Webpage activity access method, electronic equipment, storage medium and program product

Abstract

The invention provides a webpage activity access method, electronic equipment, a storage medium and a program product. Relates to the technical field of data processing. The method comprises the steps that participation information triggered by a user based on an activity page is responded, an activity template corresponding to the user is determined from a preset template library according to activity attributes carried in the participation information, and the activity template comprises multiple participation conditions; dynamically predicting a condition value range corresponding to the participation condition through a pre-trained condition generation model based on a historical behavior sequence and a current session context carried in the participation information; a target activity page is generated based on the condition value range, and the target activity page at least displays the condition value range; and receiving interaction information updated by the user based on the target activity page, and determining that the user is allowed to access an activity corresponding to the activity page under the condition that an interaction value carried in the interaction information falls into the condition value range. The effect of quickly determining whether the user can access the active page or not is achieved.

Description

Technical Field

[0001] This application relates to the field of data processing, and more particularly to a method for accessing web page activities, an electronic device, a storage medium, and a program product. Background Technology

[0002] In the wave of the digital economy, the banking industry is accelerating its digital transformation, and online marketing has become a core battleground for customer acquisition and retention. Among them, interactive activities based on HTML5 (H5) technology are widely used in holiday promotions, customer benefit upgrades, and brand communication scenarios due to their advantages such as strong cross-platform compatibility and short development cycles.

[0003] However, current HTML5 interactive activities often rely on the technical support and maintenance services of third-party platforms, which are beyond the control of banks. If technical failures or system crashes occur, the bank's activity pages may fail to display or function properly. Furthermore, current solutions mostly adopt a "template-based customization + hard-coded implementation" model: operations personnel must submit requirements documents in advance, product managers design prototypes, front-end engineers write static page code, and the back-end team then develops interfaces to complete data linkage according to business rules. Each update requires a complete overhaul; complex marketing strategy adjustments must be completed through code-level modifications, resulting in deployment cycles of several weeks, making it difficult to support agile operational needs.

[0004] Currently, there is an urgent need for a technical solution that can dynamically generate activity templates based on user participation information, and then quickly determine whether a user can access the activity page. Summary of the Invention

[0005] This application provides a method for accessing web page activities, an electronic device, a storage medium, and a program product to quickly determine whether a user can access an active page.

[0006] In a first aspect, embodiments of this application provide a method for accessing web page activities, comprising: responding to participation information triggered by a user based on an activity page; determining an activity template corresponding to the user from a preset template library based on activity attributes carried in the participation information, wherein the activity template includes multiple participation conditions; dynamically predicting the range of condition values ​​corresponding to the participation conditions through a pre-trained condition generation model based on the historical behavior sequence and current session context carried in the participation information; generating a target activity page based on the range of condition values, wherein the target activity page at least displays the range of condition values; receiving interactive information updated by the user based on the target activity page; and determining that the user is allowed to access the activity corresponding to the activity page if the interactive value carried in the interactive information falls within the range of condition values.

[0007] In one possible implementation, the historical behavior sequence is temporally encoded to obtain a user behavior representation vector; the user behavior representation vector is concatenated with the current session context features and input into a condition generation model; the condition generation model outputs the probability distribution of each participating condition and truncates it based on a preset confidence threshold to obtain the range of condition values.

[0008] In one possible implementation, multidimensional tags are extracted from the activity attributes, including geographic location tags, target user tags, and channel tags; the multidimensional tags and metadata of each library template are input into a shared semantic encoder to obtain a first semantic vector and a second semantic vector; the similarity between the first semantic vector and each second semantic vector is calculated, and the library template with the highest similarity is determined as a candidate template.

[0009] In one possible implementation, the method further includes: when the number of candidate templates is greater than 1, obtaining historical adaptation indicators for each candidate template, the historical adaptation indicators including historical call count, user completion rate, and matching degree with the current user group profile; weighting and adjusting each historical adaptation indicator based on the real-time context features of the current session to obtain a context-aware template score; and determining the candidate template with the highest template score as the activity template corresponding to the user.

