Interface display processing method and device, electronic equipment and storage medium
By predicting user behavior data and preloading the data displayed on the interface, the problem of slow interface response speed was solved, and rapid interface updates were achieved.
Patent Information
- Application Number
- CN202511055242.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-30
- Publication Date
- 2025-11-14
AI Technical Summary
In existing technologies, user interface response speed is slow, and data acquisition and loading are only performed after receiving interface-triggered operations.
By predicting user behavior data, a pre-trained long short-term memory network model is used to predict the user's behavior data at the next moment. The interface display data is obtained according to a preset data acquisition mechanism, preloaded into the storage space, and the interface is directly updated when an operation is triggered.
It enables rapid response to interface trigger operations, improves the response speed of the interface display, and shortens the waiting time for the interface to update.
Smart Images
Figure CN120949979A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of computer technology, and in particular to a method, apparatus, electronic device and storage medium for interface display processing. Background Technology
[0002] With the rapid development of internet technology, users have increasingly higher requirements for the user experience of front-end applications, especially in terms of interface responsiveness. In related technologies, when users interact with the front-end application interface, data is typically acquired and loaded in real-time only after an actual trigger operation is received to update the target interface. This results in a technical problem of slow interface display responsiveness. Summary of the Invention
[0003] This invention provides a method, apparatus, electronic device, and storage medium for processing interface displays, so as to achieve a relatively fast acquisition of interface display information corresponding to the interface trigger operation after receiving an interface trigger operation, thereby improving the response speed of the interface display.
[0004] According to one aspect of the present invention, a method for processing interface display is provided, the method comprising:
[0005] In response to a first interface trigger operation by the target object on the target interface, first object behavior data of the target object on the target interface at the current time is obtained;
[0006] The first object behavior data is input into a pre-trained prediction model to determine the second object behavior data of the target object at the next moment;
[0007] In response to the determination event of the second object behavior data, the interface display data corresponding to the second object behavior data is obtained according to the preset data acquisition mechanism;
[0008] The interface display data is preloaded into the target storage space so that, upon receiving the second interface trigger operation, the target interface is updated based on the preloaded interface display data in the target storage space.
[0009] According to another aspect of the present invention, a processing apparatus for displaying an interface is provided. The apparatus includes:
[0010] An operation response module is used to respond to a first interface trigger operation of a target object on a target interface and obtain first object behavior data of the target object's operation on the target interface at the current moment.
[0011] The data prediction module is used to input the first object behavior data into a pre-trained prediction model to determine the second object behavior data of the target object at the next moment.
[0012] The data acquisition module is used to respond to the determination event of the second object behavior data and acquire the interface display data corresponding to the second object behavior data according to the preset data acquisition mechanism;
[0013] The data preloading module is used to preload the interface display data into the target storage space, so as to update the target interface based on the interface display data preloaded in the target storage space when the second interface trigger operation is received.
[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:
[0015] One or more processors;
[0016] Storage device for storing one or more programs.
[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement the interface display processing method as described in any of the embodiments of the present invention.
[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the interface display processing method described in any of the present invention.
[0019] The technical solution of this invention, in response to a first interface trigger operation by a target object on a target interface, obtains first object behavior data of the target object's operation on the target interface at the current moment; inputs the first object behavior data into a pre-trained prediction model to quickly and effectively determine the second object behavior data of the target object at the next moment; in response to the determination event of the second object behavior data, acquires interface display data corresponding to the second object behavior data according to a preset data acquisition mechanism; and preloads the interface display data into the target storage space. In this way, when the second interface trigger operation is actually received, there is no need to temporarily acquire and load the relevant interface display data; instead, the target interface can be directly updated based on the interface display data preloaded in the target storage space. The technical solution of this invention solves the technical problem in related technologies where data acquisition and loading can only be performed after receiving an interface trigger operation, resulting in slow interface display response. It achieves faster acquisition of interface display information corresponding to the interface trigger operation, improves the response speed of the interface display, and shortens the waiting time for interface display updates.
[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description
[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0022] Figure 1 A flowchart illustrating a method for processing interface display according to an embodiment of the present invention;
[0023] Figure 2 A flowchart illustrating a method for processing interface display according to an embodiment of the present invention;
[0024] Figure 3 A schematic diagram of the structure of a processing device for interface display provided in an embodiment of the present invention;
[0025] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation
[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0028] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.
[0029] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.
