System for generating machine learning model through website crawling automation

Abstract

The present invention relates to a machine learning model generation system through website crawling automation. By automatically collecting HTML documents from various websites while minimizing user intervention through machine learning model development (ML Dev) and machine learning model operation (ML Ops), and by minimizing the performance gap between data and machine learning models that occurs between ML Dev and ML Ops, the invention has the effect of popularizing data analysis and service development through the development of machine learning model operation (ML Ops) that can be automatically operated even by non-experts.

Description

Technology Field

[0001] The present invention relates to a machine learning model generation system through website crawling automation. Background Technology

[0002] Recently, the exponentially increasing volume of big data has surpassed zettabytes and reached yottabytes.

[0003] To analyze such vast amounts of data, data collection must be carried out first. Data collection should be limited to websites where it is permitted. Furthermore, even for websites where collection is allowed, user intervention becomes required whenever a new website is added, as the coding methods and styles of each HTML document vary.

[0004] Therefore, to increase work efficiency by reducing user intervention, more advanced and automated data collection technology than the current method is required.

[0005] Before explaining these advanced technologies, Machine Learning Developments (ML Dev) refers to the process of developing models for machine learning. When developing a machine learning model, tasks such as adjusting hyperparameter values ​​to improve accuracy are required. Furthermore, Machine Learning Operations (ML Ops) refers to the stage where machine learning model development (ML Dev) is completed and the model is deployed for actual service operation.

[0006] It also refers to a system built so that once machine learning model development (ML Dev) is completed, it is automatically deployed to a service through machine learning model operation (ML Ops) without user intervention to start operations immediately, and allows for immediate data training when necessary.

[0007] Finally, advanced technology refers to a technology that utilizes the machine learning model development (ML Dev) and machine learning model operation (ML Ops) mentioned in the technology field to autonomously learn the diverse coding methods and styles of HTML documents and automatically extract and collect only the data desired by the user.

[0008] As mentioned above, in order to collect vast amounts of data, it is necessary to read HTML documents from websites where data collection is permitted and extract only the data desired by the user. This requires significant user intervention, and there is a need for technology that can simplify data collection and minimize user intervention by utilizing the aforementioned machine learning model development (ML Dev) and machine learning model operation (ML Ops).

[0009] Furthermore, not only are different websites affected, but even within a single website, styles often change depending on the season or event. Whenever this occurs, users are required to intervene to collect data; consequently, there is a need for technology that minimizes user intervention to prevent a decline in work efficiency. Prior art literature

[0010] Korean Patent Publication No. 10-2021-0055934 (Published on May 18, 2021) The problem to be solved

[0011] The present invention has been devised to solve the aforementioned problems, and the objective of the present invention is to provide a machine learning model generation system through website crawling automation that automatically collects HTML documents from different websites while minimizing user intervention through machine learning model development (ML Dev) and machine learning model operation (ML Ops), minimizes the performance gap between data and machine learning models occurring between ML Dev and ML Ops, and popularizes data analysis and service development through the development of machine learning model operation (ML Ops) that can be automatically operated even by non-experts. means of solving the problem

[0012] To achieve the aforementioned objective, one aspect of the present invention comprises: an HTML document collection device that collects HTML documents using a pre-configured list of website URLs; a data extraction device that receives HTML documents collected from the HTML document collection device and extracts data desired by a user from the collected HTML documents using a pre-distributed machine learning model based on pre-configured HTML collection rule information; a data collection device that collects the data desired by the user extracted from the data extraction device; and a data verification device that receives the data desired by the user collected from the data collection device and verifies the collected data desired by the user as correct data or incorrect data according to a pre-configured threshold value using a pre-configured data semantic analysis technique based thereon. The present invention provides a machine learning model generation system through website crawling automation, comprising a machine learning model generation device that uses a pre-configured artificial intelligence (AI)-based machine learning method based on the pre-configured website URL list and HTML collection rule information, along with correct data verified by the data verification device, to automatically discover the location of the HTML document where the data desired by the user is located, and then repeatedly trains the pre-distributed machine learning model until it reaches a pre-configured accuracy, and then generates the machine learning model that has reached the pre-configured accuracy as a new machine learning model for operating a website crawling automation service and automatically distributes it to the data extraction device.

