., rebates, criminal records, and fraud records can be used. In addition, even if all the information is not available in reality, any information that can be collected by being disclosed to the outside may be used as expert data classified as structured information or unstructured information.

Meanwhile, some of the above-described structured information and unstructured information may be difficult to quantify as data. A case in which it is difficult to quantify data may mean a case where data quantification is impossible because the standard is not established. In this case, it goes without saying that data that are difficult to quantify can be directly input based on the surrounding evaluation and can be quantified through various types of algorithms.

Meanwhile, in this disclosure, the term project refers to projects and business-related tasks ordered by the government, local governments, public institutions, associations, and companies, domestic and international procurement bidding projects, and other various projects and projects that require experts. It can mean related work. Projects can include various types, such as in-kind and services, and are not limited to specific types. For example, a project may be a task to deliver a specific product, a research service, a professional service service, etc.

Meanwhile, in the present disclosure, data on a project may be data on various configurations included in the project. In one embodiment, the data on the project is data on the subject of registration of the project, data related to the category of the project, data on the starting point or completion point of the project, data on the subject performing the project, and It may mean data about.

In this case, the data on the project may include fixed data that does not change regardless of the project execution and change data that changes as the project is executed. The fixed data may mean, for example, data on the subject of registration of the project, data related to the category of the project, and data on the start time or completion time of the project. Also, the change data may mean data on the subject of the project and data on the project's performance task. However, the present invention is not limited thereto, and fixed data and modified data may be changed according to the nature of each project. In one embodiment, if there is no possibility of change because the subject of performing the project is determined in advance, data on the subject of performing the project is classified as fixed data, but if the subject of performing the project is undecided or can be changed, added, or deleted in the middle. In this case, data on the subject of the project can be classified as change data. Classification of data for a project may be performed by a server, or may be classified by information included when providing a project.

In another embodiment, if necessary, fixed data and change data may be determined ex post by comparing a project result obtained after performing a project with a project initially provided.

Meanwhile, in the present disclosure, a result of a project and data extracted from a result of the project may refer to the data for the above-described project and data calculated by the expert who performed the project. Specifically, the project results and data for the fixed data may be the same data as the fixed data classified in the initial project. Meanwhile, the project result and data for the change data may be data changed by an expert from the change data of the initial project.

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

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

Referring to FIG. 1, a server 100, blockchain holding servers 200, and user terminals 300 are shown.

In the disclosed embodiment, each of the user terminals 300 transmits information to at least one of the server 100 or the blockchain holding servers 200, and the transmitted information is propagated to the blockchain holding servers 200. , Stored and managed on each server.

In the disclosed embodiment, the blockchain holding servers 200 authenticate and record data on a project, a result of a project, data extracted from the result of a project, and expert data including structured information and unstructured information. This refers to the installed servers.

Each of the user terminals 300 may transmit and receive various data to and from the server 100 according to the disclosed embodiment. In one embodiment, when the user terminal 300 is a terminal corresponding to the project registration subject, the user terminal 300 transmits data on a project, a result of the project, and data extracted from the result of the project with the server 100 You can send and receive. In another embodiment, when the user terminal 300 is a terminal corresponding to a project execution subject (for example, an expert, etc.), the user terminal 300 includes expert data including structured information and unstructured information, and Data extracted from the result product and the project result may be transmitted and received with the server 100.

Meanwhile, according to various embodiments of the present disclosure, the block chain holding server 200 may be part of a plurality of nodes for creating, storing, and managing a block chain. Furthermore, each of the user terminals 300 may be part of a plurality of nodes for creating, storing, and managing a block chain.

The server 100 may acquire, store, and verify information necessary for the operation of the expert platform and project management by using the information stored in the blockchain holding servers 200.

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

The cryptocurrency according to the disclosed embodiment may be a cryptocurrency in the form of a coin managed by a separate mainnet and managed accordingly, or may be a cryptocurrency in the form of a token that utilizes the infrastructure of other coins such as the Ethereum network. Not limited.

In an embodiment, the project management method according to the disclosed embodiment may be serviced as a form of utilizing the infrastructure of an existing blockchain platform such as Ethereum, and may be serviced using an independent infrastructure when the mainnet is developed in the future.

