KR102042796B1 - Method and system for managing project results generated by a specialist based on bigdata - Google Patents

Abstract

A method for managing a project result generated by a specialist based on big data and a system thereof are provided. According to an aspect of the present invention, a big-data-based management method performed by a server comprises the steps of: matching, by the server, a first project with a first expert for performing the first project (S105); acquiring a first result according to a result of performing the matched first project (S110); extracting first data about the first result based on the first result (S115); and storing the extracted first data and the first result in a blockchain (S120).

Description

TECHNICAL AND SYSTEM FOR MANAGING PROJECT RESULTS GENERATED BY A SPECIALIST BASED ON BIGDATA}

The present invention relates to a method and system for managing a project result generated by an expert on a big data basis.

In recent years, researches on methods of collecting big data to build big data and providing various services using the big data are increasing. In order to construct big data, it is important to select and store what data and how to use the stored data. Furthermore, there is a need for a method for safely storing the stored data.

Block chain refers to a data distribution processing technology in which all users participating in a network distribute and store all data managed. It is also called 'Distributed Ledger Technology (DLT)' or 'Public Transaction Book' in that the ledger containing transaction information is not held by a trading entity or a specific institution but is divided among all network participants. . Blockchain is a name given as a chain of blocks containing transactions.

Blockchain focuses on the decentralization of the P2P (Peer to Peer) transaction, which breaks away from the existing financial system that secures and manages all transactions in financial institutions. P2P is a communication network connecting 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 blockchain based on P2P method, transaction information is put in blocks and shared by all participants.

Virtual currency, also known as electronic money or cryptocurrency, refers to money that is traded online without any kind of bills or coins. Cryptocurrency is valued according to the rules set by the original creator, unlike general currency issued by governments or central banks. In addition, due to the decentralized feature, certain institutions, such as the government, do not guarantee value or payment because they are distributed based on blockchain technology without managing transactions in the government or central bank.

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

There is a need for such an expert platform how to match different experts for different projects. In addition, the expert platform does not consume one-time project results, and there is also a need for a project management method for efficient use in new projects occurring later.

Registered Patent Publication No. 10-0339055, Registered on May 21, 2002

The problem to be solved by the present invention is to provide a method for managing a project result generated by an expert according to the above-mentioned needs based on big data.

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

In the big data-based management method performed by the server according to an aspect of the present invention for solving the above problems, the server, matching the first project and the first expert for performing the first project ( S105); Acquiring a first result according to a result of performing the matched first project (S110); Extracting first data about the first result based on the first result (S115); And storing the extracted first data and the first result in a blockchain (S120). It includes.

In this case, the matching of the first expert (S105) may include collecting data about the first project (S210); Collecting the atypical information and atypical information for each of the plurality of experts (S220); Matching an expert for the first project based on the data about the first project and the structured information and the unstructured information about each of the plurality of experts (S230); The data of the first project may include data related to a category of the first project, data on a task performed by the first project, data about the first project registration subject, and the first project performer. It may include at least one information of the data for.

At this time, the management method, the step of obtaining a plurality of results according to the results of the execution of the plurality of projects for a plurality of projects (S125); Extracting a plurality of data for the plurality of results (S130); Storing (S135) a plurality of results according to a result of the execution of the plurality of projects and a plurality of data extracted for the plurality of results in the blockchain; Receiving a new second project (S140); Collecting data on the second project (S145); Selecting a project associated with the second project among the plurality of projects (S150); And when the selected project is the first project, acquiring first-first data corresponding to data of the second project among the first data (S155). S105) matching the expert based on the data other than the 2-1 data among the data for the second project (S240); It may include.

