KR20210045283A - Method, Apparatus and System for Automatic Interlocking Between Heterogeneous Platform - Google Patents

Abstract

Disclosed are an automatic interworking method, a device and a system between heterogeneous platforms. An interworking method between heterogeneous platforms extracts HTML (Hypertext Mark-up Language) data from a web page of a second shopping mall for interworking with a first shopping mall, collects xpath data corresponding to elements included in the web page from extracted HTML data, classifies X-Pass data by associated X-Pass data based on a learned artificial intelligence model, modularizes the classified X-Pass data, creates a task execution module for performing a specific task in the second shopping mall based on the modularized X-Pass data, and performs the generated task execution module.

Description

Method, Apparatus and System for Automatic Interlocking Between Heterogeneous Platform}

The present invention relates to an automatic linkage method, device, and system between heterogeneous platforms, and more specifically, an automatic linkage method between heterogeneous platforms capable of automatically linking operations between shopping malls based on different platforms, and It's about the system.

In recent years, the popularization of online communication such as the Internet has dramatically changed the commerce environment more than ever. Now, the word “online” or “online shopping” is unfamiliar, and the word “internet” or “online” 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 through online, increase exponentially, shopping malls are striving to link data with various platforms to enhance their image and expand their business. For example, a shopping mall implemented based on a specific platform attempts to link services of a heterogeneous shopping mall implemented based on a heterogeneous platform to its own shopping mall through interworking with a heterogeneous platform.

In general, interworking of heterogeneous platforms to shopping malls is based on collecting and processing an application programming interface (API). For example, a shopping mall checks and collects an API corresponding to a target platform for a specific service. If there is no corresponding API, the shopping mall must request the provision of the API to the target shopping mall.

However, in this case, a considerable amount of time is required from the request to provide the API until the API is provided. In addition, since the production of AIP is performed by a professional manpower, it consumes cost and time, and even if an API is collected from a target shopping mall or an API is provided, there are difficulties in responding to and maintaining issues when an issue occurs in the service. Accordingly, there is an urgent need to develop a solution capable of easily interworking between heterogeneous platforms based on a completely different method from the prior art.

Publication Patent No. 2012-0020502

The present invention is to solve this problem, and provides an automatic linkage method, device, and system between heterogeneous platforms that enable easy and automatic service linkage between heterogeneous platforms based on X-Pass, which is the absolute location information of an element. There is a purpose.

In order to achieve this object, the present invention provides an interworking method between heterogeneous platforms in one aspect. The interworking method between different platforms is performed by a server interlocking with a plurality of shopping malls, and extracting HTML (Hypertext Mark-up Language) data from a web page of a 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; Collecting xpath data corresponding to elements included in the web page from the extracted HTML data ??each x-pass data includes x-pass, which is the absolute position information of the corresponding element; Classifying the collected X-Pass data according to 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 execution module.

The collected X-Pass data may include a URL (Universal Resource Locator), which is link information of the web page; Keywords corresponding to the elements; 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 to perform the operation 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 in the second shopping mall from the first 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 a web page of the second shopping mall when the task is performed. have.

The automatic linkage method between heterogeneous platforms may further include learning the artificial intelligence model. The learning of the artificial intelligence model may include: extracting HTML data from a learning web page of a shopping mall to be studied; Collecting X-pass data corresponding to elements included in the learning web page from the HTML data extracted from the learning web page; Learning, by the artificial intelligence model, based on X-Pass data corresponding to elements included in the learning web page, so that the artificial intelligence model can classify each related 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 a user of the first shopping mall.

The database interworking with the artificial intelligence model includes, for each element, a keyword corresponding to the element; A vector value 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; An initial creation date corresponding to the element; And it is possible to manage the table including the latest 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; And grouping the X-pass data for each associated X-pass data based on the feature information and the similar keyword group.

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 order information, and task execution target X-pass information.