In one embodiment, the virtual currency according to the disclosed embodiment is provided as a reward for the use of the service according to the disclosed embodiment, can be used for transactions between users, and can also be provided to users with stakes through the initial ICO. have.

In an embodiment, the virtual currency provided to each of the user terminals 300 is managed by the server 100 as already generated or issued, and may be provided according to the activity of each of the user terminals 300.

For example, the server 100 may perform an operation to generate a block that stores information and activity details of the user terminals 300, and may generate a block through Proof Of Work. According to an embodiment, the server 100 may receive a predetermined virtual currency generated in return and distribute it to the user terminals 300.

As another example, the server 100 may manage virtual currency that has already been issued, and may issue additional virtual currency in some cases. The server 100 may verify the information stored in the blockchain holding servers 200 to determine its integrity, and provide virtual currency to the user terminals 300 according to the activity based on this.

In an embodiment, the server 100 may directly verify the information or may verify the information through an external verification server.

In another embodiment, as information is recorded by each of the user terminals 300, a virtual currency may be generated according to a preset rule and provided to the user terminals 300, which is then mined (mining) of virtual currency. Expressed as

In general, cryptocurrency mining is performed by one of Proof Of Work (POW), Proof Of Stake (POS), and Proof Of Importance (POI). The above-described methods are one of the distributed summation algorithms used to guarantee the reliability of a distributed system.

In the disclosed embodiment, a transaction between user terminals 300 and a method of providing virtual currency accordingly may be performed by a smart contract. Depending on the embodiment, a project execution contract according to project matching may also be executed by a smart contract, and at least some or all of the rewards according to the project execution may be paid through virtual currency.

Meanwhile, in various embodiments according to the present invention, various types of blockchains such as private blockchains and consortium blockchains may be used as the blockchain as well as public blockchains that are generally used.

Public blockchain refers to a blockchain in which everyone can freely mine the blockchain without the management of an authoritative organization. In a public blockchain, each terminal corresponding to each node can copy and store data stored in the blockchain, and participate in the creation of a new block through hash operation, and thus virtual currency can be provided. Data stored in the public blockchain has the advantage that it is difficult to forgery, but it has the disadvantage that the speed is slow and that even unauthorized participants can participate in the blockchain. In the public blockchain, anyone can create transactions, and proof-of-work and proof-of-stake algorithms can be used.

Private blockchain refers to a blockchain in which only authorized users can participate in the blockchain network. Private blockchain has the advantage of strengthening confidentiality and preventing forgery, but it needs centralization, so it does not meet the purpose of decentralization. There is a characteristic that it is not necessary to provide virtual currency according to the creation of the blockchain. In the private blockchain, only authorized users can create transactions, and a BFT series consensus algorithm that does not allow partial branching can be used.

Consortium Blockchain is a block chain that has the characteristics of an intermediate level between public and private blockchains, and has a structure in which several organizations or companies form a group to form a blockchain network, and each node is given a specific role. There are features that can be done.

In the embodiment illustrated in FIG. 1, the first user terminal 310 may transmit and receive necessary information with the server 100 providing a project management method according to the disclosed embodiment.

Hereinafter, each of the steps will be described as being performed by the server 100, but a subject performing each step is not limited thereto, and all or part of each step may be performed by a different subject.

2 is a flowchart schematically illustrating a method of managing a project according to an embodiment of the present invention.

In step S105, the server 100 may match the first project with a first expert for performing the first project.

Specifically, the server 100 may obtain project information from the first project and match an expert corresponding to the project information of the first project among a plurality of experts. Hereinafter, an expert matching the first project is referred to as a first expert. A method of matching experts to the project will be described later.

In step S110, the server 100 may obtain a first result according to the result of executing the matched first project.

As described above, the first result may include data on the first project and data changed/added/deleted by an expert who performed the first project.

In step S115, the server 100 may extract first data for the first result based on the first result.

In step S120, the server 100 may store the first data and the first result in the blockchain.

At this time, the data stored in the blockchain can be managed through various methods. In one embodiment, the server 100 may store and manage data stored in the blockchain for each project. In other words, each project result for each project can be stored and managed in the blockchain. At this time, each project result stored in the blockchain may include an index for search. For example, the index may be an index for easily searching for a registration subject, a performing subject, and a category.

