Information display method, information processing method and corresponding devices

Abstract

Embodiments of the present application disclose an information display method, an information processing method and corresponding devices. The information display method comprises: displaying an intermediate page of a target application, the intermediate page comprising description information of the target application and a first component triggering downloading of the target application; and displaying personalized recommendation information for a current user in a preset area of the intermediate page, the personalized recommendation information being used to provide a decision basis for downloading the target application, the current user being a user bound to the user terminal, and the personalized recommendation information being obtained based on an association between feature data of the target application and feature data of the current user. The present application changes a traditional fixed content display page into a personalized content display interface, so that a user can directly and quickly obtain more valuable information from the intermediate page, and then make a quick decision, thereby improving user experience.

Description

Technical Field

[0001] This application relates to user interaction technology in the field of information technology, and in particular to an information display method, an information processing method, and a corresponding device. Background Technology

[0002] In application promotion scenarios based on content platforms, the application intermediary page serves as a key interface connecting users and the target application. Its technical goal is to efficiently and accurately convey the core value and functional information of the application to users, thereby improving the accuracy of information delivery and the effectiveness of interaction.

[0003] Traditional application intermediary pages typically display static, pre-defined content, such as the application's text description, icon, screenshots, and components that trigger the application download. This makes it difficult for users to obtain more effective information to make quick decisions. Summary of the Invention

[0004] This application provides an information display method, an information processing method, and a corresponding device to provide users with more effective information and to provide users with a basis for quick decision-making.

[0005] This application provides the following solution: According to the first aspect, an information display method is provided, applied to a user terminal, the method comprising: An intermediate page for displaying the target application is provided, the intermediate page including a description of the target application and a first component that triggers the download of the target application. Personalized recommendation information for the current user is displayed in a preset area of ​​the middle page. The personalized recommendation information is used to provide a decision basis for downloading the target application. The current user is a user bound to the user terminal. The personalized recommendation information is obtained based on the correlation between the feature data of the target application and the feature data of the current user.

[0006] Optionally, the personalized recommendation information includes matching information between the target application and the current user, or the personalized recommendation information includes matching information of the target application and recommendation reason information; The matching degree information is obtained based on the correlation between the feature data of the target application and the feature data of the current user.

[0007] Optionally, the preset area may also display a second component that triggers the display of recommended function points; In response to the operation that triggers the second component, information on at least one feature of the target application recommended to the current user is displayed in a preset area of ​​the intermediate page. The information on the at least one feature is obtained based on the association between the information on the feature of the target application and the feature data of the current user.

[0008] Optionally, the personalized recommendation information includes information on at least one feature of the target application recommended to the current user, wherein the information on the at least one feature is obtained based on the association between the information on the feature of the target application and the feature data of the current user.

[0009] Optionally, the information of the at least one functional point includes: an interactive interface corresponding to an image, video clip, or code clip that embodies the at least one functional point of the target application.

[0010] Optionally, the interactive interface corresponding to the code snippet is displayed in the following manner: Obtain and cache the code snippet, the code snippet embodying at least one functional point of the target application; Create a canvas or view in a preset area of ​​the middle page; Render the initial interface corresponding to the code snippet within the canvas or view, where each visual element in the initial interface is in its initial display state. In response to an operation on the initial interface, based on the correspondence between the operation parameters included in the code snippet and the interface interaction logic corresponding to the at least one function point, the interface interaction logic corresponding to the operation is determined, and the interface interaction logic includes the update display state of visual elements. Based on the defined interface interaction logic, the corresponding visual elements are re-rendered within the canvas or view.

[0011] Optionally, displaying personalized recommendation information for the current user in a preset area of ​​the intermediate page includes: An interactive entry point for triggering the display of personalized recommendation information is shown in a preset area of ​​the middle page; In response to the operation that triggers the interactive entry point, personalized recommendation information for the current user is displayed in a preset area of ​​the intermediate page instead of the interactive entry point.

[0012] Optionally, the interactive entry point includes: a sliding component; The operation of triggering the interaction entry includes: sliding the swipeable component from the icon position of the target application to the avatar position of the current user, or sliding the swipeable component from the avatar position of the current user to the icon position of the target application.

[0013] Optionally, the intermediate page displaying the target application includes: Display a media playback page or a live streaming page. The media playback page is used to play media files published by the influencer, and the live streaming page is used to play live streaming content initiated by the influencer. The media playback page or the live streaming page includes a link to an intermediate page of the target application. The influencer is a content creator who meets preset conditions. In response to the action that triggers the link, an intermediate page of the target application is displayed.

[0014] Optionally, the feature data of the target application includes: interaction data between the expert and the target application; The current user's feature data includes the interaction data between the current user and the expert.

[0015] According to the second aspect, an information processing method is provided, applied on a server side, the method comprising: In response to a user's request for an intermediate page of a target application, personalized recommendation information is obtained for the current user based on the correlation between the feature data of the target application and the feature data of the current user. The personalized recommendation information is used to provide a decision basis for downloading the target application. The current user is a user bound to the user's client. The personalized recommendation information and the intermediate page data of the target application are sent to the user terminal. The intermediate page data includes the description information of the target application and the download address information of the target application.

[0016] Optionally, obtaining personalized recommendation information for the current user based on the association between the feature data of the target application and the feature data of the current user includes: The feature data of the target application and the feature data of the current user are provided to a first language model. The first language model then infers the matching degree information between the target application and the current user based on the association between their feature data; or... The feature data of the target application and the feature data of the current user are provided to the second language model. After the second language model extracts function points based on the feature data of the target application, it infers information about at least one function point of the target application to recommend to the current user based on the association between the function points and the feature data of the current user.