On the other hand, in order to achieve the object of the present invention described above, the present invention provides an interworking device between heterogeneous platforms in another aspect. The interworking device between heterogeneous platforms, in an interworking device between heterogeneous platforms interworking with a plurality of shopping malls, an extraction unit that extracts HTML (Hypertext Mark-up Language) data from a web page of a second shopping mall for interworking 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 x-pass data includes x-pass, which is absolute position information of the corresponding element; A classification unit for classifying the collected X-Pass data according to related X-Pass data based on the learned artificial intelligence model; A modularization unit that modulates the classified X-pass data; A task execution module generation unit for generating a task execution module for performing a specific task in the second shopping mall based on the modularized X-pass data; And a performing unit that performs the task execution module.

The collected X-Pass data may include a URL (Universal Resource Locator), which is link information of the web page; Keywords corresponding to the elements; 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 interworking device between the heterogeneous platforms may further include a receiver configured to receive a message requesting to perform the task 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 in the second shopping mall from the first 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 a web page of the second shopping mall when the task is performed. have.

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

The database interworking with the artificial intelligence model includes, for each element, a keyword corresponding to the element; A vector value 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; An initial 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 the similar keyword group. Based on the X-Pass data, the X-Pass data may be grouped according to 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 order information, and task execution target X-pass information.

Meanwhile, in order to achieve the object of the present invention described above, the present invention provides an interworking system between heterogeneous platforms in another aspect. The interworking 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 interworking 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 the elements included in the web page are collected, and each x-pass data includes x-pass, which is the absolute position information of the corresponding element. Classify the X-Pass data by related X-Pass data based on the learned artificial intelligence model, modularize the classified X-Pass data, and perform 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 perform and performs the task execution module.

The server may receive a message requesting to perform the task in a second shopping mall from the first shopping mall, and the task execution 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 a web page of the second shopping mall when the task is performed. have.

The server extracts HTML data from a learning web page of a shopping mall to be taught, collects X-pass data corresponding to elements included in the learning web page from the HTML data extracted from the learning web page, and X-pass data The artificial intelligence model is trained based on X-Pass data corresponding to elements included in the learning web page so that the related X-Pass data can be classified according to feature information and keyword information corresponding to. Intelligence models can be stored in a database.

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 the similar keyword group. Based on the X-Pass data, the X-Pass data may be grouped according to 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 order information, and task execution target X-pass information.

On the other hand, in order to achieve the object of the present invention described above, the present invention provides a step of extracting HTML (Hypertext Mark-up Language) data from a web page of a second shopping mall for linking with the 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 x-pass data includes x-pass, which is the absolute position information of the corresponding element; Classifying the collected X-Pass data according to 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 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 the absolute location information of an element. Accordingly, a seller performing sales in a shopping mall can easily sell products in completely different shopping malls based on heterogeneous platforms other than the currently sold shopping mall.

1 is a block diagram showing the configuration of a system for realizing an automatic linkage 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 an interworking device between heterogeneous platforms according to a preferred embodiment of the present invention.
3 to 4 are flowcharts illustrating an operation flow of an interworking device between heterogeneous platforms, and illustrating an interworking method between 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 product sale web page 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 shows an exemplary user interface used to extract link data.
9 is an exemplary diagram showing 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 as an example.
11 is a flowchart illustrating a classification procedure performed by a classification unit of a service server.
12 shows data managed by interworking with an artificial intelligence model in a database in response to X-pass data.
13 is a diagram illustrating a list of data managed by FIG. 12 by way of example.
14 is a diagram showing data associated with the modularization unit and managed in a database.
15 is an exemplary diagram illustrating a list of data managed by FIG. 14 by way of example.
16 is a diagram illustrating information on a unit module included in a task execution module by way of example.
17 is a diagram showing a table associated with task performance information managed in association with a task execution module.
18 is a flowchart illustrating a procedure for learning an artificial intelligence model by a service server.
19 shows a user interface for inputting a setting value for a platform for which a user wants to collect and register.
FIG. 20 exemplarily shows a user interface through which an operator can check related contents as necessary, since X-pass collection is performed according to a setting value registered in the page, and subsequent collection and registration are automatically processed.

