User behavior data high-frequency acquisition and playback method

Abstract

The invention discloses a user behavior data high-frequency acquisition and playback method, which comprises the following steps of: acquiring user behavior Event event information from a browser cache through a method of monitoring a trigger event based on a document object model (DOM), intercepting and processing data in the Event event information through a JavaScript asynchronous programming mechanism, and uploading the data to a server to realize process data acquisition; the collected process data are transmitted and then stored in different data tables of different databases; when the process data needs to be exported, different process data are taken out from different databases and integrated and exported, so that researchers can conveniently look up and use the process data. The method is high in collection accuracy, high in real-time performance and comprehensive and flexible in collection, different collection strategies and collection information can be customized according to different behavior event types and trigger conditions, and the multi-modal data collection requirements of the process data are met.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to a method for high-frequency collection and playback of user behavior data. Background Art

[0002] Most of the current front-end systems are developed using single-page applications. As time goes by and the number of developed functions increases, the front-end engineering of single-page applications becomes huge and difficult to maintain. Such applications are usually called monolithic applications. In order to solve the problems caused by this phenomenon, the existing technology proposes the concept of micro frontends. Micro frontends are not a simple front-end framework or tool, but a set of architecture systems, similar to the microservices architecture, which decomposes the front-end application into some smaller and simpler applications, and then combines them into a larger whole.

[0003] CLASS is a Web application that uses a browser as a client. The collection of procedural data on user operations must be based on Web front-end technology, mainly including the Document Object Model (DOM) and event mechanisms based on the DOM model, JavaScript asynchronous programming, browser cache technology, etc. The procedural data collected by the system is divided into three categories: chat data, which is generated in the IM module and records the content of user chats; behavioral data, which is generated from the internal operations of the task component and records the actions and sequence of users in solving problems in the process of collaborative course tasks; playback data, which is generated from the internal operations of the task component and records the order and attribute changes of HTML elements in the task component. Both behavioral data and playback data are derived from the user's behavior in the course unit component interface and rely on mouse behavior and text typing; chat data is generated from text typing when users communicate using IM tools. Currently, there are the following problems in the collection of procedural data:

[0004] First, process data is generated in the collaborative process of users. Each task instance corresponds to thousands of pieces of information, which places high demands on the efficiency and real-time performance of data collection. Second, the recorded actions need to be very detailed, resulting in very small granularity of process data, so the accuracy of the data collected by the system is required to be high. Finally, the multimodality of process data leads to large differences in the structure of different data, which poses great challenges to the implementation of unstructured storage and integration of data. Existing CSCL (Computer Supported Collaborative Learning) systems rarely focus on the collection and evaluation of process data, and the accuracy and granularity of the collected data naturally cannot meet the research needs of CPS capabilities. Summary of the invention

[0005] The purpose of the present invention is to provide a method for high-frequency collection and playback of user behavior data. The method has high collection accuracy and real-time performance, and the collection is comprehensive and flexible. Different collection strategies and collection information can be customized for different behavior event types and trigger conditions to meet the needs of multimodal data collection of process data.

[0006] The objective of the present invention is achieved through the following technical solutions:

[0007] A method for high-frequency collection and playback of user behavior data, the method comprising:

[0008] Step 1: Obtain user behavior Event information from the browser cache by monitoring the triggering event based on the Document Object Model DOM, intercept and process the data in the Event information through the JavaScript asynchronous programming mechanism, and upload it to the server to realize procedural data collection;

[0009] Step 2: The collected process data is transmitted and stored in different data tables of different databases;

[0010] Step 3: When process data needs to be exported, different process data are taken out from different databases and integrated and exported to facilitate researchers' reference and use.