[0010] In one possible implementation, a lightweight attention mechanism is used to dynamically generate a first weight and a second weight based on the current device type, network status, and time period characteristics; a first score is obtained based on the preset first weight and the number of historical calls; a second score is obtained based on the preset second weight and the user's historical rating; and the sum of the first score and the second score is determined as the score of the target library template.

[0011] In one possible implementation, the interaction frequency corresponding to the interaction information is determined; if the interaction frequency is greater than a preset frequency threshold, a reminder message is sent to the user, wherein the reminder message instructs the user to reduce the interaction frequency.

[0012] In one possible implementation, after receiving the user's updated interaction information based on the target activity page, the method further includes: determining the interaction type and operation timing characteristics corresponding to the interaction information; matching the interaction type with the conditional interaction type indicated in the participation conditions, and verifying whether the operation timing characteristics fall within the historical normal operation window corresponding to the interaction type; if the matching is successful and the timing characteristics are valid, then determining that the current interaction information is valid interaction information, and allowing the determination of whether it falls within the condition value range based on the interaction value.

[0013] Secondly, embodiments of this application provide a webpage activity access device, comprising: a response module, configured to respond to participation information triggered by a user based on an activity page, and determine an activity template corresponding to the user from a preset template library based on activity attributes carried in the participation information, wherein the activity template includes multiple participation conditions; a generation module, configured to dynamically predict the range of condition values ​​corresponding to the participation conditions based on the historical behavior sequence carried in the participation information and the current session context through a pre-trained condition generation model, and generate a target activity page based on the range of condition values, wherein the target activity page displays at least the range of condition values; and a determination module, configured to receive interactive information updated by the user based on the target activity page, and determine, if the interactive value carried in the interactive information falls within the range of condition values, allow the user to access the activity corresponding to the activity page.

[0014] Thirdly, embodiments of this application provide a device for accessing web page activities, including: a memory and a processor;

[0015] The memory stores computer-executed instructions;

[0016] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0018] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0019] The webpage activity access method, electronic device, storage medium, and program product provided in this application, while parsing the activity attributes triggered by the user and dynamically loading the corresponding template from a preset library, incorporates multi-dimensional participation rules. Subsequently, it combines user historical behavior data to calculate the target value range for each condition, thereby generating a target page displaying the conditional range. Then, it captures user interaction data and compares it in real-time with the preset range; if the rules are met, it authorizes entry into the activity process. This enables precise, personalized operation, serving as a means to reduce development costs and achieving the technical solution effect of quickly determining whether a user can access the activity page. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] Figure 1 The process of accessing the webpage activity provided in this application Figure 1 ;

[0022] Figure 2 The process of accessing the webpage activity provided in this application Figure 2 ;

[0023] Figure 3 A schematic diagram of the structure of the webpage access device provided in this application;

[0024] Figure 4 A schematic diagram of the device for accessing web page activities provided in this application.

[0025] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0027] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0028] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0029] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0030] Figure 1 Flowchart of the method for accessing web page activities provided in this application Figure 1 ,like Figure 1 As shown, the method includes S101-S103:

[0031] S101. In response to the user's participation information triggered by the activity page, determine the activity template corresponding to the user from the preset template library according to the activity attributes carried in the participation information.

[0032] The activity template includes several participation conditions.

[0033] It should be noted that participation conditions refer to a series of predefined rules or restrictions in the activity template, used to determine whether a user's interaction behavior meets the activity's admission criteria. It is one of the core bases for the system's automated processing flow.

[0034] Specifically, when a user performs a specific action on an H5 page (i.e., an "activity page") (such as clicking a button, submitting a form, scanning a QR code, etc.), the system will capture the data packet generated by this action, which is called "participation information".