[0013] Here, a user input device that outputs a specific input signal by user operation is further included, and it is preferable that the website URL list and the HTML collection rule information are pre-set according to the specific input signal output from the user input device.

[0014] Preferably, a user input device that outputs a specific input signal by user operation is further included, wherein incorrect data verified by the data verification device may be deleted or stored in a separate data storage device according to the specific input signal output from the user input device.

[0015] Preferably, a user input device that outputs a specific input signal by user operation is further included, and if incorrect data verified by the data verification device according to the specific input signal output from the user input device is determined to be correct data, the determined correct data may be stored in a separate data storage device.

[0016] Preferably, the machine learning model generation device can repeatedly train the pre-distributed machine learning model until it reaches a preset accuracy, using the preset artificial intelligence (AI)-based machine learning method based on the preset website URL list and HTML collection rule information along with the correct data stored in the data storage device, so as to automatically discover the location of the HTML document where the data desired by the user is located.

[0017] Preferably, a data storage device may further be included to store and manage the above-mentioned pre-set website URL list, the above-mentioned pre-set HTML collection rule information, HTML documents collected from the above-mentioned HTML document collection device, and correct data verified by the above-mentioned data verification device in a database (DB).

[0018] Preferably, the data verification device receives the data desired by the user collected from the data collection device and, based on this, uses a pre-set data semantic analysis technique to determine if the collected data desired by the user is greater than or equal to the pre-set threshold value; if so, it passes as collected correct data and stores it in a separate data storage device, and if it is smaller than the pre-set threshold value, it verifies it as incorrect data.

[0019] Preferably, the machine learning model generation device can repeatedly learn the coding method or style of the collected HTML document through the pre-distributed machine learning model by finding the optimal value while automatically adjusting the pre-set HTML collection rule information in order to extract only the data desired by the user from the HTML document collected from the HTML document collection device.

[0020] Preferably, the above-mentioned pre-configured HTML collection rule information may include hyperparameter values ​​for training the above-mentioned pre-distributed machine learning model.

[0021] Preferably, the artificial intelligence (AI)-based machine learning method applied to the machine learning model generation device may consist of at least one artificial intelligence learning method among a neural network, a support vector machine (SVM), a multi-layer perception (MLP), and deep learning.

[0022] Preferably, the incorrect data verified by the data verification device may consist of advertising data or test data. Effects of the invention

[0023] According to the machine learning model generation system through website crawling automation of the present invention as described above, there is an advantage in that HTML documents from different websites are automatically collected with minimal user intervention through machine learning model development (ML Dev) and machine learning model operation (ML Ops), and the performance difference between data and machine learning models between machine learning model development (ML Dev) and machine learning model operation (ML Ops) is minimized, and data analysis and service development can be popularized through the development of machine learning model operation (ML Ops) that can be automatically operated even by non-experts.

[0024] In addition, according to the present invention, there is an advantage of increasing work efficiency by not requiring much user intervention in cases where data collection must be done in multiple places or where HTML documents change every season and event on a specific website, requiring the collection rules to be changed and reset each time.

[0025] In addition, according to the present invention, since the structure of an HTML document is automatically and repeatedly learned through a one-time initial setting and data can be collected according to the changed structure, the user can focus on more important tasks, which has the advantage of not only improving work efficiency but also extracting highly accurate data when collecting data. Brief explanation of the drawing

[0026] FIG. 1 is an overall block diagram illustrating a machine learning model generation system through website crawling automation according to one embodiment of the present invention. Specific details for implementing the invention

[0027] The aforementioned objectives, features, and advantages are described in detail below with reference to the attached drawings, and accordingly, a person skilled in the art to which the present invention pertains will be able to easily implement the technical concept of the present invention. In describing the present invention, detailed descriptions of known technologies related to the present invention are omitted if it is determined that such descriptions may unnecessarily obscure the essence of the present invention.