[0017] According to a third aspect, an information display device is provided for use on a user terminal, the device comprising: The first display unit is configured to display an intermediate page of the target application, the intermediate page including description information of the target application and a first component that triggers the download of the target application; The second display unit is configured to display personalized recommendation information for the current user in a preset area of ​​the middle page. The personalized recommendation information is used to provide a decision basis for downloading the target application. The current user is a user bound to the user terminal. The personalized recommendation information is obtained based on the correlation between the feature data of the target application and the feature data of the current user.

[0018] According to the fourth aspect, an information processing apparatus is provided, applied to a server, the apparatus comprising: The request retrieval unit is configured to receive requests from the user client for an intermediate page of the target application; The recommendation generation unit is configured to respond to a request from the user terminal for an intermediate page of a target application, and obtain personalized recommendation information for the current user based on the correlation between the feature data of the target application and the feature data of the current user. The personalized recommendation information is used to provide a decision basis for downloading the target application, and the current user is a user bound to the user terminal. The data sending unit is configured to send the personalized recommendation information and the intermediate page data of the target application to the user terminal, wherein the intermediate page data includes the description information of the target application and the download address information of the target application.

[0019] According to a fifth aspect, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first and second aspects above.

[0020] According to a sixth aspect, an electronic device is provided, comprising: One or more processors; and A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any one of the first and second aspects above.

[0021] According to a seventh aspect, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of the method described in any one of the first and second aspects above.

[0022] As can be seen from the above technical solutions, this application dynamically generates and displays personalized recommendation information for the current user by linking the feature data of the target application with the feature data of the current user. This transforms the traditional fixed content display page into a personalized content display interface for the current user, enabling the user to directly and quickly obtain more valuable information from the intermediate page, thereby making a quick decision. Furthermore, the first component can trigger the download of the target application, thus improving the user experience. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a system architecture diagram applicable to the embodiments of this application.

[0025] Figure 2 A flowchart illustrating the information display method provided in this application embodiment.

[0026] Figure 3a This is a schematic diagram of an intermediate page of a target application provided in an embodiment of this application.

[0027] Figure 3b This is a schematic diagram of an intermediate page for another target application provided in an embodiment of this application.

[0028] Figure 4 This is a schematic diagram of an intermediate page for displaying personalized recommendation information, provided as an embodiment of this application.

[0029] Figure 5 This is a schematic diagram of another intermediate page for displaying personalized recommendation information provided in an embodiment of this application.

[0030] Figure 6 This is a schematic diagram of another intermediate page for displaying personalized recommendation information provided in an embodiment of this application.

[0031] Figure 7 This is a flowchart of an information processing method executed by the server side, provided in an embodiment of this application.

[0032] Figure 8 A schematic block diagram of an information display device provided in an embodiment of this application.

[0033] Figure 9 A schematic block diagram of an information processing apparatus provided in an embodiment of this application.

[0034] Figure 10 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0036] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0037] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0038] Depending on the context, the word "if" as used herein can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the condition or operation of the statement)" can be interpreted as "when determination," "in response to determination," "when detection (of the condition or operation of the statement)," or "in response to detection (of the condition or operation of the statement)."

[0039] In traditional intermediary page technology solutions, static, pre-defined display logic is typically used to maintain the universality and determinism of the content presentation. After in-depth research, the inventors of this application discovered that this static threshold-based display logic results in a weak correlation between page information and individual user needs, thus affecting the targeted nature of information delivery and the adaptability and efficiency of the interaction process.

[0040] In view of this, this application proposes a new method of information display. To facilitate understanding of this application, the system architecture on which this application is based will be described first. Figure 1 An exemplary system architecture that can be applied to embodiments of this application is shown, such as Figure 1 As shown, the system architecture may include: a client and a server.

[0041] The server-side and the client-side are the two main components of an application service. The server-side, using a server as its primary hardware infrastructure, may include one or more software service modules. In this embodiment, it is mainly responsible for sending page data to the user, forming a collaborative front-end and back-end with the client, enabling the client to display the application's intermediate pages based on the received page data, using the methods provided in this embodiment.

[0042] The user terminal can include, but is not limited to, smart mobile terminals, wearable devices, and PCs (Personal Computers). Smart mobile devices can include mobile phones, tablets, PDAs (Personal Digital Assistants), and connected car terminals. Wearable devices can include smartwatches, smart glasses, smart bracelets, VR (Virtual Reality) devices, AR (Augmented Reality) devices, and mixed reality devices (devices that support both virtual and augmented reality), etc.

[0043] The user end can be an application located on the user terminal, or it can be a plugin or software development kit (SDK) or other functional unit set in the application.

[0044] A server can be a standalone server, a server cluster, or a cloud server. A cloud server, also known as a cloud computing server or cloud host, is a hosting product within the cloud computing service system, designed to address the shortcomings of traditional physical hosts and Virtual Private Servers (VPS) services, such as high management difficulty and weak service scalability.

[0045] It should be understood that Figure 1 The server and client shown are merely illustrative. Depending on implementation needs, there can be any number of server and client components.

[0046] Figure 2 This is a flowchart illustrating an information display method provided in an embodiment of this application. The method can be... Figure 1 The user-side execution in the system shown is as follows. Figure 2 As shown, the method may include the following steps: Step 201: Display the intermediate page of the target application, which includes a description of the target application and the first component that triggers the download of the target application.

[0047] Step 203: Display personalized recommendation information for the current user in the preset area of ​​the middle page. The personalized recommendation information is used to provide a decision basis for downloading the target application. The current user is the user bound to the user terminal. The personalized recommendation information is obtained based on the correlation between the feature data of the target application and the feature data of the current user.

