Wallpaper generation method and device and computer readable storage medium

Abstract

The embodiment of the invention relates to the technical field of vehicles, and discloses a wallpaper generation method and device and a computer readable storage medium, and the method comprises the steps: collecting real-time data of a vehicle, the real-time data comprising in-vehicle music data, vehicle driving data and out-vehicle environment data; and inputting the real-time data into a pre-trained wallpaper generation model, extracting wallpaper feature data based on the real-time data, fusing the wallpaper feature data through a shared vector space, and generating corresponding wallpaper based on the fused wallpaper feature data. By applying the technical scheme of the invention, the wallpaper adaptive to the current music data of the vehicle, the driving data of the vehicle and the external environment data of the vehicle can be automatically generated, and the use requirements of different scenes are met.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of vehicle technology, and specifically to a wallpaper generation method, device, and computer-readable storage medium. Background Art

[0002] The in-vehicle entertainment system, also known as the in-vehicle infotainment system (IVI), is an important part of the smart cockpit, providing information and entertainment services to drivers and passengers. With the development of automobile intelligence and networking, the in-vehicle entertainment system has evolved from a simple radio and GPS navigation to a comprehensive system that integrates multiple functions such as real-time map navigation, multimedia entertainment, audio and video communication, and vehicle control. As a visual component of the in-vehicle entertainment system, car wallpaper can affect the user's overall perception of the in-vehicle entertainment system and their continued use intention.

[0003] The existing wallpaper generation method usually requires users to manually select limited wallpaper elements from a database, or download sample wallpaper materials online to form vehicle wallpapers, which results in the generated wallpapers being relatively fixed and single, and difficult to meet usage requirements. Summary of the invention

[0004] In view of the above problems, the embodiments of the present application provide a wallpaper generation method, device and computer-readable storage medium, which are used to solve the technical problem that the existing wallpaper generation method is usually that the user manually selects limited wallpaper elements in a database, or downloads sample wallpaper materials online to form a car wallpaper, resulting in the generated wallpaper being relatively fixed and single, and difficult to meet usage requirements.

[0005] According to one aspect of an embodiment of the present application, a wallpaper generation method is provided, comprising:

[0006] Collecting real-time data of the vehicle, the real-time data including in-vehicle music data, vehicle driving data and external environment data;

[0007] The real-time data is input into a pre-trained wallpaper generation model, wallpaper feature data is extracted based on the real-time data, the wallpaper feature data is fused through a shared vector space, and the corresponding wallpaper is generated based on the fused wallpaper feature data.

[0008] In an optional manner, fusing the wallpaper feature data through a shared vector space includes:

[0009] The wallpaper feature data is mapped into a shared vector space, and the wallpaper feature data is fused through a shared layer or a fully connected layer of the shared vector space.

[0010] In an optional manner, the wallpaper feature data includes music features and scene features;

[0011] The generating corresponding wallpaper based on the fused wallpaper feature data includes:

[0012] Identify the music features of the in-car music data, and determine a first wallpaper element associated with the music features, wherein the first wallpaper element includes a wallpaper pattern, a wallpaper color, and a dynamic element;

[0013] Determine a scenario feature by combining the vehicle driving data and the vehicle exterior environment data, and determine a second wallpaper element based on the scenario feature, wherein the second wallpaper element includes a scenario element;

[0014] The first wallpaper element and the second wallpaper element are combined to generate a wallpaper.

[0015] In an optional manner, after generating the corresponding wallpaper based on the fused wallpaper feature data, the method further includes:

[0016] A text prompt is generated based on the wallpaper feature data, and the text prompt is displayed on the generated wallpaper.

[0017] In an optional manner, the wallpaper generation method further includes:

[0018] The current driving period and the ambient light intensity are determined according to the scenario characteristics, and wallpaper display parameters are adjusted according to the current driving period and the ambient light intensity, wherein the wallpaper display parameters include at least one of brightness and contrast.

[0019] In an optional manner, after generating the corresponding wallpaper based on the fused wallpaper feature data, the method further includes:

[0020] Collect user feedback information, update the loss function in the wallpaper generation model according to the user feedback information, and optimize the model parameters in the wallpaper generation model based on the updated loss function.

