KR102493490B1 - System using artificial intelligence for data crawling - Google Patents

Abstract

The present invention relates to a data crawling system using artificial intelligence. A marketing evaluation system, according to the present invention, comprises a client and a server, wherein the server includes a first processor, a first memory, and a first transceiver, and the client includes an input unit, a second processor, a second memory, and a second transceiver. Accordingly, by simply inputting a keyword, marketing or advertising effects related to the keyword can be analyzed on various platforms.

Description

Data crawling system using artificial intelligence {SYSTEM USING ARTIFICIAL INTELLIGENCE FOR DATA CRAWLING}

Embodiments of the present invention relate to a data crawling technique, and relate to a data crawling technique using a neural network.

Continuous interest and experimental attempts in big data in modern society are attributed to the purpose of providing and managing information suitable for individualized members of society, predicting diversified modern society more elaborately and providing information to operate efficiently. do. For example, in the private sector, customer preference is analyzed based on credit card usage history and social media web data, etc., and customer purchasing patterns and actual purchase trends are identified to provide customized information tailored to individual customer needs. are doing

Crawling means collecting and sorting data. The main target of crawling is data that exists in various forms. Data is classified into structured, semi-structured, and unstructured data depending on the style of data creation, but it can also be classified into data generated by companies (or institutions/organizations) and users according to the entity that produces the data. Data generated by organizations includes news and broadcasting videos provided by the media, product-related information provided by private companies, and content-type data produced by media industries such as movies, music, and games.

Data generated by users includes data automatically created by consumption of services and products (number of times news articles are read, number of visitors to online shopping websites, etc.), news comments, product reviews, and activities on messengers, blogs, online cafes, YouTube, and social media. contains the data produced by Since this data is data that can be read through a user terminal, it is necessary to collect it using a crawling or scraping method.

Embodiments may provide a system for analyzing marketing or advertisement effects related to a corresponding keyword on various platforms only by simple keyword input at the client end.

Embodiments may provide a system for more precisely collecting data having a high relevance to a keyword entered in a data crawling process and a low relevance to a different keyword.

Embodiments may provide a system for increasing the accuracy of crawled data and improving the reliability of analysis results by simultaneously analyzing text and images.

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

A marketing evaluation system according to an embodiment includes a client and a server, wherein the server and the client are connected through a network, and the server includes: a first processor; a first memory; and a first transceiver, wherein the client comprises: an input unit; a second processor; a second memory; and a second transceiver, wherein the first memory stores a program for a server and address information of a plurality of platforms, and the second memory stores a program for a client and address information of the server, and the client: A keyword input signal representing a mutual keyword and a menu keyword for each platform is generated based on a user input through the second processor, the keyword input signal is converted into keyword data through the second processor, and the second transceiver The keyword data is transmitted to the server, and the server: extracts the mutual keyword and the menu keyword from the keyword data through the first processor, and converts the mutual keyword and the menu keyword to the address information of each platform. to generate a URL for each platform, access each platform through the network using the URL for each platform, and use the URL for each platform to access a mutual name related to the mutual keyword from the source data of each platform. Crawl raw data or menu raw data related to the menu keyword, the source data means data stored in the platform, and parse the mutual raw data or the menu raw data crawled from each platform, respectively to output mutual analysis data or menu analysis data corresponding to each platform, and to correspond to each platform based on the mutual analysis data or menu analysis data corresponding to each platform using a marketing analysis model generating a marketing analysis result, generating a result page using the marketing analysis result corresponding to each platform, and transmitting the result page to the client through the first transceiver.

The first processor generates a marketing analysis result corresponding to each platform using a marketing analysis model composed of a neural network, the marketing analysis model includes an input layer, one or more hidden layers, and an output layer, and mutual analysis A plurality of learning data related to data or menu analysis data is input to the input layer of the marketing analysis model and passes through the one or more hidden layers and output layers to output an output vector, and the output vector is a loss connected to the output layer. It is input to a function 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 parameter of the marketing analysis model is a parameter of the marketing analysis model that reduces the loss value. direction can be learned.