[0011] It can be seen from the technical solution provided by the present invention that the above method has high collection accuracy and real-time performance, and is comprehensive and flexible in collection. Different collection strategies and collection information can be customized for different behavioral event types and trigger conditions to meet the needs of multimodal data collection of process data. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0013] Figure 1 A schematic diagram of a flow chart of a method for high-frequency collection and playback of user behavior data provided by an embodiment of the present invention;

[0014] Figure 2 A schematic diagram of the composition structure of the procedural data field according to an embodiment of the present invention;

[0015] Figure 3 A schematic diagram of the composition of the unified identification field according to an embodiment of the present invention;

[0016] Figure 4A schematic diagram of the composition of the unstructured field according to an embodiment of the present invention;

[0017] Figure 5 This is a schematic diagram of the chat data collection process according to an embodiment of the present invention;

[0018] Figure 6 A schematic diagram of the behavior data and playback data collection process according to an embodiment of the present invention;

[0019] Figure 7 This is a schematic diagram of an example of exporting procedural data according to an embodiment of the present invention;

[0020] Figure 8 A schematic diagram of a data model after the behavior data and the playback data are combined according to an embodiment of the present invention;

[0021] Fig. 9 A schematic diagram of optimizing the export data format according to an embodiment of the present invention;

[0022] Fig.10 This is a schematic diagram of the results of the data collection correctness test of the example given in the present invention;

[0023] Fig.11 This is a schematic diagram of the results of the integrity comparison of some of the collected data in the examples given in the present invention. DETAILED DESCRIPTION

[0024] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, which does not constitute a limitation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] like Figure 1 The figure is a flow chart of a method for high-frequency collection and playback of user behavior data provided by an embodiment of the present invention, the method comprising:

[0026] Step 1: Obtain user behavior Event information from the browser cache by monitoring the triggering event based on the Document Object Model DOM, intercept and process the data in the Event information through the JavaScript asynchronous programming mechanism, and upload it to the server to realize procedural data collection;

[0027] In this step, the process data has multimodal characteristics and is composed of a unified identification field and unstructured fields, such as Figure 2 FIG. 1 is a schematic diagram of the composition structure of the procedural data field according to an embodiment of the present invention, wherein:

[0028] The unified identification field is a common component of multimodal data and consists of id, unitId, classroomId, curriculumId, groupId, userId, and createTime, such as Figure 3 FIG. 1 is a schematic diagram showing the composition of the unified identification field according to an embodiment of the present invention, where id represents the unique identifier of the data, unitId represents the course unit ID, classroomId represents the classroom ID, curriculumId represents the course ID, groupId represents the group ID, userId represents the user ID, and createTime represents the time when the data is uploaded to the server;

[0029] Unstructured fields include chat data, behavior data, and playback data, such as Figure 4 FIG. 1 is a schematic diagram showing the composition of the unstructured field according to an embodiment of the present invention, wherein:

[0030] The chat data consists of the chatGroupId, content, msgRead, msgType, fromUserid, and recordId fields; chatGroupId indicates the current group ID, content indicates the chat content, msgRead indicates whether it has been read, msgType indicates the message type, fromUserid indicates the source user ID of the message, and recordId indicates the original message ID;

[0031] Behavior data consists of count, classKey, and behaviorData fields; count indicates count, classKey indicates page ID, and behaviorData indicates custom fields of different course units, which are composed of arrays;

[0032] The playback data consists of the fields moduleKey, classKey, eventTime, orderNo, moduleValue, location, and baseData. Among them, moduleKey, classKey, baseData, and location are standard fields passed by the course unit component to the system, indicating the custom operation information within the course unit component.

[0033] In the specific implementation, due to the huge amount of behavior data and playback data, in order to alleviate bandwidth pressure, the system will pre-store the data in the browser cache and upload it to the server after reaching the specified data volume. As a result, the unified identification field createTime in the same batch of data will record the same time node, and the introduced eventTime and orderNo fields indicate the occurrence time and order of each data.

[0034] In the process of procedural data collection, the objects of procedural data collection include chat data, behavior data and playback data, among which:

[0035] The chat data is generated from the native input box event under the Document Object Model DOM element. By binding the native input box and the keyboard Enter event, and monitoring the changes in the input box value; when the content inside the input box is not empty and the user presses the keyboard Enter, the content in the input box is intercepted and converted into a JSON string and sent directly to the server; Figure 5 The diagram shows the chat data collection process of the embodiment of the present invention. First, the Input native box in HTML and the Enter event of the keyboard are bound, and the sendMessage() function is called to pass parameters to the server through the keydownHandle() processing function; the keydownHandle() function declares that the function prevents the occurrence of the Enter line break behavior to ensure that the Enter message sending event behavior is correctly monitored. Compared with the behavior data, the data collected by the chat data only needs to focus on the final text input result, and the number is not huge, so it does not need to be temporarily stored in the browser cache for batch transmission. The system can collect them one by one and directly transmit them to the server.