[0021] In an optional manner, the user feedback information includes rating information, like information and comment information;

[0022] The updating of the loss function in the wallpaper generation model according to the user feedback information comprises:

[0023] The score difference information is determined by the score information, the score difference information is used as the loss item of the loss function, the like information is used as the plus item, and the negative comments of the comment information are used as the minus item, and the loss function in the wallpaper generation model is updated.

[0024] According to another aspect of an embodiment of the present application, there is provided a wallpaper generating device, including:

[0025] A real-time data acquisition module is used to collect real-time data of the vehicle, wherein the real-time data includes in-vehicle music data, vehicle driving data and external environment data;

[0026] The wallpaper generation module is used to input the real-time data into a pre-trained wallpaper generation model, extract wallpaper feature data based on the real-time data, fuse the wallpaper feature data through a shared vector space, and generate a corresponding wallpaper based on the fused wallpaper feature data.

[0027] In an optional manner, the wallpaper generation module further includes:

[0028] The feature data fusion unit is used to map the wallpaper feature data into a shared vector space, and fuse the wallpaper feature data through a shared layer or a fully connected layer of the shared vector space.

[0029] In an optional manner, the wallpaper feature data includes music features and scene features;

[0030] The wallpaper generation module is also used for:

[0031] Identify the music features of the in-car music data, and determine a first wallpaper element associated with the music features, wherein the first wallpaper element includes a wallpaper pattern, a wallpaper color, and a dynamic element;

[0032] Determine a scenario feature by combining the vehicle driving data and the vehicle exterior environment data, and determine a second wallpaper element based on the scenario feature, wherein the second wallpaper element includes a scenario element;

[0033] The first wallpaper element and the second wallpaper element are combined to generate a wallpaper.

[0034] In an optional manner, the wallpaper generating device further includes a prompt display module, which is used to:

[0035] A text prompt is generated based on the wallpaper feature data, and the text prompt is displayed on the generated wallpaper.

[0036] In an optional manner, the wallpaper generating device further includes a display parameter adjustment module, which is used to:

[0037] The current driving period and the ambient light intensity are determined according to the scenario characteristics, and wallpaper display parameters are adjusted according to the current driving period and the ambient light intensity, wherein the wallpaper display parameters include at least one of brightness and contrast.

[0038] In an optional manner, the wallpaper generating device further includes a model optimization module, which is used to:

[0039] Collect user feedback information, update the loss function in the wallpaper generation model according to the user feedback information, and optimize the model parameters in the wallpaper generation model based on the updated loss function.

[0040] In an optional manner, the user feedback information includes rating information, like information and comment information;

[0041] The model optimization module is also used to:

[0042] The score difference information is determined by the score information, the score difference information is used as the loss item of the loss function, the like information is used as the plus item, and the negative comments of the comment information are used as the minus item, and the loss function in the wallpaper generation model is updated.

[0043] According to another aspect of an embodiment of the present application, there is provided a wallpaper generating device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0044] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations such as the above-mentioned wallpaper generation method.

[0045] According to another aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein at least one executable instruction is stored in the storage medium. When the executable instruction is executed on a wallpaper generating device / apparatus, the wallpaper generating device / apparatus performs the operation of the wallpaper generating method as described above.

[0046] The embodiment of the present application collects real-time data of the vehicle, extracts wallpaper feature data based on multiple real-time data, and fuses the wallpaper feature data through a shared vector space, so that real-time data of different modes form a consistent representation in the shared vector space, thereby improving the accuracy of real-time data fusion, and can accurately and automatically generate wallpapers that are adapted to the vehicle's current music data, vehicle driving data, and external environment data to meet the usage requirements of different scenarios.

[0047] Furthermore, the embodiment of the present application updates the loss function in the wallpaper generation model according to user feedback information, takes into account the user's actual feedback to update the model loss function and model parameters, and can generate wallpapers that better meet user expectations and usage preferences, thereby effectively improving the user experience; and updating the loss function can make the model training more focused on features that users care about, thereby improving the overall performance of the model and effectively improving the efficiency of wallpaper generation.

