Device application skin changing method and device based on large model, terminal and medium

Abstract

The invention provides an equipment application skin changing method and device based on a large model, a terminal and a medium. The method comprises the following steps: identifying a received skin changing instruction issued by a user through a pre-trained large model to extract skin changing demand information; self-defined skin resources of the skin to be replaced in the skin replacement demand information are constructed through the large model according to the skin replacement demand information, and the resources are returned to the application to be subjected to skin replacement so that the application to be subjected to skin replacement can store the resources to a local database; performing resource matching in a local database according to the identification information corresponding to the to-be-changed skin to obtain a target skin resource corresponding to the identification information; and replacing a corresponding skin resource in a currently used skin package in the application to be subjected to skin replacement by using the target skin resource so as to realize skin replacement when the application to be subjected to skin replacement is reloaded. According to the method, the skin changing instruction is identified through the large model, the instruction is accurately understood, and the user-defined skin resources are constructed, so that deep customization of autonomous skin changing is realized, and the experience feeling of a user is improved.

Description

Technical Field

[0001] The present invention relates to the field of skinning technology, and in particular to a device application skinning method, device, terminal and medium based on a large model. Background Art

[0002] With the rapid development of science and technology, smart TVs have been popularized in every household, and users have higher and higher requirements for the aesthetics of the interface. Many applications provide a skin-changing function, allowing users to choose their favorite UI color scheme. However, the current skin-changing function often selects a set of skins from multiple skin packages defined by developers, and cannot be deeply customized. That is, the current skin-changing function can only be selected from multiple skin packages designed by developers, and once switched, all UIs will change color; therefore, the user's choices are not diverse enough, and the desired effect cannot be obtained through customization, and it is easy to get bored after using it for a period of time. In addition, although the current voice recognition technology and image recognition technology can be guaranteed in terms of recognition accuracy, they cannot understand the user's intentions, making it impossible to achieve the skin-changing effect that the user wants.

[0003] In view of this, providing a solution to the above-mentioned technical problems has become an urgent concern for those skilled in the art. Summary of the invention

[0004] The technical problem to be solved by the present invention is that, in response to the above-mentioned defects of the prior art, a device application skinning method, device, terminal and medium based on a large model are provided, which can accurately understand the user's complex skinning instructions, enable the user to deeply customize the UI, thereby realizing deep customization of autonomous skinning and enhancing the user experience.

[0005] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0006] A device application skinning method based on a large model, wherein the method comprises:

[0007] Receive a skin-changing instruction issued by a device user, and identify the skin-changing instruction through a pre-trained large model to extract corresponding skin-changing requirement information;

[0008] Constructing the custom skin resource of the skin to be changed in the skin change demand information according to the skin change demand information through the large model, and returning the custom skin resource to the application to be skinned so that the application to be skinned can save the custom skin resource to the local database;

[0009] Perform resource matching in the local database according to the identification information corresponding to the skin to be replaced to match the target skin resource corresponding to the identification information, and read the target skin resource from the local database;

[0010] The target skin resource is used to replace the corresponding skin resource in the skin package currently used in the application to be skinned, so as to complete the corresponding skinning operation when the application to be skinned is reloaded.

[0011] In one implementation, before the receiving device user issues a skin-changing instruction, the method further includes:

[0012] The current skin changing mode is switched to a target skin changing mode selected by a device user; wherein the target skin changing mode is a voice skin changing mode or an image skin changing mode.

[0013] In one implementation, the receiving device user sends a skin changing instruction, and identifies the skin changing instruction by using a pre-trained large model to extract corresponding skin changing requirement information, including:

[0014] In the voice skinning mode, a skinning voice instruction issued by a device user is received through a pre-built voice receiving interface;

[0015] Recognize the skin-changing voice instruction by using a pre-trained large model to determine first semantic information corresponding to the skin-changing voice instruction, and determine whether the skin-changing voice instruction is an instruction related to skin-changing according to the first semantic information;

[0016] When the skin-changing voice instruction is an instruction related to skin-changing, the position information and color information of the skin to be changed are extracted from the first semantic information.