In the present invention, various modifications may be made and various embodiments may be provided, and specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the present invention to a specific embodiment, it should be understood to include all changes, 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 elements, but the elements should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another component. For example, without departing from the scope of the present invention, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. The term and/or includes a combination of a plurality of related listed items or any of a plurality of related listed items.

When an element is referred to as being'connected' or'connected' to another element, it is understood that it may be directly connected or connected to the other element, but other elements 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 there is no other component in the middle.

The terms used in the present 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 indicates otherwise. In the present application, terms such as'comprise' or'have' are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof does not preclude in advance.

Unless otherwise defined, 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 technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in the present application. Does not.

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

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

As shown in FIG. 1, the service server 100 may interwork with a plurality of shopping malls, such as a first shopping mall M1 and a second shopping mall M2. The shopping malls are built on the basis of different heterogeneous platforms. Here, the shopping mall may include all types of online shopping malls that sell products through online. For example, the shopping mall may include a shopping mall in the form of an open market such as Amazon, Auction, and Interpark, a large shopping mall such as a department store shopping mall, an outlet shopping mall, and a personal shopping mall operated with a unique domain. In addition, if a shopping mall sells products, various types such as SNS, blogs, online cafes, and communities can be included in the category.

The service server 100 interlocks with the shopping malls of these different platforms and allows the work 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 enables a seller performing sales in the first shopping mall M1 to sell products in the second shopping mall M2 of a platform completely different from the first shopping mall M1. The operation may be various types of operations such as not only selling products, but also changing user information, logging in, purchasing products, and updating posts. In the present embodiment, among these tasks, a task related to the sale of a product 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 an interworking device between heterogeneous platforms according to a preferred embodiment of the present invention. 3 to 4 are flowcharts illustrating an operation flow of an interworking device between heterogeneous platforms, and illustrating an interworking method between 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. It may include 170, a task execution module generation unit 180, a learning unit 200, a database 190, and the like.

The service server 100 may be built on the basis of at least one computer terminal. The computer terminal may be a server-class computer including a memory storing a program, a processor executing a program stored in the memory, an input unit for inputting information, an output unit for outputting information, and the like. Each part is interoperable and may be built on the basis of at least one computer terminal.

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

In other words, it is assumed that the first shopping mall M1 is a shopping mall that 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 be a single shopping mall or may refer to a plurality of shopping malls.

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

5 is an exemplary view illustrating an example of displaying a request icon on a product sale web page of a first shopping mall M1.

As shown in FIG. 5, a guide phrase such as "Would you like to sell this product in a second shopping mall?" is displayed on one side of the product sale web page of the first shopping mall M1, and such intention is displayed in the adjacent part of the guide phrase. A request icon (IC1) for requesting is displayed. That is, the request icon IC1 is displayed in association with identification information indicating the second shopping mall M2, for example, the phrase.

When the icon IC1 is selected, for example, clicked or touched by the user, a request signal for requesting to sell the product sold in the first shopping mall M1 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, identification information of a product, and the like.

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

As illustrated in FIG. 6, the display unit 120 may display a user interface in the first shopping mall M1 to select a second shopping mall M2 that is a target shopping mall. 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 the second shopping mall M2 desired by the user among the list is displayed. ) Can be selected. When the second shopping mall M2 is selected, a request signal for requesting to sell the product sold in the first shopping mall M1 at the selected second shopping mall M2 may be generated.

For example, when the user selects an icon IC3 for directly inputting the second shopping mall M2 from the list, an input window for inputting the identification information of the second shopping mall M2 desired by the user is displayed, and , Identification information of the second shopping mall M2, such as a URL (Universal Resource Locator), and a name, may be received from the user's input. When the identification information of the second shopping mall M2 is input, the display unit 120 requests a request signal to sell the product sold in the first shopping mall M1 at the second shopping mall M2 corresponding to the input identification information. Can occur.

Meanwhile, there may be a plurality of products to be sold. In this case, the display unit 120 displays a product list or input window to be sold in the second shopping mall M2 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. I 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, identification information of at least one product, and the like.