[0036] Behavioral data and playback data are generated inside the task component. Since the course unit component itself does not have the function of data collection, the system actively monitors the occurrence of DOM element events inside the course unit component and completes the data collection operation. The system actively retrieves the preset standard parameter information from the course unit component, processes these parameters in a unified manner and puts them in the browser cache. When the data of a certain item is stored, it is uploaded to the server to realize data collection. Among them, unlike the behavioral data, the collection of playback data is based on the changes in the HTML elements inside the task component. By reproducing the changes and sequence of elements, it can achieve the effect of visual playback of user behavior.

[0037] like Figure 6The figure shows a schematic diagram of the behavior data and playback data collection process described in an embodiment of the present invention. Taking behavior data collection as an example, the Widget course component unit will preset the embedded data, that is, define the DOM event elements that need to be collected, and the system will declare the custom behavior field data. When the event occurs, the task component sends it to the system in a standard format; the system micro-front-end framework will listen to the message event sent by the task component to the system through the addEventListener() function, and call the SavaBehaviorDataMsgOption method through the system storage class to save the data to the browser cache, and then transmit it to the server. Unlike behavior data, playback data has stricter requirements on data real-time performance. Therefore, in addition to the server time createTime, an eventTime field is added to record the local behavior occurrence time. Since the system uses the sessionStorage temporary session storage mechanism, the system determines whether it is data after the page is refreshed and recalculates eventTime.

[0038] Step 2: The collected process data is transmitted and stored in different data tables of different databases;

[0039] In this step, in the process of transmitting the collected procedural data, the behavior data and playback data come from the micro-operations of user behavior, which have the characteristics of large cardinality and high requirements for real-time performance and system data management capabilities. Therefore, the behavior data and playback data are cached in the client browser through the LocalStorage object provided by HTML5, and the JSON data exchange format is used to achieve the purpose of sending them to the server in batches and in a quantitative manner to ensure the stability of data bandwidth and the integrity of collection; compared with behavior data and playback data, chat data has a low collection frequency and a small cardinality, so there is no need to use browser cache technology as a transfer to reduce the pressure on transmission bandwidth;

[0040] When storing procedural data in different data tables of different databases, the unstructured field structure of behavior data and playback data is flexible and contains a large number of custom fields. Therefore, the behavior data and playback data are submitted to the server and stored in the unstructured database MongoDB. Chat data has only two types: private chat and one-on-one chat. The unstructured field information is fixed, so it is stored as structured data in the database MySql.

[0041] In the specific implementation, the system uses the localStorage method to implement browser caching of data. When behavioral events are monitored, these unstructured data fields will be encapsulated in messages according to the data model of the preset behavioral data standard. The Micro-App sub-application will send data to the CLASS base framework. The micro-frontend framework will monitor the messages sent by the sub-application and submit the data to the cache through the localStorage.setItem() method. When the number of entries reaches the preset limit or the page is closed or the course ends, the data will be actively uploaded to the server and the cache will be cleared. The system uses JavaScript asynchronous mechanism for data transmission. Without refreshing the page, the HttpPost interface call is implemented to upload to the server in the form of body. Before that, the system will convert the value information in its field from the default string type to JSON format through the JSON.stringify() method for storage.

[0042] Step 3: When process data needs to be exported, different process data are taken out from different databases and integrated and exported to facilitate researchers' reference and use.

[0043] In this step, the standard of the process data export field consists of a unified identification field and an unstructured field. The unified identification field represents the basic information corresponding to the process data.

[0044] In the unstructured field, type is the data type; 1 is behavior data; 2 is chat data; operationModuleValue is the template content; location is the level name, which is a custom data field for the component; and the data value represents the variable value in the template content.