[0048] The above description is only an overview of the technical solution of the embodiment of the present application. In order to more clearly understand the technical means of the embodiment of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present application. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0050] Figure 1 A schematic diagram of the process of the wallpaper generation method provided in the embodiment of the present application is shown;

[0051] Figure 2 A schematic diagram of wallpaper generation model training provided in an embodiment of the present application is shown;

[0052] Figure 3 A schematic diagram of the structure of a wallpaper generating device provided in an embodiment of the present application is shown;

[0053] Figure 4 A schematic structural diagram of a wallpaper generating device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0054] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0055] Figure 1 FIG. 1 is a flowchart of a first embodiment of a wallpaper generation method of the present application, and the method is executed by a wallpaper generation device. Figure 1 As shown, the method comprises the following steps:

[0056] Step 110: Collecting real-time data of the vehicle, the real-time data including in-vehicle music data, vehicle driving data and external environment data;

[0057] In the embodiment of the present application, the in-car music data can be obtained by identifying the music played by the in-car music platform, and the music data includes audio data and corresponding text data. The vehicle driving data includes the current driving speed, acceleration and driving mode, etc., and the external environment data includes the real-time image taken by the external camera.

[0058] Step 120: input the real-time data into a pre-trained wallpaper generation model, extract wallpaper feature data based on the real-time data, fuse the wallpaper feature data through a shared vector space, and generate a corresponding wallpaper based on the fused wallpaper feature data.

[0059] In an embodiment of the present application, before the real-time data is input into a pre-trained wallpaper generation model, the real-time data may be preprocessed. The preprocessing may include: data cleaning, data screening and normalization. Data cleaning may remove or correct inconsistencies, errors and outliers in the data, thereby improving the accuracy and reliability of the data; data screening may help remove irrelevant or redundant data, so that the data set is more focused on the analysis goal and improves the availability of the data; normalization may ensure that all features are on the same scale, thereby avoiding disproportionate effects of certain features on the model results due to their large numerical range.

[0060] In an embodiment of the present application, the wallpaper generation model can be a large multimodal model built based on the Transformer architecture. The Transformer architecture can process long-term dependencies in sequence data. In the wallpaper generation model, the Transformer architecture can be extended to support data input in different modalities, so that the wallpaper generation model can process text, images, audio and other modal data.

[0061] The embodiment of the present application collects real-time data of the vehicle, extracts wallpaper feature data based on a variety of real-time data, and can automatically generate wallpapers that adapt to the vehicle's current music data, vehicle driving data, and external environment data to meet the usage requirements of different scenarios.

[0062] In an optional manner, step 120: fusing the wallpaper feature data through a shared vector space includes the following sub-steps:

[0063] Step 121: Mapping the wallpaper feature data into a shared vector space, and fusing the wallpaper feature data through a shared layer or a fully connected layer of the shared vector space.

[0064] In the embodiment of the present application, the wallpaper feature data of multiple modes can be feature extracted by their corresponding encoders to obtain a high-dimensional feature vector, i.e., wallpaper feature data. When the wallpaper feature data is mapped to a shared vector space, the wallpaper feature data can be normalized or standardized to ensure that the wallpaper feature data of different modes are on the same scale, and the convergence speed can be accelerated to improve the training efficiency and accuracy of the model.

[0065] In an embodiment of the present application, wallpaper feature data of different modalities may contain complementary information. After the wallpaper feature data are fused through a shared vector space, effective cross-modal retrieval, matching, and reasoning can be performed. For example, the features in one modality can be used to supplement the missing ambiguous information in another modality, so that the generated wallpaper meets the user's usage needs.

[0066] In the embodiment of the present application, the wallpaper feature data is mapped to a shared vector space, so that the model can understand and process the complex interactions between the wallpaper feature data. In addition, the embodiment of the present application uses contrastive learning and joint embedding methods to optimize the loss function, so that the feature data of different modalities are as close as possible in the same shared vector space, and the alignment between the modalities is achieved, so that different wallpaper feature data can be uniformly represented and integrated, which can reduce the consumption of computing resources and improve the efficiency of wallpaper generation.

