KR102513048B1 - Method and apparatus for transmitting diagnostic information to a mentor terminal based on report information related to a support project and progress information related to the support project using a neural network - Google Patents

Abstract

The present invention relates to a method and a device for transmitting diagnostic information to a mentor terminal based on report information related to a support project and progress information related to the support project using a neural network, which can determine more accurate diagnostic information. The method of the present invention comprises the steps of: obtaining report information related to a support project and company information; requesting the company terminal for progress information; determining priorities for a plurality of support project fields; and transmitting diagnostic information.

Description

Method and apparatus for transmitting diagnostic information to a mentor terminal based on report information related to a support project and progress information related to a support project using a neural network A SUPPORT PROJECT AND PROGRESS INFORMATION RELATED TO THE SUPPORT PROJECT USING A NEURAL NETWORK}

Embodiments of the present disclosure relate to a technology for transmitting diagnosis information to a mentor terminal based on report information related to a support project and progress information related to the support project, and report information and support project related to a support project using a neural network. This relates to a technology for transmitting diagnosis information to a mentor terminal based on related progress information.

On the other hand, due to the advancement of the industrial structure, technology-based companies that possess innovative technologies and ideas, such as start-ups and venture companies, operate their business with investment are increasing. In line with this, the government's business to support startups and venture companies, that is, the scale of government support projects, is also increasing, and startups and venture companies receive support through government support projects in various fields.

At this time, government support projects have mentor companies that provide mentoring or mentoring for startups and venture companies depending on the field, that is, partner organizations, and government support projects are mainly conducted by beneficiary companies, partner organizations, and government agencies. . However, due to various government-supported projects, it is difficult for government agencies or cooperative organizations to identify government-supported projects managed by other government agencies. In addition, in order to check which government support projects the beneficiary companies are currently carrying out and to what extent, government agencies or partner organizations review the submitted reports one by one to determine the status of the beneficiary companies. You have to figure it out.

Accordingly, the server related to the government agency obtains report information related to the support project and progress information related to the support project, and diagnoses information through a neural network based on the obtained report information related to the support project and progress information related to the support project. A method for determining and providing the diagnostic information to a mentor terminal is required.

Embodiments of the present disclosure may provide a method and apparatus for transmitting diagnosis information to a mentor terminal based on report information related to a supported project and progress information related to the supported project using a neural network.

Technical tasks to be achieved in the embodiments are not limited to those mentioned above, and other technical tasks not mentioned will be considered by those skilled in the art from various embodiments to be described below. can

A method in which a server transmits diagnosis information to a mentor terminal based on report information related to a supported project and progress information related to the supported project using a neural network according to an embodiment includes report information related to the supported project and information about the company. Obtain, and the report information related to the support project includes the field of the support project, a report file, a feedback score for the report, information on the mentor terminal that submitted the report, the number of reports submitted, and the number of standard submissions. Based on the fact that the number of reports submitted and the reference number of submissions are the same, progress information related to the supported project is requested from the enterprise terminal, and based on receiving the progress information related to the supported project from the enterprise terminal, the support Based on the report information related to the project, the progress information related to the support project, and the type of the company, priorities are determined for the fields of a plurality of support projects through a prioritization model using a first neural network, and the plurality of Based on the priorities of the fields of the support project, report information related to the support project, and progress information related to the support project, synthesis of each of the plurality of fields of the support project is performed through a diagnosis model using a second neural network. The method may include determining a score and a progress level, and transmitting diagnosis information including a comprehensive score and a progress level for each of the fields of the plurality of support projects to a plurality of mentor terminals. For example, the plurality of mentor terminals may be mentor terminals determined based on overall scores and progress for each of the fields of the plurality of support projects. For example, the type of company may be determined based on fields of the plurality of supported projects and information about the company.

The method receives curriculum information on a field of a supported project from two or more mentor terminals among a plurality of mentor terminals, transmits information on the two or more mentor terminals to the enterprise terminal, and transmits information on the two or more mentor terminals from the enterprise terminal. The method may further include receiving a selection message including a first mentor terminal selected from two or more mentor terminals, and connecting the first mentor terminal and the enterprise terminal based on the selection message. For example, the field of the support project may be a field determined by the overall score and progress of each of the plurality of fields of the support project. The plurality of mentor terminals may be mentor terminals related to the field of the support project. The information on the two or more mentor terminals may include information on the mentor terminals and curriculum information on the field of the support project.

For example, the information about the company may include the year of establishment of the company, information about the industry, information about manpower, information about technology development, information about marketing, information about financial status, and information about certification. there is. Fields of the plurality of support projects may include human resources field, development field, marketing field, finance field, overseas expansion field, and certification field. The field of the support project may be a field having the lowest overall score and the lowest progress value among the fields of the plurality of support projects.

For example, the first neural network may include a first input layer, one or more first hidden layers, and a first output layer. Each learning data consisting of report information related to a plurality of support projects, progress information related to a plurality of support projects, types of a plurality of companies, and priorities for fields of correct answer support projects is sent to the first input layer of the neural network. The first output vector is input and output as a first output vector after passing through the one or more first hidden layers and the first output layer, the first output vector is input to a first loss function layer connected to the first output layer, and the first The loss function layer outputs a first loss value by using a first loss function that compares the first output vector with a first correct answer vector for each training data, and the parameter of the first neural network is the first loss value. The prioritization model, which is learned in a direction in which the value decreases, may be created.

For example, the second neural network may include a second input layer, one or more second hidden layers, and a second output layer. Each learning data composed of a plurality of priority sets, report information related to a plurality of support projects, progress information related to a plurality of support projects, and a comprehensive score and progress in the field of a correct answer support project is the second input of the neural network. It is input to a layer, passes through the at least one second hidden layer and a second output layer, and is output as a second output vector, and the second output vector is input to a second loss function layer connected to the second output layer. The second loss function layer outputs a second loss value by using a second loss function that compares the second output vector with a second answer vector for each training data, and the parameters of the second neural network are the first 2 The diagnostic model, which is learned in a direction in which the loss value decreases, may be created.

For example, the total score for each of the fields of the plurality of correct answers may be determined by the following equation.

In the above equation, s is the total score for the field of the support project, pr is the priority for the field of the support project, n _c is the total number of fields of the plurality of support projects, and the k is the number of reports submitted in the field of the support project, q _i is the quality score of the ith report, f _c is the sum of average satisfaction and average utilization for completed support projects in the field of support project, and , The v _s may be a value representing the state of the company in the field of the support project.

Additionally, for example, the quality score may be determined by the following equation.

In the above equation, q is the quality score, f is a feedback score for the report, a is a weight for the feedback score, C _s is the number of first categories, and I _total is the report is the total number of images included in, I _th is the number of reference images, P _total is the total number of pages of the report, P _th is the number of reference pages, C _im is the number of the second category, The C _th may be the number of reference categories.