[0045] The following table 1 is an example of the process data field export standard according to the embodiment of the present invention:

[0046]

[0047]

[0048] like Figure 7 FIG. 1 is a schematic diagram of an example of exporting process data according to an embodiment of the present invention, wherein some of the exported process data information items are as follows: Figure 7 As shown, the template content is [A]send[B], which means that user A sent a message with content B to the group, and data1 and data2 are the values ​​of A and B respectively.

[0049] In addition, in the process of collecting procedural data, since both behavior and playback data are collected from student behaviors in the task component, they are composed of unified identification fields and unstructured fields. There are overlapping fields in the unstructured fields. Separate collection not only easily increases the pressure on the transmission bandwidth and thus affects the system response efficiency, but also causes a waste of storage resources. Therefore, the behavior data and playback data can be merged and collected uniformly. Specifically:

[0050] like Figure 8 The figure shows a schematic diagram of a data model after the behavior data and the playback data are merged according to an embodiment of the present invention. The fields of the behavior data and the playback data are merged, and a type field is added to the unstructured field: 0 is playback data, 1 is behavior data, and 2 is behavior plus playback data; the count field unique to the behavior data is deleted and replaced with the fields eventTime and orderNo to avoid duplication of field identifiers;

[0051] In the process of exporting procedural data, the type in the process data export field is used to distinguish chat data from behavior data. Considering that the behavior data in the template content of the exported data is defined by a third party, in order to reduce unnecessary fields, the two data types are distinguished in the template content field, such as Fig. 9 The figure shows a schematic diagram of the optimized export data format according to an embodiment of the present invention, and defines the template content of the chat data: the group chat template content is [A]send[B], wherein [A] and [B] are the contents in the data fields data1 and data2, respectively indicating the person sending the information and the content; the single chat template content is [A]send[B]to[C], wherein [A] and [B] are the contents in the data fields data1, data2 and data3, respectively indicating the person sending the information, the content and the person receiving the information.

[0052] In addition, the embodiment of the present application is also equipped with a playback engine, which reproduces user behavior by collecting and reproducing playback data. For the reproduction of playback data and chat data, the implementation of data reproduction is divided into two steps: system preparation of data and distribution of data; playback data is taken and distributed in the database, and the distributed playback data is obtained in the system page. By comparing the classKey value, the data is left for processing. The data in the course unit component is sent out using the microApp.setData() method. When each page receives the playback data, it only needs to assign the value of the playback data to the current data. Specifically:

[0053] (1) System preparation for playback data

[0054] The system includes two ways to prepare playback data: First, when the initial playback request is made or the playback object is switched, the system requests the server to obtain fixed-point or full playback data; the fixed-point data is used to locate the playback time node, and the full playback data is used to reproduce the user operation at the current time node; second, when the playback data is updated or the playback data is requested for the second drag, the system will obtain 5 seconds of playback data through the playback data interface; after the system playback data is prepared, data distribution is enabled;

[0055] (2) System distribution data to achieve data reproduction

[0056] After the system is ready for playback data, it opens and loads the playback page containing the course unit component. When the page is ready, it will notify the playback page to distribute the data. The system listens to the distribution message, and the base application receives the playback data. The data of the base application will be kept for processing, and the data of the course unit component sub-application will be distributed using the Micro-App sub-page. After the Micro-App sub-page receives the playback data, it compares the classKey value and assigns the compared data to the corresponding attributes of the current page to achieve data reproduction.

[0057] It is worth noting that the contents not described in detail in the embodiments of the present invention belong to the prior art known to professional and technical personnel in the field.

[0058] The effect of the method described in the embodiment of the present invention is verified by a specific example below. This example verifies the process data management function from the two dimensions of the correctness and completeness of the collected process data. Taking the basic course of writing tasks as an example, this example designs a set of data collection automation test scripts to verify whether the process data management function meets the requirements.

[0059] The scope of verification of data collection accuracy is to conduct a longitudinal comparison of the accuracy of data collected from individual students.

