Interface display method and device, electronic equipment and storage medium
By acquiring and inputting multimodal data into the adjustment model of the target device, the problem of the inability to dynamically analyze the interface configuration of kitchen appliances was solved, thereby improving the real-time performance and accuracy of interface adjustments and enhancing the user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NINGBO FOTILE KITCHEN WARE CO LTD
- Filing Date
- 2026-02-02
- Publication Date
- 2026-06-19
AI Technical Summary
In existing technologies, the interface configuration of kitchen appliances cannot continuously and dynamically analyze multimodal data, resulting in low real-time performance and accuracy of interface adjustments, and a decline in user experience.
By acquiring current device control information, device operation information, and environmental information, and inputting them into a target adjustment model trained based on historical training data of the target device, the display attribute adjustment information of the target interface is obtained, and the interface attributes are adjusted accordingly. The model is continuously and dynamically analyzed and updated through multimodal data.
It improves the real-time performance and precision of interface adjustments, provides accurately adapted personalized interface configurations, and enhances the user experience.
Smart Images
Figure CN122240218A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of artificial intelligence technology, and in particular to an interface display method, apparatus, electronic device, and storage medium. Background Technology
[0002] With the intelligent development of kitchen appliances, the interface functions of various kitchen appliances are becoming increasingly rich. How to provide users with suitable interface configurations has become the key to improving the user experience.
[0003] In related technologies, user behavior data is often input into a model to generate control signals, which are then used to adjust the terminal's interface parameters. However, in these technologies, the input data modalities on which the model relies are relatively singular, and some key signals, such as language commands, ambient lighting, and device operating status, are often ignored. This results in the inability to provide accurately adapted personalized interface configurations and the inability to continuously and dynamically analyze multimodal data, leading to low real-time performance and accuracy of interface adjustments, which in turn degrades the user experience. Summary of the Invention
[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for displaying an interface, to at least solve the problems in related technologies, such as the inability to continuously and dynamically analyze multimodal data, resulting in low real-time performance and accuracy of interface adjustments, and consequently, a decline in user experience. The technical solution of this disclosure is as follows: According to a first aspect of the present disclosure, a method for displaying an interface is provided, the method being applied to a target device, the method comprising: In response to a device control command triggered on the target interface, current adjustment information is obtained; the current adjustment information includes current device control information, current device operation information, and current environment information. The current device control information, the current device operation information, and the current environment information are input into the target adjustment model to obtain the first display attribute adjustment information corresponding to the target interface; the target adjustment model is trained based on the historical training data corresponding to the target device; the historical training data includes the device control information, device operation information, and environment information within the historical time period corresponding to the target device; the historical training data is updated periodically based on a preset cycle; Based on the first display attribute adjustment information, the display attributes of the target interface are adjusted.
[0005] In an optional embodiment, the target conditioning model is trained in the following manner: Obtain the historical training data and the preset display attribute adjustment information corresponding to the historical training data; The historical training data is input into the adjustment model to be trained to adjust the display attributes, thereby obtaining the predicted display attribute adjustment information. Based on the predicted display attribute adjustment information and the preset display attribute adjustment information, the adjustment model to be trained is trained to obtain the target adjustment model.
[0006] In an optional embodiment, the method further includes: Determine the amount of training data corresponding to the historical training data; If the amount of training data does not reach the preset threshold, pseudo-training data is generated based on the historical training data and the preset generation model, so that the amount of training data corresponding to the historical training data and the pseudo-training data reaches the preset threshold.
[0007] In an optional embodiment, the method further includes: The step of inputting the historical training data into the model to be trained for display attribute adjustment to obtain predicted display attribute adjustment information includes: The historical training data adjustment information is input into the training spatial feature extraction layer and the training temporal feature extraction layer of the training adjustment model to extract features, thereby obtaining historical spatial features and historical temporal features. The historical spatial features and the historical temporal features are input into the training adjustment layer of the training adjustment model to adjust the display attributes and obtain the predicted display attribute adjustment information.
[0008] In an optional embodiment, the method further includes: The current regulation information is stored as training data in the target device, and the target regulation model is trained based on the current regulation information.
[0009] In an optional embodiment, the method further includes: In response to a model update command triggered based on a preset time interval, update information corresponding to the parameters of the target adjustment model is obtained; The update information is encrypted to obtain encrypted update information; The encrypted update information is sent to the server so that the server updates the target adjustment model based on the encrypted update information and the encrypted update information corresponding to at least one preset device.
