KR102545374B1 - Method for mice event managingusing a neural network - Google Patents

Abstract

Embodiments suggest a method and apparatus in which a server provides information on a matching rate between a participant and an observer using a neural network. In the method according to an embodiment, a first message requesting participation in a first event is received from each of a plurality of first terminals, and the first message includes information about participants related to the first terminal and products. information, and receives a second message requesting a visit to the first event from each of a plurality of second terminals, wherein the second message includes personal information about the visitor related to the second terminal, and related to the second terminal. Including information on the observer and information on the field of interest, determining the reliability of the participant and the reliability of the observer based on the information on the preset transaction details, the information on the preset transaction details is a plurality of It includes information on sellers and buyers related to transactions made in the second event, information on the transaction product that is the target of the transaction, and feedback message information on the transaction, and includes the reliability of the participant, the reliability of the observer, and the participant. A first matching rate between the participant and the observer is determined through a matching model using a neural network based on the information about, the information about the observer, the information about the product, and the information about the area of interest, and a preset matching rate is determined. Based on event information, the first matching rate is adjusted to a second matching rate, and list information about an observer whose second matching rate is greater than or equal to a preset first rate is transmitted to each of the plurality of first terminals, and the plurality of first terminals are notified. and transmitting list information about participants whose second matching rate is greater than or equal to a preset second rate to each of the second terminals of the second terminal.

Description

MICE event management method using neural network {METHOD FOR MICE EVENT MANAGINGUSING A NEURAL NETWORK}

Embodiments of the present disclosure relate to a technology for managing the entire cycle of an event hosting an MICE event between a hosting organization, participants, and visitors, and to a technology for managing the entire cycle of an MICE event using a neural network.

Meanwhile, in line with the global era, countries around the world are recognizing the added value of the MICE industry and are constructing large-scale convention facilities and exhibition halls or sparing no support for national-level activities to foster the MICE industry. The MICE industry is an industry that realizes economic benefits by hosting international conferences, exhibitions, incentive tours and events with specialized facilities such as large-scale conference halls and exhibition halls. It is an industry. Here, MICE stands for Meetings, Incentives Travel, Conventions, and Exhibitions/Events.

On the other hand, when MICE event participants and visitors participate in a MICE event held by a host organization, the MICE event participants and visitors may experience difficulties in making various reservations, business meetings, and networking. Specifically, when a MICE event participant provides a product or service as a seller, the exchange of information about which visitors or visitors are interested in the product or service and plan to purchase it requires a lot of resources from all participating companies and visitors. You have to spend your time. Similarly, if a visitor to a MICE event or an observer wants to purchase a product or service as a buyer, determining which participant is providing the product or service and whether the participant can be trusted and transacted with can be done simply by exchanging information. There are limits.

As such, there is a need for an efficient online event management system to address different needs in relation to MICE (meeting, incentive tour, convention, exhibition) events where various stakeholders gather.

Embodiments of the present disclosure may provide a technique for managing the entire cycle of an MICE event 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

According to an embodiment, in a MICE event management method in which a server manages MICE events using a neural network, each participant account is generated in response to a first subscription request message received from each of a plurality of first terminals. doing; generating an observer account in response to the second subscription request message received from each of the plurality of second terminals; generating a host organization account in response to a third subscription request message received from each of the plurality of third terminals; opening candidate events in response to an opening request message requesting opening of each of a plurality of candidate events received from each of the plurality of third terminals; providing a promotional media list including a plurality of promotional media to the plurality of third terminals, wherein the establishment request message includes information on candidate events; performing a public relations contract procedure with a public relations medium corresponding to the selection signal received from each of the plurality of third terminals; Receiving a first message requesting participation in a first event among the plurality of candidate events from each of the plurality of first terminals; The first message includes information about participants related to the first terminal, information about products, booth selection information, and ancillary service selection information, and requests to observe the first event from each of the plurality of second terminals. receiving a second message; The second message includes personal information about a visitor related to the second terminal, information about the observer related to the second terminal, and information about a field of interest, and includes participants related to the plurality of first terminals and the plurality of second messages. Determining an observer related to a terminal and providing a matching result to each of the plurality of first terminals and the plurality of second terminals through a matching model using a neural network; The plurality of first terminals and the plurality of second terminals. Settling the cost paid by the terminal; Receiving feedback information about the determined observer from the plurality of first terminals; and receiving feedback information on the confirmed participant from the plurality of second terminals.

In the method, based on information about a participant corresponding to one of the plurality of second terminals and information about the plurality of candidate events, information on a recommended event is obtained through a recommendation model using a neural network. The step of transmitting to any one of the second terminals may be further included.

The information about the candidate event may include information about the type of event, the scale of the event, the location of the event, and whether or not a VIP service is selected.

The opening may include providing a rental space list to each of the plurality of third terminals based on the size of the event and the location of the event; receiving a selection signal for a rental space included in the rental space list from each of the plurality of third terminals; and proceeding with a rental contract procedure between each of the plurality of third terminals and an owner of a rental space corresponding to the selection signal.

The method may include providing selection information of promotional media corresponding to the selection signal to the plurality of first terminals; and receiving a selection signal corresponding to the selection information from one or more first terminals among the plurality of first terminals, wherein the selection information of the promotional media indicates whether to include a participant together with a hosting organization in promotional content. You can include optional information.

The step of creating each participant account includes providing a QR code unique to the participant account to each corresponding first terminal, and the step of creating each participant account includes a QR code unique to the participant account. It may include providing a code to each corresponding second terminal.

The method may include receiving identification information of a QR code unique to an observer account from any one of the plurality of first terminals; and mapping information about the observer corresponding to the identification information to a participant account of the first terminal that transmitted the identification information.

The providing of the matching result may include determining reliability of the determined participant and reliability of the determined observer based on information on predetermined transaction details; The information on the preset transaction details may include information on sellers and buyers related to transactions made in a plurality of second events, information on a transaction product that is a target of the transaction, and feedback message information on the transaction.

The method is based on the confidence level of the determined participant, the confidence level of the determined observer, the information about the determined participant, the information about the determined observer, the information about the product, and the information about the field of interest. determining a first matching rate between the confirmed participant and the confirmed observer through a matching model using the neural network; adjusting the first matching rate to a second matching rate based on preset event information; transmitting, to each of the plurality of first terminals, list information about an observer whose second matching rate is greater than or equal to a preset first rate; and transmitting, to each of the plurality of second terminals, list information about a participant whose second matching rate is greater than or equal to a preset second rate.

The preset event information, in each of the plurality of second events, information on the size of the participant's booth, information on the number of visitors to the participant's booth, information on the stay time of the visitors who visited the participant's booth, Information on the number of booths of participants visited by the visitor and information on participants and visitors with whom transactions have been concluded, and the second matching rate is the number of participants, the size of the booths of the participants, and the length of stay of the visitors who visited the booths of the participants , may be determined based on the number of visitors to the booth of the participant, the number of visitors, and the number of participants visited by the visitor.

The method may include receiving a first matching success message from at least one first terminal; Receiving a second matching success message from at least one second terminal matched with the at least one first terminal; Based on receiving the first matching success message and the second matching success message, the preset transaction related to the at least one second terminal matched with the at least one first terminal to the at least one first terminal. Transmitting information about details; Based on receiving the first matching success message and the second matching success message, the preset transaction related to the at least one first terminal matched with the at least one second terminal to the at least one second terminal. A step of transmitting information about the details may be further included.

The method may include receiving a first feedback message for the at least one second terminal from the at least one first terminal; Transmitting information about an event related to the at least one first terminal to the at least one first terminal based on the first feedback message; Receiving a second feedback message for the at least one first terminal from the at least one second terminal; And based on the second feedback message, further comprising transmitting information about an event related to the at least one second terminal to the at least one second terminal, wherein the first feedback message comprises the at least one second terminal. Text data for ratings and feedback for the second terminal may be included, and the second feedback message may include text data for ratings and feedback for the at least one first terminal.

The neural network includes an input layer, one or more hidden layers, and an output layer, and includes reliability of a plurality of participants, reliability of a plurality of observers, information about a plurality of participants, information about a plurality of observers, and reliability of a plurality of products. Each learning data composed of information on information, information on a plurality of areas of interest, and correct answer matching rates is input to the input layer of the neural network, passes through one or more hidden layers and output layers, and is output as an output vector. A vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each training data, and outputs a loss value, and the parameters of the neural network The matching model, which is learned in a direction in which the loss value decreases, may be generated.

According to embodiments, the MICE event management method may manage the entire cycle of the MICE event using a neural network.

According to embodiments, the MICE event management method may provide an efficient online event management technology for solving different needs in relation to an MICE event in which various interested parties gather.

According to the embodiments, the server determines the reliability of the participant and the observer based on the information on the transaction details previously set related to the previous MICE event, and when the server determines the matching rate between the participant and the observer through the neural network, The server may increase the reliability of the determined matching rate by reflecting the reliability level in addition to the information on the participant and the information on the observer.

According to embodiments, the server provides list information about participants and list information about visitors based on a matching rate between participants and visitors predicted through a neural network, so that participants are interested in their products and have a probability of purchasing them. This high observer can be identified, and the observer can facilitate judgment as to which participant is providing goods or services, and whether such participant can be trusted and transacted.

According to embodiments, the server may determine a more reliable matching rate by adjusting a matching rate between predicted participants and observers based on information related to an upcoming MICE event.