[0060] The test script is specially designed to verify the correctness of data collection. In the input function student_input1(driver), send_keys("ABC DEF") and send_keys(Keys.BACK_SPACE) are used to implement the text input of "ABC+space+DEF" and the operation of deleting the letter F. The for a in range(0,400) loop function is used to repeat the text input. The purpose of this is that the data in the system participating in and exporting CSV will show a special graphical pattern, which is convenient for verifying the correctness of data collection.

[0061] like Fig.10The figure shows a result diagram of the correctness test of data collection of the example given in the present invention. The column where the value of the CSV file is located shows an increasing trend of the string. Due to the operations of typing a space and deleting F, there will be repeated rows after displaying the last letter C and a rollback phenomenon when displaying the last letter F. The collected data meets the design expectations and the test passes.

[0062] Verification of data test integrity. Due to the large amount of data collected and the maximum capacity of the character box, a longitudinal comparison is adopted during the test. That is, the system collects process data of different devices performing the same operation at the same time for consistency comparison to verify the integrity of data collection. By repeatedly executing the student_input1() script function, the data collection when multiple students perform the same operation in collaborative learning is simulated. Fig.11 The figure shows the result of the integrity comparison of some collected data in the example of the present invention. In the figure, the size of the exported Excel files is 20.4MB and the number of data cells contained therein is consistent, which proves that the data collected by the automated test script is complete and has passed the verification.

[0063] To sum up, the method described in the embodiment of the present invention has the following advantages compared with traditional data collection methods (manual collection, web crawler technology, log file analysis, etc.): First, the collection accuracy is high, and the collected data is derived from the corresponding behavioral event information captured when the user interacts with the web page, which meets the basic needs of process data collection for user collaborative learning behavior; second, the real-time performance is high, and data collection is performed when the behavioral event is triggered, and the corresponding data processing or operation is triggered by monitoring the DOM event; third, the collection is comprehensive and flexible, and different collection strategies and collection information can be customized for different behavioral event types and trigger conditions to meet the needs of multimodal data collection of process data.

[0064] In addition, a person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be implemented by instructing related hardware through a program, and the corresponding program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0065] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or in any form that the information constitutes prior art known to those skilled in the art.

Claims

1. A method for high-frequency collection and playback of user behavior data, characterized in that: The method comprises: Step 1: Obtain user behavior Event information from the browser cache by monitoring the triggering event based on the Document Object Model DOM, intercept and process the data in the Event information through the JavaScript asynchronous programming mechanism, and upload it to the server to realize procedural data collection; Step 2: The collected process data is transmitted and stored in different data tables of different databases; Step 3: When process data needs to be exported, different process data are taken out from different databases and integrated and exported to facilitate researchers' reference and use.

2. The method for high-frequency collection and playback of user behavior data according to claim 1, characterized in that: The process data has multimodal characteristics and is composed of a unified identification field and an unstructured field, wherein: The unified identification field is a common component of multimodal data, consisting of id, unitId, classroomId, curriculumId, groupId, userId, and createTime; id represents the unique identifier of the data, unitId represents the course unit ID, classroomId represents the classroom ID, curriculumId represents the course ID, groupId represents the group ID, userId represents the user ID, and createTime represents the time when the data is uploaded to the server; Unstructured fields include chat data, behavior data, and playback data, among which: The chat data consists of the chatGroupId, content, msgRead, msgType, fromUserid, and recordId fields; chatGroupId indicates the current group ID, content indicates the chat content, msgRead indicates whether it has been read, msgType indicates the message type, fromUserid indicates the source user ID of the message, and recordId indicates the original message ID; Behavior data consists of count, classKey, and behaviorData fields; count indicates count, classKey indicates page ID, and behaviorData indicates custom fields of different course units, which are composed of arrays; The playback data consists of moduleKey, classKey, eventTime, orderNo, moduleValue, location, and baseData fields; among them, moduleKey, classKey, baseData, and location are standard fields passed by the course unit component to the system, indicating the custom operation information within the course unit component; the introduced eventTime and orderNo fields indicate the occurrence time and order of each piece of data.