In another embodiment, the server 100 may extract a plurality of data from a result of each of a plurality of projects, and classify and manage the extracted plurality of data according to a preset criterion. That is, unlike the above-described embodiment, the server 100 may classify and manage data for all stored projects according to preset criteria (not managed for each project). In this case, the preset standards may be various standards, such as standards related to the type of project, standards related to the business field, standards related to budget, and standards related to the project registration entity.

In another embodiment, the server 100 may extract a plurality of data from a result of each of a plurality of projects, cluster the extracted plurality of data to group a plurality of data, and manage the grouped data. It goes without saying that the server 100 may label grouped data based on data included in the grouped data. At this time, the process of clustering may be performed at the time the project result is registered in the blockchain, but is not limited thereto, and of course, it may be performed in the process of matching a project suitable for a specific expert.

3 is a flowchart illustrating a method of matching experts on a project according to an embodiment of the present invention.

In step S210, the server 100 may collect data on the first project.

Data on the project may include data on the subject of registration of the project, data related to the category of the project, data on the start or completion point of the project, data on the subject of the project, and data on the work performed by the project. have. Furthermore, the data on the project may include information on the needs of the project registrant for the project. In other words, data on the project is data on the expertise of the project execution subject based on the standards desired by the project registration subject (e.g., experience in domestic or overseas project execution, and experience in project execution by organizer or orderer such as government, public institutions, local governments, companies, etc. It may include whether it has experience in performing projects by size according to the total budget, etc.).

In step S220, the server 100 may collect expert data including structured information and unstructured information for each of the plurality of experts.

In this specification, an expert may be understood to mean all kinds of people with a predetermined expertise in a specific field, and the criteria are not limited. All kinds of experienced persons, educated persons, or talented persons capable of performing work in a specific field can all be referred to as experts, but preferably understood to mean persons with a predetermined experience in a specific field. Can be.

Accordingly, expert data may mean information on the career of a corresponding expert, and the career may include various information according to embodiments. For example, information such as the workplace where each expert worked, the academic background of each expert, the thesis published by each expert, and the project each expert participated in may be included. Furthermore, the record of each expert's activities in online communities or clubs, etc. It can be understood to mean that it encompasses all various formal and unstructured information, such as projects performed as a hobby or materials created as a hobby.

In step S230, the server 100 may match the experts for the first project based on the data for the first project and the structured and unstructured information for each of the plurality of experts.

Specifically, the server 100 may match a first project with an expert having a degree of correlation equal to or greater than a preset value with the first project. In one embodiment, the server 100 determines a project cluster to which the first project belongs among a plurality of project clusters generated by clustering a plurality of project data, obtains an expert cluster closest to the focus of the determined project cluster, and obtains Experts corresponding to the created expert cluster may be matched with the first project.

In another embodiment, the server 100 determines an expert cluster to which the first expert belongs among a plurality of expert clusters generated by clustering a plurality of expert data including structured information and unstructured information, and determines the focus of the determined expert cluster and The nearest project cluster may be acquired, and a project corresponding to the acquired project cluster may be matched with the first expert (in this case, the matched project may be the first project).

In addition, according to various embodiments of the present invention, when a specific expert needs an expert in a heterogeneous field, the specific expert inputs a condition for the required expert in a heterogeneous field, and the server 100 Of course, it is possible to provide experts in different fields according to specific experts. In this case, the conditions for experts in heterogeneous fields input by a specific expert may include information on a project to be performed by a specific expert with experts in heterogeneous fields.

4 is a flowchart illustrating a method of matching experts for a new project using data on a plurality of projects according to an embodiment of the present invention.

In step S125, the server 100 may obtain a plurality of results according to the results of performing a plurality of projects for a plurality of projects.

In step S130, the server 100 may extract a plurality of data for a plurality of results.

In step S135, the server 100 may store a plurality of results according to the execution results of the plurality of projects and a plurality of data extracted for the plurality of results in the blockchain.

That is, steps S125 to S135 mean a step of storing the executed project and the result in the blockchain when the project is performed by an expert.

In step S140, the server 100 may register a new second project.

In step S145, the server 100 may collect data on the second project.

In step S150, the server 100 may select a project associated with the second project from among a plurality of projects.

In an embodiment, a method of selecting a project related to the first project may be a method of selecting a project that exists in the closest distance to the second project among project data expressed in the vector space.