[0035] For example, a user sees an advertisement page for a "limited-time event" on an e-commerce website (this is the event page) and clicks the "Participate Now" button. This click is then packaged as a "participation information" message.

[0036] The "Participation Information" includes a key identifier—the event number. This number represents a specific marketing campaign, raffle, survey, etc. Multiple different events may run simultaneously on the same platform, each with its own unique number.

[0037] Optionally, the platform backend prepares a series of standardized configuration schemes in advance, called a "template library." This library contains general frameworks or rule sets designed for various possible event types. Each template defines a complete process for an event, including its operational logic, interface style requirements, eligibility restrictions, and prize settings.

[0038] It should be noted that when creating the H5 activity front-end package, the event operator can use the model standard SDK to access functions such as reliable user information recognition certified by industry rules, industry data tracking, activity configuration parameters, gameplay achievement judgment, and message push by integrating the model standard SDK.

[0039] S102. Based on the historical behavior sequence and current session context carried in the participation information, dynamically predict the range of condition values ​​corresponding to the participation conditions through a pre-trained condition generation model, and generate the target activity page based on the range of condition values.

[0040] The target activity page must at least display a range of conditional values.

[0041] Optionally, historical information can be information about a user's past participation in similar activities or their past visits to the current activity page. It should be noted that access to historical information can only be made with the user's authorization.

[0042] By leveraging users' historical data to determine the specific numerical range for each condition (i.e., the "condition value range"), the newly generated target page will clearly display these numerical ranges, allowing users to clearly understand how far they are from achieving the goal. This enables dynamic customization of activity page information based on different users.

[0043] Extracted from the "participation information" submitted by users:

[0044] Historical behavior sequence: Retrieve the user's most recent N interaction records from the user behavior log database;

[0045] Current session context: Parses real-time characteristics such as device, network, and location from the front-end SDK or back-end request headers.

[0046] The historical behavior sequence is temporally encoded to generate a fixed-dimensional user behavior embedding vector; the current session context is normalized / discrete to form a context feature vector; the two are concatenated or fused through a cross-network to form the model input vector.

[0047] The fused input vector is fed into a pre-trained conditional generation model;

[0048] For each participation condition (such as "level", "activity level", "region") defined in the activity template, the model outputs a recommended range of condition values.

[0049] Example: Output 1: User level ∈ [3, 5] Output 2: Number of logins in the last 7 days ≥ 2 (i.e., [2, +∞)) Output 3: Region ∈ {Beijing, Shanghai, Guangzhou} (discrete set).

[0050] Inject the above condition value range into the front-end template engine; render a personalized target activity page, such as: displaying the prompt: "Your current level is 4, which meets the participation conditions"; hiding inapplicable options (such as non-Beijing users not being able to see "Local Exclusive Prizes"); and dynamically adjusting form fields (such as requiring additional verification for high-risk users).

[0051] S103. Receive the user's updated interaction information based on the target activity page. If the interaction value carried in the interaction information falls within the range of condition values, determine that the user is allowed to access the activity corresponding to the activity page.

[0052] For example, when a user interacts with the target page, each change is sent back to the backend as new "interaction information." Only when the data change resulting from this interaction falls within the previously set conditions is it considered valid participation. Once all relevant conditions are met, full activity benefits are immediately unlocked, allowing the user to access the corresponding activity on the activity page.

[0053] The webpage activity access method provided in this application not only parses the activity attributes triggered by the user and dynamically loads the corresponding template from a preset library, but also incorporates multi-dimensional participation rules. It then combines historical user behavior data to calculate the target value range for each condition, thereby generating a target page with a conditional range display. Next, it captures user interaction data and compares it in real-time with the preset range; if the rules are met, it authorizes entry into the activity process. This enables precise, personalized operation, serving as a means to reduce development costs and achieving the technical solution effect of quickly determining whether a user can access the activity page.

[0054] Figure 2 Flowchart of the method for accessing web page activities provided in this application Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the method for accessing web page activities is described in detail, which includes...