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. 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 may be executed (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 provides HTML (Hypertext Mark-up Language) from the web page of the second shopping mall M2 for interworking 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 product sales. Here, the web page for product sales may be all web pages necessary for a product sales procedure in the second shopping mall M2 such as log-in, product sales, inventory check, payment, order status check, and so on.

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 an X-pass, which is absolute position information of a corresponding element. The x-pass may mean a unique path value for a specific element in the HTML document.

According to a preferred embodiment of the present invention, the service server 100 uses the X-Pass scraping method as described above. X-pass scraping may mean automatically extracting and collecting X-passer information corresponding to a unique path value corresponding to an element. When the API provision request pointed out in the above-described conventional problem must be received or cannot be provided, the use of the X-Pass scraping method not only eliminates restrictions on accessible platforms, but also saves time and cost resources.

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

9 is an exemplary diagram showing X-pass data of elements collected by the service server 100. 10 is an exemplary diagram illustrating a collection list of X-Pass data of elements included in a web page as an 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 is "URL", which is a URL (Universal Resource Locator), which is link information of the web page, "Title", which is a keyword corresponding to the element, and is a tag type corresponding to the element. It may include "Tag_type", "Tag_info" that is tag information (or tag name) corresponding to the element, and "xpath" that is an X-Path value that is an absolute position value corresponding to the element.

As illustrated in FIG. 10, X-pass data corresponding to a plurality of elements collected from a web page of a link "Cafe24.com/prdctregister" may be collected. For example, the Xpass value of the sale status radio button that selects to wait for sale on the web page of the link "Cafe24.com/prdctregister" may be "//[@id="eDescriptionADDOptionM"], thus the only path of the element. The absolute position of an element can be known through the value of the access pass.

Next, the classification view of the service server 100 may classify the collected X-pass data for each associated 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.

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 the artificial intelligence model (step: S22). Next, the classification unit 160 may group the X-Pass data by associated X-Pass data based on the feature information and the similar keyword group based on the artificial intelligence model (step: S23).

12 shows data managed by interworking with an artificial intelligence model in the database 190 in response to X-pass data. 13 is a diagram illustrating a list of data managed by FIG. 12 by way of example.

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

As shown in Fig. 13, for the "login submission image" element, the keyword "login", vector value, similar keyword group, such as "Login, Log in, login, ...", tag type "Submit image", initial creation 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, you can associate data of related elements with similar characteristics and keywords. 14 is a diagram showing data associated with the modularization unit 170 and managed by the database 190. 15 is an exemplary diagram illustrating a list of data managed by FIG. 14 by way of example.

As illustrated in FIG. 14, a table including "Title" indicating a keyword of an element and "Tag_type" indicating a tag type of an element may be managed by the database 190 for modular data. As shown in FIG. 15, a keyword "login" and a tag type "submit" may be managed for the first element of the list, which is a login submission element.

Subsequently, the task execution module generation 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 a task 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 operation is to sell a product in the second shopping mall (M2), various operations such as login, product registration, product list collection, inventory check, sales reception, etc. Tasks must be performed, and task execution modules corresponding to unit tasks for performing tasks may be referred to as unit modules. The unit module may be a batch file for performing a unit operation.

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

As shown in FIG. 16, the unit module includes "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 the task execution target X. It may include path information "Target xpath".

As shown in FIG. 17, in order to perform a task, the task execution module may manage information of a plurality of unit modules. For example, here, the action order of the unit module is determined according to the action order 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 You can perform a series of unit tasks for product sales by registering products in ).

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 location requesting the input data on a web page of the second shopping mall (M2) when performing the task. can do.

For example, after receiving user information (e.g., ID, password) from the first shopping mall M1 for logging in, automatically inputting the user information into the user information input unit of the login web page of the second shopping mall M2, and then selecting the login button. After logging in and receiving product information from the first shopping mall M1, the product may be registered in a required location in the product registration unit of the second shopping mall M2, and then a registration button may be selected to register the product.

The input data from the first shopping mall M1 may be received by the task execution module by requesting from the first shopping mall M1 in real time, or the 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 allows easy interworking with the target shopping mall even if APIs corresponding to platforms of heterogeneous target shopping malls are not checked and collected or requested to be provided. In terms of system development, construction, and maintenance, time and human resources can be significantly reduced.