[0067] See also Figure 2 In an optional manner, the training method of the wallpaper generation model may include:

[0068] S1. Collect audio samples and their corresponding music tags from a music database or online music platform, where the music tags include tags related to style, rhythm and melody, and collect text data corresponding to the audio samples, including lyrics and music descriptions; collect wallpaper samples, and classify wallpapers according to the wallpaper elements of each wallpaper sample; collect vehicle driving data through on-board sensors; and collect real-time landscape images around the vehicle through an external camera; wherein the audio samples are reference audio and the real-time landscape images are reference images;

[0069] S2. Extract features from audio data, segment and encode text data, scale and normalize wallpaper samples and real-time landscape images, and format vehicle driving data. Different multimodal data are encoded according to the multimodal compiler to extract corresponding features. For example, the audio encoder extracts audio features; the text encoder uses pre-trained language models such as BERT or GPT to encode text to obtain text features; the image encoder uses visual models such as ResNet and ViT to encode images to obtain image features; a special neural network structure is used as an encoder to extract features of vehicle driving data;

[0070] S3. By constructing positive samples and negative samples (, the encoder is optimized using a contrastive learning loss function (such as InfoNCE loss) so that related samples are closer in the vector space and irrelevant samples are farther away; the wallpaper feature data output by encoders of different modalities are mapped to a shared mapping space, the wallpaper feature data is fused through a shared layer and a fully connected layer, and the model is trained.

[0071] In an optional manner, the wallpaper feature data includes music features and scene features;

[0072] Step 120: generating a corresponding wallpaper based on the fused wallpaper feature data, including the following sub-steps:

[0073] Step 1201: Identify the music features of the in-car music data, and determine the first wallpaper element associated with the music features, where the first wallpaper element includes wallpaper patterns, wallpaper colors, and dynamic elements;

[0074] In the embodiment of the present application, the music features include:

[0075] Style characteristics: such as jazz, classical, pop, rock, folk, etc.;

[0076] Emotional characteristics: such as happiness, sadness, tranquility, excitement, etc.;

[0077] Instrument characteristics: such as piano, guitar, violin, saxophone, etc.

[0078] Theme characteristics: such as love, friendship, nature, history, etc.;

[0079] Characteristics of the times: such as the classical music period, the romantic music period, the modern music period, etc.;

[0080] Regional characteristics: such as European music, American music, Asian music, etc.;

[0081] Singer / band characteristics: such as Jay Chou, Mayday, G.E.M., etc.;

[0082] Language characteristics: such as Chinese, English, Japanese, etc.;

[0083] Emotional characteristics: such as love, friendship, happiness, etc.

[0084] Each music feature is associated with at least one wallpaper element, so that the generated wallpaper can change dynamically according to the music rhythm, thereby improving the user's interactive experience.

[0085] Step 1202: Determine a scenario feature by combining the vehicle driving data and the vehicle exterior environment data, and determine a second wallpaper element based on the scenario feature, wherein the second wallpaper element includes a scenario element;

[0086] In the embodiment of the present application, the scenario characteristics include driving style characteristics, driving period characteristics, and driving route characteristics, wherein the driving style characteristics include steady driving style, aggressive driving style, high-speed driving style, and slow driving style. The steady driving style is a relatively smooth driving process with little sudden acceleration or braking, while the aggressive driving style is a relatively intense driving process with frequent acceleration and braking.

[0087] The embodiment of the present application may also be combined with a map application to determine the current driving scenario, for example, identifying the current driving route based on the map application.

[0088] Step 1203: Combine the first wallpaper element and the second wallpaper element to generate a wallpaper.

[0089] The embodiment of the present application combines the first wallpaper element and the second wallpaper element to generate a wallpaper, which integrates the multimodal data of text data, image data and audio data in the current real-time data to generate a wallpaper associated with a specific scenario, which can effectively improve user usage needs.

[0090] In an optional manner, step S120: after generating the corresponding wallpaper based on the fused wallpaper feature data, further includes:

[0091] Step S130: Generate a text prompt based on the wallpaper feature data, and display the text prompt on the generated wallpaper.

[0092] In this embodiment, NLP technology can be used to generate text prompts based on wallpaper feature data, so that the text prompts are adapted to the generated wallpaper, and multi-language text generation and display can be achieved to meet the needs of different users.

[0093] In an optional manner, the wallpaper generation method further includes:

[0094] Step S131: determining the current driving period and the ambient light intensity according to the scenario characteristics, and adjusting wallpaper display parameters according to the current driving period and the ambient light intensity, wherein the wallpaper display parameters include at least one of brightness and contrast.