At this time, the step of extracting the first data (S115), the step of dividing the first result according to a predetermined reference (S310); Acquiring a first data for the first result based on the divided data (S320), and matching an expert to the project (S230), by clustering information about a plurality of projects by a plurality of Obtaining a project cluster (S410); Clustering data for a plurality of experts to obtain a plurality of expert clusters (S420); Acquiring a first project cluster to which the first project belongs among the plurality of project clusters (S430); Determining an association degree between the first project cluster and the plurality of expert clusters (S440); Acquiring a first expert cluster of which the association degree is greater than or equal to a preset value among the plurality of expert clusters (S450); Matching the first project to an expert corresponding to the first expert cluster (S460); The reference may include: data about a subject, data about a category of the first result, data about a start time and an end time of the first project, and a form used for the first project. The data may be a criterion related to data related to the summary of the first project.

In this case, the management method may include classifying the first result into a common result and a general result according to a type (S160); Setting a user accessible to the first result according to the classified result (S165); It may include.

At this time, the step of storing in the blockchain (S120), if the first result is a common result, storing the first result in a private block chain accessible only to participants of the project (S510); And if the first result is a general purpose result, storing the first result in a public block chain accessible to all users (S520); It may include.

At this time, the management method, if the user other than the participants of the first project using the first result, providing a virtual currency to the participants of the first project (S170); It may include.

At this time, the step of storing in the blockchain (S120), the step of encrypting the first data and the first result (S610); Storing the encrypted data in the blockchain (S620); It may include.

According to various embodiments of the present invention described above, the project management system has an effect that can safely manage the performed project, and can be efficiently used for a new project.

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 a project result generated by an expert according to an embodiment of the present invention on a big data basis.
2 is a flowchart for explaining a method of managing a project according to an embodiment of the present invention.
3 is a flowchart illustrating a method of matching experts for a project according to an embodiment of the present invention.
4 is a flowchart illustrating a method of matching an expert for a new project using data about a plurality of projects according to an embodiment of the present invention.
5 is a flowchart illustrating a method of matching experts for a project using clustering according to an embodiment of the present invention.
6 is a flowchart illustrating a project management method according to the type of output of the project according to an embodiment of the present invention.
7 is a flowchart illustrating a project storage method according to the type of the result of the project, according to an embodiment of the present invention.
8 is a flowchart illustrating a method of encrypting and storing a result of a project according to an embodiment of the present invention in a blockchain.
9 is a block diagram of an apparatus according to an embodiment of the present invention.

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

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

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

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

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

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

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

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

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

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

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

Therefore, the expert data may include all kinds of information for evaluating the expertise of each expert, and the information for evaluating the expert's expertise may be related to the expertise of the expert's specific specialty or more than a predetermined reference value. It may mean information having, but is not limited thereto.

Further, the expert data may mean information about the career of the expert, and in some embodiments, the career may include various information. For example, you can include information such as the workplace where each professional worked, the educational background of each professional, the papers published by each professional, and the projects each professional participated in. It can be understood as encompassing a wide range of both structured and unstructured information, such as projects and hobbyist materials.

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

In one embodiment, the formal information may refer to information (or quantifiable information) that has already been quantitatively proved to be associated with a specialty of a specific specialty. 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 a department in a particular field, a career in a project or a task in a particular specialty, or an academic background or publication information in a specific field.

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

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

In one embodiment, expert data for a new area of expertise that does not previously store structured information may be obtained. In this case, since no formal information is stored as a criterion for evaluating the specialty, all expert data can be evaluated as unstructured information.

For example, even if information from a job or department in a particular specialty, or information on a project or assignment has been performed, if there is no pre-evaluated or stored information about that specialty, it will be evaluated as unstructured information for that specialty. It can be used to assess the expertise of each expert.

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

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

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

Meanwhile, in the present disclosure, a project refers to projects and business related orders issued by the government, local governments, public institutions, cooperation organizations, companies, domestic and international procurement bidding projects, and various projects and projects requiring professionals. It can mean related tasks. Projects can include many different types of goods, services, etc., but are not limited to specific types. For example, a project may be a task to deliver a specific product, or it may be a research service or a professional services service.

Meanwhile, in the present disclosure, data about a project may be data about various configurations included in the project. In one embodiment, the data for a project may include data about a project's registrant, data related to a project's category, data about a project's start or completion time, data about a project's performer, and a project's performance. It may mean data about.