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 is a flowchart illustrating the entire operation of the MICE event management method according to an embodiment.
4 is a flowchart illustrating an operation between a server and a terminal of an MICE event management method according to an embodiment.
5 is a diagram showing the structure of a recommendation model of the MICE event management method according to an embodiment.
6 is an exemplary diagram of information on a recommended event recommended by a recommendation model according to an embodiment. FIG. 7 illustrates a method for a server to provide information on a matching rate between a participant and an observer according to an embodiment. indicate
8 is a diagram illustrating a method for determining, by a server, reliability or reputation of a participant or observer based on information on a preset transaction history according to an embodiment.
9 is a diagram of a method for transmitting list information about participants and list information about observers by a server according to an embodiment.
10 is a diagram for a method in which a server reflects a feedback message according to an embodiment.
11 is an example of a matching model using a neural network according to an embodiment.
12 is a signal exchange diagram illustrating a procedure for a server to transmit information about an event related to a terminal according to an embodiment.
13 is an example of a screen on which list information on participants is displayed and a screen on which list information on observers is displayed according to an embodiment.
14 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, a legacy communication module). 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 work 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™, Tizen™, or Bada™. 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. Here, an edge or link interconnecting an input node and an output node has a weight that can be variably applied by a user or an algorithm to perform a function desired by the neural network. 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 is a flowchart illustrating the entire operation of the MICE event management method according to an embodiment.

Due to the corona issue, face-to-face meetings or business trips are continuously restricted, and inevitable meetings tend to be digitally converted. In the case of conferences, seminars, fairs, or events where a large number of people or companies gather, it is inefficient for several stakeholders to perform individual preliminary work offline.

Host organizations generally hope for a successful event through the participation of various companies and individuals. However, the hosting organization may face the problem of operating costs at each stage of the event preparation process. For example, in order to hold an event, a high cost burden may be incurred when performing offline in parts such as search for an event venue, rental, promotion, meeting with participants and visitors, and the like. In addition, even when each step is conducted online to reduce costs, the hosting organization may have difficulty securing operational personnel for opening a site related to the event or developing and maintaining an operation promotion registration system.

Participants generally aim to expand business partners such as buyers and affiliates by effectively providing information about their products or services. Even after providing company and product information, participants go through the process of revising and may incur additional costs. Participants may go through the process of modifying the promotional materials even after they are produced, and additional costs may be incurred.

Visitors generally aim to acquire various business information, grasp product and service information, and build networking between individuals and companies. Observers may have difficulty collecting corporate information and building personal connections offline at the event. For example, it may be difficult for a participant to select and visit only a desired booth, and when visiting a plurality of booths, it may be inconvenient because the amount of promotional materials for a company increases. In addition, participants may experience difficulties in networking with observers after the event ends.

As such, there is a need for an efficient online event management system to address different needs in relation to MICE (meeting, incentive tour, convention, exhibition) events where various stakeholders gather. Here, MICE encompasses international conferences, corporate meetings, tourism, conventions, exhibitions and fairs, and many other events.

According to one embodiment, the MICE event management system may provide an event management solution that provides convenience in hosting, participating in, and managing events. The MICE event management system can provide an efficient solution from recruitment to operation management. The MICE event management system can support the entire cycle of MICE events, from contracting a good booth to managing visiting customers through CRM. According to one embodiment, the MICE event management system can manage the entire cycle of MICE events using a neural network.

According to one embodiment, the MICE event management system may include a server, a plurality of first terminals, a plurality of second terminals, one or more third terminals, and a network. A terminal used by the hosting organization may be referred to as a third terminal, a terminal used by a participant may be referred to as a first terminal, and a terminal used by an observer may be referred to as a second terminal. The MICE event management system can provide users with applications implementing MICE event management methods. The user can install the application on the terminal and execute the MICE event management method through the application.

According to one embodiment, at step 301, the MICE event management system may perform account creation. The MICE event management system may create an account in response to a user's account creation request signal. Here, the user may include a host organization, participants, and observers. Organizing organization means the organization hosting the event. A participant is a person or company that participates in an event, sets up a booth, and promotes its own products or services. An observer is a person or business that attends an event and visits one or more participants.

According to an embodiment, the server may create a participant account in response to the first subscription request message received from each of the plurality of first terminals. The server may create each observer account in response to the second subscription request message received from each of the plurality of second terminals. The server may provide each of the first terminals corresponding to a QR code unique to the participant account. Hereinafter, participants and observers may include corporations or individuals.

The server may create a hosting organization account in response to the third subscription request message received from each of the plurality of third terminals. The order of account creation from the first terminal to the third terminal is exemplary and not limited thereto, and each account may be created in a different order. The server may provide each of the second terminals corresponding to a QR code unique to the observer account.

According to one embodiment, in step 303, the MICE event management system may perform an event establishment. The hosting organization may apply for opening an event using an application through a third terminal. The hosting organization can enter optional information related to the opening of the event. For example, selection information related to opening an event may include event theme, event location, event scale, event timing, budget, expected number of participants and visitors, whether or not a public relations company connection service has been requested, and the like.

The server may open each of the candidate events in response to an opening request message requesting opening of each of the plurality of candidate events received from each of the plurality of third terminals. The establishment request message may include information about candidate events. Information about the candidate event may include information about the type of event, the size of the event, the location of the event, and whether a VIP service is selected.

In order to open an event, first, the server may provide a rental space list to each of a plurality of third terminals based on the size and location of the event. The server may receive a selection signal for a rental space included in the rental space list from each of the plurality of third terminals. The server may proceed with a rental contract procedure between each of the plurality of third terminals and the owner of the rental space corresponding to the selection signal.

According to one embodiment, in step 311, the MICE event management system may perform a public relations company connection service. In step 303, if the hosting organization has applied for a public relations agency connection service, the MICE event management system may transmit a list of a plurality of public relations services to the third terminal. The server may provide a publicity media list including a plurality of publicity media to a plurality of third terminals.

The hosting organization may select one publicity service through the third terminal and make a payment according to a preset payment method. The MICE event management system can support the contract process between the public relations company providing the selected public relations service and the host organization online. The server may proceed with a public relations contract procedure with a public relations medium corresponding to a selection signal received from each of a plurality of third terminals.

In the public relations agency connection service, first, the server may provide selection information of promotional media corresponding to a selection signal to a plurality of first terminals. Here, the promotional media selection information may include information for selecting whether or not to include a participant together with a hosting organization in the promotional content. The server may receive a selection signal corresponding to selection information from one or more first terminals among a plurality of first terminals.

According to one embodiment, at step 313, the MICE event management system may perform event curation. The MICE event management system can provide event curation services to attendees. The MICE event management system can provide AI event recommendation services based on the field and experience of the attendees.

The MICE event management system can build a database by collecting data such as age, gender, job group, and preferred events of users. The MICE event management system can recommend events that users are likely to participate in using a neural network based on the collected data. For example, the MICE event management system can recommend events that the current user is likely to participate in based on data such as preferences, time of participation, and related fields of events attended by other users in the same occupational group as the user through a neural network. . The MICE event management system may recommend an event by additionally inputting the analysis result of the data analyst based on the current user's data to the neural network. The recommendation result may be reflected in the database as new data.

The server transmits information about a recommended event to one of a plurality of second terminals through a recommendation model using a neural network based on information about a participant corresponding to any one of a plurality of second terminals and information about a plurality of candidate events. It can be transmitted to 2 terminals.

For example, the neural network may include an input layer, one or more hidden layers, and an output layer. Each learning data composed of information on a plurality of participants, information on a plurality of candidate events, and information on a correct answer recommendation event is input to the input layer of the neural network, passes through one or more hidden layers and output layers, and is output. It is output as a vector, and the output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct vector for each learning data. and the parameters of the neural network may be learned in a direction in which the loss value becomes smaller.

This recommendation service may be provided in various forms. For example, the server may provide the user with a recommended event suitable for the user among events scheduled for each period as part of a subscription service.

The MICE event management system can provide one-touch schedule management service. The MICE event management system can curate events in your interests and automatically add them to your personal calendar. Schedules can be automatically shared with team members.

According to one embodiment, in step 305, the MICE event management system may receive a participation application and a visit application. The server may receive a first message requesting participation in a first event among a plurality of candidate events from each of the plurality of first terminals. The first message may include information about participants related to the first terminal, information about products, booth selection information, and ancillary service selection information. The server may receive a second message requesting a visit to the first event from each of the plurality of second terminals. The second message may include personal information about the visitor related to the second terminal, information about the visitor related to the second terminal, and information about fields of interest.

According to one embodiment, in step 307, the MICE event management system may perform a participant determination and matching service. The server may determine participants associated with a plurality of first terminals and observers associated with a plurality of second terminals. The server may provide matching results to each of the plurality of first terminals and the plurality of second terminals through a matching model using a neural network. This matching service is described in detail in FIG. 7 .

The MICE event management system can filter the event information the user wants and register the selected event information. The MICE event management system can assist in selecting events to visit, booking consultation visits, and exchanging business cards.

The MICE event management system can provide a one-stop reservation service. The MICE event management system can recommend or automate hotel and flight reservations based on the location of the venue where the event is being held. The MICE event management system can even support transportation reservations in the country or city of the venue.

The MICE event management system can assist with the management of visit information, participants' business cards, and arrangement of consultation contents. The MICE event management system can provide a CRM solution, a personal networking management system capable of exchanging business cards, managing visit records, and managing the relationship network between companies and buyers related to exhibitions. The MICE event management system can provide an ERP solution, an integrated resource information linkage system that integrates and manages settlement information such as schedule, visit information, and expenditure details.

In the process of exchanging business cards, the server may receive identification information of a QR code unique to the visitor account from any one of the first terminals among the plurality of first terminals. The server may map information about the observer corresponding to the identification information to a participant account of the first terminal that has transmitted the identification information.