Here, the first category may be a category that satisfies the number of reference texts set for each category in the report. For example, the number of first categories may be the number of categories in which content is written with the number of texts equal to or greater than the number of reference texts set for each category in the report. Here, the second category may be an important category included in the report. For example, the number of second categories may be the number of important categories included in the report. Here, the criterion category may be an important category set in the report. Here, the reference page may be the total number of pages set in the report. Here, the number of reference images may be the total number of images set in the report.

Additionally, for example, the progress for each of the fields of the plurality of support projects as the correct answer may be determined by the following equation.

In the above equation, p is the progress of the field of the support project, p _r is the priority of the field of the support project, n _c is the total number of fields of the plurality of support projects, R _c is the number of completed support projects in the field of the support project, R _p is the number of support projects in progress in the field of the support project, and R _total is the total number of support projects for the field of the support project can be

According to embodiments, the server prioritizes the fields of a plurality of supported projects through a prioritization model using a first neural network based on report information related to the supported project, progress information related to the supported project, and type of company. By determining the priority, it is possible to determine more accurate diagnosis information by reflecting the priority of the field of support project for each enterprise terminal.

According to embodiments, the server performs a diagnosis model using a second neural network based on priorities of fields of a plurality of supported projects, report information related to the supported projects, and progress information related to the supported projects. By determining the overall score and progress for each field of the support project, the status of the enterprise terminal can be efficiently grasped through the overall score and progress determined by reflecting the priority for each field of the support project, and based on the overall score and progress As a result, the enterprise terminal can be connected with a suitable mentor terminal.

Effects obtainable from the embodiments are not limited to the effects mentioned above, and other effects not mentioned are clearly derived and understood by those skilled in the art based on the detailed description below. It can be.

BRIEF DESCRIPTION OF THE DRAWINGS Included as part of the detailed description to aid understanding of the embodiments, the accompanying drawings provide various embodiments and, together with the detailed description, describe technical features of the various embodiments.
1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.
2 is a diagram showing the configuration of a program according to an embodiment.
3 illustrates a method in which a server transmits diagnosis information to a mentor terminal using a neural network based on report information related to a supported project and progress information related to the supported project.
4 is a flowchart of a method for determining priorities of fields of a plurality of supported projects by a server based on report information related to the supported projects and progress information related to the supported projects according to an embodiment.
5 is a flowchart of a method for a server to transmit diagnostic information including comprehensive scores and progress for fields of a plurality of support projects to a plurality of mentor terminals and to connect a company terminal and a mentor terminal according to an embodiment. am.
6 is a block diagram illustrating a configuration of a server according to an exemplary embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form not combined with other components or features. In addition, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.

In the description of the drawings, procedures or steps that may obscure the gist of various embodiments are not described, and procedures or steps that can be understood by those skilled in the art are not described. did

Throughout the specification, when a part is said to "comprising" or "including" a certain element, it means that it may further include other elements, not excluding other elements, unless otherwise stated. do. In addition, terms such as “… unit”, “… unit”, and “module” described in the specification mean a unit that processes at least one function or operation, which is hardware or software or a combination of hardware and software. can be implemented as Also, “a or an”, “one”, “the” and like terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, both the singular and the plural can be used.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description set forth below in conjunction with the accompanying drawings is intended to describe exemplary embodiments of various embodiments, and is not intended to represent a single embodiment.

In addition, specific terms used in various embodiments are provided to help understanding of various embodiments, and the use of these specific terms may be changed into other forms without departing from the technical spirit of various embodiments. .

1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.

1 is a block diagram of an electronic device 101 within a network environment 100, according to various embodiments. Referring to FIG. 1 , in a network environment 100, an electronic device 101 communicates with an electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or through a second network 199. It may communicate with at least one of the electronic device 104 or the server 108 through (eg, a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 . According to an embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or the antenna module 197 may be included. In some embodiments, in the electronic device 101, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added. In some embodiments, some of these components (eg, sensor module 176, camera module 180, or antenna module 197) are integrated into a single component (eg, display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (eg, the program 140) to cause at least one other component (eg, hardware or software component) of the electronic device 101 connected to the processor 120. It can control and perform various data processing or calculations. According to one embodiment, as at least part of data processing or operation, the processor 120 transfers instructions or data received from other components (e.g., sensor module 176 or communication module 190) to volatile memory 132. , processing commands or data stored in the volatile memory 132 , and storing resultant data in the non-volatile memory 134 . According to one embodiment, the processor 120 may include a main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit ( NPU: neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, when the electronic device 101 includes the main processor 121 and the auxiliary processor 123, the auxiliary processor 123 may use less power than the main processor 121 or be set to be specialized for a designated function. can The secondary processor 123 may be implemented separately from or as part of the main processor 121 .

The secondary processor 123 may, for example, take the place of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or the main processor 121 is active (eg, running an application). ) state, together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to one embodiment, the auxiliary processor 123 (eg, image signal processor or communication processor) may be implemented as part of other functionally related components (eg, camera module 180 or communication module 190). there is. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model.

AI models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself where the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning or reinforcement learning, but in the above example Not limited. The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the foregoing, but is not limited to the foregoing examples. The artificial intelligence model may include, in addition or alternatively, software structures in addition to hardware structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101 . The data may include, for example, input data or output data for software (eg, program 140) and commands related thereto. The memory 130 may include volatile memory 132 or non-volatile memory 134 .

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142 , middleware 144 , or an application 146 .

The input module 150 may receive a command or data to be used by a component (eg, the processor 120) of the electronic device 101 from the outside of the electronic device 101 (eg, a user). The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101 . The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback or recording playback. A receiver may be used to receive an incoming call. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 may visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor set to detect a touch or a pressure sensor set to measure the intensity of force generated by the touch.

The audio module 170 may convert sound into an electrical signal or vice versa. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device connected directly or wirelessly to the electronic device 101 (eg: Sound may be output through the electronic device 102 (eg, a speaker or a headphone).

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a bio sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device 101 to an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 may be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 may convert electrical signals into mechanical stimuli (eg, vibration or motion) or electrical stimuli that a user may perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 may manage power supplied to the electronic device 101 . According to one embodiment, the power management module 188 may be implemented as at least part of a power management integrated circuit (PMIC), for example.

The battery 189 may supply power to at least one component of the electronic device 101 . According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). Establishment and communication through the established communication channel may be supported. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, : a local area network (LAN) communication module or a power line communication module). Among these communication modules, a corresponding communication module is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a telecommunications network such as a computer network (eg, a LAN or a WAN). These various types of communication modules may be integrated as one component (eg, a single chip) or implemented as a plurality of separate components (eg, multiple chips). The wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 may be identified or authenticated.

The wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, NR access technology (new radio access technology). NR access technologies include high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and access of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low latency (URLLC)). -latency communications)) can be supported. The wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data rate, for example. The wireless communication module 192 uses various technologies for securing performance in a high frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. Technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna may be supported. The wireless communication module 192 may support various requirements defined for the electronic device 101, an external electronic device (eg, the electronic device 104), or a network system (eg, the second network 199). According to one embodiment, the wireless communication module 192 is a peak data rate for eMBB realization (eg, 20 Gbps or more), a loss coverage for mMTC realization (eg, 164 dB or less), or a U-plane latency for URLLC realization (eg, Example: downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) may be supported.

The antenna module 197 may transmit or receive signals or power to the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is selected from the plurality of antennas by the communication module 190, for example. can be chosen A signal or power may be transmitted or received between the communication module 190 and an external electronic device through the selected at least one antenna. According to some embodiments, other components (eg, a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module 197 in addition to the radiator.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, the mmWave antenna module includes a printed circuit board, an RFIC disposed on or adjacent to a first surface (eg, a lower surface) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, array antennas) disposed on or adjacent to a second surface (eg, a top surface or a side surface) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( e.g. commands or data) can be exchanged with each other.

According to an embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or part of operations executed in the electronic device 101 may be executed in one or more external electronic devices among the external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 instead of executing the function or service by itself. Alternatively or additionally, one or more external electronic devices may be requested to perform the function or at least part of the service. One or more external electronic devices receiving the request may execute at least a part of the requested function or service or an additional function or service related to the request, and deliver the execution result to the electronic device 101 . The electronic device 101 may provide the result as at least part of a response to the request as it is or additionally processed. To this end, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an internet of things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199 . The electronic device 101 may be applied to intelligent services (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide a service to the connected electronic device 101 . In addition, the server 108 may proceed with a membership sign-up procedure, store and manage various types of information of users subscribed as members, and provide various purchase and payment functions related to services. In addition, the server 108 may share execution data of service applications executed in each of the plurality of electronic devices 101 in real time so that the service can be shared among users. This server 108 may have the same configuration as a conventional web server or service server in terms of hardware. However, in terms of software, it may include a program module that is implemented through any language such as C, C++, Java, Python, Golang, or kotlin and performs various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives requests from clients or other servers to perform tasks and derives and provides work results. It means a computer system and the computer software (server program) installed for it. In addition, the server 108, in addition to the above-described server program, a series of application programs operating on the server 108 and various databases (DB: Database, hereinafter referred to as It should be understood as a broad concept including DB"). Accordingly, the server 108 classifies member registration information and various information and data about games, stores them in a DB, and manages them. This DB may be implemented inside or outside the server 108 . In addition, the server 108 may be implemented using server programs that are provided in various ways according to operating systems such as Windows, Linux, UNIX, and Macintosh on general server hardware, As a representative example, a web service can be implemented using IIS (Internet Information Server) used in a Windows environment and CERN, NCSA, APPACH, TOMCAT, etc. used in a Unix environment. In addition, the server 108 may interoperate with an authentication system and a payment system for user authentication of services or payment for purchases related to services.

The first network 198 and the second network 199 are a connection structure capable of exchanging information between nodes such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. means (Network). The first network 198 and the second network 199 include Internet, Local Area Network (LAN), Wireless Local Area Network (Wireless Local Area Network), Wide Area Network (WAN), Personal Area Network (PAN), and 3G , 4G, LTE, 5G, Wi-Fi, etc., but are not limited thereto. The first network 198 and the second network 199 may be closed first networks 198 and second networks 199 such as LAN and WAN, but are preferably open such as the Internet. The Internet includes protocols such as the TCP/IP protocol, TCP, and User Datagram Protocol (UDP), and various services that exist in the upper layer, such as HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), and DNS (Domain Name System). ), Simple Mail Transfer Protocol (SMTP), Simple Network Management Protocol (SNMP), Network File Service (NFS), and Network Information Service (NIS). ) structure.

A database may have a general data structure implemented in a storage space (hard disk or memory) of a computer system using a database management program (DBMS). The database may have a data storage form in which data can be freely searched for (extracted), deleted, edited, added, and the like. Databases are relational database management systems (RDBMS) such as Oracle, Informix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and XML Native Databases such as Excelon, Tamino, and Sekaiju, and its functions may have appropriate fields or elements to achieve.

2 is a diagram showing the configuration of a program according to an embodiment.

2 is a block diagram 200 illustrating a program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable in the operating system 142 for controlling one or more resources of the electronic device 101. can include The operating system 142 may include, for example, Android™, iOS™, Windows™, Symbian™ or Tizen™. At least some of the programs 140 are, for example, preloaded in the electronic device 101 at the time of manufacture, or when used by a user, an external electronic device (eg, the electronic device 102 or 104), or a server ( 108)) can be downloaded or updated. All or part of program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or reclamation) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively include other hardware devices of electronic device 101 , such as input module 150 , sound output module 155 , display module 160 , audio module 170 . , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that the function or information provided from one or more resources of the electronic device 101 may be used by the application 146 . The middleware 144 includes, for example, the application manager 201, the window manager 203, the multimedia manager 205, the resource manager 207, the power manager 209, the database manager 211, and the package manager 213. ), connectivity manager 215, notification manager 217, location manager 219, graphics manager 221, security manager 223, call manager 225, or voice recognition manager 227. can

The application manager 201 may manage the life cycle of the application 146 , for example. The window manager 203 may manage one or more GUI resources used in a screen, for example. The multimedia manager 205 identifies, for example, one or more formats necessary for reproducing media files, and encodes or decodes a corresponding media file among the media files using a codec suitable for the selected format. can be done The resource manager 207 may manage a source code of the application 146 or a memory space of the memory 130 . The power manager 209 may manage, for example, the capacity, temperature, or power of the battery 189, and determine or provide related information necessary for the operation of the electronic device 101 by using corresponding information among them. . According to an embodiment, the power manager 209 may interoperate with a basic input/output system (BIOS) (not shown) of the electronic device 101 .

The database manager 211 may create, search, or change a database to be used by the application 146, for example. The package manager 213 may manage installation or update of applications distributed in the form of package files, for example. The connectivity manager 215 may manage, for example, a wireless connection or a direct connection between the electronic device 101 and an external electronic device. The notification manager 217 may provide a function for notifying a user of occurrence of a designated event (eg, an incoming call, message, or alarm), for example. The location manager 219 may manage location information of the electronic device 101, for example. The graphic manager 221 may manage, for example, one or more graphic effects to be provided to a user or a user interface related thereto.

Security manager 223 may provide system security or user authentication, for example. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101 . The voice recognition manager 227 transmits, for example, the user's voice data to the server 108, and at least partially based on the voice data, a command corresponding to a function to be performed in the electronic device 101; Alternatively, text data converted at least partially based on the voice data may be received from the server 108 . According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least part of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142 .