[0030] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.
[0031] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.
[0032] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.
[0033] Figure 1 This is a flowchart illustrating a method for processing interface display according to an embodiment of the present invention. This embodiment is applicable to situations involving the processing of interface display information. The method can be executed by an interface display processing device, which can be implemented in hardware and / or software and can be configured in electronic devices such as computers or servers. Figure 1 As shown, the method in this embodiment includes:
[0034] S110. In response to the first interface trigger operation of the target object on the target interface, obtain the first object behavior data of the target object operating on the target interface at the current time.
[0035] In this invention, the target object can be understood as an object that interacts with the target interface. In this embodiment, the target object can be a user. For example, the target object can be a business processing personnel involved in resource transfer. The target interface can be understood as a visual interface used to display specific information or provide specific functions for interaction with the target object. In this embodiment, the target interface can be a pre-set interface, such as a login interface; or it can be a switched interface obtained based on an interface switching operation, such as a business processing interface. The first interface triggering operation can be understood as a certain operation performed by the target object on the target interface, which can be used to trigger subsequent data acquisition and processing flows. In this embodiment, when the target interface is a login interface, the first interface triggering operation can be a login operation; when the target interface is a business processing interface, the first interface triggering operation can be a business processing operation, such as a login operation, a data entry operation, etc.
[0036] In this embodiment of the invention, the first object behavior data can be understood as the object behavior data obtained after the target object downloads and operates the target interface at the current moment. The first object behavior data may include basic behavior data, request history data, timestamp information, and context data. The basic behavior data can be understood as events of interface operations, such as control click events, information input events, or page switching events. The request history data can be understood as recording the specific content of page requests, such as page address (URL), request parameters, request duration, response time, etc. The timestamp information can be understood as recording the time when the target object performs operations on the target interface, in order to determine the target object's operation mode. The context data may at least include object identifier, object role information, object operating device, browser type, and network status.
[0037] Specifically, in response to a display trigger operation of the target interface, the target interface is displayed. In response to a first interface operation of the target object on the target interface, first object behavior data of the target object's operation on the target interface can be obtained based on the first interface trigger operation.
[0038] In one embodiment, the target interface is a login interface, and the step of obtaining first object behavior data of the target object's operation on the target interface at the current moment in response to the first interface trigger operation of the target object on the target interface may include: responding to the login operation of the target object on the login interface; and obtaining the operation behavior data of the target object's operation on the login interface based on the login operation.
[0039] In another embodiment, the target interface is a business processing interface. The step of responding to the first interface trigger operation of the target object on the target interface and obtaining the first object behavior data of the target object's operation on the target interface at the current moment may include: responding to the business processing operation of the target object on the business processing interface; and then obtaining the operation behavior data of the target object's operation on the business processing interface through the business processing operation.
[0040] In this embodiment of the invention, obtaining first object behavior data based on the first interface trigger operation can include: based on the first interface trigger operation, a predefined object behavior data acquisition method for obtaining object behavior data can be invoked; thereby obtaining the object behavior data of the target object operating on the target interface at the current moment, i.e., the first object behavior data. In this embodiment of the invention, the computer programming language of the object behavior data acquisition method is a front-end programming language, such as JavaScript.
[0041] Based on the above embodiments, after obtaining the first object behavior data, the first object behavior data can be sent to the target server through a preset communication protocol (e.g., WebSocket), so that the target server records data in each request and stores the first object behavior data in a preset database.
[0042] Based on the above embodiments, in order to ensure data quality, the first object behavior data obtained based on the operation triggered by the first interface can be preprocessed. Optionally, the data preprocessing may include at least one of missing value removal, outlier removal, and noise removal.
[0043] S120. Input the first object behavior data into the pre-trained prediction model to determine the second object behavior data of the target object at the next moment.
[0044] In this embodiment of the invention, the second object behavior data can be understood as the object behavior data of the target object at the next time step, predicted by a pre-trained prediction model based on the first object behavior data. The prediction model can be used to predict the second object behavior data of the target object at the next time step based on the first object behavior data. In this embodiment of the invention, the prediction model can be a Long Short-Term Memory (LSTM) network model. In this embodiment of the invention, the LSTM network model can include an input layer, a convolutional layer, a first LSTM network layer, an attention mechanism layer, a random deactivation layer, and a second LSTM network layer.