According to one embodiment, at step 309, the MICE event management system may perform cost settlement and feedback information collection. The MICE event management system can provide smart automatic settlement services to participants and observers. The MICE event management system can automatically save receipts in a pre-set file format for each expenditure. Receipts stored in this way can be printed out on a printer linked to the app or sent to the user's e-mail address. The server may settle costs paid by the plurality of first terminals and the plurality of second terminals.

The server may receive feedback information on the determined observer from the plurality of first terminals. The server may receive feedback information on the determined participant from a plurality of second terminals. In this way, the MICE event management system can provide a personalized event management platform ranging from networking between participants and observers to cost settlement and establishment of feedback information. Additionally, the MICE event management system can provide a webinar service. Webinar is a compound word of web and seminar, and means an online seminar. The MICE event management system may send invitations to webinars to participants or observers. The MICE event management system can share the recordings through the webinar service to those who have registered to participate in the event but have not been able to attend the event. The MICE event management system can collect immediate feedback from webinar participants in real time, reflect it, and use it for future webinar event planning. The MICE event management system can plan Q&A sessions during webinars to provide immediate answers to participants' questions or concerns. The MICE event management system provides a platform for users to attend real-time webinars and provides post-participation services for past events. By automatically generating business cards of users, it is possible to provide a simple transfer service of business cards between attendees. The MICE event management system can also provide the ability to follow user accounts corresponding to celebrities.

4 is a flowchart illustrating an operation between a server and a terminal of an MICE event management method according to an embodiment.

According to one embodiment, the MICE event management system may include a server, one or more first terminals, one or more second terminals, one or more third terminals, and a network. The first terminal may be a participant's terminal, the second terminal may be an observer's terminal, and the third terminal may be a terminal of the hosting organization.

According to one embodiment, the server may interwork with an application implementing the MICE life cycle management platform. The server may manage the reception of data necessary for the operation of the application, the storage of data, the processing of data, and the transmission of data.

According to one embodiment, in step 401, the third terminal may transmit an establishment request signal for opening an MICE event to the server. The opening request signal may include selection information related to event opening. For example, the opening request signal may include the subject of the event, the venue of the event, the size of the event, the time of the event, the budget, the expected number of participants and visitors, and whether or not a public relations company connection service has been requested.

According to one embodiment, in step 403, the server may perform MICE event establishment in response to the establishment request signal. The server may determine whether to apply for a public relations agency connection service based on the opening request signal. When the public relations agency connection service is requested, steps 405 and 407 are performed, and when the public relations agency connection service is not requested, the corresponding steps may be omitted.

According to an embodiment, in step 405, the server may transmit a promotion option list to the third terminal. The server may provide selection information of promotional media corresponding to the selection signal to the plurality of first terminals. Here, the promotional media selection information may include information for selecting whether or not to include a participant together with a hosting organization in the promotional content. For example, the promotional content may be a promotional video for the event itself or a promotional video for the event and major participants.

According to an embodiment, in step 407, the third terminal may select a promotion option and transmit a selection signal to the server. Based on the selection signal, the server may online support a contract procedure between a corresponding public relations company and a hosting organization of the third terminal.

According to an embodiment, in step 409, the server may perform event curation and transmit it to the second terminal. The server may build a database by collecting data such as a user's age, gender, job group, and preferred events. The server may recommend an event in which the user is highly likely to participate by using a neural network based on the collected data. For example, the server may recommend an event in which the current user is highly likely to participate based on data such as preference, time of participation, related field, etc. of events in which other users of the same occupational group as the user participated, through a neural network. The server may recommend an event by additionally inputting an analysis result of a data analyst based on current user data into the neural network. The recommendation result may be reflected in the database as new data.

According to one embodiment, in step 411, the first terminal may transmit a participation application for a specific event to the server. The first terminal may transmit a first message requesting participation in a first event among a plurality of candidate events. The first message may include information about participants related to the first terminal, information about products, booth selection information, and ancillary service selection information.

According to one embodiment, in step 413, the second terminal may transmit a participation application for a specific event to the server. The second terminal may transmit a second message requesting observation of the first event from each of a plurality of second terminals. The second message may include personal information about the visitor related to the second terminal, information about the visitor related to the second terminal, and information about fields of interest.

According to an embodiment, in step 415, the server may determine participants associated with a plurality of first terminals and observers associated with a plurality of second terminals. The server may determine matching rates of the plurality of participants and the plurality of observers determined by using the matching model.

According to an embodiment, in step 417, the server may provide the determined matching result to the first terminal. The server may transmit a list of observers whose matching rate is greater than or equal to a threshold value to the first terminal. According to an embodiment, in step 419, the server may provide the determined matching result to the second terminal. The server may transmit a list of participants whose matching rate is greater than or equal to the threshold to the second terminal.

Afterwards, a confirmed event is held at the scheduled time, and participants and subcontractors can attend.

According to an embodiment, in step 421, the observer may exchange business cards with the participant's first terminal through the second terminal. For example, the first terminal may scan the QR code assigned to the user account of the second terminal and transmit the scan result to the server. The server may recognize the QR code and map business card information of the second terminal and business card information of the first terminal. The server may transmit the mapping result to the first terminal and the second terminal.

According to an embodiment, in step 423, the first terminal may transmit information about the cost paid before and after the event to the server. For example, the first terminal may transmit not only online payment information but also an image of a receipt issued offline to the server.

According to one embodiment, in step 425, the second terminal may transmit information about the cost paid before and after the event to the server. For example, the second terminal may transmit not only online payment information but also an image of a receipt issued offline to the server.

According to one embodiment, in step 427, in response to the termination of the offline event, the server may terminate the corresponding event online. With the end signal, subsequent steps can be executed.

According to one embodiment, in step 429, the server may settle the payment information received from the first terminal and transmit cost settlement information to the first terminal. For example, cost settlement information may include settlement results for each item based on supply value, value-added tax, and other taxes, and may be output as a pdf file. For example, the expense settlement information may convert each receipt into the same template and output each converted receipt as a pdf file.

According to one embodiment, in step 431, the server may settle the payment information received from the second terminal and transmit cost settlement information to the first terminal. For example, cost settlement information may include settlement results for each item based on supply value, value-added tax, and other taxes, and may be output as a pdf file. For example, the expense settlement information may convert each receipt into the same template and output each converted receipt as a pdf file.

According to one embodiment, in step 433, the first terminal may transmit feedback information to the server. According to one embodiment, in step 435, the second terminal may transmit feedback information to the server. Feedback information may consist of ratings and text. Ratings may be composed of ratings for each product or service of each company. In the case of text, it may be limited to text of a certain length and may be composed of preset positive and negative words. For example, positive words may include words such as “neat,” “friendly,” or “satisfying.” For example, negative words may include words such as “unfortunate,” “unkind,” or “unsatisfactory.”

According to one embodiment, in step 437, the server may generate feedback data based on the received feedback information. Feedback data may be used as training data of a matching model. Thereafter, the matching model may provide a matching result more suitable for the user's taste.

5 is a diagram showing the structure of a recommendation model of the MICE event management method according to an embodiment.

Referring to FIG. 11 , a neural network may include an input layer 510, one or more hidden layers 520, and an output layer 530. Each learning data composed of information on a plurality of participants, information on a plurality of candidate events, and information on a correct answer recommendation event is input to the input layer of the neural network, passes through one or more hidden layers and output layers, and is output. It is output as a vector, and the output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct vector for each learning data. and the parameters of the neural network may be learned in a direction in which the loss value becomes smaller.

The server may perform data pre-processing on information on a plurality of participants, information on a plurality of candidate events, and information on a correct answer recommendation event. The server may perform data pre-processing based on sales, number of employees, number of patents and product codes of a plurality of participants, timing, location, and subject of a plurality of candidate events. That is, the server performs data preprocessing based on sales, number of employees, number of patents, and product codes for a plurality of participants. A second input vector composed of the input vector and [value obtained by converting the time, location, and subject code of the candidate event] may be generated. In addition, a loss value is output using a loss function that compares the output vectors of the first input vector and the second input vector with the vector value of the correct answer recommendation event, and the parameter of the neural network is the direction in which the loss value decreases. can be learned as

6 is an exemplary diagram of information about a recommended event recommended by a recommendation model according to an embodiment.

The server may build a database by collecting data such as a user's age, gender, job group, and preferred events. The server may recommend an event in which the user is highly likely to participate by using a neural network based on the collected data. For example, the server may recommend an event in which the current user is highly likely to participate based on data such as preference, time of participation, related field, etc. of events in which other users of the same occupational group as the user participated, through a neural network. The server may recommend an event by additionally inputting an analysis result of a data analyst based on current user data into the neural network.

Referring to FIG. 6 , a screen 610 is an example of a recommended event list provided through a recommendation model. For example, the recommended event list may provide event name, event theme, schedule, and location information. The ranking may represent a ranking of recommended events that are determined to be most appropriate for the observer.

For example, in the recommended event list, the first priority is an aviation fair, which may be an event held with a theme of new aircraft, the schedule is from 7.31 to 8.5, and the location may be COEX. In the list of recommended events, the second priority is the new engine seminar, which may be an event held with the theme of aircraft engines, the schedule is July 30, and the location may be KINTEX. In the list of recommended events, the third priority is an aviation academic seminar, which may be an event held with the theme of the latest trends in aircraft-related technology, the schedule is August 21, and the location may be xx university.

7 illustrates a method in which a server provides information on a matching rate between a participant and an observer according to an embodiment. One embodiment of FIG. 7 may be combined with various embodiments of the present disclosure.