[0028] Terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but said components are not limited by said terms. Such terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The terms used in this application are used merely to describe specific embodiments and are not intended to limit the present invention. A singular expression includes a plural expression unless the context clearly indicates otherwise.

[0029] The terms used in this invention have been selected based on currently widely used general terms, taking into account their functions within the invention; however, these terms may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this invention should be defined not merely by their names, but based on their meanings and the overall content of the invention.

[0030] When a part of a specification is described as "comprising" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "...part" or "module" as used in the specification refer to a unit that processes at least one function or operation, and this may be implemented in hardware or software, or as a combination of hardware and software.

[0031] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the embodiments of the present invention exemplified below may be modified in various different forms, and the scope of the present invention is not limited to the embodiments described below. The embodiments of the present invention are provided to more completely explain the present invention to those skilled in the art.

[0032] Combinations of each block of the attached block diagram and each step of the flowchart may be executed by computer program instructions (execution engines), and since these computer program instructions may be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment create means to perform the functions described in each block of the block diagram or each step of the flowchart. Since these computer program instructions may also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement the function in a specific way, the instructions stored in computer-available or computer-readable memory may also produce a manufactured item containing instruction means to perform the function described in each block of the block diagram or each step of the flowchart.

[0033] And, since computer program instructions can be loaded onto a computer or other programmable data processing equipment, instructions that execute a computer or other programmable data processing equipment by performing a series of operation steps on the computer or other programmable data processing equipment to create a process executed on the computer can also provide steps for executing the functions described in each block of the block diagram and each step of the flowchart.

[0034] Additionally, each block or each step may represent a module, segment, or part of code containing one or more executable instructions for executing specific logical functions, and it should be noted that in some alternative embodiments, the functions mentioned in the blocks or steps may occur out of order. For example, two blocks or steps described in succession may actually be performed substantially simultaneously, and the blocks or steps may also be performed in the reverse order of the corresponding functions as needed.

[0035] FIG. 1 is an overall block diagram illustrating a machine learning model generation system through website crawling automation according to one embodiment of the present invention.

[0036] Referring to FIG. 1, a machine learning model generation system through website crawling automation according to one embodiment of the present invention comprises, broadly, an HTML document collection device (100), a data extraction device (200), a data collection device (300), a data verification device (400), and a machine learning model generation device (500). Additionally, a machine learning model generation system through website crawling automation according to one embodiment of the present invention may further include a user input device (600), a data storage device (700), etc. Meanwhile, since the components shown in FIG. 1 are not essential, a machine learning model generation system through website crawling automation according to one embodiment of the present invention may have more components or fewer components.

[0037] Hereinafter, the components of a machine learning model generation system through website crawling automation according to one embodiment of the present invention will be examined in detail as follows.

[0038] The HTML document collection device (100) performs the function of collecting HTML documents through a communication network (10) using a pre-configured list of website URLs. At this time, the communication network (10) is a high-speed backbone network of a large communication network capable of providing large-capacity, long-distance voice and data services, and may be a next-generation wireless communication network including WiFi, WiGig, WiBro (Wireless Broadband Internet), WiMAX (World Interoperability for Microwave Access), etc., for providing Internet or high-speed multimedia services.

[0039] The above Internet refers to a global open computer network structure that provides the TCP / IP protocol and various services existing in the upper layer, namely HTTP (Hyper Text Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP (Simple Network Management Protocol), NFS (Network File Service), NIS (Network Information Service), etc., and provides an environment that enables an HTML document collection device (100) to connect to a web server (not shown) operating a specific website. Meanwhile, the above Internet may be a wired or wireless Internet, and may also be a core network integrated with a wired public network, a wireless mobile communication network, or a mobile Internet.

[0040] If the communication network (10) is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. As an example of the asynchronous mobile communication network, a WCDMA (Wideband Code Division Multiple Access) type communication network may be cited. In this case, although not shown in the drawing, the mobile communication network may include, for example, an RNC (Radio Network Controller). Meanwhile, although the WCDMA network was given as an example, it may be a next-generation communication network such as a cellular-based 3G network, LTE network, 4G network, 5G network, or other IP-based IP networks. Such a communication network (10) performs the role of mutually transmitting signals and data between the HTML document collection device (100) and the web server.