[0055] S201. Extract multidimensional labels from the activity attributes; input the multidimensional labels and metadata of each library template into the shared semantic encoder to obtain the first semantic vector and the second semantic vector; calculate the similarity between the first semantic vector and each second semantic vector, and determine the library template with the highest similarity as the candidate template.

[0056] The multidimensional tags include: geolocation tags, target user tags, and channel tags.

[0057] To achieve intelligent matching of activity templates, the system first parses activity attributes from user-triggered participation requests. Activity attributes refer to structured or semi-structured information describing the current activity context, typically carried by the front-end page when a user clicks on the activity entry point. Examples include activity identifiers, distribution channel sources, target audience types, and geographic location ranges. Multidimensional tags include at least the following three categories: geographic location tags, used to identify the geographic area targeted by the activity, such as "Beijing," "East China," or "first-tier cities"; target user tags, used to identify the user group targeted by the activity, such as "university students," "high-net-worth clients," or "registered users who have not placed an order"; and channel tags, used to identify the traffic source or reach path of the activity, such as "mini-program" or "SMS push."

[0058] Through the above extraction operations, the original activity attributes are transformed into a structured set of tags, such as: {geographic location tag: “Shanghai”, target user tag: “youth”, channel tag: “Xiaohongshu”}, which facilitates subsequent semantic calculations.

[0059] Subsequently, the system inputs the multidimensional tags and the metadata of each library template in the preset template library into a shared semantic encoder. The metadata of the library template refers to the descriptive information configured when each activity template is created. It also contains fields corresponding to the three types of tags mentioned above, used to describe the business scenarios to which the template is applicable. For example, the metadata of a library template can be represented as: {Geographic Location Tag: [“Beijing”, “Shanghai”], Target User Tag: [“Active Users”], Channel Tag: [“App Push”, “WeChat”]}.

[0060] Furthermore, the system standardizes the activity attributes and extracts multidimensional labels from them. Multidimensional labels refer to classification identifiers with multiple orthogonal dimensions, abstracted from activity attributes, used to represent the semantic features of the activity.

[0061] A shared semantic encoder is a neural network model with shared parameters (e.g., composed of embedding layers and multilayer perceptrons) that is configured as follows:

[0062] It can accept multidimensional labels from either side (active side or multi-template side) as input;

[0063] Vectorize each label field separately (e.g., using a pre-trained word embedding table or category embedding).

[0064] After fusing the vectors of each field, a nonlinear transformation is performed to output a semantic vector of fixed dimensions.

[0065] The key is that regardless of whether the input comes from activity attributes or library template metadata, it is encoded using the exact same network parameters.

[0066] By employing a shared semantic encoder, this invention ensures that activities and templates are mapped to the same semantic vector space, enabling effective measurement of their semantic similarity and providing a technical foundation for subsequent template matching based on vector similarity. This design avoids semantic biases caused by inconsistent encoding methods, significantly improving the accuracy and generalization ability of template matching.

[0067] For example, when the number of candidate templates is greater than 1, the historical adaptation indicators of each candidate template are obtained. The historical adaptation indicators include the number of historical calls, the user completion rate, and the matching degree with the current user group profile. Based on the real-time context features of the current session, the historical adaptation indicators are weighted and adjusted to obtain a context-aware template score. The candidate template with the highest template score is determined as the activity template corresponding to the user.

[0068] When the number of candidate templates obtained is greater than 1, the system further performs refined selection. Specifically, it obtains the historical adaptation indicators of each candidate template, including: the total number of times the template is called (reflecting stability), the proportion of users who successfully complete the activity after using the template (i.e., user completion rate, reflecting effectiveness), and the matching degree between the user group applicable to the template and the current user's group profile (such as age, region, behavioral preferences) (reflecting population adaptability).