[mathematical expression]

The loss function follows the above equation, where N is the number of the plurality of training data, n is a natural number identifying the learning data, k is a natural number identifying the value of the nth learning data, nk is the nth Means the k-th value of the learning data, t means the correct answer data, y means the output vector, E means the loss value, and a plurality of learning data related to mutual analysis data or menu analysis data The number of searches by time period for the trade name or the menu, preference, comparison sentences with other trade names or other menus, evaluation sentences for the menu itself, evaluation sentences for accessibility to stores corresponding to the trade name, atmosphere, price, taste, It may include at least one of an image related to the trade name and an image related to the menu.

The plurality of platforms include a first platform and a second platform, and the first processor: receives mutual row data related to the mutual keyword or menu row data related to the menu keyword during a first time interval from the first platform. crawling, and crawling mutual raw data related to the mutual keyword or menu raw data related to the menu keyword during a second time interval from the second platform, wherein the first time interval and the second time interval are all or part of may overlap.

The first processor: generates a marketing analysis result for the first platform through a URL for the first platform through an event loop using a single thread, and After the marketing analysis result is generated, the marketing analysis result for the second platform is generated through the URL for the second platform through the event loop, and after the marketing analysis result for the second platform is generated, the second platform is generated. The result page may be generated using the marketing analysis result for the first platform and the marketing analysis result for the second platform.

The first processor: converts the mutual keywords to generate mutual parameters, converts the menu keywords to generate menu parameters, combines the mutual parameters or the menu parameters behind the address information of the respective platforms, A URL for each platform is generated, and the first processor crawls mutual row data related to the mutual keyword or menu row data related to the menu keyword from each platform using the URL for each platform. can

The source data includes text data and image data, and the first processor: outputs a text vector corresponding to the text data and an image vector corresponding to the image data using a raw data preprocessing model; Concatenation is performed on the text vector and the image vector to generate a preprocessing vector, and based on the preprocessing vector from each platform using a data crawling model, mutual raw data related to the mutual keyword or Menu raw data related to the menu keyword may be crawled.

The first processor: obtains a feature vector by inputting the preprocessing vector using the data crawling model, and based on a comparison result of the feature vector with a pre-registered reference vector representing a specific business name or menu, the mutual row Data or the menu row data may be output.

The data crawling model is pre-learned based on a plurality of training data, a learning feature vector is obtained from training data belonging to a first criterion among a plurality of training data using the data crawling model, and a learning feature vector is obtained using the data crawling model. A residual between each reference vector of a criterion different from the first reference and the learning feature vector is obtained, and a reference residual between a first reference vector of the first reference and a second reference vector of a second reference and the first reference A high-intensity learning parameter is calculated based on a feature residual between a vector and the learning feature vector, and between a residual to which the high-intensity learning parameter is applied to the feature residual and a reference vector of a criterion different from the first criterion and the feature vector. A loss value may be calculated through a loss function including a residual, and parameters of the data crawling model or the one or more reference vectors may be learned based on the loss value.

According to embodiments, a marketing or advertising effect related to a corresponding keyword can be analyzed on various platforms only by simple keyword input at the client side.

According to embodiments, data having a high relevance to a keyword entered in a data crawling process and a low relevance to a different keyword may be more accurately collected.

According to embodiments, by simultaneously analyzing text and images, accuracy of crawled data may be increased and reliability of analysis results may be improved.

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 diagram showing the overall configuration of a marketing evaluation system according to an embodiment.
4 is a flowchart illustrating the operation of a marketing evaluation system according to an embodiment.
5 is an example illustrating an asynchronous operation of a marketing evaluation system according to an embodiment.
6 is a diagram illustrating the structure of a data crawling model according to an embodiment.
7 is a diagram illustrating a configuration of a server according to an embodiment.