In another embodiment, the server 100 may assign a weight to each of the project data extracted from the second project. The server 100 may determine a project associated with the second project based on project data having the highest weight among project data to which each weight is assigned.

At this time, the subject to which the weight is assigned may be the subject of project registration. As another example, the subject to which the weight is assigned may be the server 100. Specifically, the server 100 may assign a weight to project data based on data on other projects registered by the registration subject of the second project. For example, the project data included in the second project is the first data and the second data, and the project data included in the other project registered by the registration entity of the second project is the first data, the third data, the fourth data, If the project data included in another project registered by the second registration entity is the first data, the second data, and the fifth data, the server 100 assigns the highest weight to the first data (all other projects include). And the second highest weight may be assigned to the second data that is included most frequently.

In another embodiment, the server 100 may select a project related to the second project based on change data among data extracted for a plurality of results. That is, since change data is more useful in terms of utilizing a project that has been performed in the past rather than fixed data, the server 100 can select a project related to the second project by comparing the data for the second project with the change data. have.

In step S155, when the selected project is the first project, the server 100 may obtain 1-1 data corresponding to the data for the second project among the first data.

For example, assume that the second project data includes data of A, B, C, and D, and that the first data includes data of A', B', E, and F. In this case, the 1-1th data may be A', B'data.

That is, the 1-1 data may mean data that can be used in the second project among data of the first project.

In step S240, the server 100 may match experts on the basis of data excluding the 1-1 data among the data for the second project. For convenience of explanation, data excluding the first-first data among the first data is referred to as the 1-2 data, and among the data for the second project, the data corresponding to the first-first data is referred to as the 2-1 data and the second data. Data excluding -1 data is referred to as 2-2 data.

That is, since the 1-1 data and the 2-1 data are similar data, the project execution subject may be an expert on the 2-2 data. Through the above-described process, the expert platform can minimize the waste of manpower and match experts on the project to have high efficiency. Furthermore, the matched experts also use the previously performed 1-1 project part for the project part corresponding to the 2-1 data, and the project part corresponding to the 2-2 data, which is their specialty, the matched expert. By exercising your own expertise, you can increase the efficiency of the second project.

The user terminal 300 corresponding to the matched expert may receive the 1-1th data from the server 100. The user terminal 300 may add/delete/change a project-related task to the 2-1 data based on the 1-1 data.

On the other hand, it goes without saying that the server 100 may classify the 1-1 data into fixed data and change data and provide them to the user terminal 300. In this case, the user terminal 300 may utilize the contents of the fixed data as it is.

Meanwhile, the above-described embodiment relates to a method of performing a new project using the result of an already performed project by viewing each project as a unit. However, in another embodiment of the present invention, data on a plurality of project data and project results included in a project may be managed as a unit.

In an embodiment, the server 100 may classify data on a plurality of projects and a plurality of data extracted from a result of a plurality of projects performed by an expert according to a preset criterion. The preset criteria are data on the subject of execution, data on the category of results, data on the start and end of the project, data related to the format used in the project, data related to the summary of the project, and the work category of the project. It may be a criterion related to data for

The server 100 may classify and group data for a plurality of projects and data extracted from a result of the project according to the above criteria. For example, the server 100 classifies a plurality of data into a first group grouped based on the subject of execution, a second group grouped based on the subject of registration, and a third group grouped based on the business category of the project. I can.

That is, as described above, the server 100 may not group a plurality of data by project, but may group a plurality of data according to a preset classification criterion.

When a new project is registered, the server 100 may collect data on the project. For example, the data on the project may be data on the subject of project execution, data on the subject of project registration, and data on the business category of the project.

The server 100 may determine, from each of the grouped first to third groups, project data having the highest correlation with each of the collected project data. For example, the server 100 determines that the data on the project execution subject among the data on the new project corresponds to the data on the project execution subject of the first project, and the project registration subject among the data on the new project It may be determined that the data corresponds to the data on the project registration subject of the second project, and the data on the work category among the data on the new project corresponds to the data on the work category of the third project.

The server 100 may transmit the determined project data to the user terminal 300 corresponding to the matched expert. For example, the server 100 may transmit data on a project execution subject of a first project, data on a project registration subject of a second project, and data on a work category of a third project to the user terminal 300. .