[0095] In the embodiment of the present application, the current driving period includes the daytime driving period, the nighttime driving period and the morning rush hour driving period, and the way to obtain the ambient light intensity includes: a photosensitive sensor, an image sensor or a computer vision algorithm, etc. The embodiment of the present application adjusts the wallpaper display parameters according to the current driving period and the ambient light intensity, which can avoid excessively high or low brightness distracting the driver's attention or affecting the line of sight when the wallpaper is displayed, thereby effectively improving driving safety.

[0096] In an optional manner, after step S120: generating a corresponding wallpaper based on the fused wallpaper feature data, the method further includes:

[0097] Step S132: Collect user feedback information, update the loss function in the wallpaper generation model according to the user feedback information, and optimize the model parameters in the wallpaper generation model based on the updated loss function.

[0098] In an embodiment of the present application, user feedback information can be collected through the platform, and the loss function in the wallpaper generation model can be updated according to the user feedback information. The model loss function and model parameters are updated by taking into account the actual feedback of the user, so that wallpapers that better meet the user's expectations and usage preferences can be generated, thereby effectively improving the user experience; and updating the loss function can make the training of the model more focused on the features that the user cares about, thereby improving the overall performance of the model and effectively improving the efficiency of wallpaper generation.

[0099] In an optional manner, the user feedback information includes rating information, like information and comment information;

[0100] The updating of the loss function in the wallpaper generation model according to the user feedback information comprises:

[0101] The score difference information is determined by the score information, the score difference information is used as the loss item of the loss function, the like information is used as the plus item, and the negative comments of the comment information are used as the minus item, and the loss function in the wallpaper generation model is updated.

[0102] In the embodiment of the present application, the score difference information can be determined based on the model prediction score and the user score, and the text data in the comment information can be extracted to analyze and determine whether it is a negative comment. The embodiment of the present application updates the loss function according to the comprehensive situation of the user feedback information to adjust the model parameters of the wallpaper generation model, for example, giving different weights to different generation algorithms.

[0103] The embodiment of the present application updates the loss function in real time to reflect the latest user feedback, so that the wallpaper generation model can quickly adapt to changes in user needs and accurately generate wallpapers that meet user needs.

[0104] Implementing the embodiments of the present application has the following beneficial effects:

[0105] The embodiment of the present application collects real-time data of the vehicle, extracts wallpaper feature data based on a variety of real-time data, and can automatically generate wallpapers that adapt to the vehicle's current music data, vehicle driving data, and external environment data to meet the usage requirements of different scenarios.

[0106] Furthermore, the embodiment of the present application updates the loss function in the wallpaper generation model according to user feedback information, takes into account the user's actual feedback to update the model loss function and model parameters, and can generate wallpapers that better meet user expectations and usage preferences, thereby effectively improving the user experience; and updating the loss function can make the model training more focused on features that users care about, thereby improving the overall performance of the model and effectively improving the efficiency of wallpaper generation.

[0107] Figure 3 FIG. 1 is a schematic diagram showing the structure of an embodiment of the wallpaper generating device of the present application. Figure 3 As shown, the device 300 includes: a real-time data acquisition module 310 and a wallpaper generation module 320.

[0108] The real-time data collection module 310 is used to collect real-time data of the vehicle, including in-vehicle music data, vehicle driving data and external environment data;

[0109] The wallpaper generation module 320 is used to input the real-time data into a pre-trained wallpaper generation model, extract wallpaper feature data based on the real-time data, fuse the wallpaper feature data through a shared vector space, and generate a corresponding wallpaper based on the fused wallpaper feature data.

[0110] In an optional manner, the wallpaper generation module 320 further includes:

[0111] The feature data fusion unit is used to map the wallpaper feature data into a shared vector space, and fuse the wallpaper feature data through a shared layer or a fully connected layer of the shared vector space.

[0112] In an optional manner, the wallpaper feature data includes music features and scene features;

[0113] The wallpaper generation module is also used for:

[0114] Identify the music features of the in-car music data, and determine a first wallpaper element associated with the music features, wherein the first wallpaper element includes a wallpaper pattern, a wallpaper color, and a dynamic element;

[0115] Determine a scenario feature by combining the vehicle driving data and the vehicle exterior environment data, and determine a second wallpaper element based on the scenario feature, wherein the second wallpaper element includes a scenario element;

[0116] The first wallpaper element and the second wallpaper element are combined to generate a wallpaper.