[0048] As can be seen from the above process, this application dynamically generates and displays personalized recommendation information for the current user by linking the feature data of the target application with the feature data of the current user. This transforms the traditional fixed content display page into a personalized content display interface for the current user, enabling the user to directly and quickly obtain more valuable information from the intermediate page, thereby making a quick decision. Furthermore, the first component can trigger the download of the target application, thus improving the user experience.

[0049] The following describes in detail each step of the above process and the effects that can be further produced, with reference to the embodiments. It should be noted that the terms "first" and "second" involved in this disclosure do not have limitations in terms of size, order, or quantity, but are only used to distinguish them by name. For example, "first component" and "second component" are used to distinguish two components by name.

[0050] First, the above step 201, namely "displaying the intermediate page of the target application", will be described in detail with reference to the embodiments.

[0051] In this application, "intermediate page of the target application" refers to any page displayed on the user's end for promoting the target application. This intermediate page typically includes a description of the target application and a first component that triggers the download of the target application. Typically, in response to a user's request for an intermediate page of the target application, the server sends the intermediate page data, which includes at least the description of the target application and its download address.

[0052] The description information of the target application may include one or any combination of the following: the target application's name, category tags, developer information, version number information, permission information, user rating information, etc.

[0053] The first component triggers the download of the target application. In response to the event of the first component being triggered, a request to download the target application is sent to the server, which then returns the installation package of the target application for the user's terminal device to install. In addition, the intermediate page of the target application may also include screenshots and other information.

[0054] As one feasible approach, the intermediate page displaying the target application can specifically be either a media playback page or a live streaming page. The media playback page displays the media content stream published by the influencer, while the live streaming page displays the real-time audio and video stream generated by the influencer. The influencer can be a content producer who meets preset conditions. These preset conditions may include at least one of the following: the quantity of generated content meets a first quantity requirement; the quality of generated content meets a preset quality requirement; the number of content consumers who follow the content meets a second preset quantity requirement; and the number of comments received meets a third preset quantity requirement.

[0055] To improve the effectiveness of app promotion, a Uniform Resource Identifier (URI) pointing to an intermediate page of the target app can be included on the media playback page or live stream page. Then, in response to an action triggered by this URI, the user is redirected to the intermediate page displaying the target app. For example, when a user watches a video posted by an influencer on a content playback page, a text component is also displayed. This text component contains promotional content for the target app and includes a link to the intermediate page of the target app. When the user clicks this text component, the content playback page redirects to the intermediate page of the target app.

[0056] This approach allows for the embedding of an entry point into the target application's intermediate page within the content consumption scenario, enabling users to access the target application's intermediate page more quickly and conveniently when consuming content.

[0057] In addition to triggering the display of the target application's intermediate page from the content playback page and the live streaming page, it can also be triggered by events on other pages, such as: click events on the application list within the platform, redirection requests from external links, response actions to system notifications, or selection operations on search results.

[0058] In a specific example, such as Figure 3a and 3b The diagram illustrates the intermediate page of the target application. At the top of the intermediate page, descriptive text including the target application's icon, name, developer information, version number, and permissions is displayed. At the bottom, a button component (e.g., a button containing the text "Download") serves as the first component. Those skilled in the art will understand that the view layout of the intermediate page, the content of the descriptive information, and the specific type of the first component (e.g., button, switch, gesture area) can all be flexibly configured.

[0059] The following describes step 203, namely "displaying personalized recommendation information for the current user in a preset area of ​​the middle page," in detail with reference to an embodiment.

[0060] The personalized recommendation information for the current user can originate from the server. The server can send the intermediate page data of the target application and the personalized recommendation information for the current user to the user's client.

[0061] One feasible approach is to directly display personalized recommendations in a pre-defined area of ​​the target application's intermediate page. That is, after navigating to the intermediate page showcasing the target application, personalized recommendations are immediately displayed there.

[0062] As another possible approach, an interactive entry point that triggers the display of personalized recommendation information can be first displayed in a preset area of ​​the intermediate page of the target application; then, in response to the operation that triggers the interactive entry point, personalized recommendation information for the current user can be displayed in the preset area of ​​the intermediate page instead of the interactive entry point.

[0063] In this implementation, the intermediate page of the target application serves as the basic view for information carrying. Its main technical function is to present the static description information of the target application, provide the first component as the component that triggers the subsequent application installation process, and at the same time, display an interactive entry point. Users can choose whether to trigger the further display of personalized recommendation information through this interactive entry point. On the one hand, it is more flexible, and on the other hand, this method of triggering the display of personalized recommendation information only when the user initiates an explicit display request through the interactive entry point can improve the performance of the intermediate page's initial load.

[0064] As one possible implementation, the aforementioned interactive entry point can include a button component. The operation that triggers the interactive entry point can include triggering the operation of that button component. For example... Figure 3a As shown, a button component is displayed in a preset area. Text indicating the function of the button component is displayed for the button component, such as "Test if this app is right for you" or "Take a test". An icon 301 of the target app and a user avatar 302 can also be displayed. In response to the operation of triggering the button component, personalized recommendation information for the current user is displayed in the preset area instead of displaying the interaction entry.

[0065] As one possible implementation, the aforementioned interaction entry point can include a scrollable component. The operation that triggers this interaction entry point can include: scrolling the scrollable component from the target application's icon position to the current user's avatar position, or scrolling the scrollable component from the current user's avatar position to the target application's icon position. See also... Figure 3b The preset area contains the target application's icon 301 and a user avatar 302, with a sliding component between them, for example... Figure 3bA draggable square slider 303 is used. The user drags the square slider 303 from the position of the target application icon 301 to the position of the user avatar 302. After this gesture operation event is captured, personalized recommendation information is displayed. At this time, the preset area is updated with personalized recommendation information.