[0041] The data extraction device (200) receives an HTML document collected from the HTML document collection device (100) and performs the function of extracting data desired by the user from the collected HTML document using a machine learning model distributed in advance based on pre-set HTML collection rule information.

[0042] At this time, it is desirable that the aforementioned pre-configured HTML collection rule information include, for example, hyperparameter values ​​for training the pre-distributed machine learning model. The hyperparameter values ​​are values ​​reflected in the training process of the machine learning model; simply put, they are values ​​directly set by the developer of the machine learning model. That is, the hyperparameter values ​​consist of values ​​that are tuned by repeating training and validation until the model, which is set for data learning, achieves the desired performance. These hyperparameters may include, for example, the learning rate, the number of hidden layers, the batch size, the number of epochs, the type of loss function, C, gamma, depth, etc.

[0043] The data collection device (300) performs the function of collecting the data desired by the user extracted from the data extraction device (200).

[0044] The data verification device (400) receives the data desired by the user collected from the data collection device (300) and performs the function of verifying the collected data desired by the user as correct data or incorrect data according to a pre-set threshold value using a pre-set data semantic analysis technique based on the data.

[0045] That is, the data verification device (400) receives the data desired by the user collected from the data collection device (300) and, based on this, uses a pre-set data semantic analysis technique. If the collected data desired by the user is greater than or equal to the pre-set threshold value, it passes as collected correct data and stores it in a separate data storage device (700), and if it is smaller than the pre-set threshold value, it can verify it as incorrect data.

[0046] In addition, it is preferable that the incorrect data verified by the data verification device (400) consists of, for example, advertising data or test data.

[0047] Meanwhile, the aforementioned established data semantic analysis technique is intended to verify whether the collected data is correct data desired by the user or incorrect data not desired by the user. Various conventional Natural Language Processing (NLP) algorithms can be applied for this purpose, and since such NLP algorithms are well known and the present invention is not characterized by limiting such algorithms, a description thereof is replaced by known technology.

[0048] It is preferable that each of the device components described above, namely the HTML document collection device (100), the data extraction device (200), the data collection device (300), and the data verification device (400), be applied to a machine learning model operation (ML Ops) system.

[0049] And, the machine learning model generation device (500) performs the function of automatically distributing the machine learning model that has been previously distributed until it reaches a pre-set accuracy, using a machine learning method based on a pre-set artificial intelligence (AI) based on the pre-set website URL list and HTML collection rule information along with the correct data verified by the data verification device (400) to automatically discover the location of the HTML document where the data desired by the user is located, and then generating the machine learning model that has reached the pre-set accuracy as a new machine learning model for operating a website crawling automation service and automatically distributing it to the data extraction device (200).

[0050] In addition, the machine learning model generation device (500) can perform the function of repeatedly training the pre-distributed machine learning model until it reaches a pre-set accuracy, using the pre-set artificial intelligence (AI)-based machine learning method based on the correct data stored in the data storage device (700) and the pre-set website URL list and HTML collection rule information, so that it can automatically discover the location of the HTML document where the data desired by the user is located.

[0051] In addition, the machine learning model generation device (500) can perform the function of repeatedly learning the coding method or style of the collected HTML document through the machine learning model that was previously distributed, by finding the optimal value while automatically adjusting the pre-set HTML collection rule information in order to extract only the data desired by the user from the HTML document collected from the HTML document collection device (100).

[0052] In addition, the artificial intelligence (AI)-based machine learning method applied to the machine learning model generation device (500) is preferably composed of at least one artificial intelligence learning method among, for example, a neural network, a support vector machine (SVM), a multi-layer perception (MLP), and / or deep learning.

[0053] It is preferable that the machine learning model generation device (500) described above be applied to a machine learning model development (ML Dev) system.