In this case, the data about the project may include fixed data that does not change regardless of performing the project and change data that is changed by performing the project. The fixed data may mean, for example, data about a subject of registration of a project, data related to a category of a project, data about a start time or a completion time of a project. In addition, the change data may refer to data on the subject of the project and data on the task of the project. However, the present invention is not limited thereto, and the fixed data and the change data may be changed according to the properties of each project. In one embodiment, when the subject of the project is predetermined and there is no possibility of change, the data about the subject of the project is classified as fixed data, but the subject of the project may be undecided or changed, added, or deleted in the interim. The data on the subject of the project may be classified as change data. The classification of data about a project may be performed by a server or classified by information included in providing a project.

In another embodiment, if necessary, the fixed data and the change data may be post-determined by comparing the first-provided project with the project output obtained after carrying out the project.

Meanwhile, in the present disclosure, the data extracted from the result of the project and the result of the project may mean data about the above-described project and data calculated by the expert who performed the project. Specifically, the output and data of the project for the fixed data may be the same data as the fixed data classified in the original project. On the other hand, the output and data of the project for the change data may be data changed by the expert in the change data of the original project.

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

Each step 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. Each server is stored and managed.

In the disclosed embodiment, the blockchain holding server 200 is a blockchain for authenticating and recording data about the project, the output of the project, the data extracted from the output of the project, expert data including structured information and unstructured information, and the like. This means onboard servers.

Each of the user terminals 300 may transmit / receive various data according to the disclosed embodiment with the server 100. In one embodiment, when the user terminal 300 is a terminal corresponding to the project registration subject, the user terminal 300 and the server 100 and the data extracted from the data about the project, the output of the project, the output of the project Can send and receive In another embodiment, when the user terminal 300 is a terminal corresponding to a project performer (for example, an expert), the user terminal 300 may include expert data, including structured information and unstructured information, about a project. The result, data extracted from the result of the project can be transmitted and received with the server 100.

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

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

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

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

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

In one embodiment, the cryptocurrency according to the disclosed embodiment may be provided as a reward for the use of the service according to the disclosed embodiment, may be used for transactions between users, and may be provided to users who hold a stake through the initial ICO. have.

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

For example, the server 100 may perform an operation for generating a block for storing information and activity history 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 the same to the user terminals 300.

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

In one embodiment, the server 100 may verify the information directly, 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 predetermined rule and provided to the user terminals 300, which may be mined (mining). Express this.

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

In the disclosed embodiment, a transaction between the user terminals 300 and a method for providing virtual currency may be performed by a smart contract. According to an 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 of the project execution may be paid through virtual currency.

On the other hand, in various embodiments according to the present invention, the blockchain is not only a public blockchain generally used, but also a blockchain of various methods such as a private blockchain and a consortium blockchain may be used.

Public blockchain means a blockchain that anyone can freely mine in the blockchain without the management of an authoritative organization. The public blockchain is a structure in which each terminal corresponding to each node copies and stores data stored in the blockchain, participates in the creation of a new block through hash operation, and accordingly, virtual currency can be provided. Data stored in the public blockchain has the advantage that it is difficult to forgery, but there are disadvantages that it is slow and that unauthorized participants can participate in the blockchain. Anyone in the public blockchain can create transactions, and proof-of-work, proof-of-stake algorithms can be used.

Private blockchain means a blockchain in which only authorized users can participate in the blockchain network. Private blockchains have the advantages of enhanced confidentiality and prevention of forgery and alteration, but they do not serve the purpose of decentralization because they require centralization. There is a feature that it is not essential to provide virtual currency according to blockchain creation. Private blockchains can only create transactions by authorized users, and a consensus algorithm of the BFT family that does not allow partial branching can be used.

The consortium blockchain is a blockchain that has the characteristics of an intermediate stage between public blockchain and private blockchain.A consortium blockchain has a structure in which several organizations or enterprises form a blockchain network and assigns a specific role to each node. 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.

In the following, each step is described as being performed by the server 100, the subject performing each step is not limited to this, all or part of each step may be performed by another subject.