[0066] By concretizing the interaction entry point into a swipeable component and limiting the triggering operation to swiping from the target application's icon to the current user's avatar or vice versa, this design establishes a clear and visually visible triggering path at the technical level. Users must complete a continuous swipe operation with a clear start and end point to trigger the display of personalized recommendations. This requires a more explicit intent and more precise control than a simple click, effectively avoiding accidental triggers caused by unintentional clicks or touches on intermediate pages and improving the accuracy of interaction intent recognition.

[0067] In this step, the personalized recommendations displayed in the preset area of ​​the middle page are derived from the correlation between the feature data of the target application and the feature data of the current user. Here, the current user refers to the user bound to a client application, such as a user who logs in to the corresponding service through a client application.

[0068] The characteristic data of a target application refers to a set of parameters used to describe the attributes, functions, content, or technical requirements of the target application. For example, it may include, but is not limited to: functional information, category information, user information (including content producers and consumers) who interact with the target application, and their interaction metrics (e.g., whether they use the target application, the number of times they use the target application, etc.). The user information and interaction metrics that interact with the target application can be, for example, interaction data between influencers and target users, such as whether the influencer uses the target application, or the number of times the influencer uses the target application.

[0069] The current user's characteristic data refers to the data set used to describe the user's attributes, historical behavior, preferences, or user relationships (such as the interaction data between the current user and the expert).

[0070] The target application's feature data can be the interaction data between the influencer and the target application, while the current user's feature data can be the interaction data between the current user and the influencer, establishing a data chain linking "user-influencer-application". This allows the personalized recommendation information to not only rely on the direct association between the user and the application, but also to delve deeper into and utilize the interaction relationship between the influencers (i.e., content creators) that the user follows and the target application (e.g., whether the influencer uses, recommends, or creates content for the application). This indirect association data based on social influence and interest transmission can more accurately infer the user's potential interests and trust in the target application, thereby generating more persuasive and personalized recommendations that align with the user's social interests.

[0071] In this embodiment of the application, personalized recommendation information may include, but is not limited to, the following two types: The first approach: Personalized recommendations can include information on how well the target application matches the current user.

[0072] The matching degree information is derived from the correlation between the feature data of the target application and the feature data of the current user. It is a quantifiable or intuitive expression that the user can readily perceive, calculated from the correlation between the feature data of the target application and the feature data of the current user.

[0073] Matching information may include at least one of matching degree and recommendation suggestion text. Matching degree is the degree of match between the feature data of the target application and the feature data of the current user, and can be a scalar value (such as 0.85), a percentage (85%), an identifier in a ranking sequence (such as four out of five stars), or a descriptive text label (such as "high match").

[0074] The recommended suggestion text is determined based on the matching degree, and different suggested suggestion texts can be predefined according to different matching degrees. For example, when the matching degree is between 60% and 70%, the suggested suggestion text "Fate is wonderful" can be displayed; when the matching degree is between 70% and 80%, the suggested suggestion text "This app really understands you" can be displayed; when the matching degree is between 80% and 90%, the suggested suggestion text "This app is a perfect match for you" can be displayed; and when the matching degree is above 90%, the suggested suggestion text "This app was made for you" can be displayed.

[0075] like Figure 4 As shown in the image, the matching accuracy is 88%, and the recommended suggestion text is "This app really understands you".

[0076] The matching degree between the feature data of the target application and the feature data of the current user can be obtained by calculating the similarity between the feature representations corresponding to the feature data of the target application and the feature data of the current user. Other calculation methods can also be used, such as calling a large language model to calculate the matching degree between the feature data of the target application and the feature data of the current user. This application does not impose any particular restrictions on this.

[0077] In addition to this, the personalized information displayed can further include recommendation rationale information. Recommendation rationale information can provide a readable contextual explanation of the quantified matching degree information, clarifying the specific basis for the association, thereby increasing the understandability and credibility of the output information. For example... Figure 4 The results show "Based on the types of apps you usually like", "Two influencers you follow also like it", and "Two fans are also using it".

[0078] It can be seen that by transforming the originally internally calculated relationships into user-perceptible interface information, namely matching degree information and recommendation reasons, users can intuitively and quickly understand the relationship between the target application and themselves, and use this as a basis and reference to quickly make a decision on whether to download the target application.

[0079] The second approach: Personalized recommendation information includes information on at least one feature of the target application recommended to the current user.

[0080] At least one feature's information is derived from the correlation between the target application's feature information and the current user's characteristic data. Feature information can be understood as data that identifies and describes specific characteristics, modules, or gameplay within the target application, such as "multi-role reading" or "page-turning animation" in reading applications. Another example is "disco mode" or "colorful lyrics" in music applications.

[0081] The functionalities of the target application can be pre-defined by the target application provider and provided in the form of a file, or they can be summarized and extracted from a large model.

[0082] The server can select at least one feature from all functionalities of the target application that best matches the current user's feature data as personalized recommendations. For example, a set of feature tags can be predefined for each feature (e.g., "suitable for photography enthusiasts," "requires geolocation," "emphasizes real-time interaction"), while a set of interest preference tags can be extracted from the current user's feature data (e.g., "frequently browses photography content," "frequently uses local services," "active in social interactions"). By calculating the semantic similarity between the tag sets or using a large language model, the degree of association between each feature and the current user can be quantified, and at least one feature with the highest degree of association can be selected. Other methods can also be used to determine the degree of association between each feature and the current user, which will not be listed here.

[0083] By seamlessly integrating personalized recommendations of functional points into the intermediate page of the target application, the number of operation steps and waiting time required for users to obtain the core value of the target application is reduced. Furthermore, the personalized recommendation information adopts the functional point approach, making the recommendation information more specific and concrete. This solves the problem of user ambiguity caused by overly general recommendation information and improves the accuracy of information delivery at the technical level.