[0069] Subsequently, by combining the real-time contextual features of the current session (such as device type, network conditions, access time, etc.), the weights of various indicators are dynamically adjusted through a lightweight attention mechanism or preset rules, and historical adaptation indicators are weighted and fused to generate a context-aware template score.

[0070] Ultimately, the candidate template with the highest score is selected as the target campaign template. This method overcomes the limitations of traditional static scoring, enabling template selection to not only rely on historical data but also adapt to the current interactive environment, significantly improving campaign conversion efficiency and user experience.

[0071] For example, a lightweight attention mechanism is used to dynamically generate a first weight and a second weight based on the current device type, network status, and time period characteristics; a first score is obtained based on the preset first weight and the number of historical calls; a second score is obtained based on the preset second weight and the user's historical rating; and the sum of the first score and the second score is determined as the score of the target library template.

[0072] To achieve accurate sorting of multiple target library templates, the system introduces a lightweight attention mechanism to dynamically generate scoring weights.

[0073] Specifically, the system obtains the real-time context features of the current session, including device type (such as iOS, Android, or Web), network status (such as 4G, WiFi, or weak network), and time period features (such as weekday, night, or holiday). These features are then input into a lightweight neural network (such as a single-layer fully connected network with Softmax) and output a first weight and a second weight used to adjust the importance of historical metrics.

[0074] The system employs a two-factor algorithm: a first weighting factor to weight the template's historical call count, reflecting its stability and versatility; and a second weighting factor to weight user historical ratings (such as user satisfaction feedback on past activities using the template), reflecting user experience quality. The first score is calculated by multiplying the first weight by the historical call count, and the second score is calculated by multiplying the second weight by the user historical rating. These two factors are then summed to obtain the overall score for the target template. This context-aware dynamic weighting mechanism allows the system to flexibly adapt to different access environments, avoiding suboptimal selections caused by fixed weights, thereby improving the accuracy of template matching and the effectiveness of activities.

[0075] S202. Determine the interaction frequency corresponding to the interaction information; if the interaction frequency is greater than the preset frequency threshold, send a reminder message to the user.

[0076] Among them, the reminder message instructs users to reduce the frequency of interaction.

[0077] Interaction frequency, corresponding to interactive information, refers to the statistical count of the number of times a user triggers a certain action per unit of time (e.g., per hour / per day). This includes the number of times a button is clicked on a mobile H5 interface; the system records the timestamp of each interaction. For example, if a user made 8 search requests in the past 5 minutes, the current interaction frequency = 8 times / 5 minutes.

[0078] Then, the calculated actual frequency is compared with the preset safety threshold: if the actual frequency is less than or equal to the threshold, the normal process continues; if the actual frequency is greater than the threshold, the intervention process is initiated (a reminder message is sent).

[0079] Optionally, the reminder message can be text, voice, or image, etc., and this application does not impose any restrictions on this.

[0080] Optionally, the reminder message could be something like, "Your recent operations have been too frequent. We suggest you take a break before continuing," "Abnormal activity detected. To ensure account security, please reduce manual refreshes," or "The system has automatically paused some functions and will resume after [specific time]."

[0081] In addition, to avoid the system misjudging whether a user has generated a valid interaction, it can be determined from the type of user interaction.

[0082] S203. Determine the interaction type and operation timing characteristics corresponding to the interaction information; match the interaction type with the conditional interaction type indicated in the participation conditions, and verify whether the operation timing characteristics fall within the historical normal operation window corresponding to the interaction type; if the match is successful and the timing characteristics are valid, determine that the current interaction information is valid interaction information, and allow judgment based on the interaction value whether it falls within the range of condition values.

[0083] In one embodiment of the present invention, in order to improve the accuracy and security of interactive verification, the system not only verifies the interactive content, but also makes multi-dimensional judgments based on the interactive behavior patterns.

[0084] Specifically, after receiving the interactive information submitted by the user based on the target activity page, the system first identifies the type of interaction (such as "upload ID card", "enter mobile phone number" or "complete swipe verification") and extracts the operation timing features, including time-dimensional indicators such as operation time, click interval, and input speed.