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

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

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

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

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

In operation S115, the server 100 may extract first data about the first result based on the first result.

In operation 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 an embodiment, the server 100 may store and manage data stored in the blockchain for each project. In other words, each project output itself for each project can be stored and managed in the blockchain. In this case, each project result stored in the blockchain may include an index for searching. For example, the index may be an index for easily searching for a registration subject, a performing subject, a category, and the like.

In another embodiment, the server 100 may extract a plurality of data from the results of each of the plurality of projects, and classify and manage the extracted plurality of data according to preset criteria. That is, unlike the above-described embodiment, the server 100 may classify and manage data about all stored projects according to preset criteria (rather than managing by project). In this case, the preset criteria may be various criteria such as criteria related to the type of project, criteria related to the field of work, criteria related to budget, and criteria related to the project registration subject.

In another embodiment, the server 100 may extract a plurality of data from the results of each of the plurality of projects, group the plurality of data by clustering the extracted plurality of data, and manage the grouped data. The server 100 may label the grouped data based on the data included in the grouped data. In this case, the clustering process may be performed at the time when the project result is registered in the blockchain, but is not limited thereto, and 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 for a project according to an embodiment of the present invention.

In operation S210, the server 100 may collect data about the first project.

The data for a project may include data about the project's registrants, data related to the category of the project, data about when the project is started or completed, data about the project's performers, and data about the project's work. have. Furthermore, the data about the project may include information about the needs of the project registrar for the project. In other words, the data on the project includes data on the professionalism of the project performer based on the criteria of the project registrant (e.g., experiences of domestic or overseas project execution and experience of project execution by organizer or client such as government, public institution, local government, company, etc.) Whether the project has been carried out by size according to the total budget, etc.).

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

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

Therefore, the expert data may mean information about the career of the expert, and in some embodiments, the career may include various information. For example, you can include information such as the workplace where each professional worked, the educational background of each professional, the papers published by each professional, and the projects each professional participated in. It can be understood as encompassing a wide range of both structured and unstructured information, such as projects and hobbyist materials.

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

In detail, the server 100 may match the expert having the degree of association with the first project or more with a predetermined value to the first project. In an 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 midpoint of the determined project cluster, and obtains it. Experts corresponding to the expert clusters can be matched with the first project.

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

In addition, according to various embodiments of the present disclosure, when a specific expert needs an expert in a heterogeneous field, the specific expert inputs a condition for an expert in a heterogeneous field that is needed, and the server 100 may apply to the input condition. Of course, it is possible to provide specific experts with heterogeneous fields accordingly. In this case, the condition for the specialist in the heterogeneous field input by the specific specialist may include information about the project to be performed by the specialist in the heterogeneous sector.

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

In operation S125, the server 100 may obtain a plurality of results according to a plurality of project execution results for the plurality of projects.

In operation S130, the server 100 may extract a plurality of data about the plurality of results.

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

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

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

In operation S145, the server 100 may collect data about the second project.

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

According to an embodiment, the method of selecting a project associated with the first project may be a method of selecting a project existing at the closest distance to the second project among the project data represented 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 the highest weighted project data among the respective weighted project data.

In this case, the weighting subject may be a project registration subject. As another example, the weighting subject may be the server 100. In detail, the server 100 may assign a weight to the project data based on data on other projects registered by the registrar of the second project. For example, the project data included in the second project is the first data, the second data, and the project data included in another project registered by the registrar 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 registrar is the first data, the second data, and the fifth data, the server 100 assigns the highest weight to the first data (which all other projects include). The second highest weight may be given to the second data that is much included next.

In another embodiment, the server 100 may select a project associated with the second project based on the change data among the data extracted for the plurality of results. That is, since the change data is more useful in terms of utilizing the project in which the change data was performed in the past, the server 100 may select a project associated with the second project by comparing the data for the second project with the change data. have.

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

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

That is, the 1-1 data may refer to data available in the second project among the data of the first project.