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

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

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

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

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

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

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

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

The secondary processor 123 may, for example, take the place of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or the main processor 121 is active (eg, running an application). ) state, together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to one embodiment, the auxiliary processor 123 (eg, image signal processor or communication processor) may be implemented as part of other functionally related components (eg, camera module 180 or communication module 190). there is. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. AI models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself where the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning or reinforcement learning, but in the above example Not limited. The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the foregoing, but is not limited to the foregoing examples. The artificial intelligence model may include, in addition or alternatively, software structures in addition to hardware structures.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The server 108 is connected to the electronic device 101 and can provide a service to the connected electronic device 101 . In addition, the server 108 may proceed with a membership sign-up procedure, store and manage various types of information of users subscribed as members, and provide various purchase and payment functions related to services. In addition, the server 108 may share execution data of service applications executed in each of the plurality of electronic devices 101 in real time so that the service can be shared among users. This server 108 may have the same configuration as a conventional web server or WAP 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, Visual Basic, or Visual C 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 uses a server program provided in various ways according to operating systems such as DOS, Windows, Linux, UNIX, and Macintosh in general server hardware. As a representative example, a website used in a Windows environment, Internet Information Server (IIS), and CERN, NCSA, APPACH, etc. used in a Unix environment can be used. In addition, the server 108 may interoperate with an authentication system and a payment system for user authentication of services or payment for purchases related to services.

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

The 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.

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.

3 is a diagram showing the overall configuration of a marketing evaluation system according to an embodiment.

According to one embodiment, the marketing evaluation system may evaluate the marketing effect of a business operated by a user through various platforms. The marketing evaluation system can search for keywords related to business entities for each of the various platforms, and crawl and analyze data highly relevant to the keywords.

To this end, the marketing evaluation system includes a server 310 (eg, server 108 in FIG. 1 ), a client 320 (eg, electronic device 101 in FIG. 1 ), and a network 340 (eg, in FIG. 1 ). A second network 199), and one or more platform servers 331 and 333 may be included. Server 310 and client 320 may be connected by network 340 . The marketing evaluation system may further include a database 311 (eg, the database of FIG. 1 ).

The database 311 may be connected to the server 310 . The database 311 may be connected to the server 310 through an intra network or may be connected to the server 310 through an external network 340 . The server 310 may include a first processor, a first memory, and a first transceiver. The client 320 may include an input unit, a second processor, a second memory, and a second transceiver. The first memory may store a server program (eg, the program 140 of FIG. 2 ) and address information of a plurality of platforms. The second memory may store address information of a client program (eg, the program 140 of FIG. 2 ) and the server 310 .

A user may enter one or more user inputs into the client 320 . For example, the client program of the client 320 may display an input window corresponding to each of the platform servers 331 and 333 . A user may input a user input into each input window. The user input may include keywords related to the business. For example, the user input may include a trade keyword or a menu keyword. For example, a user may input a mutual keyword or a menu keyword into the input unit of the client 320 and press a search button.

The client 320 may generate a keyword input signal indicating a mutual keyword and a menu keyword for each platform based on a user input through the input unit. The client 320 may generate keyword data by converting the keyword input signal.

The client 320 may transmit keyword data to the server 310 through the second transceiver.

The server 310 may extract mutual keywords and menu keywords by analyzing keyword data through the first processor.

The server 310 may crawl data from each of the platform servers 331 and 333 using a GET method. The server 310 may generate a URL for each platform server 331 or 333 by combining a mutual keyword and a menu keyword with address information of each platform server 331 or 333 . The first processor may generate mutual parameters by converting mutual keywords, and generate menu parameters by converting menu keywords. The first processor may generate a URL for each platform by combining mutual parameters or menu parameters to the back of the address information of each platform.

The server 310 may access each of the platform servers 331 and 333 of the network using URLs for each of the platform servers 331 and 333 . The server 310 may collect data related to user input in each of the platform servers 331 and 333 based on the URL. The server 310 may collect data related to user input from each of the platform servers 331 and 333 at the same time or at the same time.

The server 310 may crawl mutual raw data related to mutual keywords or menu raw data related to menu keywords from the source data of each platform. Here, the source data may mean data stored in the platform. Source data may include text data and image data. For example, the source data may include text or images included in various contents stored in the platform servers 331 and 333 . The first processor may crawl mutual row data related to mutual keywords or menu row data related to menu keywords from each platform by using the URL for each platform. Here, mutual raw data may mean source data whose relevance with a mutual keyword among source data is greater than or equal to a threshold value, and menu row data may mean menu data whose relevance to a menu keyword among source data is greater than or equal to a threshold value.