[0085] Subsequently, the interaction type is compared with the preset conditional interaction types in the activity template to ensure that the user is performing the expected operation. Simultaneously, the behavior statistics of this interaction type among historical normal user groups are queried to determine its corresponding historical normal operation window (e.g., "ID card upload typically takes 3-30 seconds"). If the interaction type matches successfully, and the current operation timing characteristics (e.g., actual time taken 8 seconds) fall within this window range, the interaction information is determined to be valid. Only under this premise does the system further determine whether the interaction value (e.g., whether the uploaded document is compliant, whether the entered value is within the condition value range) meets the access conditions. This mechanism effectively prevents the risk of automated scripts or abnormal operations bypassing front-end verification, significantly enhancing the security and robustness of activity access control.

[0086] First, it's necessary to clarify what type of interaction the user is currently performing. For example, clicking a button or sharing a link. Each activity template predefines a set of allowed "conditional interaction types." For instance, the standard operation for a lottery activity can only be "spinning the wheel" and not "directly entering text."

[0087] The nature of an interaction is marked through frontend event binding or backend API parameter parsing. An interaction is considered valid only when the actual type of interaction exactly matches the expected type specified in the activity settings.

[0088] If the activity requires "scanning a QR code to participate", then users must use their camera to scan the QR code for the activity to be accepted; ordinary page scrolling or link redirection will be ignored.

[0089] In tasks that require voice password unlocking, a user attempting to type in a password is an invalid interaction because the types do not match.

[0090] By comparing the interaction type corresponding to the interaction information with the conditional interaction type, only interactions that exactly match the expected type specified in the activity settings are considered valid. This not only prevents users from bypassing process restrictions through non-standard methods but also ensures that business processes are executed as designed. For example, it prevents bots from automatically filling out forms and replacing manual operations.

[0091] In addition, multiple conditions can be combined for a single determination:

[0092] For example, when a user helps a friend on the activity page, the H5 page calls the SDK's gameplay achievement determination interface to check if the user meets the requirements for helping, specifically whether the user has already helped 5 times. The wfId and activityCode can be used to accurately obtain and determine the gameplay achievement standards configured for the activity.

[0093] For example, the historical behavior sequence is temporally encoded to obtain a user behavior representation vector; the user behavior representation vector is concatenated with the current session context features and input into the condition generation model; the condition generation model outputs the probability distribution of each participating condition and truncates the condition value range based on a preset confidence threshold.

[0094] Specifically, a lightweight temporal encoder (e.g., a single-layer LSTM or Transformer encoder) is used to process the sequence, outputting a fixed-dimensional user behavior representation vector to capture the user's long-term behavior patterns and activity levels. This representation vector is then concatenated with the contextual features of the current session (including real-time information such as device type, network status, geographic location, and access time) to form a fusion vector, which is then input into a pre-trained conditional generation model (e.g., a multilayer perceptron). For each participation condition defined in the activity template (e.g., "user level" or "number of logins in the last 7 days"), the model outputs its corresponding probability distribution (e.g., the probability of level 3 is 0.85, and the probability of level 4 is 0.12). Finally, the system truncates or accumulates the probability distribution based on a preset confidence threshold (e.g., 90%), selecting high-confidence value intervals to determine the condition value range for that participation condition (e.g., user level ∈ [3, 3]). This method abandons static rule configuration and realizes data-driven, context-aware dynamic admission strategy generation.

[0095] The webpage activity access method provided in the application embodiment, while parsing the activity attributes triggered by the user and dynamically loading the corresponding template from a preset library, incorporates multi-dimensional participation rules. It then combines user historical behavior data to calculate the target value range for each condition, thereby generating a target page displaying the conditional range. Next, it captures user interaction data and compares it in real-time with the preset range; if the rules are met, it authorizes entry into the activity process. This enables precise, personalized operation, serving as a means to reduce development costs and achieving the technical solution effect of quickly determining whether a user can access the activity page.