Referring to FIG. 7 , in step S701, a server (eg, the server 108 of FIG. 1 ) requests participation in a first event from each of a plurality of first terminals (eg, the electronic device 101 of FIG. 1 ). A first message may be received. Here, the first event is an event related to MICE. For example, the first message may include information about a product and information about a participant related to the first terminal. Also, for example, the first message may include information on the first event. Here, the information on the participant related to the first terminal includes the name of the participant, information on the field of the participant, information on the sales of the participant, information on the number of employees of the participant, the date the participant was established, and information about the technology possessed by the participant. It may include at least one of information, the number of registered patents possessed by the participant, and information on companies with which the participant has transacted prior to the first event. Here, the information about the product is information about the product that the participant is selling. Here, products include goods or services. For example, information about a product may include a name of the product, a product classification code for the product, or a service classification code for the product. Here, information on product classification codes or service classification codes may be preset in the server. The product classification code is a code for classifying products. The service classification code is a code for classifying a service. The product classification code may be a harmonized system code (HS) code or a NICE international product classification code. Here, the information on the first event may include the area of the booth desired by the participant and the location of the booth desired by the participant.

In step S702, the server may receive a second message requesting a visit to the first event from each of a plurality of second terminals (eg, the electronic device 102 of FIG. 1). For example, the second message may include personal information about the visitor related to the second terminal, information about the visitor related to the second terminal, and information about fields of interest. Also, for example, the second message may include information about a desired date and time to attend the first event from among schedules for the first event. For example, personal information about a visitor may include at least one of a visitor's name, a position of the visitor, a department of the visitor, or a contact information of the visitor. For example, information on the observer related to the second terminal may include the name of the observer, information on the field of the observer, information on sales of the observer, information on the number of employees of the observer, the date the observer was established, or the It may include at least one of information about a company transacted prior to the event. The information on the field of interest is information on the field of interest to the viewer. For example, the information on the field of interest may include at least one of information on the purpose of observation, a name of a product of interest, a product classification code of the product of interest, or a service classification code of the product of interest. Information on the purpose of visit is information indicating the purpose of the visit, such as investment, product purchase, and cooperation.

In step S703, the server may determine a trust level for the participant and a trust level for the observer based on the information on the preset transaction details.

Here, the information on the preset transaction details is information on transactions between participants and observers in a plurality of second events performed before the first event. Here, the plurality of second events are fields identical to or similar to those of the first event. A plurality of second events corresponding to fields identical to or similar to those of the first event may be pre-input into the server. A second event included in the same cluster as the first event may be determined as an event in the same or similar field.

Additionally, the field of the event may be determined as n fields through a clustering technique using a neural network based on participant information on a plurality of events collected through web crawling. Here, information on participants in a plurality of events may include industry classification codes for companies participating in the event. Web crawling is an operation of collecting data from multiple web sites. Clustering may refer to unsupervised learning that groups data having similar properties into a certain number of clusters. For example, the industry classification code may include a major classification code, a middle classification code, and a small classification code. The field of the event may be determined as n types through a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique. DBSCAN assumes that if a specific point belongs to a cluster, it must be located close to many other points in the cluster, and radius and minimum points can be used for this calculation. Additionally, the element is a value obtained by converting the industry classification code of the participant collected through the web crawling into three-dimensional coordinates. The 3D coordinates may be [values for a large classification code, values for a middle classification code, and values for a small classification code]. For example, the diameter may be a radius based on a specific data element, and may be referred to as a dense area. The minimum element may indicate how many elements are required 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. Through the 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. As a result, the field of the event is not determined as a fixed type, but is dynamically changed according to the company information currently collected by the server, so that more diverse fields of the event can be determined. In addition, events may be classified based on participants by determining n types of events through industry classification codes of companies participating in the event.

Information on preset transaction details may be pre-input into the server. For example, the information on the preset transaction details includes information on sellers and buyers related to transactions made in a plurality of second events, information on a transaction product that is the target of the transaction, and information on the transaction. Information on the seller and buyer related to the transaction includes information on contracts concluded for each of the plurality of second events, information on participants or observers corresponding to the seller in transactions made by the contracts, and information on the buyer. It may contain information about participants or observers who For example, the product information on the transaction product that is the target of the transaction may include information such as the name, transaction amount, and transaction period of the product that is the transaction target. For example, the feedback message information on the transaction may include a feedback message transmitted from the counterpart seller or the counterpart buyer to the server after the transaction is completed.

Here, the reliability of the participant may include reliability when the participant is a seller and reliability when the participant is a buyer for transactions made in a plurality of second events. Here, the reliability of the observer may include reliability when the observer is a seller and reliability when the observer is a buyer for transactions made in a plurality of second events. Here, reliability is a score indicating how diligently the transaction was performed. For example, the feedback message information may include a feedback message for the seller and a feedback message for the buyer. For example, the feedback message may include text data about ratings and feedback.

Additionally, transaction information for participants and transaction information for observers may be determined based on the information on the preset transaction details. For example, the transaction information for the participant includes information about the total number of transactions related to the participant in a plurality of second events, information about a feedback message for the participant, and a transaction identical to the product among transactions related to the participant. It may contain information about the number of transactions related to the product. For example, the transaction information for the observer may include information about the total number of transactions related to the observer in a plurality of second events, information about a feedback message for the observer, and a transaction identical to the product among transactions related to the observer. It may contain information about the number of transactions related to the product.

The reliability of the participant or the reliability of the observer may be determined by Equation 1 below.

In the case of the reliability of the participant, in Equation 1, R is the reliability, n _d is the total number of transactions related to the participant in the plurality of second events, and n _a is the reliability of the participant. The number of feedback messages including any one of a plurality of preset first words among feedback messages, and n _m is the number of messages including any one of a plurality of preset second words among feedback messages for the participant. number, wherein n _e is the number of transactions related to the same transaction product as the product among transactions related to the participant in the plurality of second events, and G is the number of ratings included in feedback messages for the participant. is the average value for The rating may be a value greater than 0 and less than or equal to 10.

In the case of trust for the observer, in Equation 1, R is the trust, n _d is the total number of transactions related to the observer in the plurality of second events, and n _a is the trust for the participant. The number of feedback messages including any one of a plurality of preset first words among feedback messages, and n _m is the number of messages including any one of a plurality of preset second words among feedback messages for the participant. number, wherein n _e is the number of transactions related to the same trade product as the product among transactions related to the observer in the plurality of second events, and G is the number of ratings included in feedback messages for the observer. is the average value for

The plurality of preset first words may be pre-input into the server. For example, the plurality of preset first words may include positive words. For example, positive words may include words such as “neat,” “friendly,” or “satisfying.”

The plurality of preset second words may be pre-entered into the server. For example, the plurality of preset first words may include negative words. For example, negative words may include words such as “unfortunate,” “unkind,” or “unsatisfactory.”

The greater the positive feedback, the higher the reliability, and the greater the negative feedback, the lower the reliability. In addition, since the higher the feedback on the product sold at the current event, the higher the reflection rate on the rating, so the reliability can be more accurate. As a result, it is possible to determine the reliability of the participant more effectively by reflecting positive and negative feedback based on the text of the feedback as well as the rating included in the feedback message and reflecting the ratio of the product for which the transaction was made.

In step S704, the server performs matching using a neural network based on the reliability of the participant, the reliability of the observer, the information about the participant, the information about the observer, the information about the product, and the information about the field of interest. A first matching rate between the participant and the observer may be determined through a model.

For example, the neural network may include an input layer, one or more hidden layers, and an output layer. Reliability of multiple participants, reliability of multiple observers, information on multiple participants, information on multiple observers, information on multiple products, information on multiple fields of interest, and correct answer matching rate. Training data is input to the input layer of the neural network, passes through the one or more hidden layers and output layers, and is output as an output vector. The output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each training data, and the parameters of the neural network are learned in a direction in which the loss value becomes smaller, and the matching model is generated. can

In step S705, the server may adjust the first matching rate to a second matching rate based on preset event information.

Here, the preset event information is information on the plurality of second events. Pre-set event information may be pre-input into the server. For example, the preset event information, in each of the plurality of second events, information about the size of the participant's booth, information about the number of visitors to the participant's booth, and the length of stay of the visitors who visited the participant's booth Information about booths, information on the number of booths of participants visited by the observer, and information on participants and observers with whom transactions have been concluded. For example, the information on the size of the participant's booth may include the area and location of the booth installed by the participant at each of the plurality of second events. The information about the staying time of the visitor who visited the participant's booth is information on the time the visitor who visited the participant's booth stayed in the participant's booth at each of the plurality of second events. The information on the number of booths of the participants visited by the observer is the total number of booths of the participants visited by the observer in one second event for each of the plurality of second events. If there are a plurality of visitors to one visitor, the total number of booths of the participants visited by the visitors at the second event is the total number of booths of the participants visited by each visitor at the second event. For example, the information on the participant and observer with whom the transaction was concluded may include the name of the company with which the transaction was concluded and the name of the observer with whom the transaction was concluded.

The second matching rate may be set by Equation 2 below.

In Equation 2, m ₂ is the second matching rate, n is the number of participants participating in the first event, s is the average size of booths of matched participants, and si is the i-th The average size of the participant's booth, t is the average staying time of visitors to the booth of the matched participant, ti is the average staying time of the visitors visiting the i-th participant's booth, and v is the booth of the matched participant is the average number of visitors to , vi is the average number of visitors to the booth of the ith participant, k is the number of visitors to the first event, and p is the booth of the participant visited by the matched visitor. is the average number of , pj is the average number of participant booths visited by the j-th observer, and m ₁ is the first matching rate.