The first processor may obtain raw data by pre-processing the source data. The first processor may output a text vector corresponding to text data and an image vector corresponding to image data by using a raw data preprocessing model. The first processor may generate a preprocessing vector by performing concatenation on the text vector and the image vector.

The first processor may crawl mutual row data related to mutual keywords or menu row data related to menu keywords from each platform based on the preprocessing vector by using a data crawling model. The first processor may obtain a feature vector by inputting a preprocessing vector using a data crawling model. The first processor may output mutual row data or menu row data based on a comparison result between a pre-registered reference vector representing a specific business name or menu and a feature vector.

A data crawling model may be pre-learned based on a plurality of training data. A learning feature vector may be obtained from training data belonging to a first criterion among a plurality of training data by using a data crawling model. Here, the criterion means a classification target, and may include, for example, a specific trade name or menu. Residuals between each reference vector of a criterion different from the first criterion and the learning feature vector may be obtained by using the data crawling model.

A high-intensity learning parameter may be calculated based on a reference residual between a first reference vector of the first reference and a second reference vector of the second reference and a feature residual between the first reference vector and the learning feature vector. For example, the first reference vector may be a vector representing the user's business name or menu, and the second reference vector may be a vector representing a business name or menu unrelated to the user's business.

A loss value may be calculated through a loss function including residuals between feature vectors and reference vectors of residuals to which high-intensity learning parameters are applied to feature residuals and criteria different from the first criterion.

Based on the loss values, parameters of the data crawling model or one or more reference vectors may be learned. The learning device may learn parameters or one or more reference vectors of a data crawling model in a direction in which a loss value becomes smaller or larger.

와 제2 기준의 제2 기준 벡터

간의 기준 레지듀얼

및 제1 기준 벡터

와 특징 벡터

간의 특징 레지듀얼

를 기초로 고강도 학습 파라미터

을 계산할 수 있다. 여기서, 제2 기준 벡터

는 하나 이상의 기준 벡터 중에서 제1 기준 벡터

와 레지듀얼의 차이가 가장 작은 기준 벡터일 수 있다.The learning device may further improve the ability of the neural network to answer correctly and the discrimination ability by applying the high-intensity learning parameter. For example, the learning device may use the loss function of Equation 1. The learning device is a first reference vector of the first reference according to Equation 4

and the second criterion vector of the second criterion

Baseline residual of the liver

and the first reference vector

and feature vector

Liver Characteristics Residual

High-intensity learning parameters based on

can be calculated. Here, the second reference vector

Is a first reference vector among one or more reference vectors

It may be a reference vector having the smallest difference between the residual and the residual.

는 수학식 3에 의해 계산될 수 있다. 제1 기준 벡터

와 제j 기준의 제j 기준 벡터 wj의 내적을 코사인 값으로 하는 레지듀얼이 기준 레지듀얼

일 수 있다. 특징 레지듀얼

는 수학식 2에 의해 계산될 수 있다. 제1 기준 벡터

와 특징 벡터

의 내적을 코사인 값으로 하는 레지듀얼이 특징 레지듀얼

일 수 있다.Criteria Residual

can be calculated by Equation 3. First reference vector

and the residual whose cosine is the dot product of the jth criterion vector wj of the jth criterion is the criterion residual

can be Features Residual

can be calculated by Equation 2. First reference vector

and feature vector

Residual characterized by a residual whose cosine is the dot product of

can be

에 대한 특징 레지듀얼

및 기준 레지듀얼

간의 차이의 비율을 계산할 수 있다. 수학식 3을 참조하면, 특징 레지듀얼이 작을수록 고강도 학습 파라미터는 커지고, 기준 레지듀얼이 클수록 고강도 학습 파라미터가 커진다. 특징 레지듀얼이 작다는 것은 입력 이미지의 특징 벡터와 정답 기준을 의미하는 제1 기준의 제1 기준 벡터의 유사도가 높다는 것을 의미하며, 입력 이미지가 속한 기준을 잘 맞추는 것을 의미한다. 기준 레지듀얼이 크다는 것은 서로 다른 기준 간의 구별 능력이 크다는 것을 의미한다. The learning device may calculate a ratio of a difference between a feature residual to a reference residual and a reference residual as a high-intensity learning parameter. The learning device is the reference residual according to Equation 3