[0096] Figure 3 A schematic diagram of the structure of the access device for web page activities provided in this application is shown below. Figure 3 As shown, the webpage activity access device 30 provided in this embodiment includes:

[0097] The response module 301 is used to respond to the participation information triggered by the user based on the activity page, and to determine the activity template corresponding to the user from the preset template library according to the activity attributes carried in the participation information. The activity template includes multiple participation conditions.

[0098] The generation module 302 is used to dynamically predict the range of condition values ​​corresponding to the participation conditions based on the historical behavior sequence carried in the participation information and the current session context through a pre-trained condition generation model, and generate a target activity page based on the range of condition values, wherein the target activity page displays at least the range of condition values.

[0099] The determination module 303 is used to receive the user's updated interaction information based on the target activity page, and if the interaction value carried in the interaction information falls within the range of condition values, determine that the user is allowed to access the activity corresponding to the activity page.

[0100] In one possible implementation, the response module 301 is used to extract multidimensional tags from the activity attributes, wherein the multidimensional tags include: geographic location tags, target user tags, and channel tags; input the multidimensional tags and the metadata of each library template into the shared semantic encoder to obtain a first semantic vector and a second semantic vector; calculate the similarity between the first semantic vector and each second semantic vector, and determine the library template with the highest similarity as a candidate template.

[0101] In one possible implementation, the response module 301 is used to obtain the historical adaptation indicators of each candidate template when the number of candidate templates is greater than 1. The historical adaptation indicators include the number of historical calls, user completion rate, and matching degree with the current user group profile. Based on the real-time context features of the current session, the historical adaptation indicators are weighted and adjusted to obtain a context-aware template score. The candidate template with the highest template score is determined as the activity template corresponding to the user.

[0102] In one possible implementation, the response module 301 is used to dynamically generate a first weight and a second weight based on the current device type, network status, and time period characteristics through a lightweight attention mechanism.

[0103] The first score is obtained based on the preset first weight and the number of historical calls, and the second score is obtained based on the preset second weight and the user's historical rating. The sum of the first score and the second score is determined as the score of the target library template.

[0104] In one possible implementation, the first determining module 302 is used to perform temporal encoding on the historical behavior sequence to obtain a user behavior representation vector; the user behavior representation vector is concatenated with the current session context features and then input into the condition generation model; the condition generation model outputs the probability distribution of each participating condition and truncates the condition value range based on a preset confidence threshold.

[0105] In one possible implementation, the device further includes: a prompting module (not shown in the figure), used to determine the interaction frequency corresponding to the interaction information; and to send a reminder message to the user when the interaction frequency is greater than a preset frequency threshold, wherein the reminder message instructs the user to reduce the interaction frequency.

[0106] In one possible implementation, the above-mentioned device further includes: an interaction module (not shown in the figure) for determining the interaction type and operation timing characteristics corresponding to the interaction information; matching the interaction type with the conditional interaction type indicated in the participation conditions, and verifying whether the operation timing characteristics fall within the historical normal operation window corresponding to the interaction type; if the matching is successful and the timing characteristics are valid, then determining that the current interaction information is valid interaction information, and allowing the determination of whether it falls within the range of conditional values ​​based on the interaction value.

[0107] The webpage activity access device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0108] Figure 4 A schematic diagram of the device used to access the webpage activity provided in this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the electronic device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus.

[0109] In the specific implementation process, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-mentioned method of accessing web page activities.