Meanwhile, the learning unit 200 of the service server 100 may learn an artificial intelligence model. 18 is a flowchart illustrating 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 a learning web page of a shopping mall to be learned (step: S31). The learning unit 200 may collect X-pass data corresponding to elements included in the learning web page from the HTML data extracted from the learning web page (step: S32).

Next, the learning unit 200 may classify X-Pass data according to related X-Pass data according to feature information and keyword information corresponding to the X-Pass data, so that the artificial intelligence model corresponds to the elements included in the learning web page. It is possible to learn based on the 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 a second shopping mall M2 requested by a user of the first shopping mall M1, but may be a plurality of shopping malls in addition to the requested second shopping mall M2. That is, the shopping mall to be learned may be a plurality of shopping malls, and the accuracy of classification may also increase as the number of shopping malls to be learned increases.

19 shows a user interface for inputting a setting value for a platform for which a user wants to collect and register. FIG. 20 exemplarily shows a user interface through which X-pass collection is performed according to a setting value registered through the user interface, and then collection and registration are automatically processed, so that an operator can check related contents as needed.

The present invention has been described by exemplifying preferred embodiments thereof, but those skilled in the art will variously modify and change the present invention within the scope not departing from the technical matters and scope of the present invention described in the following claims. You will be able to understand that it can be done. Therefore, changes in the embodiments of the present invention will not be able to depart from the technology of the present invention.

M1: No. 1 shopping mall
M2: 2nd shopping mall
100: service server
110: receiver
120: display
130: executive
140: extraction unit
150: collection unit
160: classification unit
170: modularization unit
180: task execution module generation unit
190: database
200: Learning Department

Claims (27)