[0054] Additionally, the user input device (600) performs the function of outputting a specific input signal by user operation. Furthermore, it is preferable that the website URL list and the HTML collection rule information are pre-set according to the specific input signal output from the user input device (600).

[0055] Additionally, incorrect data verified by the data verification device (400) may be deleted or stored in a separate data storage device (700) according to a specific input signal output from the user input device (600).

[0056] Additionally, if incorrect data verified by the data verification device (400) according to a specific input signal output from the user input device (600) is determined to be correct data, the determined correct data can be stored in a separate data storage device (700).

[0057] This user input device (600) is a module that outputs a specific key input signal by having at least one key button, and can output a key input signal corresponding to a key input by the user by having multiple character keys, number keys and various function keys.

[0058] Meanwhile, the user input device (600) is preferably implemented to include a plurality of key buttons, but is not limited thereto. For example, it may be composed of a keyboard, mouse, remote control, etc., and in some cases, it may be composed of at least one mobile terminal device among a smartphone, smart pad, or smart note that communicates via wireless internet or mobile internet. In addition, it may comprehensively refer to all wired / wireless home appliances / communication devices having a user interface for outputting a specific key input signal, such as a personal PC, notebook PC, Palm PC, mobile game console (Mobile PlayStation), DMB (Digital Multimedia Broadcasting) phone with communication function, tablet PC, iPad, etc.

[0059] The data storage device (700) performs the function of storing and managing at least one of the following data in a database (DB): the pre-set website URL list, the pre-set HTML collection rule information, HTML documents collected from the HTML document collection device (100), and / or correct data verified from the data verification device (400).

[0060] The data storage device (700) may include at least one type of storage medium among, for example, a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), 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, a magnetic disk, and an optical disk.

[0061] It is preferable that each of the device components described above, namely the user input device (600) and the data storage device (700), be applied to both the machine learning model development (ML Dev) system and the machine learning model operation (ML Ops) system.

[0062] In addition, although not shown in the drawings, a power supply device is further included to supply power to each of the aforementioned device components, namely, an HTML document collection device (100), a data extraction device (200), a data collection device (300), a data verification device (400), a machine learning model generation device (500), a user input device (600), and / or a data storage device (700). For continuous power supply, it is preferable to implement a commercial AC power source (e.g., AC 220V) to convert DC and / or AC power sources, but is not limited thereto, and may be implemented by including a conventional portable battery.

[0063] In addition, the power supply unit may include a power management unit (not shown) that performs the function of protecting components from external power shocks and outputting a constant voltage. The power management unit may comprise an ESD (Electro Static Damage) protector, a power detector, a rectifier, and a power circuit breaker.

[0064] Here, the ESD protector is configured to protect electrical components from electrostatic discharge or sudden power shocks. The power detector is configured to send a cutoff signal to the power breaker when a voltage outside the allowable voltage range is introduced, and to transmit a step-up or step-down signal to the rectifier according to voltage changes within the allowable voltage range. The rectifier is configured to perform a step-up or step-down rectification operation according to the signal from the power detector to minimize fluctuations in the input voltage and supply a constant voltage. The power breaker is configured to cut off the power supplied from the battery according to the cutoff signal transmitted from the power detector.

[0065] The various embodiments described herein may be implemented, for example, in a recording medium readable by a computer or similar device using software, hardware, or a combination thereof.

[0066] According to hardware implementation, the embodiments described herein may be implemented using at least one of Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Data Arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and electrical units for performing functions. In some cases, such embodiments may be implemented by at least one of the aforementioned device components.

[0067] According to the software implementation, embodiments such as procedures or functions may be implemented together with separate software modules that perform at least one function or operation. The software code may be implemented by a software application written in a suitable programming language. Additionally, the software code may be stored in a data storage device (700) or a separate storage device (not shown) and may be executed by at least one of the aforementioned device components.