[0010] In an optional embodiment, the method further includes: In response to a display attribute adjustment command, obtain second display attribute adjustment information; The first display attribute adjustment information is updated based on the second display attribute adjustment information.
[0011] According to a second aspect of the present disclosure, an interface display device is provided, the device being applied to a target device, the device comprising: The current adjustment information acquisition module is used to acquire current adjustment information in response to a device control command triggered on the target interface; the current adjustment information includes current device control information, current device operation information, and current environment information; The first information acquisition module is used to input the current device control information, the current device operation information, and the current environment information into the target adjustment model to obtain the first display attribute adjustment information corresponding to the target interface; the target adjustment model is trained based on the historical training data corresponding to the target device; the historical training data includes the device control information, device operation information, and environment information within the historical time period corresponding to the target device; the historical training data is updated periodically based on a preset period. The display attribute adjustment module is used to adjust the display attributes of the target interface based on the first display attribute adjustment information.
[0012] In an optional embodiment, the target conditioning model is trained in the following manner: The preset information acquisition module is used to acquire the historical training data and the preset display attribute adjustment information corresponding to the historical training data; The prediction adjustment information acquisition module is used to input the historical training data into the adjustment model to be trained to adjust the display attributes and obtain the prediction display attribute adjustment information. The training module for the adjustment model to be trained is used to train the adjustment model to be trained based on the predicted display attribute adjustment information and the preset display attribute adjustment information to obtain the target adjustment model.
[0013] In an optional embodiment, the apparatus further includes: The training data volume determination module is used to determine the training data volume corresponding to the historical training data; The pseudo-training data determination module is used to generate pseudo-training data based on the historical training data and the preset generation model when the amount of training data has not reached the preset threshold, so that the amount of training data corresponding to the historical training data and the pseudo-training data reaches the preset threshold.
[0014] In an optional embodiment, the prediction adjustment information acquisition module includes: The feature extraction unit is used to input the historical training data adjustment information into the training spatial feature extraction layer and the training temporal feature extraction layer of the training adjustment model to extract features and obtain historical spatial features and historical temporal features. The display attribute adjustment unit is used to input the historical spatial features and the historical temporal features into the adjustment layer to be trained in the adjustment model to adjust the display attributes and obtain the predicted display attribute adjustment information.
[0015] In an optional embodiment, the apparatus further includes: The target regulation model training module is used to store the current regulation information as training data in the target device and train the target regulation model based on the current regulation information.
[0016] In an optional embodiment, the apparatus further includes: The update information acquisition module is used to acquire update information corresponding to the parameters of the target adjustment model in response to a model update command triggered based on a preset time interval. An encrypted update information acquisition module is used to encrypt the update information to obtain encrypted update information; The target adjustment model update module is used to send the encrypted update information to the server, so that the server updates the target adjustment model based on the encrypted update information and the encrypted update information corresponding to at least one preset device.
[0017] In an optional embodiment, the apparatus further includes: The second information acquisition module is used to acquire second display attribute adjustment information in response to display attribute adjustment instructions; The first information update module is used to update the first display attribute adjustment information based on the second display attribute adjustment information.
[0018] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the method as described in any one of the first aspects above.
[0019] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided such that, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the first aspects of the present disclosure.
[0020] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects: In response to a device control command triggered on the target interface, the system acquires current adjustment information, including current device control information, current device operation information, and current environmental information. This information is then input into a target adjustment model trained on historical training data corresponding to the target device. This yields the first display attribute adjustment information for the target interface. The historical training data includes device control information, device operation information, and environmental information for a historical time period corresponding to the target device. This historical training data is updated periodically based on a preset cycle. This allows for the use of multimodal data as input to the target adjustment model, improving the accuracy of the display attribute adjustment information. Furthermore, continuous dynamic analysis of multimodal data provides precisely tailored personalized interface configurations. Based on the first display attribute adjustment information, the system adjusts the display attributes of the target interface, enhancing the real-time performance and accuracy of interface adjustments, thereby improving the user experience.
[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.
[0023] Figure 1 This is a schematic diagram illustrating an application environment according to an exemplary embodiment; Figure 2 This is a flowchart illustrating an interface display method according to an exemplary embodiment; Figure 3 This is a block diagram illustrating an interface display device according to an exemplary embodiment; Figure 4 This is a block diagram illustrating an electronic device for displaying an interface according to an exemplary embodiment; Figure 5 This is a block diagram illustrating an electronic device for displaying an interface according to an exemplary embodiment. Detailed Implementation
[0024] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.