[0045] The system comprises the following layers: an input layer for receiving first object behavior data; a convolutional layer for processing the first object behavior data input from the input layer; optionally, a one-dimensional temporal convolutional layer for extracting short-term local fluctuation features of the object behavior data; a first long short-term memory (LSTM) layer for receiving temporal data from the convolutional layers, capturing temporal dependencies in the first object behavior data, and outputting the hidden state at each time step; an attention mechanism layer for introducing feature attention weights into the hidden state sequence output by the first LSM layer, dynamically calculating importance weights at different time steps, and outputting weighted features to enhance key behavioral features; a random deactivation layer for randomly masking some neurons after the attention mechanism layer to prevent overfitting; and a second LSM layer for receiving the weighted features output by the random deactivation layer and receiving input from the first LSM layer through residual connections to avoid information loss during deep network training.
[0046] Specifically, the first object behavior data is input into a pre-trained prediction model, thereby obtaining the output of the prediction model, which is the second object behavior data of the target object at the next moment.
[0047] In this embodiment of the invention, before inputting the first object behavior data into a pre-trained prediction model, the method further includes: performing data transformation on the first object behavior data to obtain input features suitable for the prediction model. Specifically, the behavioral features, event features, interface state features, and previous request features in the first object behavior data are determined, thus obtaining each behavioral data feature. Furthermore, each behavioral data feature can be standardized to obtain standardized data features.
[0048] Among these, behavioral features can be features obtained by serializing the object's behavior. Optionally, behavioral features can be the object's behavioral trajectory, such as the order of click operations, input operations, and form submission operations. Temporal features can be the frequency of operations and dwell time of the extracted object during interface operations. Page state features can include at least one of page loading, displayed elements, and the current form state. Object features can include at least one of the object's historical operation behavior, preference settings, and device information. The previous request feature can be understood as the content of the object's previous request, such as the requested address, request parameters, and request type.
[0049] In this embodiment of the invention, feature standardization is performed in at least one of the following ways: Min-Max normalization is used for numerical features (such as dwell time, number of clicks, interface time, etc.); one-hot encoding is used for categorical features (such as page type, button type, etc.); and periodic encoding is used for temporal features (such as time series of object behavior).
[0050] In this embodiment of the invention, the method of obtaining the prediction model may specifically include: acquiring sample data and the expected output result corresponding to the sample data, wherein the sample data includes third object behavior data of the preset object on the target interface at a certain historical moment, and the expected output result is the fourth object behavior data of the preset object at the next moment of the certain historical moment; inputting the sample data into a pre-constructed initial network model to obtain the actual output result of the initial network model; and adjusting the network parameters of the initial network model according to the expected data and the actual output result to obtain the trained prediction model.
[0051] The preset object can be understood as a pre-set object. The number of preset objects can be one, two, or more, and the preset objects must include at least the target object. The third object behavior data can be understood as the operation behavior data of the preset object on the target interface at a certain historical moment. The fourth object behavior data can be understood as the operation behavior data of the preset object at the next moment after the previous historical moment. The initial network model can be understood as the network model used to train the prediction model. In this embodiment of the invention, the initial network model can be a Long Short-Term Memory (LSTM) network model; wherein the LSM network model may include an input layer, a convolutional layer, a first LSM network layer, an attention mechanism layer, a random deactivation layer, and a second LSM network layer. It is understood that the model structure of the initial network model is the same as the model structure of the prediction model.
[0052] In this embodiment of the invention, the network parameters of the initial network model are adjusted based on the expected data and the actual output results to obtain the trained prediction model. A preset loss function value is determined based on the expected data and the actual output results. The network parameters of the initial network model are then adjusted based on the function value. The convergence of the preset loss function is taken as the training objective, and the initial network model is trained to obtain the trained prediction model.
[0053] Specifically, the training error of the loss function can be used as a condition to detect whether the loss function has reached convergence. For example, this could be whether the training error is less than a preset error, whether the error trend is stable, or whether the current number of iterations equals a preset number. If convergence is detected, such as the training error of the loss function being less than the preset error or the error trend being stable, it indicates that the initial network model training is complete, and iterative training can be stopped. If convergence is not detected, training samples from the training data can be further obtained to train the initial network model until the training error of the loss function is within a preset range. When the training error of the loss function converges, the trained initial network model can be used as a prediction model. The user's behavior data for the next time step can be determined based on the trained prediction model. During training, various optimization methods can be used, such as using the Adam (Adaptive Moment Estimator) adaptive learning rate optimizer with an initial learning rate of 0.001. If the loss value of the predicted model does not decrease continuously, the learning rate is halved. Alternatively, an early stopping mechanism can be used. If the loss value of the predicted model does not decrease for a preset number of rounds, training is terminated early, and the optimal model parameters are restored. Or, regularization can be used to constrain the model weights through L2 regularization to prevent overfitting.