In operation S240, the server 100 may match the expert based on data except for the first-first data among the data for the second project. For convenience of description, the data corresponding to the 1-1 data among the 1-2 data and the data for the second project among the data except the 1-1 data among the first data, the 2-1 data, the second corresponding data Data except for -1 data is referred to as 2-2 data.

That is, since the first-first data and the second-first data are similar data, the subject of the project may be an expert on the second-two data. Through the above process, the expert platform can match the experts for the project to minimize the waste of manpower and have high efficiency. Furthermore, the matched expert Also, the project part corresponding to the 2-1 data utilizes the already performed first-first project part, and the project part corresponding to the 2-2 data, which is his specialty, is the matched expert. By carrying out their expertise, the efficiency of the second project can be improved.

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

In the meantime, the server 100 may classify the first-first data into fixed data and change data and provide the first-first data to the user terminal 300. In this case, the user terminal 300 may utilize the contents of the fixed data as it is.

On the other hand, the above-described embodiment relates to a method of performing a new project using the output of the already performed project by looking at each project as a unit. However, another embodiment of the present invention may manage a plurality of project data included in the project and data about the result of the project as one unit.

In an embodiment, the server 100 may classify the data for the plurality of projects and the plurality of data extracted from the results of the plurality of projects performed by the expert according to preset criteria. The preset criteria are based on the data on the subject, the category of the result, the data at the start and end of the project, the data related to the form used in the project, the data related to the summary of the project, and the business category of the project. Criteria for data.

The server 100 may classify and group data about a plurality of projects and data extracted from a result of the project according to the criteria. For example, the server 100 may classify a plurality of data into a first group grouped based on a subject, a second group grouped based on a registered subject, and a third group grouped based on a business category of a project. Can be.

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

If a new project is registered, the server 100 may collect data about the project. For example, data about a project may be data about a project performer, data about a project registrar, and data about a task category of the project.

In each of the grouped first to third groups, the server 100 may determine project data having the highest correlation with each of the data about the collected projects. For example, the server 100 determines that the data on the project execution subject among the data about the new project corresponds to the data on the project execution subject of the first project, and the data about the project registration subject among the data about the new project. The data may be determined to correspond to the data of the project registration subject of the second project, and the data of the work category among the data about the new project may be determined to correspond to the data of 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 the project performing subject of the first project, data on the project registering subject of the second project, and data on the work category of the third project to the user terminal 300. .

The expert corresponding to the user terminal 300 is a new project using data on the subject of the project execution of the first project similar to the new project, data on the project registration subject of the second project, and data on the work category of the third project. Can be performed.

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

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

In this case, the preset criteria means data on the subject of execution, data on the category of the first result, data about the start time and the end time of the first project, data related to the form used in the first project, and the first project. Can be a criterion associated with data relating to a summary.

That is, the server 100 may include data about a subject performing the first output, data about a category of the first output, data about a start time and an end time of the first project, data related to a form used in the first project, If it contains any of the data relating to the summary of the first project, the first data may be included in the data for the performing entity, the data for the category of the first output, at the start and end of the first project. The data can be divided into any one of data related to data, data related to a form used in the first project, and data related to a summary of the first project.

In operation S320, the server 100 may obtain first data about the first result based on the divided result.

In operation S410, the server 100 may cluster information about the plurality of projects to obtain a plurality of project clusters. In this case, the server 100 may obtain a keyword for a feature representing a plurality of project clusters and index them to the corresponding project cluster.

In operation S420, the server 100 may cluster a plurality of expert data to obtain a plurality of expert clusters. At this time, the server 100 may obtain a keyword for a feature representing a plurality of expert clusters and index the keyword to a corresponding expert cluster.

In operation S430, the server 100 may obtain a first project cluster to which the first project belongs among the plurality of clusters.

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

In operation S450, the server 100 may obtain a first expert cluster having an association degree equal to or greater than a preset value among the plurality of expert clusters. In one embodiment, clusters with similar features may be located closer to each other. Therefore, the server 100 may determine the degree of association between each cluster based on the distance.

In operation S460, the server 100 may match the first project with the experts 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 cluster with the first project and the full text.