[0084] In this application embodiment, the information of the above-mentioned at least one functional point includes, but is not limited to, at least one of the following forms: The first format: an image that represents at least one functional feature of the target application.

[0085] This refers to a static image that has been cropped or specially created to visually demonstrate the core interface, operation effects, or application scenarios of a particular feature. For example, for the "handwriting to text" feature of a note-taking application, the information could be a comparison image showing how handwritten notes are accurately recognized as regular printed text.

[0086] The second form: a video clip that demonstrates at least one functional aspect of the target application.

[0087] This refers to a short, focused, dynamic video used to demonstrate the operation process, dynamic effects, or use cases of a particular feature. For example, for a game application's "custom character outfit" feature, the information could be a quick demonstration video showing the process from choosing a hairstyle and clothing to finally generating a personalized character model.

[0088] The third form: the interactive interface corresponding to a code snippet that represents at least one functional point of the target application.

[0089] This refers to a method that uses a code snippet representing the core interactive logic of a feature to render a simulated interface in real time, providing feedback and interaction. It doesn't directly display the code text, but rather the interactive effect after the code runs. For example, in a reading app, the "page-turning animation" feature, rendered from a code snippet, allows users to experience the "page-turning animation" effect using a specific action point.

[0090] As can be seen, this application's embodiments, by introducing rich media formats such as images, videos, and interactive interfaces corresponding to code snippets, efficiently express interface layouts, dynamic effects, and visual feedback that are difficult to accurately convey through text descriptions. This solves the problems of insufficient expression and high user comprehension costs associated with pure text information when describing complex interactive functions. In particular, by providing "interactive interfaces corresponding to code snippets," a lightweight functional "trial" experience is technically achieved, allowing users to perceive the core interactive logic through simulated execution on the user's device without downloading the full application. This essentially upgrades the way functional information is delivered from "informing" to "experiencing," improving the efficiency of users obtaining reference information.

[0091] As one possible approach, the interactive interface corresponding to the code snippet can be displayed using the following steps: Step S1: Obtain and cache code snippets, which represent at least one functional point of the target application.

[0092] The code snippet above is not the complete source code of the target application, but rather a specially extracted, encapsulated, and simplified version that contains only the front-end logic necessary to achieve the core interactive effects of this feature (for example, a script written using HTML5, JavaScript, and CSS).

[0093] Step S2: Create a canvas or view in the preset area of ​​the middle page.

[0094] In this embodiment, a canvas or a native view can be created in a preset area of ​​the intermediate page. The canvas is suitable for pixel-based interactive graphics, while the native view is suitable for interactive interfaces containing standard UI controls. This step provides an isolated rendering environment for the execution of code snippets.

[0095] Step S3: Render the initial interface corresponding to the code snippet within the canvas or view. In the initial interface, each visual element is in its initial display state.

[0096] At this point, all visual elements in the interface (such as buttons, sliders, images, and draggable objects) are in their preset initial display state. For example, for a code snippet of an "image filter" function, the initial interface may display a default image and a series of filter icons that are not selected.

[0097] Step S4: In response to the operation on the initial interface, based on the operation parameters included in the code snippet and the interface interaction logic corresponding to at least one function point, determine the interface interaction logic corresponding to the operation. The interface interaction logic includes the update display state of visual elements.

[0098] The client listens for user actions on the initial interface, such as clicks, drags, and swipes. When an action is detected, the client does not need to request new interface data from the server. Instead, based on the correspondence between the operation parameters defined in the cached code snippet and the interface interaction logic corresponding to at least one functional point, the client determines the interface interaction logic to be triggered by the action in real time locally.

[0099] Step S5: Based on the determined interface interaction logic, re-render the corresponding visual elements within the canvas or view.

[0100] Based on the defined interface interaction logic, the client re-renders the affected visual elements within the canvas or view, thereby updating the interface display state and providing the user with immediate feedback. The entire process forms a closed-loop local interaction cycle.

[0101] For example, such as Figure 5 As shown, the target application is assumed to be a drawing application. The interactive interface of this application is displayed in a preset area on the middle page of the application. Above the interactive interface, there is a prompt with the text "Highlights best suited for you," which allows users to trigger "Try it" and operate and experience the interface. Let's assume the recommended feature is the "brush color blending" function of a drawing application. After the corresponding code snippet is retrieved and cached, an initial interface is rendered on the created canvas: on the left are brush buttons for the three basic colors—red, yellow, and blue—and on the right is a blank canvas. When the user clicks the "red" brush and draws a stroke on the canvas, the logic defined in the code snippet controls the display of red strokes. When the user clicks the "yellow" brush and attempts to overlay a stroke on top of the existing red stroke, a more complex interactive logic is triggered. Based on the "color blending algorithm" defined in the code snippet, the user's interaction logic at this point should be: calculate the pixel values ​​of the red and yellow overlapping area and render it as orange. Therefore, the canvas is re-rendered in real time with an orange blended area, rather than a simple yellow overlay. All of these interactions and visual updates are driven by locally cached code snippets, without the need for additional communication with the server to obtain the resulting image after each drawing.

[0102] As can be seen, by pre-encapsulating the core interactive logic of functional points into cacheable lightweight code snippets and performing rendering and logic calculations locally on the user's end, real-time interactive previews and rapid responses are achieved. Furthermore, user actions run directly on the code snippets representing the functional point logic, resulting in real-time state changes rather than pre-recorded animations, thus providing a superior user experience. In addition, this solution avoids distributing large amounts of pre-rendered media files (such as videos under various operation paths) and avoids requesting new interface data from the server for every minor interactive operation. By using code snippets to describe complex interactions and completing the loop locally, it significantly optimizes network data transmission volume and server computational load, enabling deep interactive previews in resource-constrained mobile environments.