[0025] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar different contents 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 this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.
[0026] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.
[0027] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application environment according to an exemplary embodiment, such as... Figure 1 As shown, the application environment may include target device 100 and server 200.
[0028] In an optional embodiment, the target device 100 can provide device control services through a target interface. Specifically, the target device 100 may be, but is not limited to, devices such as microwave ovens, range hoods, ovens, and refrigerators, or software running on the aforementioned devices, such as applications. Optionally, the operating system running on the device may include, but is not limited to, Android, iOS, Linux, and Windows.
[0029] In an optional embodiment, server 200 can provide background support for target device 100 and adjust interface display attributes. Optionally, server 200 can pre-train a target adjustment model. Correspondingly, interface display attribute adjustment can be performed in conjunction with the target adjustment model. Specifically, server 200 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.
[0030] In addition, it should be noted that, Figure 1The example shown is merely one application environment provided by this disclosure. In practical applications, other application environments may also be included. For example, the adjustment of interface display attributes can also be implemented on the target device.
[0031] In the embodiments described in this specification, the target device 100 and the server 200 can be directly or indirectly connected via wired or wireless communication, and this disclosure does not impose any restrictions.
[0032] Figure 2 This is a flowchart illustrating an interface display method according to an exemplary embodiment. This interface display method is used in a target device or server, or can be executed interactively by the target device and server, such as... Figure 2 As shown, it includes the following steps.
[0033] In step S201, in response to a device control command triggered on the target interface, current adjustment information is obtained; In one specific embodiment, the target interface can be the control interface corresponding to the target device, and the device control command can be the command triggered on the target interface for controlling the target device. The device control command can include current adjustment information.
[0034] In a specific embodiment, taking an oven as the target device, the target interface can be an oven control interface, and the device control command can be a control command triggered on the oven control interface. Optionally, the device control command can be an oven mode adjustment command, an oven start command, an oven temperature setting command, etc.
[0035] In a specific embodiment, the current adjustment information may be the adjustment information corresponding to the target interface. Specifically, the current adjustment information may include the current device control information, the current device operation information, and the current environment information. The current device control information may be the control information corresponding to the device control command. The current device operation information may be the current operating status information corresponding to the target device. The current environment information may be the environmental parameters of the environment in which the target device is running. Specifically, the environmental parameters may be the light intensity.
[0036] In a specific embodiment, when the target device is an oven and the device control command is an oven temperature setting command, the current adjustment information can be the adjustment information corresponding to the oven control interface. Correspondingly, the current device control information can be the control information for controlling the oven's set temperature, the current device operation information can be the oven's current operating status information, and the current environment information can be the environmental parameters of the environment in which the oven is running.
[0037] In step S203, the current device control information, current device operation information, and current environment information are input into the target adjustment model to obtain the first display attribute adjustment information corresponding to the target interface.
[0038] In one specific embodiment, the target adjustment model is trained based on the historical training data corresponding to the target device. The historical training data may include device control information, device operation information and environmental information within the historical time period corresponding to the target device. The historical training data may be updated periodically based on a preset period.
[0039] In an optional embodiment, the above-mentioned target conditioning model is trained in the following manner: Obtain historical training data and corresponding preset display attribute adjustment information; Historical training data is input into the model to be trained to adjust display attributes, thereby obtaining predicted display attribute adjustment information. Based on the predicted display attribute adjustment information and the preset display attribute adjustment information, the adjustment model to be trained is trained to obtain the target adjustment model.
[0040] In one specific embodiment, the preset display attribute adjustment information can be the display attribute adjustment information corresponding to historical training data, and the predicted display attribute adjustment information can be the display attribute adjustment information corresponding to historical training data predicted by the target adjustment model.
[0041] In a specific embodiment, the aforementioned training adjustment model can be a deep learning model to be trained. Specifically, the model structure can be set according to actual needs. Optionally, the training adjustment model can include a training spatial feature extraction layer, a training temporal feature extraction layer, and a training adjustment layer. Specifically, the training spatial feature extraction layer can be a Convolutional Neural Network (CNN), etc., which can be used to extract spatial features corresponding to historical training data. The training temporal feature extraction layer can be a Transformer encoder, which can be used to extract temporal features corresponding to historical training data. The training adjustment layer can be used to adjust display attributes.
[0042] In the above embodiments, by learning the correlation patterns between device control information, device operation information, and environmental information in historical training data and preset display attribute adjustment information, an adjustment scheme that meets expectations can be automatically generated for new scenarios, thereby improving the accuracy and reliability of model adjustment and optimizing user experience.