Features for Residual

and reference residual

The ratio of the difference between them can be calculated. Referring to Equation 3, the higher the feature residual is, the higher the high-intensity learning parameter is, and the larger the reference residual is, the higher the high-intensity learning parameter is. A small feature residual means that the similarity between the feature vector of the input image and the first reference vector of the first criterion indicating the correct answer criterion is high, and it means that the criterion to which the input image belongs is well matched. A large criterion residual means that the ability to discriminate between different criteria is large.

The performance of the neural network is recognized as degraded due to the high-intensity learning parameter, and the learning device learns the neural network more strongly. Due to the high-intensity learning parameter, as the residual inside the trigonometric function of the correct answer term in Equation 1 increases, the proportion of the correct term decreases, and the loss value of the loss function increases. As a result, the learning device learns the neural network more excessively, and the learning effect may be higher than when there is no high-intensity learning parameter. As the learning progresses and the performance of the neural network improves, the high-intensity learning parameter increases, and the learning result of the neural network can be further improved.

The server 310 may parse mutual row data or menu row data crawled from each platform and output mutual analysis data or menu analysis data corresponding to each platform. The server 310 may generate a marketing analysis result corresponding to each platform based on mutual analysis data or menu analysis data corresponding to each platform using the marketing analysis model.

The first processor may generate a marketing analysis result corresponding to each platform by using a marketing analysis model composed of a neural network. A marketing analytics model may include an input layer, one or more hidden layers, and an output layer.

The marketing analysis model may be pre-learned through learning data. The marketing analysis model may be learned by the server 310 or a learning device different from the server 310 .

A plurality of learning data related to mutual analysis data or menu analysis data may be input to an input layer of a marketing analysis model, pass through one or more hidden layers and an output layer, and output vectors. The output vector may be input to a loss function layer connected to the output layer. The loss function layer may output a loss value using a loss function that compares an output vector with a correct answer vector for each training data. Parameters of the marketing analysis model may be learned in a direction in which a loss value becomes smaller.

A loss function used in the learning process of the marketing analysis model may follow Equation 1. In Equation 1, N is the number of learning data, n is a natural number identifying the learning data, k is a natural number identifying the value of the nth learning data, nk is the kth value of the nth learning data, t denotes correct answer data, y denotes an output vector, and E may denote a loss value.

For example, the plurality of learning data related to mutual analysis data or menu analysis data corresponds to the number of times of search for each other or menu, preferences, comparison sentences with other trade names or other menus, evaluation sentences for the menu itself, and trade names. It may include at least one of evaluation sentences on accessibility, atmosphere, price, and taste of the store.

The server 310 may process requests for each of the platform servers 331 and 333 in an asynchronous manner. The first processor may crawl mutual row data related to a mutual keyword or menu row data related to a menu keyword during a first time interval from the first platform. The first processor may crawl mutual row data related to a mutual keyword or menu row data related to a menu keyword during a second time interval from the second platform. In the first processor, all or part of the first time interval and the second time interval may overlap.

The first processor may generate a marketing analysis result for the first platform through a URL for the first platform through an event loop using a single thread. After the marketing analysis result for the first platform is generated, the first processor may generate a marketing analysis result for the second platform through a URL for the second platform through an event loop.

The server 310 may generate one result page using the marketing analysis result corresponding to each platform. After the marketing analysis result for the second platform is generated, the first processor may generate a result page using the marketing analysis result for the first platform and the marketing analysis result for the second platform. The server 310 may transmit a result page to the client 320 through the first transceiver. For example, the server 310 may transmit the return value of the calling function to the client 320 through the first transceiver.

The marketing analysis result may include a preference for a company name, a relative score or an absolute score, a preference for a menu, a relative score or an absolute score, and a utility score for each platform compared to marketing costs. Marketing analysis results can be expressed in the form of sentences, images or graphs.