For example, with respect to the first matching rate between participants and visitors, if the average size of the booths of the matched participants is larger than the average size of the booths of the participants in the plurality of second events, the first matching rate is higher. can be adjusted For example, with respect to the first matching rate between participants and visitors, if the average staying time of visitors to the booth of the participant is greater than the average staying time of visitors to the booths of the participants in the plurality of second events, the matching rate It can be adjusted higher than this. For example, with respect to the first matching rate between participants and visitors, if the average number of visitors to the participant's booth is greater than the average number of visitors to the booths of the participants in the plurality of second events, the matching rate is lower. can be adjusted For example, with respect to the first matching rate between participants and visitors, if the average number of booths of the participants visited by the matched visitors is greater than the average number of booths of the participants visited by the visitors in the plurality of second events, the matching rate is can be adjusted lower. That is, through this, the matching rate may be adjusted by reflecting various variables related to a plurality of second events in addition to information on participants and information on observers.

In step S706, the server may transmit list information about an observer whose second matching rate is greater than or equal to a preset first rate to each of the plurality of first terminals. The preset first ratio may be determined differently according to participants related to the first terminal. For example, the list information on the observer may include at least one of names of the plurality of observers, fields of the plurality of observers, or matching rates between the plurality of observers and participants related to the first terminal.

In step S707, the server may transmit list information about participants whose second matching rate is greater than or equal to a preset second rate to each of the plurality of second terminals. The preset second ratio may be differently determined according to an observer associated with the second terminal. For example, the list information on the participant includes at least one of names of the plurality of participants, fields of the plurality of participants, locations of booths of the plurality of participants, or matching rates between the plurality of participants and the observer related to the second terminal. can include

According to an embodiment, the server may receive a first matching success message from at least one first terminal. The server may receive a second matching success message from at least one second terminal matched with the at least one first terminal. Here, the first matching success message may include information about the observer with whom the transaction was concluded and information about the concluded transaction. Here, the second matching success message may include information about the participant for whom the transaction was concluded and information about the concluded transaction. Based on receiving the first matching success message and the second matching success message, the server sends the at least one first terminal to the dictionary related to the at least one second terminal matched with the at least one first terminal. Information on set transaction details can be transmitted. The information on the preset transaction details related to the at least one second terminal is information on transactions performed by the at least one second terminal at a plurality of second events prior to the first event. For example, the information on the preset transaction details related to the at least one second terminal may include information about a product of a transaction related to the at least one second terminal and feedback message information about the transaction. .

Based on receiving the first matching success message and the second matching success message, the server sends the at least one second terminal to the dictionary associated with the at least one first terminal matched with the at least one second terminal. Information on set transaction details can be transmitted. The information on the preset transaction details related to the at least one first terminal is information on transactions performed by the at least one first terminal at a plurality of second events prior to the first event. For example, the information on the preset transaction details related to the at least one first terminal may include information on a product of a transaction related to the at least one first terminal and feedback message information on the transaction. .

Information on a participant related to the at least one first terminal and an observer related to the at least one second terminal may be determined as learning data of the matching model based on the first matching success message and the second matching success message. there is.

The server may receive a first feedback message for the at least one second terminal from the at least one first terminal. Based on the first feedback message, the server may transmit information about an event related to the at least one first terminal to the at least one first terminal. The information on an event related to the at least one first terminal is information on an event determined based on a field of a participant related to the at least one first terminal. Information on the event is information on a plurality of events scheduled to be performed after the first event.

The server may receive a second feedback message for the at least one first terminal from the at least one second terminal. Based on the second feedback message, the server may transmit information about an event related to the at least one second terminal to the at least one second terminal. The information on an event related to the at least one second terminal is information on an event determined based on a field of a participant related to the at least one second terminal. Information on the event is information on a plurality of events scheduled to be performed after the first event. In this case, information on an event related to at least one first terminal and information on an event related to at least one second terminal may be determined based on the classification of the above-described event fields into n fields.

For example, the first feedback message may include text data about ratings and feedback for the at least one second terminal. For example, the second feedback message may include text data about ratings and feedback for the at least one first terminal.

Information on the first feedback message and information on the second feedback message may be determined as learning data of the matching model based on the first matching success message and the second matching success message.

Additionally, the server may receive a matching failure message from at least one of the at least one first terminal or the at least one second terminal.

After the server receives the matching failure message from the at least one first terminal, the server may determine a preset third ratio that is less than the preset first ratio, and the server determines that the second matching ratio is the preset first ratio. It is possible to transmit list information about an observer having a ratio less than the third ratio and greater than or equal to the preset third ratio to the at least one first terminal.

After the server receives the matching failure message from the at least one second terminal, the server may determine a fourth preset ratio that is less than the preset second ratio. The server determines that the second matching ratio is the preset second ratio. It is possible to transmit list information on participants with a ratio less than or equal to the preset fourth ratio to the at least one second terminal.

8 is a diagram illustrating a method for determining, by a server, reliability or reputation of a participant or observer based on information on a preset transaction history according to an embodiment. The embodiments of FIG. 8 can be combined with various embodiments of the present disclosure.

Referring to FIG. 8 , in step S1201, the server receives a first message requesting participation in the first event from each of a plurality of first terminals, and invites observation of the first event from each of a plurality of second terminals. A second message requesting may be received. Step S1201 may be the same as steps S701 and S702 of FIG. 7 described above.

In step S1202, the server selects the same name as at least one of participants related to the first terminal or observers related to the second terminal among companies that have entered into transactions at a plurality of second events based on the information on the preset transaction details. It is possible to determine whether a company with The information on the preset transaction details is information on transactions between participants and observers in a plurality of second events performed prior to the first event. For example, the information on the preset transaction details may include information on sellers and buyers related to transactions made in a plurality of second events, information on a transaction product that is the target of the transaction, and feedback message information on the transaction. can The plurality of second events may be events corresponding to fields identical to or similar to those of the first event. A second event included in the same cluster as the first event may be determined as an event in the same or similar field.

In step S1203, if it is determined that a company having the same name as at least one of the participant related to the first terminal or the observer related to the second terminal exists, the server determines that the company having the same name exists in a plurality of second events. You can determine whether you are a seller in a transaction made.

In step S1204, if the company with the same name is a seller in a transaction made in a plurality of second events, the server determines the degree of reliability for the case of being a seller for the company with the same name based on the information on the preset transaction details. can decide Step S1204 may determine the degree of trust in the case of the seller as in the description of step S703 above. The server may determine the reliability of the case of the seller by Equation 1 described above.

In step S1205, if the company with the same name is not a seller in a transaction made in a plurality of second events, the server determines if the company with the same name is a buyer based on information on preset transaction details. reliability can be determined. Step S1205 may determine the degree of reliability for the case of the purchaser, as in the description of step S703 above. The server may determine the degree of reliability for the case of the purchaser by Equation 1 described above.

In step S1206, if a company with the same name does not exist, the server provides information on companies with which participants included in the first message transacted before the first event and information about the company the observer included in the second message did before the first event. It is possible to determine whether the participant and the observer are sellers in a transaction prior to the first event based on information about the company that has transacted with the participant. For example, information about a company with which a participant transacted prior to the first event may include transaction information, a name of a transacted company, and contact information of a transacted company. For example, the information about the company that the observer transacted with before the first event may include information about the transaction product, the name of the company that transacted with, and contact information of the company that transacted with.

In step S1207, if at least one of the participant or the observer is a seller, the server transmits a message requesting a reputation message based on the contact information of the transaction company to the terminal associated with the transaction company, and the transaction Based on the reputation message received from the terminal related to the enterprise, the reputation of the case where at least one of the participant or the observer is a seller may be determined. For example, a reputation message may include text data about ratings and reputation.

When at least one of the participant or the observer is a seller, the server determines the total number of transactions prior to the first event, the number of feedback messages including any one of the plurality of preset first words among the reputation messages, the The number of messages including any one of the plurality of preset second words among reputation messages, the number of transactions related to the same transaction product as the product among transactions prior to the first event, the number of transactions included in the reputation messages Based on the average value of the ratings, the reputation of the seller may be determined. The rating may be a value greater than 0 and less than or equal to 10. The server may determine the reputation of the seller by Equation 3 below.

In Equation 3, L is reputation, n' _d is the total number of transactions before the first event, and n' _a is any one of the plurality of preset first words among the reputation messages. The number of included feedback messages, n' _b is the number of messages including any one of the plurality of preset second words among the reputation messages, and n' _e is among transactions prior to the first event. It is the number of transactions related to the same transaction product as the product, and G' is an average value of ratings included in the reputation messages.

In step S1208, if at least one of the participant or the observer is a buyer, the server transmits a message requesting a reputation message based on the contact information of the transaction company to the terminal related to the transaction company, and the transaction Based on the reputation message received from the terminal related to the company, the reputation of the case where at least one of the participant or the observer is a purchaser may be determined. For example, a reputation message may include text data about ratings and reputation.

When at least one of the participant or the observer is a buyer, the server determines the total number of transactions prior to the first event, the number of feedback messages including any one of the plurality of preset first words among the reputation messages, the The number of messages including any one of the plurality of preset second words among reputation messages, the number of transactions related to the same transaction product as the product among transactions prior to the first event, the number of transactions included in the reputation messages Based on the average value of the ratings, the reputation of the buyer may be determined. The rating may be a value greater than 0 and less than or equal to 10. The server may determine the reputation of the purchaser by Equation 3 above.

In step S1209, the server executes neural data based on the reliability or reputation of the participant, the trust or reputation of the viewer, the information about the participant, the information about the viewer, the information about the product, and the information about the field of interest. A first matching rate between the participant and the observer may be determined through a matching model using a network.