[0043] In an optional embodiment, the above-mentioned inputting historical training data into the model to be trained for display attribute adjustment to obtain predicted display attribute adjustment information may include: The historical training data adjustment information is input into the spatial feature extraction layer and temporal feature extraction layer of the adjustment model to be trained for feature extraction, so as to obtain historical spatial features and historical temporal features. Historical spatial features and historical temporal features are input into the training adjustment layer of the adjustment model to adjust the display attributes, thereby obtaining the predicted display attribute adjustment information.
[0044] In a specific embodiment, historical spatial features can be spatial features corresponding to historical training data, such as sliding trajectory features, operation area distribution features, etc.; historical temporal features can be temporal features corresponding to historical training data, such as temporal features corresponding to device control information in historical training data, etc.
[0045] In the above embodiments, historical training data is processed through spatial feature extraction layer and temporal feature extraction layer respectively, and then the prediction result is output through adjustment layer. Multimodal feature separation and fusion are used to improve the accuracy and adaptability of display attribute adjustment, thereby improving the scene adaptation accuracy of display attribute adjustment.
[0046] In an optional embodiment, the above method may further include: Determine the amount of training data corresponding to the historical training data; If the amount of training data does not reach the preset threshold, pseudo-training data is generated based on historical training data and a preset generation model, so that the amount of training data corresponding to the historical training data and pseudo-training data reaches the preset threshold.
[0047] In a specific embodiment, the amount of training data can be the amount of data corresponding to historical training data, the preset threshold can be a preset data amount threshold, the preset generation model can be used to generate pseudo training data, the preset generation model can be a generative adversarial network, etc., and optionally, a mixed sample data augmentation method can be used to mix historical training data and pseudo training data.
[0048] In the above embodiments, when the amount of historical training data is small, generating pseudo training data corresponding to the historical training data can alleviate the problem of data imbalance, improve the generalization ability of the target adjustment model, enhance the adaptability of the target adjustment model to different training data, and avoid problems such as overfitting.
[0049] In step S205, the display attributes of the target interface are adjusted based on the first display attribute adjustment information.
[0050] In a specific embodiment, taking a smart refrigerator as an example, the target interface can be the display interface corresponding to the smart refrigerator. When the first display attribute adjustment information is to reduce the display brightness, the display brightness of the display interface corresponding to the smart refrigerator is reduced.
[0051] In an optional embodiment, the above method may further include: The current conditioning information is stored as training data in the target device, and the target conditioning model is trained based on the current conditioning information.
[0052] In a specific embodiment, where the target device is a microwave oven and the device control command is a heating control command, the current device control information can be controlling the microwave oven to perform a heating operation at 8:00 AM, the current device operation information can be the microwave oven's operating status at 8:00 AM, and the current environmental information can be the light intensity of the environment in which the microwave oven is located. Specifically, the information on controlling the microwave oven to perform a heating operation at 8:00 AM, the microwave oven's operating status at 8:00 AM, and the light intensity of the environment in which the microwave oven is located can be stored in the microwave oven so that the microwave oven can train a target adjustment model based on the above information.
[0053] In the above embodiments, by continuously storing new current adjustment information and continuously training the target adjustment model, real-time adjustment of the interface can be achieved without manual operation by the user, thereby improving adjustment accuracy and scene adaptability.
[0054] In an optional embodiment, the above method may further include: In response to a model update command triggered based on a preset time interval, obtain the update information corresponding to the parameters of the target adjustment model; The update information is encrypted to obtain encrypted update information; The encrypted update information is sent to the server so that the server updates the target adjustment model based on the encrypted update information and the encrypted update information corresponding to at least one preset device.
[0055] In a specific embodiment, the model update instruction can be an instruction triggered based on a preset time interval for updating the corresponding parameters of the target adjustment model. The update information can be the gradient corresponding to the parameters of the target adjustment model. Correspondingly, the encrypted update information can be the encrypted gradient corresponding to the parameters of the target adjustment model. Optionally, the update information can be encrypted by adding Gaussian noise.
[0056] In a specific embodiment, at least one preset device can be at least one device corresponding to the target device. For example, taking oven A as the target device, at least one preset device can be at least one oven (oven B, oven C, oven D, etc.). Accordingly, the target adjustment model can be updated based on the encrypted update information corresponding to oven A and the encrypted update information corresponding to oven B, oven C, oven D, etc.