As such, the marketing evaluation system can analyze marketing or advertisement effects related to the keywords on various platforms only by simple keyword input at the client side.

The marketing evaluation system can more precisely collect data that has a high relevance to an input keyword and a low relevance to a different keyword in a data crawling process.

The marketing evaluation system can increase the accuracy of crawled data and improve the reliability of analysis results by simultaneously analyzing text and images.

The marketing evaluation system provides highly readable analysis results for various indicators including customer preference for the company name or menu.

4 is a flowchart illustrating the operation of a marketing evaluation system according to an embodiment.

Referring to FIG. 4 , according to an embodiment, in operation S401, the client 320 may generate a keyword input signal representing mutual keywords and menu keywords for each platform based on a user input through an input unit. there is.

According to one embodiment, in operation S402, the client 320 combines the address information of the server 310 with the keyword input signal for each platform through the second processor to obtain the server 310 for each platform. You can create URLs for .

According to one embodiment, in operation S403, the server 310 may analyze the URL toward the server 310 through the first processor and extract mutual keywords and menu keywords.

According to an embodiment, in operation S404, the first processor may generate a URL for each platform by combining the mutual keyword and the menu keyword with address information of each platform.

According to one embodiment, in operation S405, the first processor may access each platform of the network using the URL for each platform.

According to one embodiment, in operation S406, the first processor may crawl mutual row data related to mutual keywords or menu row data related to menu keywords from the source data of each platform.

According to one embodiment, in operation S407, the first processor means that the source data means data stored in the platform, and parses mutual row data or menu row data crawled from each platform to obtain data corresponding to each platform. Mutual analysis data or menu analysis data can be output.

According to one embodiment, in operation S408, the first processor generates a marketing analysis result corresponding to each platform based on mutual analysis data or menu analysis data corresponding to each platform using the marketing analysis model. can

According to one embodiment, in operation S409, the first processor may generate a result page using the marketing analysis result corresponding to each platform.

5 is an example illustrating an asynchronous operation of a marketing evaluation system according to an embodiment.

Referring to FIG. 5 , in an algorithm 501, a client 320 may request a search for a specific keyword from the server 310. The user may input his/her company name and menu into the mobile terminal corresponding to the client 320 and press a search button. The client 320 may call a function requesting a search from the server 310 .

The client 320 may generate a URL by combining an address of the server 310 and a keyword input in response to a search button input signal. The client 320 may transmit the generated URL to the server 310 .

Server 310 may receive a URL. The server 310 may extract a desired value by analyzing the URL. The server 310 may analyze the URL and extract the keyword by separating the address of the server 310 and the keyword input by the user.

The server 310 may generate a URL by combining the extracted keyword with the address of each platform server 331 or 333 in order to search in each platform server 331 or 333 using the extracted keyword. The server 310 may access each platform server 331 and 333 using the generated URL.

Server 310 may crawl the source data from each platform server (331, 333). The server 310 may obtain raw data related to the keyword by parsing the source data. The server 310 may analyze raw data and output analysis data. Server 310 may generate a marketing analysis result by synthesizing the analysis data of each platform server (331, 333).

Although the algorithm 501 has been described above, the above process may be performed in the same or similar manner in the algorithms 502, 503, 504, and 505. However, the process of acquiring source data may be further subdivided or simplified according to the unique structure of the platform.

In operation 506, the server 310 may generate a result page using the marketing analysis result of each platform server (331, 333). In operation 507, the server 310 may transmit a result page to the client 320 as a response value.

6 is a diagram illustrating the structure of a data crawling model according to an embodiment.

According to an embodiment, the learning device may include a preceding layer 603, a feature extraction unit 605, a classification unit 607, a high-intensity learning parameter calculator 609, and a loss value calculator 611. Each of the feature extractor 605, classifier 607, high-intensity learning parameter calculator 609, and loss value calculator 611 may be composed of one or more layers. The preceding layer 603, feature extraction unit 605, classification unit 607, high-intensity learning parameter calculation unit 609, and loss value calculation unit 611 may be included in one neural network or configured as separate objects. may be

The classification unit 607 may include a plurality of layers corresponding to a plurality of criteria. Parameters of the layer corresponding to each criterion may form a vector. Here, a vector of a layer corresponding to each criterion may be referred to as a criterion vector.