[0103] When a user triggers an interaction entry point in a preset area on the intermediate page, besides directly displaying one of the two methods mentioned above in the preset area to replace the interaction entry point, the user can also first use the first method, where the displayed personalized recommendation information can include the matching degree information between the target application and the current user. Simultaneously, the preset area also displays a second component that triggers the display of recommendation functions, such as... Figure 6 The "Experience Highlight Features" button component shown in (a) . In response to triggering the second component, information about at least one feature of the target application recommended to the current user is displayed in a preset area of ​​the middle page. For example, if the user triggers Figure 6 The button component for "Experience Highlights" shown in (a) displays the following in the preset area: Figure 6 The information of the functional points shown in (b) allows users to experience the functional points on the interactive interface.

[0104] As can be seen, this approach, by adding a triggerable second component in a preset area, decouples the display of in-depth information such as functional points from the initial loading logic, transforming it into a secondary loading triggered by the user. It allows the page to present only core, basic application matching information (such as matching degree information) during the initial rendering, keeping the interface concise; only after the user expresses an intention to explore further (triggering the second component) is more specific functional point information dynamically loaded and displayed. This on-demand loading and layered display mechanism optimizes the initial use of system resources and rendering performance, achieving greater flexibility.

[0105] Figure 7 This is a flowchart illustrating an information processing method executed by a server, as provided in an embodiment of this application. Figure 7 As shown, the method may include the following steps: Step 701: In response to the client's request for the intermediate page of the target application, based on the correlation between the feature data of the target application and the feature data of the current user, obtain personalized recommendation information for the current user. The personalized recommendation information is used to provide a decision basis for downloading the target application. The current user is the user bound to the client.

[0106] The request carries at least the identification information of the target application and the identification information of the current user bound to the user's client. The server first retrieves the target application's feature data from the application feature database based on the target application's identification. This may include, but is not limited to: the target application's functional information, category information, information about users (including content producers and consumers) who interact with the target application, and their interaction metrics (e.g., whether they use the target application, the number of times they use the target application, etc.). The user information and interaction metrics that interact with the target application can be, for example, the interaction data between influencers and the target user, such as whether the influencer uses the target application, or the number of times the influencer uses the target application. Furthermore, the server retrieves the current user's feature data from the user feature database based on the current user's identification. This data may include the user's attributes, interest tags, historical behavior, and user relationships (e.g., interaction data between the current user and influencers).

[0107] In this step, based on the correlation between the feature data of the target application and the feature data of the current user, personalized recommendation information for the current user can be obtained in, but is not limited to, the following two ways: The first approach involves providing the feature data of the target application and the feature data of the current user to the first language model. The first language model then infers the degree of matching between the target application and the current user based on the correlation between their feature data.

[0108] The server-side processes the feature data of the target application and the current user, appropriately formatting and constructing the prompts, and provides them as prompts to the first language model. This first language model is a pre-trained model trained on massive amounts of text and task-specific data, possessing powerful semantic understanding and content generation capabilities. Based on the two types of input data, the first language model performs deep relational reasoning through its internal parameters to understand the relationship between the feature data of the target application and the feature data of the current user, and infers and outputs at least one of the following: information on the matching degree between the target application and the current user, and information on the recommendation reason.

[0109] For details regarding the matching degree information and recommendation reason information, please refer to the relevant descriptions in the previous embodiments, which will not be repeated here. Besides using the first language model to obtain at least one of the aforementioned matching degree information and recommendation reason information, other methods can also be used to obtain them.

[0110] The second approach involves providing the feature data of the target application and the feature data of the current user to the second language model. After the second language model extracts function points based on the feature data of the target application, it infers information about at least one function point of the target application to recommend to the current user based on the association between the function points and the feature data of the current user.

[0111] The second major language model is also a trained large language model, but its training data or fine-tuning tasks may focus more on product function understanding and structured information extraction. The second major language model first identifies and extracts several core functional points from application feature data. Then, it combines this functional point information with the current user's feature data to perform associative reasoning, thereby filtering and generating information on at least one functional point of the target application recommended to the current user.

[0112] In addition, other methods can be used to achieve this. For example, the functional points of the target application can be pre-set by the target application provider and provided in the form of a file. Then, the functional point information of the target application and the feature data of the current user are submitted to the large language model for association reasoning to generate information on at least one functional point of the target application recommended to the current user.

[0113] The information regarding at least one feature of the target application recommended to the current user may include: images, video clips, or code snippets that represent at least one feature of the target application.

[0114] Both of these methods leverage the powerful semantic understanding and generation capabilities of large language models to process the feature data of the target application and the feature data of the current user, thereby uncovering deep, logical connections and generating matching degree information or recommending at least one feature of the target application to the current user, making information delivery more intuitive and accurate.

[0115] Step 703: Send the personalized recommendation information and the intermediate page data of the target application to the user's terminal. The intermediate page data includes the description information of the target application and the download address information of the target application.

[0116] This method corresponds to the information display method on the user side, together forming a complete system interaction. The server side undertakes the key tasks of data aggregation, correlation analysis, and content generation.

[0117] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0118] Figure 8 This is a schematic block diagram of an information display device provided in an embodiment of this application. The device is disposed in... Figure 1 The user end in the illustrated architecture. (For example...) Figure 8 As shown, the device 800 includes: a first display unit 801 and a second display unit 802. The main functions of each component are as follows: The first display unit 801 is configured to display an intermediate page of the target application. The intermediate page includes a description of the target application and a first component that triggers the download of the target application.

