KR102475305B1 - Method, Apparatus and System for Automatic Interlocking Between Heterogeneous Platform - Google Patents

Abstract

A method, apparatus, and system for automatically linking heterogeneous platforms are disclosed. The linkage method between heterogeneous platforms extracts HTML (Hypertext Mark-up Language) data from a webpage of a second shopping mall to link with a first shopping mall, and X-Pass corresponding to elements included in the webpage from the extracted HTML data. Collect (xpath) data, classify the Xpass data by related Xpass data based on the learned artificial intelligence model, modularize the classified Xpass data, and create a second shopping mall based on the modularized Xpass data A task execution module for performing a specific task can be created in , and the created execution module can be executed.

Description

Method, apparatus and system for automatic interlocking between heterogeneous platforms {Method, Apparatus and System for Automatic Interlocking Between Heterogeneous Platform}

The present invention relates to an automatic interlocking method, apparatus, and system between heterogeneous platforms, and more particularly, to an automatic interlocking method, apparatus, and It's about the system.

In recent years, the popularization of online communication such as the Internet has radically changed the commercial environment more than ever. Now, e-commerce through online is so active that the word “online” or “internet shopping” itself is unfamiliar and feels like an unnecessary modifier. And the expansion trend of the online e-commerce market is expected to further expand.

As online e-commerce companies, that is, shopping malls that sell products online increase exponentially, shopping malls are striving to link data with various platforms to strengthen their image and expand their business. For example, a shopping mall implemented based on a specific platform wants to link services of a different shopping mall implemented based on a different platform to its own shopping mall through linkage with a different platform.

In general, integration of heterogeneous platforms into shopping malls is based on collecting and processing application programming interfaces (APIs). For example, a shopping mall checks and collects corresponding APIs in a target platform for a specific service. If there is no corresponding API, the shopping mall must request the provision of API from the target shopping mall.

However, in this case, a considerable amount of time is required from requesting API provision to receiving API provision. In addition, since the production of AIP is performed by professional personnel, cost and time are consumed, and even if an API is collected from a target shopping mall or an API is provided, there is difficulty in responding to issues and maintaining maintenance when an issue occurs in the corresponding service. Therefore, there is an urgent need to develop a solution that can easily perform linkage between heterogeneous platforms based on a method completely different from the prior art.

Patent Publication No. 2012-0020502

The present invention is to solve this problem, based on X-pass, which is absolute position information of elements, to provide an automatic interworking method, apparatus, and system between heterogeneous platforms that can easily and automatically perform service interworking between heterogeneous platforms. It has a purpose.

In order to achieve this object, the present invention provides a method for linking heterogeneous platforms in one aspect. The interworking method between the heterogeneous platforms is performed by a server that interworks with a plurality of shopping malls, and extracts HTML (Hypertext Mark-up Language) data from a web page of a second shopping mall to interwork with the first shopping mall. The first shopping mall and the second shopping mall are built based on heterogeneous platforms; Collecting xpath data corresponding to elements included in the web page from the extracted HTML data ??Each Xpath data includes an Xpath that is absolute position information of a corresponding element-; Classifying the collected X-Pass data according to the related X-Pass data based on the learned artificial intelligence model; modularizing the classified X-Pass data; generating a task execution module for performing a specific task in the second shopping mall based on the modularized X-Pass data; and performing the task performing module.

The collected X-Pass data includes URL (Universal Resource Locator), which is link information of the web page; keyword corresponding to the element; a tag type corresponding to the element; tag information corresponding to the element; and an X-pass value that is an absolute position value corresponding to the element.

The method may further include receiving a message requesting that the second shopping mall perform the task from the first shopping mall. The task execution module may be performed in response to the message. The task execution module may be a batch file that automatically performs the task from the first shopping mall in the second shopping mall.

The task execution module may receive input data for the task from the first shopping mall, and automatically input the input data to a location requesting the input data on the web page of the second shopping mall when the task is performed. have.

The method of automatically linking the heterogeneous platforms may further include learning the artificial intelligence model. The step of learning the artificial intelligence model may include extracting HTML data from a learning web page of a learning target shopping mall; collecting X-Pass data corresponding to elements included in the learning webpage from HTML data extracted from the learning webpage; learning the artificial intelligence model based on X-pass data corresponding to elements included in the learning webpage so as to classify the X-pass data by associated X-pass data according to feature information and keyword information corresponding to the X-pass data; and storing the learned artificial intelligence model in a database. The learning target shopping mall may include a plurality of shopping malls including a second shopping mall selected by the user of the first shopping mall.