The application 146 includes, for example, a home 251, a dialer 253, an SMS/MMS 255, an instant message (IM) 257, a browser 259, a camera 261, and an alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Albums (275), Watch (277), Health (279) (e.g. exercise or blood sugar) measurement of biometric information) or environmental information 281 (eg, measurement of atmospheric pressure, humidity, or temperature information). According to an embodiment, the application 146 may further include an information exchange application (not shown) capable of supporting information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transmit designated information (eg, a call, message, or alarm) to an external electronic device, or a device management application configured to manage an external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a designated event (eg, mail reception) generated in another application (eg, the email application 269) of the electronic device 101 to an external electronic device. can Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide the notification information to the user of the electronic device 101 .

The device management application is, for example, a power source (eg, turn-on or turn-off) of an external electronic device that communicates with the electronic device 101 or some component thereof (eg, a display module or a camera module of the external electronic device). ) or functions (eg brightness, resolution, or focus). The device management application may additionally or alternatively support installation, deletion, or update of an application operating in an external electronic device.

Throughout this specification, a neural network, a neural network, and a network function may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may be generally referred to as “nodes”. These “nodes” may also be referred to as “neurons”. A neural network includes at least two or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more “links”.

In a neural network, two or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

3 illustrates a method in which a server transmits diagnosis information to a mentor terminal by using a neural network based on report information related to a supported project and progress information related to the supported project according to an embodiment. One embodiment of FIG. 3 may be combined with various embodiments of the present disclosure.

Referring to FIG. 3 , in step S301, the server may obtain report information related to the supported project and information about the company.

For example, the server may request information about the company from the company terminal and receive the information about the company from the company terminal. For example, the server may request report information related to the supported project from the mentor terminal and receive report information related to the supported project from the mentor terminal.

Here, the server may be a server that manages companies and mentor companies for government agencies or organizations in charge of support projects. For example, the server may be a server associated with a government agency. Here, the report information related to the support project may include the field of the support project, a report file, a feedback score for the report, information on the mentor terminal that submitted the report, the number of reports submitted, and the standard number of submissions. For example, the field of the support project may be a field of a support project conducted by a company, and may be any one of a human resources field, a development field, a marketing field, a finance field, an overseas expansion field, or a certification field. For example, the report file may be a report file for progress results of the support project. That is, the report file is a report file that needs to be submitted in the corresponding support project, and may be a document file including text and images such as hwp and docx. For example, the mentor terminal may transmit a report file to the server. For example, the feedback score for the report may be an evaluation score of the enterprise terminal for the submitted report file. For example, the number of reports submitted may be the number of reports submitted according to each progress stage for the support project, and the standard number of submissions may be the total number of reports to be submitted determined by the organization in charge of the support project. . For example, the field of the support project is the human resources field, and it is determined that the support project for the human resource field is to conduct a total of 3 consulting sessions, the mentor company performs the first and second consulting services to the company, and the mentor terminal provides each consulting service. When a report file is submitted for , the number of submissions of the report may be two times, and the reference number of submissions may be three times.

Additionally, according to one embodiment, the feedback score for the report may be different according to the reception point of the feedback message including the feedback score for the report file from the enterprise terminal. For example, the server may transmit the report file received from the mentor terminal to the enterprise terminal. The enterprise terminal may transmit a feedback message including a feedback score for the report file to the server. In this case, a weight for the feedback score may be determined according to a time point at which the feedback message is received. For example, when the feedback message is received within a preset time from the report file received, the weight for the feedback score may be determined as a first weight. For example, when the feedback message is received after a preset time from the report file, the weight for the feedback score may be determined as a second weight. In this case, the first weight may be a value smaller than the second weight. Accordingly, the server may apply different weights to the feedback points according to a time point at which the feedback message including the feedback points for the report file is received from the enterprise terminal. Through this, a more objective feedback score may be reflected by reducing the feedback score when the review of the report file is not sufficient.

Here, the information about the company may include the year of establishment of the company, information about the type of business, information about manpower, information about technology development, information about marketing, information about financial status, and information about certification. For example, the information on the type of business is information indicating the type of business of the company and may include the Korean standard industry classification code of the company. For example, information about manpower may include information about employees employed by a company. For example, information on technology development may include information on technologies owned by the company, information on patents, trademarks, and designs owned by the company, and information on development costs. For example, information on marketing may include information on marketing conducted by a company and information on marketing costs. For example, information on the financial condition of a company may include total assets, capital, total equity, sales, operating income, and net income for each period. For example, information about certification may include information about certification acquired by a company.

In step S302, the server may request progress information related to a supported project from the enterprise terminal based on the fact that the number of submissions of the report is the same as the reference number of submissions. Here, the progress information related to the support project may include information indicating the current state of the company for the support project for which consulting is completed after consulting on the support project is completed. For example, progress information related to the supported project may be obtained based on a checklist for checking the current state of the supported project. For example, based on the fact that the number of submissions of the report is the same as the number of submissions of the criterion, the server transmits a checklist matching the support project to the enterprise terminal, and obtains the checklist from the enterprise terminal based on the checklist. It is possible to receive progress information related to the supported project. For example, the progress information related to the support project may include information about satisfaction with the support project for which consulting was completed, utilization of the support project for which consulting was completed, and the current state of the company related to the support project. Here, the information on the current state of the company related to the support project may include values of indicators related to the field of the support project. For example, when the field of the support project is a development field, information on the current state of a company related to the support project may be information on the current development state of a technology supported through the support project. For example, the information on the current state of development may include indicators related to development input by the enterprise terminal (the number of technologies owned, cost received based on technology owned, development cost for technologies owned, number of patents, costs associated with patents, etc.).

In step S303, the server sets a first neural network based on report information related to the supported project, progress information related to the supported project, and the type of the company based on receiving progress information related to the supported project from the enterprise terminal. It is possible to prioritize the fields of a plurality of support projects through a prioritization model using .

Here, the type of company may be determined based on information about the company. For example, the type of company may be determined based on information about the year of establishment of the company, the type of business of the company, and the financial status of the company. For example, the type of company may be determined as one of n preset types based on information about the year of establishment of the company, the type of business of the company, and the financial condition of the company.

For example, the n types may be determined in advance through a clustering technique using a neural network based on information about a plurality of companies, the company's establishment year, the company's type of business, and the company's financial status. For example, clustering may refer to unsupervised learning that groups data having similar properties into a certain number of clusters.

At this time, for example, for each company, information about the year of establishment of the company, the type of business of the company, and the financial status of the company may be reduced to a three-dimensional vector or less through various dimensionality reduction techniques. For example, information about the year of establishment of the company, the type of business of the company, and the financial status of the company can be vectorized through data pre-processing. For example, it can be vectorized into the company's year of establishment, the value corresponding to the company's industry, the company's total assets in that year, the company's sales in that year, the company's operating profit in that year, and the company's current net profit in that year.