[0119] The second display unit 802 is configured to display personalized recommendation information for the current user in a preset area of ​​the middle page. The personalized recommendation information is used to provide a basis for decision-making for downloading the target application. The current user is a user bound to the user terminal. The personalized recommendation information is obtained based on the correlation between the feature data of the target application and the feature data of the current user.

[0120] One possible approach is to include information on the match between the target application and the current user; alternatively, personalized recommendation information may include both the match between the target application and the reason for the recommendation. Accordingly, the match information is derived from the correlation between the feature data of the target application and the feature data of the current user.

[0121] Furthermore, the preset area also displays a second component that triggers the display of recommended functional points; the second display unit 802 is also configured to: in response to the operation of triggering the second component, display information of at least one functional point of the target application recommended to the current user in the preset area of ​​the middle page, wherein the information of at least one functional point is obtained based on the association between the information of the functional point of the target application and the feature data of the current user.

[0122] As another possible approach, personalized recommendation information includes information on at least one feature of a target application recommended to the current user, the information of which is derived from the association between the feature information of the target application and the feature data of the current user.

[0123] Among them, the information of at least one functional point includes: the interactive interface corresponding to the image, video clip or code clip that embodies at least one functional point of the target application.

[0124] As one possible implementation method, the second display unit 802 can display the interactive interface corresponding to the code snippet in the following way: acquiring and caching the code snippet, the code snippet embodying at least one functional point of the target application; creating a canvas or view in a preset area of ​​the intermediate page; rendering the initial interface corresponding to the code snippet within the canvas or view, in which each visual element is in an initial display state; responding to the operation on the initial interface, determining the interface interaction logic corresponding to the operation based on the correspondence between the operation parameters included in the code snippet and the interface interaction logic corresponding to at least one functional point, the interface interaction logic including the updated display state of the visual elements; and re-rendering the corresponding visual elements within the canvas or view based on the determined interface interaction logic.

[0125] As one possible implementation method, the second display unit 802 can be specifically configured to: display an interactive entry point that triggers the display of personalized recommendation information in a preset area of ​​the middle page; and, in response to the operation of triggering the interactive entry point, display personalized recommendation information for the current user in the preset area of ​​the middle page instead of displaying the interactive entry point.

[0126] As one possible approach, the interaction entry point includes: a sliding component; Operations that trigger the interaction entry include: sliding the swipeable component from the target application's icon position to the current user's avatar position, or sliding the swipeable component from the current user's avatar position to the target application's icon position.

[0127] As one possible implementation method, the first display unit 801 can be specifically configured to: display a media playback page or a live streaming page, wherein the media playback page is used to play media files published by the influencer, and the live streaming page is used to play live streaming content initiated by the influencer. The media playback page or live streaming page includes a link to an intermediate page of the target application, and the influencer is a content creator who meets preset conditions; in response to the operation of triggering the link, the intermediate page of the target application is displayed.

[0128] As one possible approach, the target application's feature data may include: interaction data between the influencer and the target application; and the current user's feature data may include interaction data between the current user and the influencer.

[0129] Figure 9 This is a schematic block diagram of an information processing apparatus provided in an embodiment of this application. The apparatus is disposed in... Figure 1 The server side in the illustrated architecture. For example... Figure 9As shown, the device 900 may include: a request acquisition unit 901, a recommendation generation unit 902, and a data transmission unit 903. The main functions of each component are as follows: The request retrieval unit 901 is configured to receive requests from the user client for intermediate pages of the target application.

[0130] The recommendation generation unit 902 is configured to respond to a request from the user client for an intermediate page of the target application, and obtain personalized recommendation information for the current user based on the correlation between the feature data of the target application and the feature data of the current user, where the current user is the user bound to the user client.

[0131] The data sending unit 903 is configured to send personalized recommendation information and intermediate page data of the target application to the user terminal. The intermediate page data includes the description information of the target application and the download address information of the target application.

[0132] As one possible implementation method, the recommendation generation unit 902 can be configured to provide the feature data of the target application and the feature data of the current user to a first language model, obtain the matching degree information of the target application and the current user based on the association between the feature data of the target application and the feature data of the current user; or, provide the feature data of the target application and the feature data of the current user to a second language model, obtain the information of at least one function point of the target application recommended to the current user based on the association between the function point and the feature data of the current user after the second language model extracts function points based on the feature data of the target application.

[0133] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or device embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. The system and device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0134] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application, such as user characteristic data, are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0135] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0136] And an electronic device, comprising: One or more processors; and A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0137] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0138] in, Figure 10 An exemplary architecture of an electronic device is shown, which may include a processor 1010, a video display adapter 1011, a disk drive 1012, an input / output interface 1013, a network interface 1014, and a memory 1020. The processor 1010, video display adapter 1011, disk drive 1012, input / output interface 1013, network interface 1014, and memory 1020 can communicate with each other via a communication bus 1030.

[0139] The processor 1010 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs and implement the technical solution provided in this application.

[0140] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system 1021 for controlling the operation of the electronic device 1000, and the basic input / output system (BIOS) 1022 for controlling the low-level operations of the electronic device 1000. Additionally, it can store a web browser 1023, a data storage management system 1024, and an information display device 800 / information processing device 900, etc. The aforementioned information display device 800 / information processing device 900 can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 1020 and executed by the processor 1010.

[0141] Input / output interface 1013 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0142] The network interface 1014 is used to connect the communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0143] Bus 1030 includes a pathway for transmitting information between various components of the device, such as processor 1010, video display adapter 1011, disk drive 1012, input / output interface 1013, network interface 1014, and memory 1020.