[0054] In this embodiment of the invention, when continuous third-object behavior data is obtained, a sliding time window can be used to segment the continuous third-object behavior data into sequence samples (S = {s1, s2, ..., s3}) based on a predefined time step T. Each sample can contain behavior data from N time steps to predict the behavior label for the next time step. It is understood that the dimension of the initial network model input data can be (B, N, D), where B represents the batch size, D represents the total feature dimension, and N represents the number of third-object behavior data points.
[0055] S130. In response to the determination event of the second object behavior data, obtain the interface display data corresponding to the second object behavior data according to the preset data acquisition mechanism.
[0056] The preset data acquisition mechanism can be understood as a pre-set data acquisition mechanism for acquiring interface display data corresponding to the second object behavior data. In this embodiment of the invention, interface display data can be understood as data that matches the second object behavior data and is used to display on the interface. In this embodiment of the invention, interface display data can be in various forms such as icons, text, and charts, used to intuitively present information related to the second object behavior to the user. Optionally, interface display data may include interface display style data and / or business request response data.
[0057] Specifically, a preset data acquisition mechanism is used. In response to the determination event of the second object's behavior data, the interface display data corresponding to the second object's behavior data can be obtained according to the preset data acquisition mechanism.
[0058] S140. The interface display data is preloaded into the target storage space so that when the second interface trigger operation is received, the target interface is updated based on the interface display data preloaded in the target storage space.
[0059] The target storage space can be set according to actual needs and is not specifically limited here. For example, the target storage space can be memory. Specifically, after determining the interface display data, the interface display data can be preloaded into the target storage space. Upon receiving the second interface trigger operation, the target interface can be updated based on the interface display data preloaded in the target storage space.
[0060] Based on the above embodiments, in order to determine the interface loading speed in a timely manner, the interface loading speed can be obtained by at least one of the following methods in the embodiments of the present invention.
[0061] Method 1: Employing user feedback. Specifically, this can be achieved through interface feedback on loading speed. A feedback interface is displayed; in response to feedback on loading speed, information indicating slow loading is identified. For example, a "Slow Loading Report" button can be set in a preset area of the interface. After clicking, the user can select a specific slow-loading module, such as the "Transaction Entry Page" or the "User Information Query Control," to report the loading speed of the corresponding page. Furthermore, a preset number of feedback submissions per minute can be allowed from the same user or interface operation address to prevent malicious data manipulation that could interfere with model iteration. Alternatively, the context can be automatically acquired during the target user's interface operation, automatically recording the object identifier, current interface address, device type, network status, and first cache (e.g., front-end memory cache) hit rate. Another approach is to periodically train users to manually identify "feedbackable scenarios," such as continuous loading loops or unresponsive controls.
[0062] Method 2: System detection can be used, as shown in the table below:
[0063]
[0064] Method 3: Model iteration can be used. For example, the preloading strategy can be adjusted based on feedback. Specifically, manual intervention can be used: for pages with concentrated slow loading feedback, manually mark them as high-risk modules and forcibly upgrade their cache level, such as from the second cache to the first cache. Model self-iteration can be performed: use user feedback data as negative samples for reinforcement learning to adjust model parameters. For example, if the "account query control" has slow loading feedback for 5 consecutive times, try increasing its feature weight by a certain proportion. Alternatively, a gray-scale verification mechanism can be used, i.e., test grouping: the optimized strategy can be released in a gray-scale manner according to object groups, such as prioritizing the trial of 10% of interface operation objects; or, comparison indicators: the improvement of cache hit rate, the rendering time of the first content of the page. Rollback conditions: if the memory overflow rate exceeds the first preset threshold or the main function error rate reaches the second preset threshold during the gray-scale period, automatically roll back to the data of the previous version.