In this case, for example, since the vector is 7-dimensional, the server may reduce the dimension of the vector to a dimension of 3 dimensions or less through various dimension reduction techniques. For example, the server may reduce the vector to a 3D or lower order through a principal component analysis (PCA) technique. For example, the server may determine an axis of data having the highest variance when the vector data is projected onto a principal component axis, and may reduce a dimension to the determined axis. For example, the server may generate a first axis based on the largest variance among a plurality of vector data, and a second axis may generate a vector orthogonal to the first vector axis as an axis. Thereafter, the server may generate a vector perpendicular to the second axis again using the third axis as an axis. When the server projects the original data onto the generated three vector axes, the server may dimensionally reduce the original data to as many dimensions as the number of vector axes. Hereinafter, a vector obtained by performing data preprocessing on job seeker information is generated, and a vector obtained by dimensionally reducing the generated vector through various dimensionality reduction techniques may be referred to as a dimensionality reduction vector.

For example, the company type may be determined as n types through a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique based on a plurality of dimensional reduction vectors. For example, DBSCAN assumes that if a certain point belongs to a cluster, it must be located close to many other points in the cluster, and for this calculation, the radius and minimum points are can be used For example, the diameter may be a radius based on a specific data element, and may be referred to as a dense area. For example, the minimum element could indicate how many elements are needed around the core point to specify a core point. In addition, each element of the data set can be divided into a core, a border, and an outlier point.

For example, through a neural network, the server can check the size of the diameter of each element and search for how many elements there are. Thereafter, if m or more elements exist within the diameter range, the server may determine the corresponding element as a key element. And, the server may determine an element included within a range of a diameter from the core element as a boundary element. Also, the server may determine an element not included within a diameter range from the core element as an outlier element, and the outlier element may be excluded from the corresponding cluster. In addition, when the distance between core elements is smaller than the diameter, the server may classify the corresponding elements into the same cluster.

For example, the value of n may be determined according to the number of fields of support projects registered in the server. For example, when the number of supported project fields registered in the server is 6 or less, the value n may be determined as a factorial value for the number of supported project fields registered in the server. For example, when the number of fields of support projects registered in the server is 5, the value of n may be 5!, that is, 5x4x3x2x1, 120. That is, when the number of fields of support business registered in the server is 5, the type of company may be set to 120 types.

For example, when the number of fields of support projects registered in the server exceeds 6, the n value may be determined to be 1000.

For this reason, the types of companies are not determined as fixed types, but can be flexibly changed according to the number of fields of the supported project to adaptively determine the types of companies.

Additionally, for example, the server may set a basic priority for each of the fields of a plurality of supported projects for the n types. For example, if an enterprise terminal is determined to be any one enterprise type among the n types, the basic priority for each of the fields of a plurality of supported projects is that a preset number of enterprise terminals included in the type of enterprise It may be set based on the frequency of the first supported project. For example, when the fields of the plurality of support projects are human resources field, development field, marketing field, finance field, overseas expansion field, and certification field, the field of the first supported project among the preset number of corporate terminals is development. When there are the largest number of corporate terminals, which are fields, a basic priority for the development field may be set to the highest. In addition, if the field of the first supported project is the finance field, the next largest number of enterprise terminals, the finance field may be set as the second highest priority. For example, the fields of the plurality of support projects are not limited to the above examples, and fields may be added or deleted.

For example, the first neural network may include a first input layer, one or more first hidden layers, and a first output layer. Each learning data consisting of report information related to a plurality of support projects, progress information related to a plurality of support projects, types of a plurality of companies, and priorities for fields of correct answer support projects is sent to the first input layer of the neural network. The first output vector is input and output as a first output vector after passing through the one or more first hidden layers and the first output layer, the first output vector is input to a first loss function layer connected to the first output layer, and the first The loss function layer outputs a first loss value by using a first loss function that compares the first output vector with a first correct answer vector for each training data, and the parameter of the first neural network is the first loss value. The prioritization model, which is learned in a direction in which the value decreases, may be created.

In step S304, the server determines the plurality of fields of the plurality of supported projects through a diagnostic model using a second neural network based on the priorities of fields of the plurality of supported projects, report information related to the supported projects, and progress information related to the supported projects. An overall score and progress can be determined for each of the fields of the support project.

For example, the second neural network may include a second input layer, one or more second hidden layers, and a second output layer. Each learning data composed of a plurality of priority sets, report information related to a plurality of support projects, progress information related to a plurality of support projects, and a comprehensive score and progress in the field of a correct answer support project is the second input of the neural network. It is input to a layer, passes through the at least one second hidden layer and a second output layer, and is output as a second output vector, and the second output vector is input to a second loss function layer connected to the second output layer. The second loss function layer outputs a second loss value by using a second loss function that compares the second output vector with a second answer vector for each training data, and the parameters of the second neural network are the first 2 The diagnostic model, which is learned in a direction in which the loss value decreases, may be created.

For example, the total score for each of the fields of the plurality of correct answers may be determined by Equation 1 below.

In Equation 1, s is the total score for the field of the support project, _pr is the priority for the field of the support project, and n _c is the total number of fields of the plurality of support projects , where k is the number of reports submitted in the field of the support project, q _i is the quality score of the ith report, and f _c is the average satisfaction and average utilization of the completed support project in the field of the support project. It is a sum value, and v _s may be a value representing the state of the company in the field of the support project.

Here, the value representing the state of the company in the field of the support project may be a value determined based on information about the current state of the company related to the support project. For example, the information on the current state of the company related to the support project may include values for indicators related to the field of the support project.

For example, the sum of the average quality score of the report for the field of support project, the average satisfaction and average utilization for the field of support project, and the enterprise for the field of support project with low priority for the field of support project As the value representing the state of is larger, the overall score for the field of the support project may be determined as a larger value.

That is, since the server determines the overall score by considering the history of the supported project and the priority of the supported project field of the enterprise terminal, the diagnosis model is based on data considering the characteristics of the supported project field and the characteristics of the company. can be learned.

In step S305, the server may transmit diagnostic information including a comprehensive score and progress for each of the fields of the plurality of support projects to a plurality of mentor terminals.

Also, for example, the server may transmit diagnostic information including a total score and progress for each of the plurality of fields of the support project to another server. For example, the server may transmit diagnostic information including a comprehensive score and progress for each of the fields of the plurality of support projects to the enterprise terminal.

Here, the diagnosis information may include information about the progress of each of the fields of the plurality of support projects, a total score, and a degree of progress. For example, the progress information may include information on ongoing support projects and information on completed support projects.