[0110] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0111] In the above embodiments, it should be understood that the processor 401 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0112] The memory 402 may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0113] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0114] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0115] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0116] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0117] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0118] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0119] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0120] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0121] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0122] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0123] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for accessing webpage activities, characterized in that, include: In response to user participation information triggered by an activity page, the activity template corresponding to the user is determined from a preset template library based on the activity attributes carried in the participation information, wherein the activity template includes multiple participation conditions; Based on the historical behavior sequence and current session context carried in the participation information, a pre-trained condition generation model dynamically predicts the range of condition values ​​corresponding to the participation conditions; a target activity page is generated based on the range of condition values, wherein the target activity page displays at least the range of condition values. If the user receives the updated interaction information based on the target activity page, and the interaction value carried in the interaction information falls within the range of condition values, then the user is allowed to access the activity corresponding to the activity page.

2. The method according to claim 1, characterized in that, The step of dynamically predicting the range of condition values ​​corresponding to the participation conditions using a pre-trained condition generation model based on the historical behavior sequence carried in the participation information and the current session context includes: The historical behavior sequence is time-series encoded to obtain a user behavior representation vector; The user behavior representation vector is concatenated with the current session context features and then input into the conditional generation model; The condition generation model outputs the probability distribution of each participating condition, and the range of condition values ​​is obtained by truncating the condition values ​​based on a preset confidence threshold.

3. The method according to claim 1, characterized in that, Based on the activity attributes carried in the participation information, the activity template corresponding to the user is determined from a preset template library, including: Extract multidimensional tags from the activity attributes, wherein the multidimensional tags include: geographic location tags, target user tags, and channel tags; The multidimensional tags and metadata of each library template are input into the shared semantic encoder to obtain the first semantic vector and the second semantic vector. Calculate the similarity between the first semantic vector and each of the second semantic vectors, and determine the library template with the highest similarity as the candidate template.

4. The method according to claim 3, characterized in that, The method further includes: If the number of candidate templates is greater than 1, obtain the historical adaptation indicators for each candidate template. The historical adaptation indicators include the number of historical calls, user completion rate, and matching degree with the current user group profile. Based on the real-time context features of the current session, the historical adaptation indicators are weighted and adjusted to obtain a context-aware template score. The candidate template with the highest template score is selected as the activity template corresponding to the user.

5. The method according to claim 4, characterized in that, The weighted adjustment of historical adaptation metrics based on the real-time context features of the current session includes: A lightweight attention mechanism is used to dynamically generate the first and second weights based on the current device type, network status, and time period characteristics. The first score is obtained based on the preset first weight and the number of historical calls, and the second score is obtained based on the preset second weight and the user's historical rating. The sum of the first score and the second score is determined as the score of the target library template.

6. The method according to any one of claims 1-5, characterized in that, After receiving the user's updated interaction information based on the target activity page, the method further includes: Determine the interaction frequency corresponding to the interaction information; If the interaction frequency exceeds a preset frequency threshold, a reminder message is sent to the user, wherein the reminder message instructs the user to reduce the interaction frequency.

7. The method according to any one of claims 1-5, characterized in that, After receiving the user's updated interaction information based on the target activity page, the method further includes: Determine the interaction type and operation timing characteristics corresponding to the interaction information; Match the interaction type with the conditional interaction type indicated in the participation conditions, and verify whether the operation timing feature falls within the historical normal operation window corresponding to the interaction type. If the match is successful and the temporal features are valid, the current interaction information is determined to be valid, and it is allowed to determine whether it falls within the range of condition values ​​based on the interaction value.

8. A device for accessing web page activities, characterized in that, include: The response module is used to respond to the participation information triggered by the user based on the activity page, and to determine the activity template corresponding to the user from the preset template library according to the activity attributes carried in the participation information, wherein the activity template includes multiple participation conditions; The generation module is used to dynamically predict the range of condition values ​​corresponding to the participation conditions based on the historical behavior sequence carried in the participation information and the current session context through a pre-trained condition generation model, and generate a target activity page based on the range of condition values, wherein the target activity page displays at least the range of condition values. The determination module is used to receive user interaction information updated based on the target activity page, and if the interaction value carried in the interaction information falls within the range of condition values, determine that the user is allowed to access the activity corresponding to the activity page.

9. A device for accessing web page activities, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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