[0057] In the above embodiments, encryption of the target device's update information can ensure the protection of user data privacy. Furthermore, the update of the target adjustment model aggregates the update information of at least one device, which can overcome the limitations of single-device scenarios and improve the model's versatility and adaptability.
[0058] In an optional embodiment, the above method may further include: In response to a display attribute adjustment command, obtain second display attribute adjustment information; The first display attribute adjustment information is updated based on the second display attribute adjustment information.
[0059] In a specific embodiment, the second display attribute adjustment information can be the display attribute adjustment information corresponding to the display attribute adjustment instruction. Specifically, when the display attribute adjustment instruction is to enlarge the font from medium to large and the first display attribute adjustment information is to display the font as medium, the first display attribute adjustment information is updated to display the font as large.
[0060] In the above embodiments, the first display attribute adjustment information is manually updated by display attribute adjustment instructions to ensure that the interface display effect adapts to the scene and user preferences, thereby achieving a more accurate interface display.
[0061] As can be seen from the technical solutions provided in the embodiments of this specification above, this specification, in response to a device control command triggered on the target interface, obtains current adjustment information including current device control information, current device operation information, and current environment information. It then inputs the current device control information, current device operation information, and current environment information into a target adjustment model trained based on historical training data corresponding to the target device, thereby obtaining first display attribute adjustment information corresponding to the target interface. The historical training data includes device control information, device operation information, and environment information within a historical time period corresponding to the target device. This allows for the use of multimodal data as input to the target adjustment model, improving the accuracy of display attribute adjustment information. Furthermore, continuous dynamic analysis of multimodal data provides precisely adapted personalized interface configurations. Based on the first display attribute adjustment information, the display attributes of the target interface are adjusted, improving the real-time performance and accuracy of interface adjustments, thereby enhancing the user experience.
[0062] Figure 3This is a block diagram illustrating an interface display device according to an exemplary embodiment. (Refer to...) Figure 3 The device includes: The current adjustment information acquisition module 310 is used to acquire current adjustment information in response to a device control command triggered on the target interface; the current adjustment information includes current device control information, current device operation information, and current environment information; The first information acquisition module 330 is used to input the current device control information, the current device operation information, and the current environment information into the target adjustment model to obtain the first display attribute adjustment information corresponding to the target interface; the target adjustment model is trained based on the historical training data corresponding to the target device; the historical training data includes the device control information, device operation information, and environment information within the historical time period corresponding to the target device; the historical training data is updated periodically based on a preset period. The display attribute adjustment module 350 is used to adjust the display attributes of the target interface based on the first display attribute adjustment information.
[0063] In an optional embodiment, the target conditioning model is trained in the following manner: The preset information acquisition module is used to acquire historical training data and the preset display attribute adjustment information corresponding to the historical training data; The prediction adjustment information acquisition module is used to input historical training data into the adjustment model to be trained to adjust the display attributes and obtain the prediction display attribute adjustment information. The training module for the adjustment model to be trained is used to train the adjustment model to be trained based on the predicted display attribute adjustment information and the preset display attribute adjustment information to obtain the target adjustment model.
[0064] In an optional embodiment, the above-described apparatus further includes: The training data volume determination module is used to determine the training data volume corresponding to historical training data. The pseudo-training data determination module is used to generate pseudo-training data based on historical training data and a preset generation model when the amount of training data has not reached a preset threshold, so that the amount of training data corresponding to the historical training data and pseudo-training data reaches the preset threshold.
[0065] In an optional embodiment, the prediction and adjustment information acquisition module includes: The feature extraction unit is used to input historical training data adjustment information into the training spatial feature extraction layer and the training temporal feature extraction layer of the adjustment model to be trained for feature extraction, so as to obtain historical spatial features and historical temporal features. The display attribute adjustment unit is used to input historical spatial features and historical temporal features into the adjustment layer to be trained in the adjustment model to adjust the display attributes and obtain the predicted display attribute adjustment information.
[0066] In an optional embodiment, the above-described apparatus further includes: The target conditioning model training module is used to store the current conditioning information as training data in the target device and train the target conditioning model based on the current conditioning information.
[0067] In an optional embodiment, the above-described apparatus further includes: The update information acquisition module is used to obtain the update information corresponding to the parameters of the target adjustment model in response to the model update command triggered based on a preset time interval. The encrypted update information acquisition module is used to encrypt the update information to obtain encrypted update information; The target adjustment model update module is used to send encrypted update information to the server so that the server updates the target adjustment model based on the encrypted update information and the encrypted update information corresponding to at least one preset device.