The database linked with the artificial intelligence model includes, for each element, a keyword corresponding to the element; vector values corresponding to the feature information and keyword information of the element; a similar keyword group corresponding to the element; a tag type corresponding to the element; the first creation date corresponding to the element; And it is possible to manage a table including a recent update date corresponding to the element.

The classifying may include extracting feature information of the X-pass data based on link information of the X-pass data; determining a similar keyword group based on keywords of the X-Pass data; The method may include grouping the X-Pass data by related X-Pass data based on the feature information and similar keyword groups.

The task execution module may include at least one unit module. The unit module may include a URL of a target web page, task execution type information, action type information, action sequence information, and task execution target X-pass information.

On the other hand, in order to achieve the above object of the present invention, the present invention provides an interlocking device between heterogeneous platforms in another aspect. In the linking device between heterogeneous platforms that interworks with a plurality of shopping malls, the extracting unit extracts HTML (Hypertext Mark-up Language) data from a web page of a second shopping mall to link with a first shopping mall. ??The first shopping mall and the second shopping mall are built based on heterogeneous platforms-; a collection unit that collects xpath data corresponding to elements included in the web page from the extracted HTML data ??each Xpath data includes Xpath, which is absolute position information of a corresponding element; a classification unit that classifies the collected X-Pass data according to the related X-Pass data based on the learned artificial intelligence model; a modularization unit for modularizing the classified X-Pass data; a task execution module generating unit generating a task execution module for performing a specific task in the second shopping mall based on the modularized X-Pass data; And it may include an execution unit that performs the task execution module.

The collected X-Pass data includes URL (Universal Resource Locator), which is link information of the web page; keyword corresponding to the element; a tag type corresponding to the element; tag information corresponding to the element; and an X-pass value that is an absolute position value corresponding to the element.

The device for interoperating between heterogeneous platforms may further include a receiving unit for receiving a message requesting that the operation be performed in a second shopping mall from the first shopping mall. The task execution module may be performed in response to the message.

The task execution module may be a batch file that automatically performs the task from the first shopping mall in the second shopping mall. The task execution module may receive input data for the task from the first shopping mall, and automatically input the input data to a location requesting the input data on the web page of the second shopping mall when the task is performed. have.

The interlocking device between the heterogeneous platforms may further include a learning unit for learning the artificial intelligence model. The learning unit extracts HTML data from the learning web page of the learning target shopping mall, collects X-pass data corresponding to elements included in the learning web page from the HTML data extracted from the learning web page, and collects the X-pass data. The artificial intelligence model is trained based on X-pass data corresponding to elements included in the learning webpage so that it can be classified for each associated X-pass data according to feature information and keyword information corresponding to Intelligence models can be stored in databases. The learning target shopping mall may include a plurality of shopping malls including a second shopping mall selected by the user of the first shopping mall.

The database linked with the artificial intelligence model includes, for each element, a keyword corresponding to the element; vector values corresponding to the feature information and keyword information of the element; a similar keyword group corresponding to the element; a tag type corresponding to the element; the first creation date corresponding to the element; and managing a table including a recent update date corresponding to the element.

The classification unit extracts feature information of the X-pass data based on link information of the X-pass data, determines a similar keyword group based on keywords of the X-pass data, and determines the feature information and similar keyword group Based on this, the X-pass data may be grouped by related X-pass data.

The task execution module may include at least one unit module. The unit module may include a URL of a target web page, task execution type information, action type information, action sequence information, and task execution target X-pass information.

On the other hand, in order to achieve the above object of the present invention, the present invention provides a linkage system between heterogeneous platforms in another aspect. The interlocking system between the heterogeneous platforms includes: a first shopping mall; a second shopping mall; and extracting HTML (Hypertext Mark-up Language) data from the web page of the second shopping mall for linking with the first shopping mall, and ??the first shopping mall and the second shopping mall are built based on heterogeneous platforms-; From the extracted HTML data, Xpath data corresponding to elements included in the web page are collected, and each Xpath data includes Xpath, which is the absolute position information of the corresponding element. Classifying the X-Pass data by related X-Pass data based on the learned artificial intelligence model, modularizing the classified X-Pass data, and performing a specific task in the second shopping mall based on the modularized X-Pass data It may include a server including an execution unit that generates a task execution module to be performed and performs the task execution module.

The server may receive a message requesting that the second shopping mall perform the task from the first shopping mall, and the task performing module may be performed in response to the message.