3. The method for high-frequency collection and playback of user behavior data according to claim 1, characterized in that: In step 1, the objects of process data collection include chat data, behavior data and playback data, among which: The chat data is generated from the native input box event under the Document Object Model DOM element. By binding the native input box and the keyboard Enter event, and monitoring the changes in the input box value; when the content inside the input box is not empty and the user presses the keyboard Enter, the content in the input box is intercepted and converted into a JSON string and sent directly to the server; Behavior data and playback data are generated inside the task component. The system actively monitors the occurrence of DOM element events inside the course unit component and completes the data collection operation. The system actively retrieves the preset standard parameter information from the course unit component, processes these parameters uniformly and puts them into the browser cache. When the data of a certain item is stored, it is uploaded to the server to realize data collection. Among them, unlike behavioral data, the collection of playback data is based on the changes in HTML elements within the task component. By reproducing the changes and sequence of elements, the effect of visual playback of user behavior can be achieved.

4. The method for high-frequency collection and playback of user behavior data according to claim 1, characterized in that: In step 2, during the transmission of the collected procedural data, the behavior data and playback data come from the micro-operations of the user's behavior. Therefore, the behavior data and playback data are cached in the client browser through the LocalStorage object provided by HTML5, and the JSON data exchange format is used to achieve the purpose of sending them to the server in batches and in a quantitative manner, so as to ensure the stability of the data bandwidth and the integrity of the collection; When storing procedural data in different data tables of different databases, behavior data and playback data contain a large number of custom fields. Therefore, the behavior data and playback data are submitted to the server and stored in the unstructured database MongoDB. Chat data has only two types: private chat and one-on-one chat. The unstructured field information is fixed, so it is stored as structured data in the database MySql.

5. The method for high-frequency collection and playback of user behavior data according to claim 1, characterized in that: In step 3, the standard of the process data export field consists of a unified identification field and an unstructured field. The unified identification field represents the basic information corresponding to the process data. In the unstructured field, type is the data type; 1 is behavior data; 2 is chat data; operationModuleValue is the template content; location is the level name, which is a custom data field for the component; and the data value represents the variable value in the template content.

6. The method for high-frequency collection and playback of user behavior data according to claim 3, characterized in that: In the process of collecting process data, the behavior data and playback data are combined and collected uniformly. Specifically: Merge the fields of behavior data and playback data, and add a type field to the unstructured field: 0 for playback data, 1 for behavior data, and 2 for behavior plus playback data; Delete the count field that is unique to behavior data and replace it with the eventTime and orderNo fields to avoid duplication of field identifiers; During the process of exporting procedural data, the type in the procedural data export field is used to distinguish chat data from behavior data. Therefore, the template content of chat data is defined: the group chat template content is [A]send[B], where [A] and [B] are the contents of the data fields data1 and data2, indicating the sender and content of the message respectively; the single chat template content is [A]send[B]to[C], where [A] and [B] are the contents of the data fields data1, data2 and data3, indicating the sender, content and recipient of the message respectively.

7. The method for high-frequency collection and playback of user behavior data according to claim 1, characterized in that: The method also includes reproducing the playback data and chat data. The data reproduction is implemented in two steps: (1) System preparation for playback data The system includes two ways to prepare playback data: First, when the initial playback request is made or the playback object is switched, the system requests the server to obtain fixed-point or full playback data; the fixed-point data is used to locate the playback time node, and the full playback data is used to reproduce the user operation at the current time node; second, when the playback data is updated or the playback data is requested for the second drag, the system will obtain 5 seconds of playback data through the playback data interface; after the system playback data is prepared, data distribution is enabled; (2) System distribution data to achieve data reproduction After the system is ready for playback data, it opens and loads the playback page containing the course unit component. When the page is ready, it will notify the playback page to distribute the data. The system listens to the distribution message, and the base application receives the playback data. The data of the base application will be kept for processing, and the data of the course unit component sub-application will be distributed using the Micro-App sub-page. After the Micro-App sub-page receives the playback data, it compares the classKey value and assigns the compared data to the corresponding attributes of the current page to achieve data reproduction.

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