Whether to use the reliability or reputation of at least one of the participant or observer as data for determining the first matching rate according to whether a company having the same name as at least one of the participant or observer exists in the preset transaction history information can be determined. That is, reliability may be used as data when included in the preset transaction detail information, and reputation may be used as data when not included in the preset transaction detail information. For this reason, the server may determine a matching rate using the reputation of the participant or observer even when the preset transaction history information cannot be used.

9 is a diagram of a method for transmitting list information about participants and list information about observers by a server according to an embodiment. The embodiments of FIG. 9 can be combined with various embodiments of the present disclosure.

Referring to FIG. 9 , in step S901, the server may adjust a first matching rate between a participant and an observer to a second matching rate based on preset event information. For example, the preset event information, in each of the plurality of second events, information about the size of the participant's booth, information about the number of visitors to the participant's booth, and the length of stay of the visitors who visited the participant's booth Information about booths, information on the number of booths of participants visited by the observer, and information on participants and observers with whom transactions have been concluded. For example, the information on the size of the participant's booth may include the area and location of the booth installed by the participant at each of the plurality of second events. The second matching rate may be set by Equation 2 above.

In step S902, the server may determine whether the second matching rate between the participant and the observer is greater than or equal to the preset first rate. The server may determine an observer whose second matching rate with respect to the first terminal is greater than or equal to the preset first ratio among the observers associated with the plurality of second terminals. The preset first ratio may be determined based on the reliability or reputation of the participant associated with the first terminal. The preset first ratio may be different according to the reliability or reputation of the participant associated with the first terminal. The preset first ratio may be determined such that the preset first ratio has a larger value as the reliability or reputation of the participant associated with the first terminal increases.

In step S903, the server may include the number of observers whose number is equal to or greater than a preset first ratio in the list information for the observers.

For example, the matched observers for the participant related to the first terminal are company A, company B, and company C, the second matching rate for company A is 60%, the second matching rate for company B is 40%, The second matching rate for company C may be 50%. At this time, when the preset first ratio is 50%, the server may determine an observer having a second matching rate that is greater than or equal to the preset first ratio for participants associated with the first terminal as company A and company C, and the company A List information on the observers including companies and C may be transmitted to the first terminal. For example, the list information for the visitor includes the names of companies A and C, fields of companies A and C, second matching rates of companies A and C, and expected visit times of companies A and C. can include

In step S904, the server may determine whether the number of observers included in the list information on the observers is less than a preset first number. The preset first number may be set by Equation 4 below.

In Equation 4, N _v is the preset first number, n is the number of participants participating in the first event, and s _p is the matched participant requested through the first message. The size of the participant's booth, s _q is the size of the booth of the q-th participant requested by the q-th participant through the first message, and z is the booth of the matched participant requested by the matched participant through the first message is a value related to the position of , where n _s is the number of observers having a field of interest that includes products of matched participants in the first event, and k is the number of observers attending the first event. Here, the value of k/3 rounded down due to the max function and the floor function is the maximum value of the preset first number. The value related to the location of the participant's booth requested by the participant through the first message is an importance value set according to the location of each participant's booth installed in the first event. A value related to the location of the participant's booth requested by the participant through the first message may be a value between 0 and 2. A participant's booth located in an area where visitors are expected to be dense may have a large value related to the location of the participant's booth.

The larger the size of the matched participant's booth and the higher the likelihood that the location of the matched participant's booth will be crowded with visitors, the higher the preset first number, so that the matching rate for a larger number of visitors increases. It can be transmitted to the first terminal associated with. Accordingly, a matching rate for an appropriate number of visitors may be provided to the first terminal by reflecting information on the first event.

In step S905, when the number of observers is less than the preset first number, the server may decrease the preset first ratio until the preset first number of observers is determined. In step S906, the server may determine as many observers as the first preset number. In step S907, the server may transmit, to the first terminal, list information about an observer including a preset number of observers.

In step S908, when the second matching rates for the participants associated with the first terminal are all less than the preset first rate, the server may decrease the preset first rate. After that, the server may perform step S902.

In step S912, the server may determine whether a second matching rate between participants and observers is greater than or equal to a preset second rate. The server may determine an observer whose second matching rate for the second terminal is greater than or equal to a preset second ratio among the participants associated with the plurality of first terminals. The preset second ratio may be determined based on the reliability or reputation of the observer associated with the second terminal. The preset second ratio may be different according to the reliability or reputation of the observer associated with the second terminal. The preset second ratio may be determined such that the preset second ratio has a larger value as the reliability or reputation of the participant associated with the second terminal increases.

In step S913, the server may include participants whose ratio is greater than or equal to the second predetermined ratio in list information for participants.

For example, the matched participants for the observer associated with the second terminal are company D, company E, and company F, the second matching rate for company D is 40%, the second matching rate for company E is 50%, The second matching rate for company F may be 60%. At this time, when the preset second ratio is 40%, the server may determine E companies and F companies as participants having a second matching rate that is equal to or greater than the preset second ratio for the observer associated with the second terminal, and the E company And list information about participants including companies F may be transmitted to the second terminal. For example, the list information on the participants includes the names of E companies and F companies, fields of E companies and F companies, second matching rates of E companies and F companies, and booth locations of E companies and F companies. can include

In step S914, the server may determine whether the number of participants included in the participant list information is less than a preset second number. The preset second number may be set by Equation 5 below.

In Equation 5, N _v is the preset second number, k is the number of visitors attending the first event, and v _p is the number of visitors requested by the matched visitor through the second message. number, v _q is the number of visitors requested by the q th visitor through the second message, n _x is the number of participants with products included in the field of interest of the matched visitor in the first event, n is the number of participants participating in the first event. Here, due to the max function and the floor function, a value rounded down by half of n/3 may be the maximum value of the preset second number.

Since the preset second number increases as the number of visitors associated with the matched observer increases, a matching rate for a larger number of participants may be transmitted to the second terminal associated with the matched observer. Accordingly, a matching rate for an appropriate number of participants may be provided to the second terminal by reflecting information on the first event.

In step S915, if the number of participants is less than the preset second number, the server may decrease the preset second ratio until the preset second number of participants is determined. In step S916, the server may determine as many participants as the preset second number. In step S917, the server may transmit list information about participants including a preset number of participants to the second terminal.

In step S918, when all the second matching rates for the second terminal and the related observers are less than the preset second rate, the server may decrease the preset second rate. Thereafter, the server may perform step S912.

10 is a diagram for a method in which a server reflects a feedback message according to an embodiment. The embodiments of FIG. 10 can be combined with various embodiments of the present disclosure.

Referring to FIG. 10 , in step S1001, the server may determine whether to receive the first matching success message and the second matching success message. The server may receive a first matching success message from at least one first terminal and a second matching success message from at least one second terminal.

In step S1002, based on the reception of the first matching success message and the second matching success message, the server sends the at least one first terminal to the dictionary associated with the at least one second terminal matched with the at least one first terminal. Information on the set transaction details may be transmitted, and the server may transmit information on the preset transaction details related to the at least one first terminal matched with the at least one second terminal to the at least one second terminal. .

Information on a participant related to the at least one first terminal and an observer related to the at least one second terminal may be determined as learning data of the matching model based on the first matching success message and the second matching success message. there is.

In step S1003, the server may determine whether to receive the first feedback message or the second feedback message. The server may receive a first feedback message for the at least one second terminal from at least one first terminal or receive a second feedback message for the at least one first terminal from at least one second terminal. .

In step S1004, when receiving a first feedback message for the at least one second terminal from the at least one first terminal, the server sends the at least one first terminal to an event related to the at least one first terminal. information can be transmitted. When receiving a second feedback message for the at least one first terminal from at least one second terminal, the server transmits information about an event related to the at least one second terminal to the at least one second terminal. can

In step S1005, the server may reflect the first matching success message or the second matching success message as learning data of the matching model.

In step S1006, if the first matching success message and the second matching success message are not received during the preset time period, the server may determine whether the current date is within the preset time period. When only the first matching success message is received, the server may transmit a message requesting a second matching success message to the second terminal associated with the observer included in the first matching success message. When only the second matching success message is received, the server may transmit a message requesting the first matching success message to the second terminal associated with the observer included in the second matching success message.

Alternatively, when receiving a matching failure message from at least one first terminal or at least one second terminal, the server may determine that the first matching success message and the second matching success message are not received for a preset time.

The preset time is the time from the point of time when the list information on the participant or the list information on the observer is transmitted. A preset time may be pre-entered into the server. The preset period may be a progress period for the first event.

In step S1007, if it is within the preset period, the server may decrease at least one of the preset first ratio and the preset second ratio. When the first matching success message and the second matching success message are not received for a preset time period, the server may decrease the preset first ratio and the preset second ratio. When receiving the matching failure message, if it is the first terminal that has transmitted the matching failure message, the server may decrease the preset first ratio. When receiving the matching failure message, if the second terminal has transmitted the matching failure message, the server may decrease the preset second ratio.

In step S1008, the server may transmit at least one of list information for participants and list information for observers. When the preset first ratio is decreased, the server may transmit list information on new observers including an observer having a second matching ratio equal to or greater than the reduced first ratio to at least one first terminal. At this time, the previously transmitted observer may be excluded from the list information for the new observer. When the preset second ratio is reduced, the server may transmit list information on new participants including participants having a second matching ratio equal to or greater than the reduced second ratio to at least one second terminal. At this time, the previously transmitted participant may be excluded from the list information for the new participant.