[0065] The technical solution of this invention, in response to a first interface trigger operation by a target object on a target interface, obtains first object behavior data of the target object's operation on the target interface at the current moment; inputs the first object behavior data into a pre-trained prediction model to quickly and effectively determine the second object behavior data of the target object at the next moment; in response to the determination event of the second object behavior data, acquires interface display data corresponding to the second object behavior data according to a preset data acquisition mechanism; and preloads the interface display data into the target storage space. In this way, when the second interface trigger operation is actually received, there is no need to temporarily acquire and load the relevant interface display data; instead, the target interface can be directly updated based on the interface display data preloaded in the target storage space. The technical solution of this invention solves the technical problem in related technologies where data acquisition and loading can only be performed after receiving an interface trigger operation, resulting in slow interface display response. It achieves faster acquisition of interface display information corresponding to the interface trigger operation, improves the response speed of the interface display, and shortens the waiting time for interface display updates.
[0066] Figure 2 This is a flowchart illustrating a method for processing interface display according to an embodiment of the present invention. Optionally, based on the foregoing embodiments, in response to a determination event of the second object behavior data, acquiring interface display data corresponding to the second object behavior data according to a preset data acquisition mechanism includes: in response to a determination event of the second object behavior data, determining whether interface display data corresponding to the second object behavior data exists in a first cache; if so, reading the interface display data corresponding to the second object behavior data from the first cache. Technical features that are the same as or similar to those in the above embodiments will not be repeated here. Figure 2As shown, the method in this embodiment specifically includes:
[0067] S210. In response to the first interface trigger operation of the target object on the target interface, obtain the first object behavior data of the target object operating on the target interface at the current time.
[0068] S220. Input the first object behavior data into the pre-trained prediction model to determine the second object behavior data of the target object at the next moment.
[0069] S230. In response to the determination event of the second object behavior data, determine whether there is interface display data corresponding to the second object behavior data in the first cache.
[0070] The first cache can be understood as a pre-set cache used to cache interface display data. In this embodiment of the invention, the first cache can be a front-end memory cache. In this embodiment of the invention, the first cache can be used to store frequently accessed prediction data. Specifically, in response to the determination event of the second object behavior data, it can be determined whether there is interface display data corresponding to the second object behavior data in the first cache.
[0071] S240. If so, read the interface display data corresponding to the second object behavior data from the first cache.
[0072] Specifically, if the interface display data corresponding to the second object behavior data exists in the first cache, the interface display data corresponding to the second object behavior data can be read from the first cache.
[0073] In this embodiment of the invention, if no interface display data corresponding to the second object behavior data exists in the first cache, a data acquisition request for obtaining the interface display data can be generated; based on the data acquisition request, the interface display data is obtained from the target server. Alternatively, if no interface display data corresponding to the second object behavior data exists in the first cache, it can be determined whether interface display data corresponding to the second object behavior data exists in the second cache; if so, the interface display data corresponding to the second object behavior data is read from the second cache.
[0074] The second cache has a lower level than the first cache. The second cache can be a local database. Specifically, if the first cache does not contain UI display data corresponding to the second object behavior data, it can be determined whether the second cache contains UI display data corresponding to the second object behavior data. If so, the UI display data corresponding to the second object behavior data can be read from the second cache. For example, the UI display data corresponding to the second object behavior data can be read from a local database.
[0075] In this embodiment of the invention, the method further includes: if the interface display data corresponding to the second object behavior data does not exist in the second cache, a data acquisition request for acquiring the interface display data can be generated; thereby, the interface display data can be acquired from the target server based on the data acquisition request. Here, the data acquisition request can be understood as a request for acquiring the interface display data corresponding to the second object behavior data. The target server can be understood as a server for storing the interface display data corresponding to the second object behavior data. The number of target servers can be one, two, or more.
[0076] In this embodiment of the invention, after obtaining the interface display data from the target server, the method may further include: storing the interface display data in the first cache and / or the second cache, so that after receiving the second interface trigger operation of the target object, the interface display data corresponding to the second interface trigger operation can be obtained from the first cache or the second cache.
[0077] Based on the above embodiments, this embodiment of the invention may further include: updating the data stored in the cache, that is, updating the data stored in the first cache and / or the second cache. Optionally, updating the data stored in the cache includes automatic updates or passive updates. For example, when all data in the target server changes, an update command can be pushed to the front end through a preset communication protocol (e.g., WebSocket) to trigger a front end cache update, so that the first cache and the second cache are updated. Alternatively, to avoid inconsistencies between the front end cached data and the data stored in the back end server, the current data version number can be periodically requested from the back end server. If there is an inconsistency, the latest data can be retrieved from the back end server and the cache updated. For example, when the target object is a teller, when the teller logs in, the predicted data of the current role is automatically retrieved from the back end server and stored in the front end cache according to the teller's role type for the first operation. In this embodiment of the invention, the uniqueness of the data in the cache can be determined based on a combination of the user role, the request number of the front end page, and the version number.