[0068] In an optional embodiment, the above-described apparatus further includes: The second information acquisition module is used to acquire second display attribute adjustment information in response to display attribute adjustment instructions; The first information update module is used to update the first display attribute adjustment information based on the second display attribute adjustment information.
[0069] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0070] Figure 4 This is a block diagram illustrating an electronic device for displaying an interface according to an exemplary embodiment. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the electronic device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements an interface display method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse. Figure 5This is a block diagram illustrating an electronic device for displaying an interface according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, this electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a user interface display method. Those skilled in the art will understand that Figure 4 or Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the electronic device to which the present disclosure is applied. A specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. In an exemplary embodiment, an electronic device is also provided, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the interface display method as described in the embodiments of this disclosure.
[0071] In an exemplary embodiment, a computer-readable storage medium is also provided, wherein when the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the interface display method in the embodiments of this disclosure.
[0072] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.
[0073] It should be understood that this disclosure 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 this disclosure is limited only by the appended claims.
Claims
1. A method for displaying an interface, characterized in that, The method is applied to a target device, and the method includes: In response to a device control command triggered on the target interface, current adjustment information is obtained; the current adjustment information includes current device control information, current device operation information, and current environment information. The current device control information, the current device operation information, and the current environment information are input into the target adjustment model to obtain the first display attribute adjustment information corresponding to the target interface; the target adjustment model is trained based on the historical training data corresponding to the target device; the historical training data includes the device control information, device operation information, and environment information within the historical time period corresponding to the target device; the historical training data is updated periodically based on a preset cycle; Based on the first display attribute adjustment information, the display attributes of the target interface are adjusted.
2. The interface display method according to claim 1, characterized in that, The target adjustment model was trained in the following manner: Obtain the historical training data and the preset display attribute adjustment information corresponding to the historical training data; The historical training data is input into the adjustment model to be trained to adjust the display attributes, thereby obtaining the predicted display attribute adjustment information. Based on the predicted display attribute adjustment information and the preset display attribute adjustment information, the adjustment model to be trained is trained to obtain the target adjustment model.
3. The interface display method according to claim 2, characterized in that, The method further includes: Determine the amount of training data corresponding to the historical training data; If the amount of training data does not reach the preset threshold, pseudo-training data is generated based on the historical training data and the preset generation model, so that the amount of training data corresponding to the historical training data and the pseudo-training data reaches the preset threshold.
4. The interface display method according to claim 2, characterized in that, The step of inputting the historical training data into the model to be trained for display attribute adjustment to obtain predicted display attribute adjustment information includes: The historical training data adjustment information is input into the training spatial feature extraction layer and the training temporal feature extraction layer of the training adjustment model to extract features, thereby obtaining historical spatial features and historical temporal features. The historical spatial features and the historical temporal features are input into the training adjustment layer of the training adjustment model to adjust the display attributes and obtain the predicted display attribute adjustment information.
5. The interface display method according to claim 1, characterized in that, The method further includes: The current regulation information is stored as training data in the target device, and the target regulation model is trained based on the current regulation information.
6. The interface display method according to claim 1, characterized in that, The method further includes: In response to a model update command triggered based on a preset time interval, update information corresponding to the parameters of the target adjustment model is obtained; The update information is encrypted to obtain encrypted update information; The encrypted update information is sent to the server so that the server updates the target adjustment model based on the encrypted update information and the encrypted update information corresponding to at least one preset device.
7. The interface display method according to claim 1, characterized in that, The method further includes: In response to a display attribute adjustment command, obtain second display attribute adjustment information; The first display attribute adjustment information is updated based on the second display attribute adjustment information.
8. An interface display device, characterized in that, The device is applied to the target device, and the device includes: The current adjustment information acquisition module is used to acquire current adjustment information in response to a device control command triggered on the target interface; the current adjustment information includes current device control information, current device operation information, and current environment information; The first display attribute adjustment information acquisition module is used to input the current device control information, the current device operation information, and the current environment information into the target adjustment model to obtain the first display attribute adjustment information corresponding to the target interface; the target adjustment model is trained based on the historical training data corresponding to the target device; the historical training data includes the device control information, device operation information, and environment information within the historical time period corresponding to the target device; the historical training data is updated periodically based on a preset period. The display attribute adjustment module is used to adjust the display attributes of the target interface based on the first display attribute adjustment information.
9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the interface display method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the interface display method as described in any one of claims 1 to 7.