[0117] In an optional manner, the wallpaper generating device further includes a prompt display module, which is used to:

[0118] A text prompt is generated based on the wallpaper feature data, and the text prompt is displayed on the generated wallpaper.

[0119] In an optional manner, the wallpaper generating device further includes a display parameter adjustment module, which is used to:

[0120] The current driving period and the ambient light intensity are determined according to the scenario characteristics, and wallpaper display parameters are adjusted according to the current driving period and the ambient light intensity, wherein the wallpaper display parameters include at least one of brightness and contrast.

[0121] In an optional manner, the wallpaper generating device further includes a model optimization module, which is used to:

[0122] Collect user feedback information, update the loss function in the wallpaper generation model according to the user feedback information, and optimize the model parameters in the wallpaper generation model based on the updated loss function.

[0123] In an optional manner, the user feedback information includes rating information, like information and comment information;

[0124] The model optimization module is also used to:

[0125] The score difference information is determined by the score information, the score difference information is used as the loss item of the loss function, the like information is used as the plus item, and the negative comments of the comment information are used as the minus item, and the loss function in the wallpaper generation model is updated.

[0126] The embodiment of the present application collects real-time data of the vehicle, extracts wallpaper feature data based on a variety of real-time data, and can automatically generate wallpapers that adapt to the vehicle's current music data, vehicle driving data, and external environment data to meet the usage requirements of different scenarios.

[0127] Furthermore, the embodiment of the present application updates the loss function in the wallpaper generation model according to user feedback information, takes into account the user's actual feedback to update the model loss function and model parameters, and can generate wallpapers that better meet user expectations and usage preferences, thereby effectively improving the user experience; and updating the loss function can make the model training more focused on features that users care about, thereby improving the overall performance of the model and effectively improving the efficiency of wallpaper generation.

[0128] Figure 4 A schematic structural diagram of an embodiment of a wallpaper generating device of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the wallpaper generating device.

[0129] like Figure 4 As shown, the wallpaper generating device may include: a processor (processor) 402 , a communication interface (Communications Interface) 404 , a memory (memory) 406 , and a communication bus 408 .

[0130] The processor 402, the communication interface 404, and the memory 406 communicate with each other via the communication bus 408. The communication interface 404 is used to communicate with other devices such as a client or other server network elements. The processor 402 is used to execute the program 410, which can specifically execute the relevant steps in the above-mentioned embodiment of the wallpaper generation method.

[0131] Specifically, the program 410 may include program code including computer executable instructions.

[0132] The processor 402 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the wallpaper generating device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0133] The memory 406 is used to store the program 410. The memory 406 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0134] The program 410 may be specifically called by the processor 402 to enable the wallpaper generating device to perform the following operations:

[0135] Collecting real-time data of the vehicle, the real-time data including in-vehicle music data, vehicle driving data and external environment data;

[0136] The real-time data is input into a pre-trained wallpaper generation model, wallpaper feature data is extracted based on the real-time data, the wallpaper feature data is fused through a shared vector space, and the corresponding wallpaper is generated based on the fused wallpaper feature data.

[0137] In an optional manner, the program 410 is called by the processor 402 to enable the wallpaper generating device to perform the following operations:

[0138] The wallpaper feature data is mapped into a shared vector space, and the wallpaper feature data is fused through a shared layer or a fully connected layer of the shared vector space.

[0139] In an optional manner, the wallpaper feature data includes music features and scene features, and the program 410 is called by the processor 402 to enable the wallpaper generation device to perform the following operations:

[0140] Identify the music features of the in-car music data, and determine a first wallpaper element associated with the music features, wherein the first wallpaper element includes a wallpaper pattern, a wallpaper color, and a dynamic element;

[0141] Determine a scenario feature by combining the vehicle driving data and the vehicle exterior environment data, and determine a second wallpaper element based on the scenario feature, wherein the second wallpaper element includes a scenario element;

[0142] The first wallpaper element and the second wallpaper element are combined to generate a wallpaper.

[0143] In an optional manner, the program 410 is called by the processor 402 to enable the wallpaper generating device to perform the following operations:

[0144] A text prompt is generated based on the wallpaper feature data, and the text prompt is displayed on the generated wallpaper.