[0078] Based on the above embodiments, in this embodiment of the invention, data in the cache is deleted according to a preset cache data eviction policy. For example, if data in the cache exceeds its time-to-live (TTL), it automatically expires and is deleted. It should be noted that the TTL of data in the first cache and the TTL of data in the second cache can be the same or different. If the TTL of data in the first cache and the TTL of data in the second cache are different, the TTL of data in the first cache can be greater than or less than the TTL of data in the second cache. Alternatively, when the cache capacity reaches its limit, data can be deleted according to the least recently used (LRU) policy; or, when the current target object logs out, the data in the cache automatically expires and is deleted. Based on this, a randomized TTL (base value ±10% fluctuation) can also be set for different types of data to avoid concentrated expiration.
[0079] S250. The interface display data is preloaded into the target storage space so that, upon receiving the second interface trigger operation, the target interface is updated based on the preloaded interface display data in the target storage space.
[0080] The technical solution of this embodiment of the invention determines whether there is interface display data corresponding to the second object behavior data in the first cache in response to the determination event of the second object behavior data; if so, the interface display data corresponding to the second object behavior data is read from the first cache. The technical solution of this embodiment of the invention realizes the preloading of the predicted interface display data in the first cache so as to immediately display the response result of the interface processing operation when the corresponding interface trigger operation is triggered.
[0081] Figure 3 This is a schematic diagram of the structure of a processing device for interface display provided in an embodiment of the present invention. Figure 3As shown, the device includes: an operation response module 310, a data prediction module 320, a data acquisition module 330, and a data preloading module 340. The operation response module 310 is used to obtain first object behavior data of the target object's operation on the target interface at the current time in response to a first interface trigger operation by the target object on the target interface. The data prediction module 320 is used to input the first object behavior data into a pre-trained prediction model to determine second object behavior data of the target object at the next time moment. The data acquisition module 330 is used to acquire interface display data corresponding to the second object behavior data according to a preset data acquisition mechanism in response to the determination event of the second object behavior data. The data preloading module 340 is used to preload the interface display data into a target storage space so that, upon receiving the second interface trigger operation, the target interface is updated based on the preloaded interface display data in the target storage space.
[0082] The technical solution of this invention, in response to a first interface trigger operation by a target object on a target interface, obtains first object behavior data of the target object's operation on the target interface at the current moment; inputs the first object behavior data into a pre-trained prediction model to quickly and effectively determine the second object behavior data of the target object at the next moment; in response to the determination event of the second object behavior data, acquires interface display data corresponding to the second object behavior data according to a preset data acquisition mechanism; and preloads the interface display data into the target storage space. In this way, when the second interface trigger operation is actually received, there is no need to temporarily acquire and load the relevant interface display data; instead, the target interface can be directly updated based on the interface display data preloaded in the target storage space. The technical solution of this invention solves the technical problem in related technologies where data acquisition and loading can only be performed after receiving an interface trigger operation, resulting in slow interface display response. It achieves faster acquisition of interface display information corresponding to the interface trigger operation, improves the response speed of the interface display, and shortens the waiting time for interface display updates.
[0083] Optionally, the device further includes a model training module. This model training module is used to acquire sample data and the expected output results corresponding to the sample data. The sample data includes third object behavior data of the preset object on the target interface at a certain historical moment, and the expected output results are fourth object behavior data of the preset object at the next moment after the historical moment. The module inputs the sample data into a pre-constructed initial network model to obtain the actual output results of the initial network model. Based on the expected data and the actual output results, the network parameters of the initial network model are adjusted to obtain the trained prediction model.
[0084] Optionally, the initial network model is a long short-term memory network model; wherein the long short-term memory network model includes an input layer, a convolutional layer, a first long short-term memory network layer, an attention mechanism layer, a random deactivation layer, and a second long short-term memory network layer.
[0085] Optionally, the data acquisition module 330 is configured to, in response to the determination event of the second object behavior data, determine whether there is interface display data corresponding to the second object behavior data in the first cache; if so, read the interface display data corresponding to the second object behavior data from the first cache.