The learning device may receive learning data 601 as an input. Learning data 601 may be input to a preceding layer 603 . The preceding layer 603 may output a vector from the training data 601 . A vector may be input to the feature extractor 605 . The feature extractor 605 may output feature vectors. The feature vectors 613 and 319 may be transmitted to the high intensity learning parameter calculator 609 and the loss value calculator 611 .

According to one embodiment, the feature vector may be passed to the classification unit 607 . According to another embodiment, the vector output from the preceding layer 603 may be transferred to the classification unit 607 . The classification unit 607 may output a classification result of the feature vector. The classification results 617 and 319 may be transmitted to the high-intensity learning parameter calculation unit 609 and the loss value calculation unit 611 .

The high-intensity learning parameter calculation unit 609 may calculate the high-intensity learning parameter based on the feature vector 613 and the classification result 617 . The learning device calculates the high-intensity learning parameter based on a reference residual between a first reference vector of the first reference and a second reference vector of the second reference and a feature residual between the first reference vector and the feature vector. The learning device may calculate a ratio of a difference between a feature residual to a reference residual and a reference residual as a high-intensity learning parameter.

The loss value calculation unit 611 may calculate a loss value based on the feature vector 615 , the classification result 619 , the reference label 623 , and the high-intensity learning parameter 621 . The reference label 623 means a correct answer. The learning apparatus may calculate a loss value through a loss function including a residual to which a high-intensity learning parameter is applied to the feature residual. The loss function is the ratio of the exponent values for the feature residuals of the first criterion to the sum of the exponent values for the feature residuals of all criteria except the first criterion and the sum of the exponent values for the feature residuals of the first criterion. can include As the learning progresses, the high-intensity learning parameter is gradually increased and learning becomes easier. By determining and applying a high-intensity learning parameter suitable for each criterion and learning data, the learning device can further enhance the effect of learning.

7 is a diagram illustrating a configuration of a server according to an embodiment.

According to an embodiment, the server 700 (eg, the server 310 of FIG. 3) includes a memory 710 (eg, the memory 130 of FIG. 1), a processor 720 (eg, the processor of FIG. 1 ( 120)) and a transceiver 730 (eg, the communication module 190 of FIG. 1).

The memory 710 may store a server program and address information of a plurality of platforms.

The processor 720 may extract mutual keywords and menu keywords by analyzing URLs directed to the server 700 . The processor 720 may generate a URL for each platform by combining the mutual keyword and the menu keyword with address information of each platform. The processor 720 may access each platform through a network using a URL for each platform. The processor 720 may crawl mutual row data related to mutual keywords or menu row data related to menu keywords from source data of each platform. The processor 720 may parse mutual row data or menu row data crawled from each platform and output mutual analysis data or menu analysis data corresponding to each platform. The processor 720 may generate a marketing analysis result corresponding to each platform based on mutual analysis data or menu analysis data corresponding to each platform using the marketing analysis model. The processor 720 may generate a result page using marketing analysis results corresponding to each platform.

A result page may be transmitted to the client through the transceiver 730 .

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 (5)