The task execution module may receive input data for the task from the first shopping mall, and automatically input the input data to a location requesting the input data on the web page of the second shopping mall when the task is performed. have.

The server extracts HTML data from the learning webpage of the learning target shopping mall, collects Xpass data corresponding to elements included in the learning webpage from the HTML data extracted from the learning webpage, and collects the Xpass data. The artificial intelligence model is trained based on X-pass data corresponding to elements included in the learning webpage so that it can be classified for each associated X-pass data according to feature information and keyword information corresponding to Intelligence models can be stored in databases.

The server extracts feature information of the X-pass data based on link information of the X-pass data, determines a similar keyword group based on keywords of the X-pass data, and determines the feature information and similar keyword group Based on this, the X-pass data may be grouped by related X-pass data.

The task execution module may include at least one unit module. The unit module may include a URL of a target web page, task execution type information, action type information, action sequence information, and task execution target X-pass information.

On the other hand, in order to achieve the above object of the present invention, the present invention is a step of extracting HTML (Hypertext Mark-up Language) data from a web page of a second shopping mall for linking with a first shopping mall to a computer ?? The first shopping mall and the second shopping mall are built based on heterogeneous platforms; Collecting xpath data corresponding to elements included in the web page from the extracted HTML data ??Each Xpath data includes an Xpath that is absolute position information of a corresponding element-; Classifying the collected X-Pass data according to the related X-Pass data based on the learned artificial intelligence model; modularizing the classified X-Pass data; generating a task execution engine for performing a specific task in the second shopping mall based on the modularized X-Pass data; And it may provide a computer program stored in a medium to execute the step of performing the task execution module.

As described above, according to the present invention, service interworking between heterogeneous platforms can be easily and automatically performed based on xpath, which is absolute position information of elements. Accordingly, a seller who sells in a shopping mall can easily sell products in a completely different shopping mall based on a heterogeneous platform in addition to the current shopping mall.

1 is a block diagram showing the configuration of a system for realizing an automatic interworking method between heterogeneous platforms according to a preferred embodiment of the present invention.
FIG. 2 is a block diagram for explaining the detailed configuration of the service server shown in FIG. 1, and shows the configuration of a linkage device between heterogeneous platforms according to a preferred embodiment of the present invention.
3 and 4 are flowcharts showing the operation flow of the device for linking heterogeneous platforms, showing a method for linking heterogeneous platforms according to a preferred embodiment of the present invention.
5 is an exemplary diagram illustrating an example of displaying a request icon on a web page for selling products of a first shopping mall.
6 is an exemplary diagram illustrating another example of displaying a request icon in a first shopping mall.
7 shows an example of a user interface used to extract HTML tags and text data.
8 exemplarily shows a user interface used to extract link data.
9 is an exemplary diagram illustrating X-pass data of elements collected by a service server.
10 is an exemplary diagram illustrating a collection list of X-Pass data of elements included in a web page by way of example.
11 is a flowchart illustrating a classification procedure performed by a classification unit of a service server.
12 shows data managed in conjunction with an artificial intelligence model in a database corresponding to X-Pass data.
FIG. 13 is a diagram showing a list of data managed by FIG. 12 by way of example.
14 is a diagram showing data managed in a database associated with a modularization unit.
FIG. 15 is an exemplary diagram illustrating a list of data managed by FIG. 14 by way of example.
16 is a diagram showing information of unit modules included in task performance modules by way of example.
17 is a diagram showing a table associated with job performance information managed in association with a job performance module.
18 is a flowchart illustrating a procedure for learning an artificial intelligence model by a service server.
19 illustrates a user interface through which a user inputs setting values for a platform desired to be collected and registered.
FIG. 20 exemplarily illustrates a user interface through which X-pass collection is performed according to the set values registered in the page and the operator can check related contents as necessary in automatically processing subsequent collection and registration.

Since the present invention can make various changes and have various embodiments, specific embodiments are illustrated in the drawings and described in detail. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention. The terms and/or include any combination of a plurality of related recited items or any of a plurality of related recited items.

It is understood that when a component is referred to as being 'connected' or 'connected' to another component, it may be directly connected or connected to the other component, but other components may exist in the middle. It should be. On the other hand, when a component is referred to as being 'directly connected' or 'directly connected' to another component, it should be understood that no other component exists in the middle.

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as 'include' or 'have' are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in the present application, they should not be interpreted in an ideal or excessively formal meaning. don't

Hereinafter, with reference to the accompanying drawings, preferred embodiments of the present invention will be described in more detail. In order to facilitate overall understanding in the description of the present invention, the same reference numerals are used for the same components in the drawings, and redundant descriptions of the same components are omitted.