Meanwhile, the 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 an abbreviation of English letters such as meetings, incentive trips, conventions, exhibitions and events, in a narrow sense a promising industry centered around international conferences and exhibitions. In the broader sense, it means a convergence industry that includes participant-centered reward tourism and mega events. In the present specification, the MICE project information may include task information for each meeting, tourism, shopping, transportation (air, bus, etc.), accommodation, exhibition, and the like.

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 obtain 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 whose association degree with each of the plurality of second project clusters exceeds a preset value. In this case, since the server 100 matches the experts for each of the plurality of projects, the server 100 may match the most suitable expert for each of the plurality of projects (the first matching method).

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

In one embodiment, the server 100 may determine the midpoint 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 midpoints 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 an association degree from the midpoint or more. That is, the third expert cluster may mean a cluster corresponding to the median value of all project characteristics including a plurality of projects.

The first matching method or the second matching method described above may be selected by the project registration subject or determined by the server 100.

In an embodiment, the server 100 may obtain distribution values of the midpoint values of the plurality of second project clusters based on the distribution of the midpoints of the plurality of second project clusters. If the obtained dispersion value is less than or equal to the preset value, the server 100 may match the expert to the project by the second matching method. If the obtained dispersion value is greater than the preset value, the server 100 may match the expert to the first matching method. That is, since a large variance value means a low degree of association between a plurality of projects, the server 100 matches an expert who can perform each project best with respect to a plurality of projects having low association. 1 matching method). In addition, since a small variance value means that there is a high degree of association between a plurality of projects, the server 100 matches an expert who can perform the entire project well for a plurality of highly related projects (second matching). Method).

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

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

In one embodiment, the common outcome and the universal outcome may be determined by the choice of project registrar. In other words, some projects may be open to the outside, while others require confidentiality. Accordingly, the server 100 may distinguish the public output for confidentiality and the general output for disclosure to the public.

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

That is, the server 100 may set only a participant of the project as a user in the case of a common result, and set the public as a user in the case of a general result.

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

In operation S510, when the first result is a shared result, the server 100 may store the first result in a private blockchain accessible only by participants of the first project.

As described above, the private blockchain can increase security because only authorized users can create transactions or make agreements. Furthermore, in addition to project management according to the present invention, the private blockchain may be used by a plurality of experts to carry out a project when there are a plurality of experts matched with the project. If necessary, the private blockchain can be replaced with a consortium blockchain.

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

In operation S170, when the first output is a general output and the first output is used by a user other than the participants of the first project, the server 100 may provide a virtual currency to the participants of the first project. When the first result is a public result, since the private blockchain is used, the virtual currency provision process may be omitted.

At this time, the project participants who generated the result of the project may be provided with virtual currency from various subjects. Conversely, a user who wants to use the output of a project saved by another participant can provide a virtual currency to view or purchase the project output. Cryptocurrencies are created by the expert platform and can be purchased for a certain cost, or the output of the project can be stored and provided on the blockchain.

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

In another embodiment, when another user wants to use the output of the first project stored in the blockchain, the user who wants to use may provide a virtual currency to the participants of the first project and use the output of the first project. . In this case, the meaning of using the result of the first project may mean a case of simply reading the result of the first project. Alternatively, the use of the output of the first project may mean that the output 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 the expert platform with a preset ratio of the virtual currency provided to the participants of the first project.

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

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

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

In one embodiment, the server 100 may encrypt the first data and the first result with a secret key owned by the server itself and store them in the blockchain. In this case, the user terminal 300 corresponding to the user who can use the first result and the first data may have a public key and a secret key capable of decrypting the encrypted data. When the user terminal 300 receives the encrypted data, the user terminal 300 may decrypt and use the encrypted private key. According to the above method, the server 200 may use a public blockchain instead of using a private blockchain in the case of a shared result.

9 is a block diagram of an apparatus according to an embodiment of the present invention.