The expert corresponding to the user terminal 300 uses the data on the project execution subject of the first project similar to the new project, the data on the project registration subject of the second project, and the data on the business category of the third project. You can do it.

5 is a flowchart illustrating a method of matching experts on a project using clustering according to an embodiment of the present invention.

In step S310, the server 100 may divide the first result according to a preset criterion.

At this time, the preset criteria are data on the subject of execution, data on the category of the first result, data on the start and end points of the first project, data related to the format used in the first project, It may be a criterion related to data related to the summary of Korea.

That is, the server 100 includes data on the subject of the first result, data on the category of the first result, data on the start and end points of the first project, data related to the format used in the first project, If any one of the data related to the summary of the first project is included, the first data is converted to data on the subject of the first outcome, data on the category of the first outcome, and at the start and end of the first project. It can be divided into any one of data related to the first project, data related to the format used in the first project, and data related to the summary of the first project.

In step S320, the server 100 may acquire first data for the first result based on the divided result.

In step S410, the server 100 may acquire a plurality of project clusters by clustering information on a plurality of projects. In this case, the server 100 may obtain keywords for features representing a plurality of project clusters and index them into the corresponding project clusters.

In step S420, the server 100 may acquire a plurality of expert clusters by clustering data on a plurality of experts. In this case, the server 100 may acquire keywords for features representing a plurality of expert clusters and index them into the corresponding expert cluster.

In step S430, the server 100 may acquire a first project cluster to which the first project belongs from among the plurality of clusters.

In step S440, the server 100 may determine a degree of association between the first project cluster and a plurality of expert clusters.

In step S450, the server 100 may acquire a first expert cluster having a degree of association greater than or equal to a preset value among the plurality of expert clusters. In an embodiment, clusters having similar characteristics may be located closer to each other. Accordingly, the server 100 may determine the degree of association between each cluster based on the distance.

In step S460, the server 100 may match the first project with an expert corresponding to the first expert cluster. For example, the server 100 may select an expert cluster located within a predetermined distance for each project cluster, and associate the first project with the expert and the cluster.

Meanwhile, a project according to another embodiment of the present invention may be a project in which a plurality of projects are linked. For example, the plurality of projects may be a MICE project including a plurality of projects. MICE is a term after English letters such as meeting, incentive trip, convention, exhibition and event (Exhibition&Event). In a broader concept, it refers to a convergence industry that includes participant-centered reward tourism and mega events. In this specification, the MICE project information may include each task information for meetings, tourism, shopping, transportation (air, bus, etc.), accommodation, and exhibition.

In this case, the server 100 may determine a plurality of second project clusters each including the plurality of projects included in the MICE project information. That is, the server 100 may acquire a cluster for each of a plurality of projects included in the MICE project information.

The server 100 may determine a plurality of second expert clusters in which a degree of association with each of the plurality of second project clusters exceeds a preset value. In this case, since the server 100 matches experts with respect to each of the plurality of projects, the most suitable expert for each of the plurality of projects may be matched with the project (first matching method).

However, in the case of a project including a plurality of projects, there may be cases where the overall project result is more important than the result of each project. Therefore, the server 100 may match the project and the experts in different ways in order to improve the result of the overall project rather than each project (second matching method).

In one embodiment, the server 100 may determine the focus of the plurality of second project clusters. In an embodiment, the midpoint of the plurality of second project clusters may represent a median value of each characteristic. Alternatively, the midpoint of the plurality of second project clusters may be points corresponding to the centers of gravity of the midpoints of the plurality of second project clusters.

The server 100 may determine a third expert cluster having a degree of association from the focus point equal to or greater than a preset value. That is, the third expert cluster may mean a cluster corresponding to the median value of the characteristics of all projects including a plurality of projects.

It goes without saying that the above-described first matching method or second matching method may be selected by the project registration subject or may be determined by the server 100.

In an embodiment, the server 100 may obtain variance values for the weight values of the plurality of second project clusters based on the distribution of the weight points of the plurality of second project clusters. When the obtained variance value is less than or equal to a preset value, the server 100 may match an expert to the project by a second matching method, and when exceeding a preset value, match the expert by the first matching method. That is, the meaning of a large variance value means that the correlation between a plurality of projects is low, so the server 100 matches the experts who can best perform each project with respect to a plurality of projects with a low correlation. 1 Matching method) You can. In addition, since the variance value is small means that the correlation between a plurality of projects is high, the server 100 matches an expert who can perform the entire project well with respect to a plurality of projects with high correlation (second matching). Way) you can.