1 is a block diagram showing the configuration of a system for realizing an automatic interworking method between heterogeneous platforms according to a preferred embodiment of the present invention.

As shown in FIG. 1 , the service server 100 may interoperate with a plurality of shopping malls, for example, a first shopping mall M1 and a second shopping mall M2. The shopping malls are built based on different heterogeneous platforms. Here, the shopping mall may include all types of online shopping malls that sell products online. For example, the shopping mall may include open market shopping malls such as Amazon, Auction, and Interpark, large-scale shopping malls such as department store shopping malls and outlet shopping malls, and personal shopping malls operated with their own domains. In addition, shopping malls can include various forms such as SNS, blogs, online cafes or communities in their category if they sell products.

The service server 100 interworks with shopping malls of different types of platforms, and enables operations performed in the first shopping mall M1 to be performed in the second shopping mall M2, thereby enabling interworking between different types of shopping malls. .

For example, the service server 100 allows a seller who sells in the first shopping mall M1 to sell products in the second shopping mall M2 on a completely different platform from the first shopping mall M1. The task may be various types of tasks such as product sales, user information change, log-in, product purchase, post update, and the like. In this embodiment, a job related to selling a product among these jobs will be described as an example.

FIG. 2 is a block diagram for explaining the detailed configuration of the service server 100 shown in FIG. 1, and shows the configuration of a linkage device between heterogeneous platforms according to a preferred embodiment of the present invention. 3 and 4 are flowcharts showing the operation flow of the device for linking heterogeneous platforms, showing a method for linking heterogeneous platforms according to a preferred embodiment of the present invention.

As shown in FIG. 2, the service server 100 includes a receiving unit 110, a display unit 120, an execution unit 130, an extraction unit 140, a collection unit 150, a classification unit 160, and a modularization unit. 170, a task execution module generating unit 180, a learning unit 200, and a database 190 may be included.

This service server 100 may be built based on at least one computer terminal. The computer terminal may be a server-class computer including a memory for storing a program, a processor for executing a program stored in the memory, an input unit for inputting information, and an output unit for outputting information. The respective parts can be interoperable and can be built based on at least one computer terminal.

Hereinafter, with reference to FIGS. 1 to 4, a preferred embodiment of the present invention will be described. In the description of the embodiment with reference to FIGS. 1 to 4, it is assumed that the user is a seller of products selling products through the first shopping mall M1, and the user sells products sold in the first shopping mall M1 to the first shopping mall ( It is assumed that there is an intention to sell in the second shopping mall M2 operated based on a completely different platform system from M1).

In other words, it is assumed that the first shopping mall M1 is a shopping mall where the user is currently selling, and the second shopping mall M2 is a target shopping mall that is a shopping mall of a heterogeneous platform that the user wants to sell. That is, the second shopping mall M2 is a term referring to a target shopping mall, and may actually refer to one shopping mall or a plurality of shopping malls.

First, the display unit 120 of the service server 100 may display an icon in the first shopping mall M1 asking whether to perform a specific task through the second shopping mall M2 (step: S1). . For example, in the description of the present embodiment, the specific task here may be to perform sales through the second shopping mall M2. The icon may be associated with identification information representing the second shopping mall M2. Then, the user can select, eg, click or touch the displayed icon (step: S2).

5 is an exemplary diagram illustrating an example of displaying a request icon on a web page for selling products of the first shopping mall M1.

As shown in FIG. 5, a guide phrase such as "Do you want to sell this product at the second shopping mall?" A request icon (IC1) for requesting is displayed. That is, the request icon IC1 is displayed in association with identification information representing the second shopping mall M2, for example, the phrase.

When the icon IC1 is selected by the user, for example, clicked or touched, a request signal requesting that the product sold in the first shopping mall M1 be sold in the second shopping mall M2 may be generated. The request signal may include identification information of the first shopping mall M1, identification information of the second shopping mall M2, product identification information, and the like.

In the case of the example shown in FIG. 5 , a request icon is displayed in the first shopping mall M1 after specifying a target shopping mall to perform sales in advance. Meanwhile, the display unit 120 may display a user interface so that the user can select the second shopping mall M2 as the target shopping mall. 6 is an exemplary diagram illustrating another example of displaying a request icon in the first shopping mall M1.