11 is an example of a matching model using a neural network according to an embodiment. One embodiment of FIG. 11 may be combined with various embodiments of the present disclosure.

Referring to FIG. 11 , a neural network may include an input layer 1110, one or more hidden layers 1120, and an output layer 1130. Reliability of multiple participants, reliability of multiple observers, information on multiple participants, information on multiple observers, information on multiple products, information on multiple fields of interest, and correct answer matching rate. Training data is input to the input layer 1110 of the neural network, passes through one or more hidden layers 1120 and an output layer 1130, and is output as an output vector, and the output vector is output to the output layer 1130. It is input to the connected loss function layer, and the loss function layer outputs a loss value by using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameters of the neural network are such that the loss value is small. The matching model, which is learned in a losing direction, may be generated.

The server performs data pre-processing on reliability of multiple participants, reliability of multiple observers, information on multiple participants, information on multiple observers, information on multiple products, and information on multiple areas of interest. can do. The server is based on the reliability of multiple participants, the reliability of multiple observers, the number of sales for multiple participants, the number of employees, the number of patents and product codes, the number of sales for multiple observers, the number of employees, the number of patents and the product code of interest. to perform data preprocessing. That is, the server based on the reliability of a plurality of participants, the reliability of a plurality of observers, information about a plurality of participants, information about a plurality of observers, information about a plurality of products, information about a plurality of products of interest, Through data preprocessing, the first input vector consisting of [reliance on participants, sales for participants, number of employees for participants, number of patents for participants, and product code conversion values for participants] and [reliance on participants, A second input vector consisting of the number of sales for the observer, the number of employees for the observer, the number of patents for the observer, and a value obtained by converting the product code of interest to the observer] may be generated. In addition, a loss value is output using a loss function that compares a matching rate value related to the first input vector and the second input vector with a correct matching rate vector value, and the parameter of the neural network is such that the loss value becomes small. direction can be learned. For example, when the product code and the product code of interest are Nice International Product Classification Codes, the values obtained by converting the codes are values according to classification and may have values from 1 to 45. For example, when the product code and the product code of interest are HS codes, the value obtained by converting the codes is a value of the first two digits of the HS code and may have values from 1 to 97.

Or, reliability and reputation of multiple participants, reliability and reputation of multiple observers, information about multiple participants, information about multiple observers, information about multiple products, information about multiple fields of interest, and correct answers Each learning data composed of matching rates is input to the input layer 1110 of the neural network, passes through the one or more hidden layers 1120 and the output layer 1130, and is output as an output vector. The input is input to a loss function layer connected to the output layer 1130, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each training data, and outputs a loss value, and the parameters of the neural network. The matching model, which is learned in a direction in which the loss value decreases, may be generated.

The server is based on the reliability and reputation of multiple participants, the reliability and reputation of multiple observers, information about multiple participants, information about multiple observers, information about multiple products, and information about multiple areas of interest. , a first input vector consisting of [reliability for participants, reputation for participants, sales for participants, number of employees for participants, number of patents for participants, product codes for participants] through data preprocessing. And a second input vector consisting of [reliability of the observer, reputation of the observer, sales for the observer, number of employees for the observer, number of patents for the observer, product codes of interest for the observer] can be generated. there is. In addition, a loss value is output using a loss function that compares a matching rate value related to the first input vector and the second input vector with a correct matching rate vector value, and the parameter of the neural network is such that the loss value becomes small. direction can be learned.

12 is a signal exchange diagram illustrating a procedure for a server to transmit information about an event related to a terminal according to an embodiment. One embodiment of FIG. 12 may be combined with various embodiments of the present disclosure.

Referring to FIG. 12 , in step S1210, the server may receive a first message requesting participation in the first event from the first terminal-a and the first terminal-b. In step S1220, the server may receive a second message requesting a visit to the first event from the second terminal-a and the second terminal-b.

In step S1230, the server may determine a matching rate based on the first message, the second message, information on preset transaction details, and information on preset events. Here, the matching rate is the second matching rate obtained by adjusting the first matching rate.

In step S1240, the server may transmit list information on the observer to the first terminal-a and the first terminal-b. In step S1250, the server may transmit list information on participants to the second terminal-a and the second terminal-b.

The matching rate between the participant A associated with the first terminal-a and the observer C associated with the second terminal-a is 20%, the matching rate between the participant A and the observer D associated with the second terminal-b is 50%, and the matching rate of the participant A associated with the second terminal-b is 50%. A matching rate between participant B and the viewer C related to b may be determined to be 40%, and a matching rate between the participant B and the viewer D may be determined to be 30%. In this case, the information on the list of observers transmitted to the first terminal-a may be information on the number of observers D equal to or greater than a preset first ratio (eg, 30%). At this time, the list information on the observer transmitted to the first terminal-b may be information on the observer C whose ratio is greater than or equal to a preset first ratio (eg, 35%). In this case, the information on the list of participants transmitted to the second terminal-a may be information about participant B whose ratio is greater than or equal to a preset second ratio (eg, 35%). At this time, the list information on the observer transmitted to the second terminal-b may be information on the observer A whose ratio is greater than or equal to the second pre-set ratio (eg, 40%).

In step S1260, the server may receive a first matching success message and a second success message from the first terminal-a and the second terminal-a. That is, when matching is successful even though it is not included in the list information for the participant or the list information for the observer, a matching success message can be received.

In step S1270, based on receiving the first matching success message and the second success message, the server may transmit preset transaction detail information related to the second terminal-a to the first terminal-a, and the server may send 2. Preset transaction detail information related to the first terminal-a may be transmitted to terminal-a.

In step S1280, the server may receive a first feedback message from the first terminal-a and may receive a second feedback message from the second terminal-a.

In step S1290, based on the reception of the first feedback message, the server may transmit information on an event related to the first terminal-a to the first terminal-a, and based on the reception of the second feedback message, The server may transmit information about an event related to the second terminal-a to the second terminal-a. The information about the event related to the first terminal-a may be information about the event determined based on the field of the participant related to the first terminal-a. The information on the event related to the second terminal-a may be information on the event determined based on the field of the observer related to the second terminal-a.

13 is an example of a screen on which list information on participants is displayed and a screen on which list information on observers is displayed according to an embodiment. One embodiment of FIG. 13 may be combined with various embodiments of the present disclosure.

Referring to FIG. 13 , based on the interface information, a screen 1310 for list information about the above-described participants may be displayed on the second terminal. For example, the screen 1310 on which list information on participants is displayed may include a matching rate ranking, company name, matched company field, matching rate, and location of a matched company's booth. The interface information may be transmitted from the server to a plurality of first terminals and a plurality of second terminals. The interface information may be previously input to the server.

Based on the interface information, a screen 1320 for list information about the above-described observer may be displayed on the first terminal. For example, the screen 1320 on which the list information for the observer is displayed may include a matching rate ranking, a business name, a field of the matched company, a matching rate, and an expected visit time of the matched company.

The above-described screen displaying list information for participants and a screen displaying list information for observers are examples, and various screens may be configured, and are not limited to those shown in FIG. 13 .

14 is a block diagram illustrating a configuration of a server according to an exemplary embodiment. One embodiment of FIG. 14 may be combined with various embodiments of the present disclosure.

As shown in FIG. 14 , the server 1400 may include a processor 1410 , a communication unit 1420 and a memory 1430 . However, not all components shown in FIG. 14 are essential components of the server 1400 . The server 1400 may be implemented with more components than those shown in FIG. 14 or may be implemented with fewer components than those shown in FIG. 14 . For example, the server 1400 according to some embodiments may further include a user input interface (not shown), an output unit (not shown), etc. in addition to the processor 1410, the communication unit 1420, and the memory 1430. .

The processor 1410 typically controls the overall operation of the server 1400. The processor 1410 may include one or more processors to control other elements included in the server 1400 . For example, the processor 1410 may generally control the communication unit 1420 and the memory 1430 by executing programs stored in the memory 1430 . Also, the processor 1410 may perform the functions of the server 1400 described in FIGS. 7 to 13 by executing programs stored in the memory 1430 .

According to an embodiment, the processor 1410 may receive a first message requesting participation in a first event from each of a plurality of first terminals through the communication unit 1420 . For example, the first message may include information about a product and information about a participant related to the first terminal. The processor 1410 may receive a second message requesting a visit to the first event from each of a plurality of second terminals through the communication unit 1420 . For example, the second message may include personal information about a visitor related to the second terminal, information about an observer related to the second terminal, and information about a field of interest.

The processor 1410 may determine the trust level of the participant and the trust level of the observer based on the information on transaction details preset through the memory 1430 . For example, the information on the preset transaction details may include information on sellers and buyers related to transactions made in a plurality of second events, information on a transaction product that is the target of the transaction, and feedback message information on the transaction. there is.

The processor 1410, through the memory 1430, based on the reliability of the participant, the reliability of the observer, the information about the participant, the information about the observer, the information about the product, and the information about the field of interest A first matching rate between the participant and the observer may be determined through a matching model using a neural network.

The processor 1410 may adjust the first matching rate to a second matching rate based on event information preset through the memory 1430 .

The processor 1410 may transmit list information about an observer whose second matching rate is greater than or equal to a preset first rate to each of the plurality of first terminals through the communication unit 1420 . The processor 1410 may transmit list information on participants whose second matching rate is greater than or equal to a preset second rate to each of the plurality of second terminals through the communication unit 1420 .

For example, the preset event information, in each of the plurality of second events, information on the size of the participant's booth, information on the number of visitors to the participant's booth, and the length of stay of the visitors who visited the participant's booth information about the booth, information on the number of booths of participants visited by the observer, and information on participants and observers with whom transactions have been concluded.