6 is a flowchart illustrating a project management method according to a type of project result according to an embodiment of the present invention.

In step S160, the server 100 may classify the first result into a common result and a general purpose result according to the type.

In one embodiment, the common outcome and the universal outcome may be determined according to the selection of the project registration entity. In other words, some of the projects are open to the public, while others require confidentiality. Accordingly, the server 100 may distinguish between a common result for confidentiality and a general purpose result for public disclosure.

In step S165, the server 100 may set a user who can access the first result according to the classified result.

That is, in the case of a public result, the server 100 may set only participants of the project as users, and in the case of a general result, all the public may be set as users.

7 is a flowchart illustrating a method of storing a project according to a type of a result of a project according to an embodiment of the present invention.

In step S510, when the first result is a public result, the server 100 may store the first result in a private blockchain that only participants of the first project can access.

As described above, in the private blockchain, only users who are granted access can create transactions or make agreements, so security can be increased. Furthermore, the private blockchain can be used not only for project management according to the present invention, but also for multiple experts to perform a project when there are multiple experts matched to the project. If necessary, the private blockchain can be replaced by a consortium blockchain.

In step S520, when the first result is a general purpose result, the server 100 may store the first result in a public blockchain accessible to all users.

In step S170, when the first result is a general purpose result and the first result is used by a user other than a participant of the first project, the server 100 may provide virtual currency to the participants of the first project. If the first result is a public result, a private blockchain is used, so the process of providing virtual currency may be omitted.

At this time, the project participant who created the outcome of the project can receive virtual currency from various entities. Conversely, users who want to use the project results saved by other participants can provide virtual currency to view or purchase the project results. Virtual currency is created by an expert platform and can be purchased by paying a certain fee or provided by storing the project result in the blockchain.

In one embodiment, when the result of the first project is stored in the blockchain, the participant who creates the result for the first project by performing the first project provides the virtual currency corresponding to the stored first project result from the expert platform. I can receive it.

In another embodiment, when another user wants to use the result of the first project stored in the block chain, the user who wants to use may provide virtual currency to the participants of the first project and use the result of the first project. . At this time, the meaning of using the result of the first project may mean a case of simply viewing the result of the first project. Alternatively, the meaning of using the result of the first project may mean a case where the result of the first project can be viewed and edited. In this case, the amount of virtual currency to be provided by the user when viewing the result of the first project and the amount of virtual currency to be provided by the user when viewing and editing the result of the first project may be different.

In another embodiment, a user who wants to use the result of the first project may provide a predetermined ratio of virtual currency among the virtual currency provided to the participants of the first project to the expert platform.

8 is a flowchart illustrating a method of encrypting and storing a project result in a block chain according to an embodiment of the present invention.

* In step S610, the server 100 may encrypt the first data and the first result.

In step S620, the server 100 may store the encrypted data in the blockchain.

In an embodiment, the server 100 may encrypt the first data and the first result with a secret key possessed by the server and store it in a block chain. In this case, the user terminal 300 corresponding to a user who can use the first result and the first data may have a public key and a private key capable of decrypting the encrypted data. When the user terminal 300 receives the encrypted data, the user terminal 300 can decrypt and use the encrypted private key. In the case of the above method, the server 200 may use a public blockchain instead of using a private blockchain for public results.

9 is a block 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 graphic processing unit (not shown) and/or a connection path (eg, a bus) for transmitting and receiving signals with other components. .

The processor 102 according to an exemplary embodiment executes one or more instructions stored in the memory 104 to perform the method described with reference to FIGS. 1 to 8.

Meanwhile, the processor 102 temporarily and/or permanently stores a signal (or data) processed inside the processor 102, a RAM (Random Access Memory, not shown) and a ROM (Read-Only Memory). , Not shown) may further include. In addition, 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.

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

The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. The software module includes 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 to which the present invention pertains.

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

In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those of ordinary skill in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features. You can understand. Therefore, the embodiments described above are illustrative in all respects, and should be understood as non-limiting.