As shown in FIG. 6 , the display unit 120 may display a user interface so that a second shopping mall M2, which is a target shopping mall, can be selected from the first shopping mall M1. For example, when the user selects the icon IC2 for selecting the second shopping mall M2 from the list, a list of a plurality of second shopping malls M2 is displayed, and a second shopping mall M2 desired by the user is displayed. ) to be selected. When the second shopping mall M2 is selected, a request signal requesting that products sold in the first shopping mall M1 be sold in the selected second shopping mall M2 may be generated.

For example, if the user selects the icon IC3 through which the user can directly input the second shopping mall M2 from the list, an input window is displayed to allow the user to input the identification information of the second shopping mall M2 desired by the user. , identification information of the second shopping mall M2, for example, URL (Universal Resource Locator), name, etc. may be received from the user's input. When the identification information of the second shopping mall M2 is input, the display unit 120 sends a request signal requesting that the second shopping mall M2 sell products sold in the first shopping mall M1 at the second shopping mall M2 corresponding to the input identification information. may occur.

Meanwhile, the number of products to be sold may be plural. In this case, the display unit 120 displays a list of products to be sold in the second shopping mall M2 or an input window on the first shopping mall M1, and the user selects or inputs products to be sold based on the displayed product list or input window. can

As shown in FIG. 3 , the receiving unit 110 of the service server 100 may receive a request from the first shopping mall M1 to perform a specific task through the second shopping mall M2 (step :S3). For example, the request may be a request to sell at least one product sold in the first shopping mall M1 through the second shopping mall M2. In this case, the request may include identification information of the first shopping mall M1, identification information of the second shopping mall M2, and identification information of at least one product.

In response to the received request, the execution unit 130 of the service server 100 determines whether a task execution module for automatically performing the task through the second shopping mall M2 exists in the database 190. It can be confirmed (step: S4). As a result of the check, if it is confirmed that the task execution module exists in the database 190, the task execution module can be performed (step: S5).

Meanwhile, as a result of the confirmation, if the task execution module does not exist in the database 190, procedures for generating the task execution module may be performed. As shown in FIG. 4, first, the extraction unit 140 of the service server 100 converts HTML (Hypertext Mark-up Language) from the web page of the second shopping mall M2 to interwork with the first shopping mall M1. Data can be extracted (step: S11).

For example, the service server 100 may extract HTML data from a web page of the second shopping mall M2 for selling products. Here, the web page for product sale may be all web pages necessary for product sales procedures in the second shopping mall M2, such as log-in, product sales, inventory check, payment, and order status check.

The collection unit 150 may collect xpath data corresponding to elements included in the web page from the extracted HTML data (step: S12). The X-Pass data may include X-Pass, which is absolute position information of a corresponding element. The X-path may mean a unique path value for a specific element in an HTML document.

According to a preferred embodiment of the present invention, the service server 100 uses the X-pass scraping method as described above. Xpass scraping may mean automatically extracting and collecting Xpasser information corresponding to a unique path value corresponding to an element. When the API provision request pointed out in the conventional problems described above has to be accepted or cannot be provided, using the X-Pass scraping method not only removes the accessible platform limitation, but also significantly reduces time and cost resources.

FIG. 7 exemplarily illustrates a user interface used to extract HTML tags and text data, and FIG. 8 exemplarily illustrates a user interface used to extract link data.

9 is an exemplary diagram illustrating X-pass data of elements collected by the service server 100. Referring to FIG. 10 is an exemplary diagram illustrating a collection list of X-Pass data of elements included in a web page by way of example. The X-Pass data collected in this way are stored and managed in the database 190 .

As shown in FIG. 9, the collected X-Pass data includes "URL", which is URL (Universal Resource Locator), which is link information of the web page, "Title", which is a keyword corresponding to an element, and a tag type corresponding to the element. "Tag_type", "Tag_info" which is tag information (or tag name) corresponding to an element, and "xpath" which is an X-path value which is an absolute position value corresponding to the element.

As shown in FIG. 10 , X-Pass data corresponding to a plurality of elements collected from a web page of the “Cafe24.com/prdctregister” link may be collected. For example, the xpass value of the sales status radio button that selects the sale standby of the web page of the link "Cafe24.com/prdctregister" may be "//[@id="eDescriptionADDOptionM"]. Therefore, the element's unique path The absolute position of the element can be known through the value of the value of the path.

Next, the classification view of the service server 100 may classify the collected X-pass data according to the related X-pass data based on the learned artificial intelligence model (step: S13).

11 is a flowchart illustrating a classification procedure performed by the classification unit 160 of the service server 100. Referring to FIG.