[0145] In an optional manner, the program 410 is called by the processor 402 to enable the wallpaper generating device to perform the following operations:

[0146] The current driving period and the ambient light intensity are determined according to the scenario characteristics, and wallpaper display parameters are adjusted according to the current driving period and the ambient light intensity, wherein the wallpaper display parameters include at least one of brightness and contrast.

[0147] In an optional manner, the program 410 is called by the processor 402 to enable the wallpaper generating device to perform the following operations:

[0148] Collect user feedback information, update the loss function in the wallpaper generation model according to the user feedback information, and optimize the model parameters in the wallpaper generation model based on the updated loss function.

[0149] In an optional manner, the user feedback information includes rating information, like information and comment information, and the program 410 is called by the processor 402 to enable the wallpaper generation device to perform the following operations: determine the rating difference information using the rating information, use the rating difference information as the loss item of the loss function, use the like information as the plus item, and use the negative comments of the comment information as the minus item, and update the loss function in the wallpaper generation model.

[0150] The embodiment of the present application collects real-time data of the vehicle, extracts wallpaper feature data based on a variety of real-time data, and can automatically generate wallpapers that adapt to the vehicle's current music data, vehicle driving data, and external environment data to meet the usage requirements of different scenarios.

[0151] Furthermore, the embodiment of the present application updates the loss function in the wallpaper generation model according to user feedback information, takes into account the user's actual feedback to update the model loss function and model parameters, and can generate wallpapers that better meet user expectations and usage preferences, thereby effectively improving the user experience; and updating the loss function can make the model training more focused on features that users care about, thereby improving the overall performance of the model and effectively improving the efficiency of wallpaper generation.

[0152] An embodiment of the present application provides a computer-readable storage medium, which stores at least one executable instruction. When the executable instruction is executed on a wallpaper generating device / apparatus, the wallpaper generating device / apparatus executes the wallpaper generating method in any of the above method embodiments.

[0153] The executable instructions may be specifically used to enable the wallpaper generating device / apparatus to perform the following operations:

[0154] Collecting real-time data of the vehicle, the real-time data including in-vehicle music data, vehicle driving data and external environment data;

[0155] The real-time data is input into a pre-trained wallpaper generation model, wallpaper feature data is extracted based on the real-time data, the wallpaper feature data is fused through a shared vector space, and the corresponding wallpaper is generated based on the fused wallpaper feature data.

[0156] In an optional manner, the executable instruction enables the wallpaper generating device / apparatus to perform the following operations:

[0157] The wallpaper feature data is mapped into a shared vector space, and the wallpaper feature data is fused through a shared layer or a fully connected layer of the shared vector space.

[0158] In an optional manner, the executable instruction enables the wallpaper generating device / apparatus to perform the following operations:

[0159] Identify the music features of the in-car music data, and determine a first wallpaper element associated with the music features, wherein the first wallpaper element includes a wallpaper pattern, a wallpaper color, and a dynamic element;

[0160] Determine a scenario feature by combining the vehicle driving data and the vehicle exterior environment data, and determine a second wallpaper element based on the scenario feature, wherein the second wallpaper element includes a scenario element;

[0161] The first wallpaper element and the second wallpaper element are combined to generate a wallpaper.

[0162] In an optional manner, the executable instruction enables the wallpaper generating device / apparatus to perform the following operations:

[0163] A text prompt is generated based on the wallpaper feature data, and the text prompt is displayed on the generated wallpaper.

[0164] In an optional manner, the executable instruction enables the wallpaper generating device / apparatus to perform the following operations:

[0165] The current driving period and the ambient light intensity are determined according to the scenario characteristics, and wallpaper display parameters are adjusted according to the current driving period and the ambient light intensity, wherein the wallpaper display parameters include at least one of brightness and contrast.

[0166] In an optional manner, the executable instruction enables the wallpaper generating device / apparatus to perform the following operations:

[0167] Collect user feedback information, update the loss function in the wallpaper generation model according to the user feedback information, and optimize the model parameters in the wallpaper generation model based on the updated loss function.

[0168] In an optional manner, the user feedback information includes rating information, like information and comment information, and the executable instructions enable the wallpaper generating device / apparatus to perform the following operations:

[0169] The updating of the loss function in the wallpaper generation model according to the user feedback information comprises:

[0170] The score difference information is determined by the score information, the score difference information is used as the loss item of the loss function, the like information is used as the plus item, and the negative comments of the comment information are used as the minus item, and the loss function in the wallpaper generation model is updated.