[0086] Optionally, the data acquisition module 330 is further configured to determine whether there is interface display data corresponding to the second object behavior data in the second cache if there is no interface display data corresponding to the second object behavior data in the first cache; wherein the level of the second cache is lower than the level of the first cache; if so, the interface display data corresponding to the second object behavior data is read from the second cache.
[0087] Optionally, the data acquisition module 330 is further configured to generate a data acquisition request for acquiring the interface display data when the interface display data corresponding to the second object behavior data does not exist in the second cache; and to acquire the interface display data from the target server based on the data acquisition request.
[0088] Optionally, the device further includes a data caching module. This data caching module is configured to, after obtaining the interface display data from the target server, store the interface display data in the first cache and / or the second cache.
[0089] The interface display processing device provided in the embodiments of the present invention can execute the interface display processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.
[0090] It is worth noting that the various units and modules included in the processing device shown in the above interface are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of the present invention.
[0091] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0092] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0093] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0094] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the processing methods for interface display.
[0095] In some embodiments, the interface display processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the interface display processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the interface display processing method by any other suitable means (e.g., by means of firmware).
[0096] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0097] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0098] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0100] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0101] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0102] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.
[0103] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A method for processing interface display, characterized in that, include: In response to a first interface trigger operation by the target object on the target interface, first object behavior data of the target object on the target interface at the current time is obtained; The first object behavior data is input into a pre-trained prediction model to determine the second object behavior data of the target object at the next moment; In response to the determination event of the second object behavior data, the interface display data corresponding to the second object behavior data is obtained according to the preset data acquisition mechanism; The interface display data is preloaded into the target storage space so that, upon receiving the second interface trigger operation, the target interface is updated based on the preloaded interface display data in the target storage space.
2. The method according to claim 1, characterized in that, The method further includes: Acquire sample data and the expected output result corresponding to the sample data, wherein the sample data includes the third object behavior data of the preset object on the target interface at a certain historical moment, and the expected output result is the fourth object behavior data of the preset object at the next moment of the certain historical moment; The sample data is input into a pre-built initial network model to obtain the actual output of the initial network model; The network parameters of the initial network model are adjusted based on the expected data and the actual output results to obtain the trained prediction model.
3. The method according to claim 2, characterized in that, The initial network model is a long short-term memory network model; wherein, the long short-term memory network model includes an input layer, a convolutional layer, a first long short-term memory network layer, an attention mechanism layer, a random deactivation layer, and a second long short-term memory network layer.
4. The method according to claim 1, characterized in that, The event responding to the determination of the second object behavior data involves acquiring interface display data corresponding to the second object behavior data according to a preset data acquisition mechanism, including: In response to the determination event of the second object behavior data, determine whether there is interface display data corresponding to the second object behavior data in the first cache; If so, then read the interface display data corresponding to the behavior data of the second object from the first cache.
5. The method according to claim 4, characterized in that, The method further includes: If no interface display data corresponding to the second object behavior data exists in the first cache, determine whether interface display data corresponding to the second object behavior data exists in the second cache; wherein, the level of the second cache is lower than the level of the first cache; If so, then read the interface display data corresponding to the behavior data of the second object from the second cache.
6. The method according to claim 5, characterized in that, The method further includes: If the interface display data corresponding to the second object behavior data does not exist in the second cache, a data retrieval request for retrieving the interface display data is generated. Based on the data acquisition request, the interface display data is obtained from the target server.
7. The method according to claim 6, characterized in that, After obtaining the interface display data from the target server, the method further includes: The interface display data is stored in the first cache and / or the second cache.
8. A processing device for displaying an interface, characterized in that, include: An operation response module is used to respond to a first interface trigger operation of a target object on a target interface and obtain first object behavior data of the target object's operation on the target interface at the current moment. The data prediction module is used to input the first object behavior data into a pre-trained prediction model to determine the second object behavior data of the target object at the next moment. The data acquisition module is used to respond to the determination event of the second object behavior data and acquire the interface display data corresponding to the second object behavior data according to the preset data acquisition mechanism; The data preloading module is used to preload the interface display data into the target storage space, so as to update the target interface based on the interface display data preloaded in the target storage space when the second interface trigger operation is received.
9. An electronic device, characterized in that, Its features are, The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the interface display processing 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 instructions that, when executed by a processor, implement the interface display processing method according to any one of claims 1-7.