It is performed by a server interlocking with a plurality of shopping malls,
Extracting HTML (Hypertext Mark-up Language) data from the 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-;
Collecting xpath data corresponding to elements included in the web page from the extracted HTML data ??each x-pass data includes x-pass, which is the absolute position information of the corresponding element;
Classifying the collected X-Pass data according to 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
Interworking method between heterogeneous platforms, comprising the step of performing the task execution module.
The method of claim 1, wherein the collected X-pass data,
URL (Universal Resource Locator) which is link information of the web page;
Keywords corresponding to the elements;
A tag type corresponding to the element;
Tag information corresponding to the element; And
Interworking method 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, further comprising receiving a message requesting to perform the task in a second shopping mall from the first shopping mall,
The interworking method between heterogeneous platforms, characterized in that the task execution module is performed in response to the message.
The method of claim 3, wherein the task execution module,
Interworking method between heterogeneous platforms, characterized in that it is a batch file that automatically performs the task 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 a 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,
Learning the artificial intelligence model,
Extracting HTML data from a learning web page of a shopping mall to be learned;
Collecting X-pass data corresponding to elements included in the learning web page from the HTML data extracted from the learning web page;
Learning, by the artificial intelligence model, based on X-Pass data corresponding to elements included in the learning web page, so that the artificial intelligence model can classify each related X-Pass data according to feature information and keyword information corresponding to the X-Pass data; And
And 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 linked with the artificial intelligence model
For each factor,
Keywords corresponding to the elements;
A vector value 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;
An initial creation date corresponding to the element; And
And 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 according to related X-Pass data based on the feature information and the similar keyword group.
The method of claim 1, wherein the task execution module includes at least one unit module,
The unit module,
A linking method between heterogeneous platforms, comprising: URL of a target web page, operation execution type information, action type information, action order information, and task execution target X-pass information.
In the interworking device between heterogeneous platforms interworking with a plurality of shopping malls,
An extraction unit for extracting HTML (Hypertext Mark-up Language) data from the 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-;
A collection unit that collects xpath data corresponding to elements included in the web page from the extracted HTML data ??each x-pass data includes x-pass, which is absolute position information of the corresponding element;
A classification unit for classifying the collected X-Pass data according to related X-Pass data based on the learned artificial intelligence model;
A modularization unit that modulates the classified X-pass data;
A task execution module generation unit for generating a task execution module for performing a specific task in the second shopping mall based on the modularized X-pass data; And
Interworking device between heterogeneous platforms, comprising a performing unit that performs the task execution module.
The method of claim 11, wherein the collected X-pass data,
URL (Universal Resource Locator) which is link information of the web page;
Keywords corresponding to the elements;
A tag type corresponding to the element;
Tag information corresponding to the element; And
Interworking device 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 11, further comprising a receiving unit for receiving a message requesting to perform the operation in a second shopping mall from the first shopping mall,
The interworking device between heterogeneous platforms, characterized in that the task execution module is performed in response to the message.
The method of claim 13, wherein the task execution module,
A device for interworking between heterogeneous platforms, characterized in that it is a batch file that automatically performs the task from the first shopping mall in the second shopping mall.
The method of claim 13, 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 a 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,
A feature that extracts HTML data from the learning web page of the shopping mall to be studied, collects X-Pass data corresponding to elements included in the learning web page from the HTML data extracted from the learning web page, and corresponds to the X-Pass data The artificial intelligence model is trained based on X-Pass data corresponding to elements included in the learning web page so that the related X-Pass data can be classified according to information and keyword information, and the learned artificial intelligence model is database Interworking device between heterogeneous platforms, characterized in that stored in the.
The interworking 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 linked with the artificial intelligence model
For each factor,
Keywords corresponding to the elements;
A vector value 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;
An initial creation date corresponding to the element; And
And managing a table including a recent update date corresponding to the element.
The method of claim 11, wherein 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 X-pass based on the feature information and the similar keyword group. A device for interworking between heterogeneous platforms, comprising grouping path data according to associated X-pass data.
The method of claim 11, wherein the task execution module includes at least one unit module,
The unit module,
A device for interworking between heterogeneous platforms, comprising: a URL of a target web page, information on a task execution type, information on an action type, information on an action sequence, and information on a target X-pass for performing a task.
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 ??the first shopping mall and the second shopping mall are built based on heterogeneous platforms-, extraction X-Path data corresponding to the elements included in the web page are collected from the generated HTML data, and each X-Pass data includes X-Pass, which is the absolute position information of the corresponding element. Classify X-Pass data by related X-Pass data based on the learned artificial intelligence model, modularize the classified X-Pass data, and perform a specific task in the second shopping mall based on the modularized X-Pass data An interworking system between heterogeneous platforms, comprising: a server including an execution unit for generating a task execution module for performing the task execution module and performing the task execution module.
The method of claim 21, wherein the server receives a message requesting to perform the task in the second shopping mall from the first shopping mall, and the task execution module is performed in response to the message. Interlocking system.
The method of claim 21, 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 a web page of the second shopping mall when the job is performed. Interlocking system.
The method of claim 21, wherein the server,
A feature that extracts HTML data from the learning web page of the shopping mall to be studied, collects X-Pass data corresponding to elements included in the learning web page from the HTML data extracted from the learning web page, and corresponds to the X-Pass data The artificial intelligence model is trained based on X-Pass data corresponding to elements included in the learning web page so that the related X-Pass data can be classified according to information and keyword information, and the learned artificial intelligence model is database Interworking system between heterogeneous platforms, characterized in that stored in the.
The method of claim 21, wherein 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 X-pass based on the feature information and the similar keyword group. An interworking system between heterogeneous platforms, characterized in that path data are grouped according to associated X-pass data.
The method of claim 21, wherein the task execution module comprises at least one unit module,
The unit module,
A linkage system between heterogeneous platforms, comprising: URL of a target web page, information on a task execution type, information on an action type, information on an action sequence, and information on a target X-pass to perform a task.
On the computer,
Extracting HTML (Hypertext Mark-up Language) data from the 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-;
Collecting xpath data corresponding to elements included in the web page from the extracted HTML data ??each x-pass data includes x-pass, which is the absolute position information of the corresponding element;
Classifying the collected X-Pass data according to 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
A computer program stored in a medium to execute the step of performing the task execution module.

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