[0171] The embodiment of the present application collects real-time data of the vehicle, extracts wallpaper feature data based on a variety of real-time data, and can automatically generate wallpapers that adapt to the vehicle's current music data, vehicle driving data, and external environment data to meet the usage requirements of different scenarios.

[0172] Furthermore, the embodiment of the present application updates the loss function in the wallpaper generation model according to user feedback information, takes into account the user's actual feedback to update the model loss function and model parameters, and can generate wallpapers that better meet user expectations and usage preferences, thereby effectively improving the user experience; and updating the loss function can make the model training more focused on features that users care about, thereby improving the overall performance of the model and effectively improving the efficiency of wallpaper generation.

[0173] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system or other device. In addition, the embodiments of the present application are not directed to any particular programming language.

[0174] In the specification provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. Similarly, in order to simplify the application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. Wherein, the claims that follow the specific embodiment are thus clearly incorporated into the specific embodiment, wherein each claim itself is used as a separate embodiment of the application.

[0175] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive.

[0176] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be constructed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A wallpaper generation method, characterized in that: include: Collecting real-time data of the vehicle, the real-time data including in-vehicle music data, vehicle driving data and external environment data; The real-time data is input into a pre-trained wallpaper generation model, wallpaper feature data is extracted based on the real-time data, the wallpaper feature data is fused through a shared vector space, and the corresponding wallpaper is generated based on the fused wallpaper feature data.

2. The wallpaper generation method according to claim 1, characterized in that: The wallpaper feature data is merged through a shared vector space, including: The wallpaper feature data is mapped into a shared vector space, and the wallpaper feature data is fused through a shared layer or a fully connected layer of the shared vector space.

3. The wallpaper generation method according to claim 1 or 2, characterized in that: The wallpaper feature data includes music features and scene features; The generating corresponding wallpaper based on the fused wallpaper feature data includes: Identify the music features of the in-car music data, and determine a first wallpaper element associated with the music features, wherein the first wallpaper element includes a wallpaper pattern, a wallpaper color, and a dynamic element; Determine a scenario feature by combining the vehicle driving data and the vehicle exterior environment data, and determine a second wallpaper element based on the scenario feature, wherein the second wallpaper element includes a scenario element; The first wallpaper element and the second wallpaper element are combined to generate a wallpaper.

4. The wallpaper generation method according to claim 1, characterized in that: After generating the corresponding wallpaper based on the fused wallpaper feature data, the method further includes: A text prompt is generated based on the wallpaper feature data, and the text prompt is displayed on the generated wallpaper.

5. The wallpaper generation method according to claim 1, characterized in that: Also includes: The current driving period and the ambient light intensity are determined according to the scenario characteristics, and wallpaper display parameters are adjusted according to the current driving period and the ambient light intensity, wherein the wallpaper display parameters include at least one of brightness and contrast.

6. The wallpaper generation method according to claim 1, characterized in that: After generating the corresponding wallpaper based on the fused wallpaper feature data, the method further includes: Collect user feedback information, update the loss function in the wallpaper generation model according to the user feedback information, and optimize the model parameters in the wallpaper generation model based on the updated loss function.

7. The wallpaper generation method according to claim 6, characterized in that: The user feedback information includes rating information, like information and comment information; The updating of the loss function in the wallpaper generation model according to the user feedback information comprises: The score difference information is determined by the score information, the score difference information is used as the loss item of the loss function, the like information is used as the plus item, and the negative comments of the comment information are used as the minus item, and the loss function in the wallpaper generation model is updated.

8. A wallpaper generating device, characterized in that: include: A real-time data acquisition module is used to collect real-time data of the vehicle, wherein the real-time data includes in-vehicle music data, vehicle driving data and external environment data; The wallpaper generation module is used to input the real-time data into a pre-trained wallpaper generation model, extract wallpaper feature data based on the real-time data, fuse the wallpaper feature data through a shared vector space, and generate a corresponding wallpaper based on the fused wallpaper feature data.

9. A wallpaper generating device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the operation of the wallpaper generation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one executable instruction. When the executable instruction is executed on the wallpaper generating device / apparatus, the wallpaper generating device / apparatus performs the operation of the wallpaper generating method according to any one of claims 1 to 7.

Images