In the marketing evaluation system,
The marketing evaluation system includes a client and a server, the server and the client are connected by a network,
The server includes: a first processor; a first memory; and a first transceiver;
The client includes: an input unit; a second processor; a second memory; and a second transceiver;
The first memory stores a server program and address information of a plurality of platforms, and the second memory stores a client program and address information of the server,
The client is:
Generates a keyword input signal representing a mutual keyword and a menu keyword for each platform based on a user input through the input unit;
Converting the keyword input signal into keyword data through the second processor;
Transmitting the keyword data to the server through the second transceiver;
The server:
extracting the mutual keyword and the menu keyword from the keyword data through the first processor, and generating a URL for each platform by combining the mutual keyword and the menu keyword with address information of each platform; Access to each platform through the network using the URL for each platform, and mutual raw data related to the mutual keyword or menu raw data related to the menu keyword from the source data of each platform Crawling, wherein the source data means data stored in the platform, and the mutual analysis data or menu analysis corresponding to each platform by parsing the mutual raw data or the menu raw data crawled from each platform, respectively outputting data, generating a marketing analysis result corresponding to each platform based on the mutual analysis data or the menu analysis data corresponding to each platform using a marketing analysis model, and corresponding to each platform Create a result page using the results of marketing analysis,
Sending the result page to the client through the first transceiver;
The source data includes text data and image data,
The first processor:
Using a raw data preprocessing model, outputting a text vector corresponding to the text data and outputting an image vector corresponding to the image data, performing concatenation on the text vector and the image vector, and preprocessing generating a vector, and crawling mutual row data related to the mutual keyword or menu row data related to the menu keyword based on the preprocessing vector from each platform using a data crawling model;
The first processor:
Using the data crawling model, the preprocessing vector is input to obtain a feature vector, and based on a comparison result of the feature vector with a pre-registered reference vector representing a specific business name or menu, the mutual raw data or the menu raw data outputs,
The data crawling model is pre-learned based on a plurality of training data, a learning feature vector is obtained from training data belonging to a first criterion among a plurality of training data using the data crawling model, and a learning feature vector is obtained using the data crawling model. A residual between each reference vector of a criterion different from the first reference and the learning feature vector is obtained, and a reference residual between a first reference vector of the first reference and a second reference vector of a second reference and the first reference A high-intensity learning parameter is calculated based on a feature residual between a vector and the learning feature vector, and between a residual to which the high-intensity learning parameter is applied to the feature residual and a reference vector of a criterion different from the first criterion and the feature vector. A loss value is calculated through a loss function including a residual, and parameters of the data crawling model or the one or more reference vectors are learned based on the loss value.
Marketing evaluation system.
According to claim 1,
The first processor generates a marketing analysis result corresponding to each platform using a marketing analysis model composed of a neural network;
The marketing analysis model includes an input layer, one or more hidden layers and an output layer,
A plurality of learning data related to mutual analysis data or menu analysis data is input to the input layer of the marketing analysis model, passes through the one or more hidden layers and output layers, and outputs an output vector, and the output vector is output to the output layer. It is input to the connected loss function 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 parameter of the marketing analysis model is the loss value learning in the direction of decreasing,
[mathematical expression]

The loss function follows the above equation,
In the above equation, N is the number of the plurality of training data, n is a natural number identifying the learning data, k is a natural number identifying the value of the n th learning data, nk is the k th value of the n th learning data, t means the correct answer data, y means the output vector, E means the loss value,
The mutual analysis data or the plurality of learning data related to the menu analysis data is the number of searches per time period for the trade name or the menu, preferences, comparison sentences with other trade names or other menus, evaluation sentences for the menu itself, and corresponding to the trade names. Including at least one of the accessibility to the store, the atmosphere, the price, the evaluation sentence for the taste, the image of the trade name, and the image of the menu,
Marketing evaluation system. According to claim 1,
The plurality of platforms include a first platform and a second platform,
The first processor:
Crawling mutual raw data related to the mutual keyword or menu raw data related to the menu keyword during a first time period from the first platform, and mutual raw data related to the mutual keyword during a second time period from the second platform or Crawling menu raw data related to the menu keyword, and the first time interval and the second time interval may overlap in whole or in part,
Marketing evaluation system. According to claim 3,
The first processor:
Through an event loop using a single thread, a marketing analysis result for the first platform is generated through a URL for the first platform, and a marketing analysis result for the first platform is generated After generating a marketing analysis result for the second platform through the URL for the second platform through the event loop,
After the marketing analysis result for the second platform is generated, generating the result page using the marketing analysis result for the first platform and the marketing analysis result for the second platform,
Marketing evaluation system. According to claim 1,
The first processor:
Mutual parameters are generated by converting the mutual keywords, menu parameters are generated by converting the menu keywords, and URLs for each platform are combined with the mutual parameters or the menu parameters at the end of the address information of the respective platforms. create,
The first processor crawls mutual row data related to the mutual keyword or menu raw data related to the menu keyword from each platform by using the URL for each platform.
Marketing evaluation system.

Images