[0144] It should be noted that although the above-described device only shows the processor 1010, video display adapter 1011, disk drive 1012, input / output interface 1013, network interface 1014, memory 1020, bus 1030, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0145] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer program product. This computer program product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0146] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An information display method, characterized in that, When applied to a user terminal, the method includes: An intermediate page for displaying the target application is provided, the intermediate page including a description of the target application and a first component that triggers the download of the target application. Personalized recommendation information for the current user is displayed in a preset area of ​​the middle page. The personalized recommendation information is used to provide a decision basis for downloading the target application. The current user is a user bound to the user terminal. The personalized recommendation information is obtained based on the correlation between the feature data of the target application and the feature data of the current user.

2. The method according to claim 1, characterized in that, The personalized recommendation information includes information on the matching degree between the target application and the current user, or the personalized recommendation information includes information on the matching degree of the target application and information on the reasons for the recommendation; The matching degree information is obtained based on the correlation between the feature data of the target application and the feature data of the current user.

3. The method according to claim 2, characterized in that, The preset area also displays a second component that triggers the display of recommended function points; In response to the operation that triggers the second component, information on at least one feature of the target application recommended to the current user is displayed in a preset area of ​​the intermediate page. The information on the at least one feature is obtained based on the association between the feature information of the target application and the feature data of the current user.

4. The method according to claim 1, characterized in that, The personalized recommendation information includes information on at least one feature of the target application recommended to the current user, and the information on the at least one feature is obtained based on the correlation between the information on the feature of the target application and the feature data of the current user.

5. The method according to claim 3 or 4, characterized in that, The information of the at least one functional point includes: the interactive interface corresponding to the image, video clip, or code clip that embodies the at least one functional point of the target application.

6. The method according to claim 5, characterized in that, The interactive interface corresponding to the code snippet is displayed in the following manner: Obtain and cache the code snippet, the code snippet embodying at least one functional point of the target application; Create a canvas or view in a preset area of ​​the middle page; Render the initial interface corresponding to the code snippet within the canvas or view, where each visual element in the initial interface is in its initial display state. In response to an operation on the initial interface, based on the correspondence between the operation parameters included in the code snippet and the interface interaction logic corresponding to the at least one function point, the interface interaction logic corresponding to the operation is determined, and the interface interaction logic includes the update display state of visual elements. Based on the defined interface interaction logic, the corresponding visual elements are re-rendered within the canvas or view.

7. The method according to any one of claims 1 to 4, characterized in that, The display of personalized recommendations for the current user in a preset area of ​​the middle page includes: An interactive entry point for triggering the display of personalized recommendation information is shown in a preset area of ​​the middle page; In response to the operation that triggers the interactive entry point, personalized recommendation information for the current user is displayed in a preset area of ​​the intermediate page instead of the interactive entry point.

8. The method according to claim 7, characterized in that, The interactive entry point includes: a sliding component; The operation of triggering the interaction entry includes: sliding the swipeable component from the icon position of the target application to the avatar position of the current user, or sliding the swipeable component from the avatar position of the current user to the icon position of the target application.

9. The method according to any one of claims 1 to 4, characterized in that, The intermediate page displaying the target application includes: Display a media playback page or a live streaming page. The media playback page is used to play media files published by the influencer, and the live streaming page is used to play live streaming content initiated by the influencer. The media playback page or the live streaming page includes a link to an intermediate page of the target application. The influencer is a content creator who meets preset conditions. In response to the action that triggers the link, an intermediate page of the target application is displayed.

10. The method according to claim 9, characterized in that, The characteristic data of the target application includes: interaction data between the expert and the target application; The current user's feature data includes the interaction data between the current user and the expert.

11. An information processing method, characterized in that, Applied to the server side, the method includes: In response to a user's request for an intermediate page of a target application, personalized recommendation information is obtained for the current user based on the correlation between the feature data of the target application and the feature data of the current user. The personalized recommendation information is used to provide a decision basis for downloading the target application. The current user is a user bound to the user's client. The personalized recommendation information and the intermediate page data of the target application are sent to the user terminal. The intermediate page data includes the description information of the target application and the download address information of the target application.

12. The method according to claim 11, characterized in that, The personalized recommendation information for the current user is obtained by associating the feature data of the target application with the feature data of the current user, including: The feature data of the target application and the feature data of the current user are provided to a first language model. The first language model then infers the matching degree information between the target application and the current user based on the association between their feature data; or... The feature data of the target application and the feature data of the current user are provided to the second language model. After the second language model extracts function points based on the feature data of the target application, it infers information about at least one function point of the target application to recommend to the current user based on the association between the function points and the feature data of the current user.

13. An information display device, characterized in that, The device, applied to a user terminal, includes: The first display unit is configured to display an intermediate page of the target application, the intermediate page including description information of the target application and a first component that triggers the download of the target application; The second display unit is configured to display personalized recommendation information for the current user in a preset area of ​​the middle page. The personalized recommendation information is used to provide a decision basis for downloading the target application. The current user is a user bound to the user terminal. The personalized recommendation information is obtained based on the correlation between the feature data of the target application and the feature data of the current user.

14. An information processing device, characterized in that, The device, applied to the server side, includes: The request retrieval unit is configured to receive requests from the user client for an intermediate page of the target application; The recommendation generation unit is configured to respond to a request from the user terminal for an intermediate page of a target application, and obtain personalized recommendation information for the current user based on the correlation between the feature data of the target application and the feature data of the current user. The personalized recommendation information is used to provide a decision basis for downloading the target application, and the current user is a user bound to the user terminal. The data sending unit is configured to send the personalized recommendation information and the intermediate page data of the target application to the user terminal, wherein the intermediate page data includes the description information of the target application and the download address information of the target application.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 12.

16. An electronic device, characterized in that, include: One or more processors; as well as A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1 to 12.

17. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 12.

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