100: server
200: Blockchain holding server
300: user terminal

Claims (1)

In the big data-based management method performed by the server,
The server, the step of matching a first project and a first expert for performing the first project (S105);
Obtaining a first result according to the result of executing the matched first project (S110);
Extracting first data on the first result based on the first result (S115); And
Storing the extracted first data and the first result in a blockchain (S120); Including,
The step of matching the first expert (S105),
Collecting data on the first project (S210);
Collecting structured information and unstructured information for each of a plurality of experts (S220); And
Matching the experts for the first project based on the data on the first project and structured information and unstructured information for each of the plurality of experts (S230); Including,
The data on the first project includes data related to the category of the first project, data on the task to be performed of the first project, data on the subject of registration of the first project, and the subject of the first project. It contains at least one information of the data,
The above management method,
Obtaining a plurality of results according to a result of performing a plurality of projects for a plurality of projects (S125);
Extracting a plurality of data for the plurality of results (S130);
Storing a plurality of results according to execution results of the plurality of projects and a plurality of data extracted for the plurality of results in the blockchain (S135);
Registering a new second project (S140);
Collecting data on the second project (S145);
Selecting a project associated with the second project from among the plurality of projects (S150); And
If the selected project is the first project, acquiring (S155) 1-1 data corresponding to data on the second project from among the first data; and
The matching step (S105),
Acquiring 2-1 data corresponding to the 1-1 data, wherein the 2-1 data is included in the data for the second project;
Matching (S240) experts based on data other than the 2-1 data among the data on the second project; Including,
The step of extracting the first data (S115),
Dividing the first result according to a preset criterion (S310);
Including; acquiring first data on the first result based on the divided data (S320),
The step of matching the experts to the project (S230),
Clustering information on a plurality of projects to obtain a plurality of project clusters (S410);
Clustering data on a plurality of experts to obtain a plurality of expert clusters (S420);
Obtaining a first project cluster to which the first project belongs from among the plurality of project clusters (S430);
Determining a degree of association between the first project cluster and the plurality of expert clusters (S440);
Obtaining (S450) a first expert cluster having a degree of association equal to or greater than a preset value among the plurality of expert clusters (S450); And
Matching the first project with experts corresponding to the first expert cluster (S460); Including,
The preset criteria are,
Data on the subject of execution, data on the category of the first result, data on the start and end time of the first project, data related to the format used in the first project, and a summary of the first project Is the criteria related to the relevant data,
The step of matching the experts to the project (S230),
Obtaining a plurality of second project clusters based on the second project;
Obtaining variance values for a plurality of midpoints for each of the plurality of second project clusters;
If the obtained variance value exceeds a preset value, obtaining a plurality of expert clusters for each of the plurality of second project clusters by applying a first matching method; And
If the obtained variance value is less than or equal to a preset value, obtaining expert clusters for the plurality of second project clusters by applying a second matching method; Including,
If the obtained variance value exceeds a preset value, obtaining a plurality of expert clusters for each of the plurality of second project clusters by applying a first matching method,
Acquiring a plurality of second expert clusters having a degree of association with each of the plurality of second project clusters equal to or greater than a preset value; And
Matching experts for each of the plurality of second project clusters based on each of the obtained plurality of second expert clusters; Including,
If the obtained variance value is less than or equal to a preset value, obtaining expert clusters for the plurality of second project clusters by applying a second matching method,
Acquiring a point corresponding to the center of gravity of the plurality of midpoints for each of the plurality of second project clusters as midpoints of the plurality of second clusters;
Obtaining a third expert cluster having a degree of association of the obtained plurality of second clusters from the midpoints equal to or greater than a preset value; And
Matching experts for the plurality of second clusters based on the obtained third expert cluster; Including,
The above management method,
Classifying the first result into a common result and a general purpose result according to a type (S160);
Setting a user who can access the first result according to the classified result (S165); Including,
The step of storing in the blockchain (S120),
If the first result is a public result, storing the first result in a private blockchain that only participants of the project can access (S510); And
If the first result is a general purpose result, storing the first result in a public blockchain accessible to all users (S520); Including,
The step of storing in the blockchain (S120),
Encrypting the first data and the first result (S610);
Storing the encrypted data in the block chain (S620); Management method comprising a.

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