As shown in FIG. 11 , the classification unit 160 may extract feature information of the X-pass data based on link information of the X-pass data based on an artificial intelligence model (step: S21 ). The classification unit 160 may determine a similar keyword group based on the keywords of the X-Pass data based on an artificial intelligence model (step: S22). Next, the classification unit 160 may group the X-pass data by related X-pass data based on the feature information and similar keyword groups based on the artificial intelligence model (step: S23).

12 shows data managed in conjunction with an artificial intelligence model in the database 190 corresponding to X-Pass data. FIG. 13 is a diagram showing a list of data managed by FIG. 12 by way of example.

As shown in FIG. 12, for each element, "Title", which is a keyword corresponding to the element, "Vec_Result", which is a result of vector values corresponding to the characteristic information and keyword information of the element, and a group of similar keywords corresponding to the element A table including "Synonym_Family", "Tag_type", a tag type corresponding to the element, "Created_date", the first creation date corresponding to the element, and "Updated_date", the latest update date corresponding to the element, is an artificial intelligence model. It can be managed in the database 190 in association with.

As shown in FIG. 13, for the element "login submission image", keyword "login", vector value, similar keyword group, such as "Login, Log in, login, ...", tag type "Submit image", first created The date "2019-01-01", the latest update date "2019-01-28", etc. are managed, and list data of each element may be managed in the form of a table.

Subsequently, the modularization unit 170 may modularize the classified X-Pass data (step: S14). Here, data of related elements with similar characteristics and keywords can be associated. 14 is a diagram showing data managed in the database 190 in association with the modularization unit 170 . FIG. 15 is an exemplary diagram illustrating a list of data managed by FIG. 14 by way of example.

As shown in FIG. 14 , for modularized data, a table including “Title” indicating keywords of elements and “Tag_type” indicating tag types of elements may be managed by the database 190 . As shown in FIG. 15, the keyword "login" and the tag type "submit" can be managed for the first element of the list, which is a login submission element.

Subsequently, the task execution module generating unit 180 may generate a task execution module for performing a specific task in the second shopping mall M2 based on the modularized X-Pass data (step: S15).

The task execution module may be, for example, a kind of batch file that automatically performs tasks requested from the first shopping mall M1 in the second shopping mall M2. This task execution module may include at least one unit module. For example, assuming that the requested task is to sell a product in the second shopping mall M2, various tasks such as log-in, product registration, product list collection, inventory check, sales reception, etc. are performed to sell the product. Tasks must be performed, and a task execution module corresponding to unit tasks for performing the task may be referred to as a unit module. A unit module may be a batch file for performing unit tasks.

16 is a diagram showing information of unit modules included in task performance modules by way of example. 17 is a diagram showing a table associated with job performance information managed in association with a job performance module.

As shown in FIG. 16, the unit modules include "Target_URL", which is the URL of the target web page, "Plan_type", which is task execution type information, "Action_tpye", which is action type information, "Action_No", which is action sequence information, and task execution target X. Path information "Target xpath" may be included.

As shown in FIG. 17 , a task execution module may manage information of a plurality of unit modules in order to perform a task. For example, here, the action sequence of the unit module is determined according to the action sequence information.

Next, the execution unit 130 of the service server 100 may perform a task execution module (step: S16). Then, the task execution module may automatically perform the task requested by the user in the second shopping mall M2. For example, the task execution module logs in the user to the second shopping mall M2 so that products sold in the first shopping mall M1 can be sold in the second shopping mall M2, and the user's second shopping mall M2 ) can perform a series of unit operations for product sales that register products.

The task execution module receives input data necessary for a task from the first shopping mall M1, and automatically inputs the input data to a web page of the second shopping mall M2 where the input data is requested when the task is performed. can do.

For example, after receiving user information (eg, ID and password) from the first shopping mall M1 for log-in and automatically entering the user information into the user information input unit of the login web page of the second shopping mall M2, select the log-in button After logging in and receiving product information from the first shopping mall M1, the product can be registered by selecting a register button after inputting the product information to a required location in the product register of the second shopping mall M2.

The input data from the first shopping mall M1 may be requested and received by the task execution module from the first shopping mall M1 in real time, or input data registered in advance may be received from the first shopping mall M1 and the database ( 190) and can be used when performing work.

Therefore, according to a preferred embodiment of the present invention, it is possible to provide a service that enables easy interworking with a target shopping mall even without checking, collecting, or requesting the provision of APIs corresponding to platforms of heterogeneous target shopping malls. Time and human resources can be greatly reduced in terms of system development, construction, and maintenance.