Here, the plurality of mentor terminals may be mentor terminals determined based on the overall score and progress for each of the fields of the plurality of support projects. The plurality of mentor terminals may be mentor terminals related to a field of a specific support project. For example, a field of a support project may be determined according to the distribution of the total score and the distribution of the degree of progress among the fields of the plurality of support projects.

According to an embodiment, the server may receive curriculum information on a field of support project from two or more mentor terminals among the plurality of mentor terminals. The server may transmit information about the two or more mentor terminals to the enterprise terminal. The server may receive a selection message including a first mentor terminal selected from among the two or more mentor terminals from the enterprise terminal. The server may connect the first mentor terminal and the corporate terminal based on the selection message.

For example, the information on the two or more mentor terminals may include information on the mentor and curriculum information on the field of the support project. For example, the curriculum information for the field of the support project may include information on a support project recommended for the field of the support project and a consulting process for each support project.

Additionally, when the server does not receive curriculum information on the field of support project from the plurality of mentor terminals, the server may request schedule information from the plurality of mentor terminals. For example, the schedule information may include a duration of a support project performed by the mentor terminal and the number of supported projects performed by the mentor terminal. For example, based on receiving schedule information from the plurality of mentor terminals, the server may connect the enterprise terminal with a mentor terminal having the smallest number of ongoing support projects among the plurality of mentor terminals. For example, if there are two or more mentor terminals performing the smallest number of support projects among the plurality of mentor terminals, the server selects a mentor terminal having an earlier end date of the currently ongoing support project and the enterprise terminal. can be connected For example, when the period of the currently ongoing support project ends on the same date, the server may connect a randomly selected mentor device among mentor devices having the same date to the enterprise device.

Additionally, when the comprehensive scores of the fields of the plurality of support projects are all equal to or less than the preset score, and the progress rates of the fields of the plurality of support projects are all equal to or greater than the preset value, the field of the support project is closed. field can be determined. For example, when the comprehensive scores of the fields of the plurality of support projects are all equal to or less than a preset score, and the progress rates of the fields of the plurality of support projects are all equal to or greater than a preset value, the server provides a mentor related to the field of closure of business. Diagnostic information including overall scores and progress for each of the fields of the plurality of support projects may be transmitted to the terminals.

4 is a flowchart of a method for determining priorities of fields of a plurality of supported projects by a server based on report information related to the supported projects and progress information related to the supported projects according to an embodiment. One embodiment of FIG. 4 may be combined with various embodiments of the present disclosure.

Referring to FIG. 4 , in step S401, the server may obtain report information related to the supported project and information about the company. For example, the server may request information about the company from the company terminal and receive the information about the company from the company terminal. The server may request report information related to the supported project from the mentor terminal and receive report information related to the supported project from the mentor terminal.

Here, the report information related to the support project may include the field of the support project, a report file, a feedback score for the report, information on the mentor terminal that submitted the report, the number of reports submitted, and the standard number of submissions. For example, the feedback score for the report may be different according to the reception point of the feedback message including the feedback score for the report file from the enterprise terminal.

In step S402, the server may determine whether the number of report submissions and the reference number of submissions are the same.

In step S403, based on the fact that the number of submissions of the report is the same as the reference number of submissions, the server may transmit a checklist for checking the status of the current company related to the supported project to the company terminal.

In step S404, the server may receive progress information related to the support project obtained based on the check list from the enterprise terminal.

For example, the progress information related to the support project may include information about satisfaction with the support project for which consulting was completed, utilization of the support project for which consulting was completed, and the current state of the company related to the support project.

In step S405, the server may determine whether to perform authentication of the corporate terminal. For example, the server receives identification information from the enterprise terminal, and when the identification information and the identification information registered in the server are the same, the server may determine that the enterprise terminal has performed authentication. Here, the identification information may include a picture, voice, identification code, code, ultrasound, fingerprint, iris, current location information, and the like.

In step S406, based on the authentication of the enterprise terminal, the server may apply a weight to the degree of utilization of the consulting-completed support project and satisfaction with the consulting-completed support project. For example, an enterprise terminal may perform authentication when creating a checklist received from a server. In this case, the authentication is optional, and the weight may not be applied when the corporate terminal does not perform authentication.

In step S407, if the number of reports submitted is not the same as the standard number of submissions, the server may set progress information related to the support project as a basic value. For example, the default value may be 1.

In step S408, the server may determine whether the number of fields of the supported project is less than or equal to a preset number. For example, the number of fields of the supported project may be periodically preset in the server.

In step S409, when the number of fields of the supported project is less than or equal to the preset number, the server may determine a value n, which is the number of types of enterprises, as a first value. For example, the first value may be determined as a factorial value for a preset number.

In step S410, when the number of fields of the supported project exceeds a preset number, the server may determine the n value as a second value. For example, the second value may be determined as a preset value. A preset value may be 1000.

In step S411, the server may set basic priorities for each of the n types. For example, if an enterprise terminal is determined to be any one enterprise type among the n types, the basic priority for each of the fields of a plurality of supported projects is that a preset number of enterprise terminals included in the type of enterprise It may be set based on the frequency of the first supported project. When the number of enterprise terminals in which the field of the first supported project is the development field is the largest among the preset number of enterprise terminals, the basic priority for the development field may be set to the highest. In addition, if the field of the first supported project is the finance field, the next largest number of enterprise terminals, the finance field may be set as the second highest priority.

In step S412, the server may determine a priority through a priority determination model using a first neural network based on report information related to the supported project, progress information related to the supported project, and type of company.

For example, based on receiving progress information related to the supported project from the company terminal, the server first neural network based on report information related to the supported project, progress information related to the supported project, and the type of the company. Priority can be determined through a prioritization model using .

Specifically, the server may generate a first input vector for each field of supported projects through data pre-processing based on report information related to a plurality of supported projects, progress information related to a plurality of supported projects, and types of a plurality of companies. Based on the report information related to the plurality of support projects, the progress information related to the plurality of support projects, and the type of the plurality of companies, the server [number of completed support projects, number of ongoing support projects, average quality score of the report, average satisfaction , average utilization, and basic priority] may be generated for each field of the support project.

For example, the quality score may be determined by Equation 2 below.

In Equation 2, q is the quality score, f is a feedback score for the report, a is a weight for the feedback score, C _s is the number of the first category, and I _total is The total number of images included in the report, I _th is the number of reference images, P _total is the total number of pages of the report, P _th is the number of reference pages, and C _im is the number of second categories , and C _th may be the number of reference categories.

Here, the first category may be a category that satisfies the number of reference texts set for each category in the report. For example, the number of first categories may be the number of categories in which content is written with the number of texts equal to or greater than the number of reference texts set for each category in the report. Here, the second category may be an important category included in the report. For example, the number of second categories may be the number of important categories included in the report. Here, the criterion category may be an important category set in the report. Here, the reference page may be the total number of pages set in the report. Here, the number of reference images may be the total number of images set in the report.