[0068] Although preferred embodiments of a machine learning model generation system through website crawling automation according to the present invention have been described above, the present invention is not limited thereto and can be implemented with various modifications within the scope of the claims, the detailed description of the invention, and the attached drawings, and such modifications are also included in the present invention. Explanation of the symbols

[0069] 100 : HTML document collection device, 200 : Data extraction device, 300 : Data collection device, 400 : Data verification device, 500 : Machine learning model generator, 600 : User input device, 700 : Data storage device

Claims

Claim 1 An HTML document collection device that collects HTML documents using a pre-configured list of website URLs; a data extraction device that receives HTML documents collected from the HTML document collection device and extracts data desired by a user from the collected HTML documents using a pre-distributed machine learning model based on pre-configured HTML collection rule information; a data collection device that collects the data desired by the user extracted from the data extraction device; and a data verification device that receives the data desired by the user collected from the data collection device and verifies the collected data desired by the user as correct data or incorrect data according to a pre-configured threshold value using a pre-configured data semantic analysis technique based on the data. A machine learning model generation device comprising: a machine learning model generation device that uses a pre-configured artificial intelligence (AI)-based machine learning method based on the pre-configured website URL list and HTML collection rule information, along with correct data verified by the data verification device, to automatically discover the location of an HTML document where the data desired by the user is located, and then repeatedly trains the pre-distributed machine learning model until it reaches a pre-configured accuracy, and then generates the machine learning model that has reached the pre-configured accuracy as a new machine learning model for operating a website crawling automation service and automatically distributes it to the data extraction device; wherein the machine learning model generation device is characterized by repeatedly learning the coding method or style of the collected HTML document through the pre-distributed machine learning model while finding the optimal value by automatically adjusting the pre-configured HTML collection rule information to extract only the data desired by the user from the HTML document collected by the HTML document collection device. Claim 2 A machine learning model generation system through website crawling automation according to claim 1, further comprising a user input device that outputs a specific input signal by user operation, wherein the website URL list and HTML collection rule information are pre-set according to the specific input signal output from the user input device. Claim 3 A machine learning model generation system through website crawling automation according to claim 1, further comprising a user input device that outputs a specific input signal by user operation, wherein incorrect data verified by the data verification device is deleted or stored in a separate data storage device according to the specific input signal output from the user input device. Claim 4 A machine learning model generation system through website crawling automation according to claim 1, further comprising a user input device that outputs a specific input signal by user operation, wherein if incorrect data verified by the data verification device according to the specific input signal output from the user input device is determined to be correct data, the determined correct data is stored in a separate data storage device, and the machine learning model generation device is characterized by repeatedly training the pre-distributed machine learning model until it reaches a pre-set accuracy, using the pre-set artificial intelligence (AI)-based machine learning method based on the pre-set website URL list and HTML collection rule information together with the correct data stored in the data storage device to automatically discover the location of the HTML document where the data desired by the user is located. Claim 5 A machine learning model generation system through website crawling automation according to claim 1, further comprising a data storage device that stores and manages the pre-set website URL list, the pre-set HTML collection rule information, HTML documents collected from the HTML document collection device, and correct data verified by the data verification device into a database (DB). Claim 6 A machine learning model generation system through website crawling automation according to claim 1, wherein the data verification device receives data desired by the user collected from the data collection device and, based on this, uses a pre-set data semantic analysis technique to determine if the collected data desired by the user is greater than or equal to the pre-set threshold value, passes it as collected correct data and stores it in a separate data storage device, and if it is smaller than the pre-set threshold value, verifies it as incorrect data. Claim 7 delete Claim 8 A machine learning model generation system through website crawling automation, characterized in that, in claim 1, the pre-configured HTML collection rule information includes hyperparameter values ​​for training the pre-distributed machine learning model. Claim 9 A machine learning model generation system through website crawling automation according to claim 1, wherein the artificial intelligence (AI)-based machine learning method applied to the machine learning model generation device comprises at least one artificial intelligence learning method among a neural network, a support vector machine (SVM), a multi-layer perception (MLP), and deep learning. Claim 10 A machine learning model generation system through website crawling automation, characterized in that, in claim 1, the incorrect data verified by the data verification device consists of advertising data or test data.

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