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

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

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

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

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

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

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

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

Claims (10)

In the big data-based management method performed by the server,
The server may include matching a first project with a first expert for performing the first project (S105);
Acquiring a first result according to a result of performing the matched first project (S110);
Extracting first data about the first result based on the first result (S115); And
Storing the extracted first data and the first result in a blockchain (S120); Including,
Matching the first expert (S105),
Collecting data on the first project (S210);
Collecting the atypical information and atypical information for each of the plurality of experts (S220); And
Matching an expert for the first project based on the data for the first project and the structured information and the unstructured information for each of the plurality of experts (S230); Including,
The data for the first project may include data related to a category of the first project, data about a task to be performed of the first project, data about a subject of registration of the first project, and data about a subject to perform the first project. Includes at least one piece of data,
The management method,
Acquiring a plurality of outputs according to the execution results of the plurality of projects for the plurality of projects (S125);
Extracting a plurality of data for the plurality of results (S130);
Storing (S135) a plurality of results according to a result of the execution of the plurality of projects and a plurality of data extracted for the plurality of results in the blockchain;
Receiving a new second project (S140);
Collecting data on the second project (S145);
Selecting a project associated with the second project among the plurality of projects (S150); And
If the selected project is the first project, obtaining first-first data corresponding to data of the second project among the first data (S155);
The matching step (S105),
Obtaining 2-1 data corresponding to the 1-1 data, wherein the 2-1 data is included in data for the second project;
Matching an expert based on data other than the 2-1 data among the data for the second project (S240); Including,
Extracting the first data (S115),
Dividing the first result based on a predetermined reference (S310);
And acquiring first data of the first result based on the divided data (S320).
Matching an expert to the project (S230),
Clustering information about a plurality of projects to obtain a plurality of project clusters (S410);
Clustering data for a plurality of experts to obtain a plurality of expert clusters (S420);
Acquiring a first project cluster to which the first project belongs among the plurality of project clusters (S430);
Determining an association degree between the first project cluster and the plurality of expert clusters (S440);
Acquiring a first expert cluster of which the association degree is greater than or equal to a preset value among the plurality of expert clusters (S450); And
Matching the first project to an expert corresponding to the first expert cluster (S460); Including,
The preset criterion is
Data on the subject of execution, data on the category of the first outcome, data on the start and end of the first project, data related to the form used in the first project, a summary of the first project and Is the criteria associated with the relevant data,
Matching an expert to the project (S230),
Acquiring 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;
Obtaining a plurality of expert clusters for each of the plurality of second project clusters by applying a first matching method when the obtained dispersion value is greater than a preset value; And
And obtaining an expert cluster for the plurality of second project clusters by applying a second matching method when the obtained dispersion value is equal to or less than a preset value.
If the obtained dispersion value is greater than a predetermined 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 whose association degree with each of the plurality of second project clusters is 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;
If the obtained dispersion value is less than or equal to a preset value, obtaining an expert cluster for the plurality of second project clusters by applying a second matching method,
Obtaining a point corresponding to the center of gravity of the plurality of midpoints for each of the plurality of second project clusters as the midpoint of the plurality of second clusters;
Obtaining a third expert cluster whose association degree from the midpoint of the obtained plurality of second clusters is equal to or greater than a preset value; And
Matching experts for the plurality of second clusters based on the obtained third expert clusters;
The management method,
Classifying the first result into a common result and a general result according to a type (S160);
Setting a user accessible to 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 accessible only to participants of the project (S510); And
If the first result is a general result, storing the first result in a public blockchain accessible to all users (S520); Management method comprising a. delete delete delete delete delete Claim 7 was abandoned upon payment of a set-up fee. The method of claim 1,
The management method,
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); Management method comprising a. Claim 8 has been abandoned upon payment of a set-up fee. The method of claim 1,
The step of storing in the blockchain (S120),
Encrypting the first data and the first result (S610);
Storing the encrypted data in the blockchain (S620); Management method comprising a. Memory for storing one or more instructions; And
A processor for executing the one or more instructions stored in the memory;
The processor executes the one or more instructions,
An apparatus for carrying out the method of claim 1. A computer program, coupled to a computer, which is hardware, stored on a recording medium readable by a computer to perform the method of claim 1.

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