Meanwhile, the learning unit 200 of the service server 100 may learn an artificial intelligence model. 18 is a flowchart for explaining a procedure for learning an artificial intelligence model by the service server 100.

As shown in FIG. 18, the learning unit 200 may extract HTML data from the learning webpage of the learning target shopping mall (step: S31). The learning unit 200 may collect X-Pass data corresponding to elements included in the learning webpage from HTML data extracted from the learning webpage (step: S32).

Next, the learning unit 200 allows the artificial intelligence model to classify the X-pass data according to feature information and keyword information corresponding to the X-pass data, so that the artificial intelligence model corresponds to elements included in the learning webpage. Learning may be performed based on pass data (step: S33). The learning unit 200 may store the learned artificial intelligence model in the database 190 (step: S34).

The learning target shopping mall may be the second shopping mall M2 requested by the user of the first shopping mall M1, or may be a plurality of shopping malls other than the second shopping mall M2 requested. That is, the learning target shopping mall may be a plurality of shopping malls, and the accuracy of classification may increase as the number of learning target shopping malls increases.

19 illustrates a user interface through which a user inputs setting values for a platform desired to be collected and registered. FIG. 20 exemplarily shows a user interface through which an operator can check related contents as needed in X-Pass collection in progress according to the set values registered through the user interface and subsequent collection and registration are automatically processed.

Although the present invention has been described by exemplifying preferred embodiments, those skilled in the art will variously modify and change the present invention within the scope not departing from the technical details and scope of the present invention described in the following claims. You will understand that this can be done. Accordingly, changes to future embodiments of the present invention will not deviate from the technology of the present invention.

M1: The 1st shopping mall
M2: 2nd shopping mall
100: service server
110: receiver
120: display unit
130: execution department
140: extraction unit
150: collection unit
160: classification unit
170: modularization unit
180: task performance module generation unit
190: database
200: learning unit

Claims (27)