For example, the weight for the feedback score may be determined according to a time point when the server receives a feedback message including the feedback score for the report from the enterprise terminal. For example, when the feedback message is received within a preset time from the report file received, the weight for the feedback score may be determined as a first weight. For example, when the feedback message is received after a preset time from the report file, the weight for the feedback score may be determined as a second weight. In this case, the first weight may be a value smaller than the second weight. Accordingly, the server may apply different weights to the feedback points according to a time point at which the feedback message including the feedback points for the report file is received from the enterprise terminal. Through this, a more objective feedback score may be reflected by reducing the feedback score when the review of the report file is not sufficient.

Therefore, the number of images included in the report, the total number of pages of the report, the number of important categories written in the report, and the number of categories that satisfy the number of standard texts set in the report increase, and the higher the feedback score, the higher the quality score. . Through this, the quality of the report can be effectively determined based on the evaluation score of the enterprise terminal along with the objective criteria for the report.

For example, the priority of the field of the correct answer support project may be the priority of the field of the support project actually input by the enterprise terminal. The priority of the fields of supported projects actually input by the enterprise terminal may be pre-stored in the server for each field of supported projects.

For example, each learning data composed of a first input vector for each field of the support project and a priority for each field of the correct answer support project is input to the first input layer of the neural network, and is input to the one or more first hidden layers and the first output. After passing through the layer, it is output as a first output vector, the first output vector is input to a first loss function layer connected to the first output layer, and the first loss function layer is connected to the first output vector and each learning The priority determination, wherein a first loss value is output using a first loss function that compares first answer vectors for data, and parameters of the first neural network are learned in a direction in which the first loss value becomes smaller. A model can be created.

That is, the server adjusts the basic priority set for the type of enterprise of the enterprise terminal through the above-described priority determination model to a priority suitable for the enterprise terminal based on report information related to the supported business and progress information related to the supported business. can

5 is a flowchart of a method for a server to transmit diagnostic information including comprehensive scores and progress for fields of a plurality of support projects to a plurality of mentor terminals and to connect a company terminal and a mentor terminal according to an embodiment. am. One embodiment of FIG. 5 may be combined with various embodiments of the present disclosure.

Referring to FIG. 5 , in step S501, the server uses a diagnosis model using a second neural network based on priorities for fields of a plurality of supported projects, report information related to the supported projects, and progress information related to the supported projects. An overall score and a progress level for each of the fields of the plurality of support projects may be determined.

Specifically, the server may generate a second input vector for each field of a supported project through data pre-processing based on a plurality of priority sets, report information related to a plurality of supported projects, and progress information related to a plurality of supported projects. Here, the priority set may be a set of priorities for fields of a plurality of support projects determined for one enterprise. Based on the plurality of priority sets, the report information related to the plurality of support projects, and the progress information related to the plurality of support projects, the server [number of completed support projects, number of ongoing support projects, average quality score of reports, average satisfaction , average utilization, value indicating the state of the company for the field of the supported project, and priority] may be generated for a plurality of companies for each field of the supported project.

Here, the value representing the state of the company in the field of the support project may be a value determined based on information about the current state of the company related to the support project. For example, the information on the current state of the company related to the support project may include values for indicators related to the field of the support project. That is, the value representing the state of the company in the field of the supported project may be a value determined by the server based on values of indicators related to the field of the supported project. For example, the larger the value representing the state of the company in the field of the support project, the better the state of the company may be. Indicators related to the field of the supported project may be preset in the server and may be different according to the field of the supported project.

For example, the total score for each of the fields of the plurality of correct answers may be determined by Equation 1 above.

For example, the progress for each of the fields of the plurality of support projects with the correct answer may be determined by Equation 3 below.

In Equation 3, p is the progress of the field of the support project, p _r is the priority of the field of the support project, n _c is the total number of fields of the plurality of support projects, and , wherein R _c is the number of completed support projects in the field of support project, R _p is the number of support projects in progress in the field of support project, and R _total is the number of supported projects for the field of support project. may be the total number.

For example, as the priority of the field of the support project is low, and the number of completed support projects and the number of support projects in progress are large, the progress of the field of the support project may be determined as a large value.

That is, since the server determines the progress by considering the history of the support project and the priority of the support project field of the enterprise terminal, a diagnosis model is created based on the data considering the characteristics of the support project field and the characteristics of the company. can be learned

For example, each learning data consisting of a second input vector for each field of a support project for a plurality of companies and a comprehensive score and progress for each field of a correct answer support project is input to the second input layer of the neural network, and the one After passing through the second hidden layer and the second output layer, a second output vector is output, the second output vector is input to a second loss function layer connected to the second output layer, and the second loss function layer A second loss value is output by using a second loss function that compares the second output vector with a second correct answer vector for each training data, and the parameters of the second neural network are such that the second loss value becomes small. The diagnostic model, which is learned in the direction, can be created.

In step S502, the server may determine whether preset conditions are satisfied for the overall score and progress for each of the fields of the plurality of support projects. For example, the preset condition may be a condition in which overall scores of the plurality of fields of the support project are equal to or less than a preset score, and progresses of the fields of the plurality of support projects are all equal to or greater than a preset value. For example, when the overall scores of the plurality of fields of the support project are all equal to or less than a preset score, and the progress rates of the fields of the plurality of support projects are all equal to or greater than a preset value, the server satisfies the preset condition. can decide to do it.

In step S503, when a preset condition is satisfied, the server may transmit diagnostic information to a plurality of mentor terminals related to a preset field. For example, the preset field may be a closed field.

That is, if the overall score of the plurality of support project fields is significantly low even though the support project has been performed with high progress in the plurality of support project fields, the current state of the company is not good, the server is related to the field of closure. Diagnosis information may be transmitted to a plurality of mentor terminals related to the field of business closure in order to connect the mentor terminal and the enterprise terminal.

In step S504, when the preset condition is not satisfied, the server may transmit diagnostic information to a plurality of mentor terminals. Here, the plurality of mentor terminals may be mentor terminals for the field of the support project determined according to the overall score and progress for each of the plurality of fields of the support project. For example, the server determines the field of the support project with the lowest overall score and progress among the fields of the plurality of support projects, and diagnoses the field of the support project with the lowest overall score and progress to a plurality of mentor terminals. information can be transmitted.

In step S505, the server may determine whether curriculum information has been received from at least one mentor terminal among a plurality of mentor terminals.

In step S506, when the server receives the curriculum information, the server may determine whether or not it has received the curriculum information from two or more mentor terminals.

In step S507, when the server receives curriculum information from two or more mentor terminals, the server transmits information on the two or more mentor terminals to the enterprise terminal, and connects the enterprise terminal with a mentor terminal selected from the enterprise terminals. .

In step S508, when the server receives curriculum information from one mentor terminal, the server may connect the corresponding mentor terminal to the enterprise terminal.