The processor 1410 may set the second matching rate through the above-described Equation 2 through the memory 1430.

According to an embodiment, the processor 1410 may receive a first matching success message from at least one first terminal through the communication unit 1420 . The processor 1410 may receive a second matching success message from at least one second terminal matched with the at least one first terminal through the communication unit 1420 . Based on receiving the first matching success message and the second matching success message through the communication unit 1420 and the memory 1430, the processor 1410 sends the at least one first terminal to the at least one first matching success message. Information on the preset transaction details related to at least one second terminal matched with the terminal may be transmitted. The processor 1410 sends the at least one second terminal to the at least one second terminal based on receiving the first matching success message and the second matching success message through the communication unit 1420 and the memory 1430. Information on the preset transaction details related to at least one first terminal matched with the terminal may be transmitted.

The processor 1410 may receive a first feedback message for the at least one second terminal from the at least one first terminal through the communication unit 1420 . The processor 1410 may transmit information about an event related to the at least one first terminal to the at least one first terminal based on the first feedback message through the communication unit 1420 and the memory 1430. . The processor 1410 may receive a second feedback message for the at least one first terminal from the at least one second terminal through the communication unit 1420 . The processor 1410 may transmit information about an event related to the at least one second terminal to the at least one second terminal based on the second feedback message through the communication unit 1420 and the memory 1430. . For example, the first feedback message may include text data about ratings and feedback for the at least one second terminal. For example, the second feedback message may include text data about ratings and feedback for the at least one first terminal.

For example, the neural network may include an input layer, one or more hidden layers, and an output layer.

The processor 1410 stores reliability of a plurality of participants, reliability of a plurality of observers, information about a plurality of participants, information about a plurality of observers, information about a plurality of products, and fields of interest through the memory 1430. Each learning data composed of information on and a correct answer matching rate may be input to the input layer of the neural network, pass through one or more hidden layers and an output layer, and be output as an output vector. The processor 1410 inputs the output vector to the loss function layer connected to the output layer through the memory 1430, and the processor 1410 inputs the output vector and each of the output vectors through the loss function layer through the memory 1430. A loss value may be output using a loss function for comparing correct answer vectors with respect to training data, and the matching model may be generated in which parameters of the neural network are learned in a direction in which the loss value decreases.

The communication unit 1420 may include one or more components that allow the server 1400 to communicate with other devices (not shown) and servers (not shown). Another device (not shown) may be a computing device such as the server 1400 or a sensing device, but is not limited thereto. The communication unit 1420 may receive a user input from another electronic device or data stored in an external device from an external device through a network.

The communication unit 1420 may transmit/receive a message for establishing a connection with at least one device. The communication unit 1420 may transmit information generated by the processor 1410 to at least one device connected to the server. The communication unit 1420 may receive information from at least one device connected to the server. The communication unit 1420 may transmit information related to the received information in response to information received from at least one device.

The memory 1430 may store programs for processing and control of the processor 1410 . For example, the memory 1430 may store information input to a server or information received from another device through a network. Also, the memory 1430 may store data generated by the processor 1410 . For example, the memory 1430 may include at least one of the reliability of the participant generated by the processor 1410, the reliability of the observer, the first matching rate, the second matching rate, list information about the observer, and list information about the participant. Information on preset transaction details associated with at least one second terminal matched with the first terminal of, information on preset transaction details associated with at least one first terminal matched with at least one second terminal, at least one Information on an event related to the first terminal of the device and information on an event related to at least one second terminal may be stored. The memory 1430 may store information input to or output from the server 1400 . For example, the memory 1430 may store information on preset transaction details, preset event information, preset first to fourth rates, a plurality of preset first words, a plurality of preset second words, and preset event information. Information on the first number, the preset second number, product classification code, or service classification code may be stored.

The memory 1430 may include 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. You can command the device. 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 (12)

In the method for a server to manage MICE events using a neural network,
generating a participant account in response to the first subscription request message received from each of the plurality of first terminals;
generating an observer account in response to the second subscription request message received from each of the plurality of second terminals;
generating a host organization account in response to a third subscription request message received from each of the plurality of third terminals;
opening candidate events in response to an opening request message requesting opening of each of a plurality of candidate events received from each of the plurality of third terminals;
The opening application message includes information on candidate events,
providing a publicity media list including a plurality of publicity media to the plurality of third terminals;
performing a public relations contract procedure with a public relations medium corresponding to the selection signal received from each of the plurality of third terminals;
Receiving a first message requesting participation in a first event among the plurality of candidate events from each of the plurality of first terminals;
The first message includes information about participants related to the first terminal, information about products, booth selection information, and ancillary service selection information;
Receiving a second message requesting a visit to the first event from each of the plurality of second terminals;
The second message includes personal information about the visitor related to the second terminal, information about the visitor related to the second terminal, and information about a field of interest,
A participant related to the plurality of first terminals and an observer related to the plurality of second terminals are determined, and a matching result is provided to each of the plurality of first terminals and the plurality of second terminals through a matching model using a neural network. doing;
settling costs paid by the plurality of first terminals and the plurality of second terminals;
Receiving feedback information about the determined observer from the plurality of first terminals; and
Receiving feedback information on the determined participant from the plurality of second terminals
including,
The information on the candidate event includes information about the type of event, the scale of the event, the location of the event, and whether VIP service is selected,
The opening step is
providing a rental space list to each of the plurality of third terminals based on the size of the event and the location of the event;
receiving a selection signal for a rental space included in the rental space list from each of the plurality of third terminals; and
Comprising the step of proceeding with a rental contract procedure between each of the plurality of third terminals and the owner of the rental space corresponding to the selection signal,
How to manage MICE events.
According to claim 1,
Based on the information about the participant corresponding to any one of the plurality of second terminals and the information about the plurality of candidate events, information on the recommended event is transmitted through a recommendation model using a neural network to any one of the plurality of second terminals. Further comprising transmitting to a second terminal,
How to manage MICE events.
delete delete According to claim 1,
providing selection information of promotional media corresponding to the selection signal to the plurality of first terminals;
The selection information of the promotional media includes information for selecting whether or not to include participants together with the hosting organization in the promotional content;
Receiving a selection signal corresponding to the selection information from one or more first terminals among the plurality of first terminals,
How to manage MICE events.
According to claim 1,
The step of creating each participant account,
Providing a QR code unique to the participant account to each corresponding first terminal;
The step of creating each of the observer accounts,
Providing a QR code unique to the observer account to each corresponding second terminal,
How to manage MICE events.
According to claim 6,
Receiving identification information of a QR code unique to an observer account from any one of the plurality of first terminals; and
Mapping information about the observer corresponding to the identification information to a participant account of a first terminal that transmitted the identification information,
How to manage MICE events.
According to claim 1,
The step of providing the matching result,
determining a trust level of the determined participant and a reliability level of the determined observer based on information on predetermined transaction details;
The information on the preset transaction details includes information on sellers and buyers related to transactions made in a plurality of second events, information on a transaction product that is a target of the transaction, and feedback message information on the transaction,
The neural network is based on the reliability of the determined participant, the reliability of the determined observer, the information about the determined participant, the information about the determined observer, the information about the product, and the information about the field of interest. Determining a first matching rate between the confirmed participant and the confirmed observer through a matching model using;
adjusting the first matching rate to a second matching rate based on preset event information;
transmitting, to each of the plurality of first terminals, list information about an observer whose second matching rate is greater than or equal to a preset first rate; and
Transmitting, to each of the plurality of second terminals, list information about a participant whose second matching rate is greater than or equal to a preset second rate,
How to manage MICE events.
According to claim 8,
The preset event information, in each of the plurality of second events, information on the size of the participant's booth, information on the number of visitors to the participant's booth, information on the stay time of the visitors who visited the participant's booth, Includes information on the number of booths of participants visited by the observer and information on participants and observers with whom transactions have been concluded;
The second matching rate is determined based on the number of participants, the size of the participant's booth, the length of stay of the visitor visiting the participant's booth, the number of visitors visiting the participant's booth, the number of visitors, and the number of participants visited by the visitor ,
How to manage MICE events.
According to claim 8,
Receiving a first matching success message from at least one first terminal;
Receiving a second matching success message from at least one second terminal matched with the at least one first terminal;
Based on receiving the first matching success message and the second matching success message, the preset transaction related to the at least one second terminal matched with the at least one first terminal to the at least one first terminal. Transmitting information about details;
Based on receiving the first matching success message and the second matching success message, the preset transaction related to the at least one first terminal matched with the at least one second terminal to the at least one second terminal. Further comprising transmitting information about the details,
How to manage MICE events.
According to claim 8,
Receiving a first feedback message for at least one second terminal from at least one first terminal;
Transmitting information about an event related to at least one first terminal to at least one first terminal based on the first feedback message;
Receiving a second feedback message for at least one first terminal from at least one second terminal; and
Based on the second feedback message, further comprising transmitting information about an event related to at least one second terminal to at least one second terminal,
The first feedback message includes text data about ratings and feedback for at least one second terminal,
The second feedback message includes text data for ratings and feedback for at least one first terminal,
How to manage MICE events.
According to claim 8,
The neural network includes an input layer, one or more hidden layers and an output layer;
Reliability of multiple participants, reliability of multiple observers, information on multiple participants, information on multiple observers, information on multiple products, information on multiple fields of interest, and correct answer matching rate. Training data is input to the input layer of the neural network, passes through the one or more hidden layers and output layers, and is output as an output vector. The output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameters of the neural network are learned in a direction in which the loss value decreases, the matching model is created ,
How to manage MICE events.

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