It is performed by a server interworking with a plurality of shopping malls,
Receiving a message from a first shopping mall requesting an operation to be performed in a second shopping mall -The message is a message requesting to sell at least one product sold in the first shopping mall through the second shopping mall- ;
Extracting HTML (Hypertext Mark-up Language) data from a web page of the second shopping mall to interwork with the first shopping mall -The first shopping mall and the second shopping mall are built based on heterogeneous platforms, and the web The page includes a web page required for a procedure for selling the product in the second shopping mall including at least one of log-in, product sale, inventory check, payment, and order status check-;
collecting xpath data corresponding to elements included in the web page from the extracted HTML data - each xpath data includes an xpath that is absolute position information of a corresponding element;
Classifying the collected X-Pass data according to the related X-Pass data based on the learned artificial intelligence model;
modularizing the classified X-Pass data;
generating a task execution module for performing a specific task in the second shopping mall based on the modularized X-Pass data; and
Including performing the task performing module,
The interworking method between heterogeneous platforms, characterized in that a request icon for requesting to sell the product through the second shopping mall is displayed on one side of a web page for selling products of the first shopping mall.
The method of claim 1, wherein the collected X-Pass data,
URL (Universal Resource Locator), which is link information of the web page;
keyword corresponding to the element;
a tag type corresponding to the element;
tag information corresponding to the element; and
A method of interworking between heterogeneous platforms, characterized in that it includes an X-pass value that is an absolute position value corresponding to the element.
The method of claim 1, wherein the task execution module is performed in response to the message.
The method of claim 3, wherein the task execution module,
The interworking method between heterogeneous platforms, characterized in that the batch file automatically performs the operation from the first shopping mall in the second shopping mall.
The method of claim 3, wherein the task execution module,
Receiving input data for the job from the first shopping mall, and automatically inputting the input data to a location requesting the input data on the web page of the second shopping mall when the job is performed. interlocking method.
The method of claim 1, further comprising learning the artificial intelligence model,
The step of learning the artificial intelligence model,
Extracting HTML data from the learning webpage of the learning target shopping mall;
collecting X-Pass data corresponding to elements included in the learning webpage from HTML data extracted from the learning webpage;
learning the artificial intelligence model based on X-pass data corresponding to elements included in the learning webpage so as to classify the X-pass data by associated X-pass data according to feature information and keyword information corresponding to the X-pass data; and
A method for linking heterogeneous platforms, comprising the step of storing the learned artificial intelligence model in a database.
The method of claim 6, wherein the learning target shopping mall includes a plurality of shopping malls including a second shopping mall selected by a user of the first shopping mall.
The method of claim 1, wherein the database interworking with the artificial intelligence model
for each element,
keyword corresponding to the element;
vector values corresponding to the feature information and keyword information of the element;
a similar keyword group corresponding to the element;
a tag type corresponding to the element;
the first creation date corresponding to the element; and
A method of interworking between heterogeneous platforms, comprising managing a table including a recent update date corresponding to the element.
The method of claim 1, wherein the classifying step,
extracting feature information of the X-pass data based on link information of the X-pass data;
determining a similar keyword group based on keywords of the X-Pass data;
and grouping the X-Pass data by related X-Pass data based on the feature information and similar keyword groups.
The method of claim 1, wherein the task execution module includes at least one unit module,
The unit module,
An interworking method between heterogeneous platforms, characterized in that it includes a URL of a target web page, task execution type information, action type information, action sequence information, and task execution target X-pass information.
In the interlocking device between heterogeneous platforms interworking with a plurality of shopping malls,
Receiver receiving a message from a first shopping mall requesting an operation to be performed in a second shopping mall, wherein the message is a message requesting that at least one product sold in the first shopping mall be sold through the second shopping mall- ;
Extraction unit for extracting HTML (Hypertext Mark-up Language) data from a web page of the second shopping mall for interworking with the first shopping mall - The first shopping mall and the second shopping mall are built based on heterogeneous platforms, The web page includes a web page necessary for a procedure for selling the product in the second shopping mall including at least one of log-in, product sale, inventory check, payment, and order status check-;
a collection unit that collects X-path data corresponding to elements included in the web page from the extracted HTML data - each X-path data includes an X-path that is absolute position information of a corresponding element;
a classification unit that classifies the collected X-Pass data according to the related X-Pass data based on the learned artificial intelligence model;
a modularization unit for modularizing the classified X-Pass data;
a task execution module generating unit generating a task execution module for performing a specific task in the second shopping mall based on the modularized X-Pass data; and
Including an execution unit for performing the task execution module,
Interlocking device between heterogeneous platforms, characterized in that a request icon for requesting to sell the product through the second shopping mall is displayed on one side of a web page for selling products of the first shopping mall.
The method of claim 11, wherein the collected X-Pass data,
URL (Universal Resource Locator), which is link information of the web page;
keyword corresponding to the element;
a tag type corresponding to the element;
tag information corresponding to the element; and
Interlocking device between heterogeneous platforms, characterized in that it includes an X-pass value that is an absolute position value corresponding to the element.
[Claim 12] The device of claim 11, wherein the task execution module is performed in response to the message.
The method of claim 13, wherein the task performance module,
Interlocking device between heterogeneous platforms, characterized in that it is a batch file that automatically performs the operation from the first shopping mall in the second shopping mall.
The method of claim 13, wherein the task performance module,
Receiving input data for the job from the first shopping mall, and automatically inputting the input data to a location requesting the input data on the web page of the second shopping mall when the job is performed. interlocking device.
The method of claim 11, further comprising a learning unit for learning the artificial intelligence model,
The learning unit,
Characteristics of extracting HTML data from a learning web page of a learning target shopping mall, collecting X-pass data corresponding to elements included in the learning web page from the HTML data extracted from the learning web page, and corresponding to the X-pass data. The artificial intelligence model is trained based on the X-pass data corresponding to the elements included in the learning webpage so that it can be classified according to related X-pass data according to information and keyword information, and the trained artificial intelligence model is stored in a database. An interlocking device between heterogeneous platforms, characterized in that stored in.
[17] The device of claim 16, wherein the learning target shopping mall includes a plurality of shopping malls including a second shopping mall selected by a user of the first shopping mall.
The method of claim 11, wherein the database interworking with the artificial intelligence model
for each element,
keyword corresponding to the element;
vector values corresponding to the feature information and keyword information of the element;
a similar keyword group corresponding to the element;
a tag type corresponding to the element;
the first creation date corresponding to the element; and
Interlocking device between heterogeneous platforms, comprising managing a table including a recent update date corresponding to the element.
The method of claim 11, wherein the classification unit,
Characteristic information of the X-Pass data is extracted based on link information of the X-Pass data, similar keyword groups are determined based on keywords of the X-Pass data, and based on the characteristic information and similar keyword groups, the X-Pass data is extracted. Interlocking device between heterogeneous platforms, characterized in that the pass data is grouped by related X-pass data.
The method of claim 11, wherein the task execution module includes at least one unit module,
The unit module,
A linking device between heterogeneous platforms, characterized in that it includes a URL of a target web page, task execution type information, action type information, action sequence information, and task execution target X-pass information.
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