In step S509, if the server does not receive the curriculum information, the server may determine whether a mentor terminal having the smallest number of support projects for consulting exists among a plurality of mentor terminals.

In step S510, the server may connect the mentor terminal with the smallest number of support projects for consulting to the corporate terminal.

In step S511, if there are mentor terminals with the same minimum number of support projects for consulting, the server may determine whether a mentor terminal with the earliest ending date of the ongoing support project exists.

In step S512, if there is a mentor terminal with the earliest end date of the ongoing support project, the server determines the end date of the ongoing support project among mentor terminals having the same minimum number of consulting support projects. can connect the fastest mentor terminal and corporate terminal.

In step S513, if there are mentor terminals having the same earliest date on which the period of the ongoing support project ends, the server may connect the randomly selected mentor terminal.

6 is a block diagram illustrating a configuration of a server according to an exemplary embodiment. One embodiment of FIG. 6 may be combined with various embodiments of the present disclosure.

As shown in FIG. 6 , the server 600 may include a processor 610 , a communication unit 620 and a memory 630 . However, not all components shown in FIG. 6 are essential components of the server 600 . The server 600 may be implemented with more components than those shown in FIG. 6, or the server 600 may be implemented with fewer components than those shown in FIG. For example, the server 600 according to some embodiments may further include a user input interface (not shown), an output unit (not shown), etc. in addition to the processor 610, the communication unit 620, and the memory 630. .

Processor 610, typically controls the overall operation of the server (600). The processor 610 may include one or more processors to control other elements included in the server 600 . For example, the processor 610 may generally control the communication unit 620 and the memory 630 by executing programs stored in the memory 630 . Also, the processor 610 may perform the functions of the server 600 described in FIGS. 3 to 5 by executing programs stored in the memory 630 .

The communication unit 620 may include one or more components that allow the server 600 to communicate with other devices (not shown) and servers (not shown). The other device (not shown) may be a computing device such as the server 600 or a sensing device, but is not limited thereto. The communication unit 620 may receive a user input from another electronic device or data stored in an external device from an external device through a network.

For example, the communication unit 620 may transmit/receive a message for establishing a connection with at least one device. The communication unit 620 may transmit information generated by the processor 610 to at least one device connected to the server. The communication unit 620 may receive information from at least one device connected to the server. The communication unit 620 may transmit information related to the received information in response to information received from at least one device.

The memory 630 may store programs for processing and controlling the processor 610 . For example, the memory 630 may store information input to a server or information received from another device through a network. Also, the memory 630 may store data generated by the processor 610 . The memory 630 may store information input to or output from the server 600 .

The memory 630 may be a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg SD or XD memory, etc.), RAM (RAM, Random Access Memory) SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk , an optical disk, and at least one type of storage medium.

The embodiments described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic units (PLUs), microprocessors, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (5)

A method in which a server transmits diagnostic information to a mentor terminal based on report information related to a supported project and progress information related to a supported project using a neural network, the method comprising:
Obtaining report information related to the support project and information about the company;
The report information related to the support project includes the field of the support project, a report file, a feedback score for the report, information on the mentor terminal that submitted the report, the number of reports submitted, and the number of standard submissions,
Requesting progress information related to a support project from a company terminal based on the fact that the number of submissions of the report is the same as the number of submissions of the criterion;
Based on receiving progress information related to the supported project from the company terminal, a priority determination model using a first neural network based on report information related to the supported project, progress information related to the supported project, and type of company Determining priorities for fields of a plurality of support projects through;
Based on the priorities of the fields of the plurality of support projects, report information related to the support project, and progress information related to the support project, each of the fields of the plurality of support projects is performed through a diagnosis model using a second neural network. Determining the overall score and progress for; and
Transmitting diagnostic information including a comprehensive score and progress for each of the fields of the plurality of support projects to a plurality of mentor terminals,
The plurality of mentor terminals are mentor terminals determined based on the overall score and progress for each of the fields of the plurality of support projects,
The type of the company is determined based on the fields of the plurality of supported projects and information about the company,
The first neural network includes a first input layer, one or more first hidden layers and a first output layer;
Each learning data consisting of report information related to a plurality of support projects, progress information related to a plurality of support projects, types of a plurality of companies, and priorities for fields of correct answer support projects is sent to the first input layer of the neural network. The first output vector is input and output as a first output vector after passing through the one or more first hidden layers and the first output layer, the first output vector is input to a first loss function layer connected to the first output layer, and the first The loss function layer outputs a first loss value by using a first loss function that compares the first output vector with a first correct answer vector for each training data, and the parameter of the first neural network is the first loss value. The prioritization model, which is learned in a direction in which the value decreases, is created,
The second neural network includes a second input layer, one or more second hidden layers, and a second output layer;
Each learning data composed of a plurality of priority sets, report information related to a plurality of support projects, progress information related to a plurality of support projects, and a comprehensive score and progress in the field of a correct answer support project is the second input of the neural network. It is input to a layer, passes through the at least one second hidden layer and a second output layer, and is output as a second output vector, and the second output vector is input to a second loss function layer connected to the second output layer. The second loss function layer outputs a second loss value using a second loss function that compares the second output vector with a second answer vector for each training data, and the parameters of the second neural network are the first 2 The diagnostic model, which is learned in a direction in which the loss value becomes small, is created,
The overall score for the field of the correct answer support project is determined by the following equation,

In the above equation, s is the total score for the field of the support project, pr is the priority for the field of the support project, n _c is the total number of fields of the plurality of support projects, and the k is the number of reports submitted in the field of the support project, q _i is the quality score of the ith report, f _c is the sum of average satisfaction and average utilization of completed support projects in the field of support project, , wherein v _s is a value representing the state of the company in the field of the support project,
method.
According to claim 1,
Receiving curriculum information on a field of support project from two or more mentor terminals among the plurality of mentor terminals;
Transmitting information about the two or more mentor terminals to the enterprise terminal;
Receiving a selection message including a first mentor terminal selected from among the two or more mentor terminals from the enterprise terminal; and
Based on the selection message, connecting the first mentor terminal and the enterprise terminal; further comprising,
The field of the support project is a field determined by the overall score and progress for each of the fields of the plurality of support projects,
The plurality of mentor terminals are mentor terminals related to the field of the support project,
The information on the two or more mentor terminals includes information on the mentor terminal and curriculum information on the field of the support project.
method.
According to claim 2,
The information about the company includes the year of establishment of the company, information about the industry, information about manpower, information about technology development, information about marketing, information about financial status, and information about certification,
The fields of the plurality of support projects include human resources field, development field, marketing field, finance field, overseas expansion field and certification field,
The field of the support project is a field having the lowest overall score and the lowest progress value among the fields of the plurality of support projects,
method.
delete delete

Images