[0017] In one implementation, before extracting the position information and color information of the skin to be replaced from the first semantic information, the method further includes:

[0018] Determining whether the first semantic information lacks skinning information required for skinning;

[0019] When the skin-changing information required for skin-changing is missing in the first semantic information, generating corresponding prompt information according to the missing skin-changing information;

[0020] A new skin-changing voice instruction that continues to be sent by the receiving device user according to the prompt information, and the new skin-changing voice instruction is continuously recognized by the large model to determine the second semantic information corresponding to the new skin-changing voice instruction;

[0021] The step of extracting the position information and color information of the skin to be replaced from the first semantic information includes:

[0022] The position information and color information of the skin to be replaced are extracted from the first semantic information and the second semantic information.

[0023] In one implementation, the step of constructing the custom skin resource to be replaced in the skin replacement requirement information by using the large model according to the skin replacement requirement information includes:

[0024] The custom skin resource of the skin to be replaced is constructed through the large model according to the position information and the color information of the skin to be replaced.

[0025] In one implementation, the receiving device user sends a skin changing instruction, and identifies the skin changing instruction by using a pre-trained large model to extract corresponding skin changing requirement information, including:

[0026] In the image skinning mode, a picture input by a device user and pre-specified location information of the skin to be replaced are received through a pre-built image receiving interface;

[0027] The image is recognized by a pre-trained large model to extract corresponding image information.

[0028] In one implementation, the step of constructing the custom skin resource to be replaced in the skin replacement requirement information by using the large model according to the skin replacement requirement information includes:

[0029] The custom skin resource of the skin to be replaced is constructed through the large model according to the picture information and the position information of the skin to be replaced.

[0030] The present invention also discloses a device for skinning equipment based on a large model, wherein the device comprises:

[0031] A receiving module, used for receiving a skin-changing instruction issued by a device user;

[0032] A large model recognition module, used to recognize the skin-changing instruction through a pre-trained large model to extract corresponding skin-changing requirement information;

[0033] A skin resource construction module, used to construct the custom skin resource of the skin to be changed in the skin change demand information through the large model according to the skin change demand information, and return the custom skin resource to the application to be skinned so that the application to be skinned can save the custom skin resource to the local database;

[0034] A resource matching module, configured to perform resource matching in the local database according to the identification information corresponding to the skin to be replaced, so as to match a target skin resource corresponding to the identification information, and read the target skin resource from the local database;

[0035] The resource replacement module is used to replace the corresponding skin resource in the skin package currently used in the application to be skinned with the target skin resource, so as to complete the corresponding skinning operation when the application to be skinned is reloaded.

[0036] The present invention also discloses a terminal, which includes: a memory, a processor, and a large model-based device application skinning program stored in the memory and executable on the processor. When the large model-based device application skinning program is executed by the processor, the steps of the large model-based device application skinning method described above are implemented.

[0037] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the device application skinning method based on a large model as described above.

[0038] The present invention provides a device application skinning method, device, terminal and medium based on a large model. The device application skinning method based on a large model includes: receiving a skinning instruction issued by a device user, and identifying the skinning instruction through a pre-trained large model to extract the corresponding skinning requirement information; constructing a custom skin resource of the skin to be changed in the skinning requirement information according to the skinning requirement information through the large model, and returning the custom skin resource to the application to be skinned so that the application to be skinned saves the custom skin resource to a local database; performing resource matching in the local database according to the identification information corresponding to the skin to be changed to match the target skin resource corresponding to the identification information, and reading the target skin resource from the local database; using the target skin resource to replace the corresponding skin resource in the skin package currently used in the application to be skinned, so as to complete the corresponding skinning operation when the application to be skinned is reloaded. It can be seen that the present invention recognizes the complex skinning instructions issued by the device user through the large model, and can accurately understand the user's complex skinning instructions, and then constructs the corresponding custom skin resources, so that the user can deeply customize the UI, thereby realizing the deep customization of autonomous skinning and improving the user's experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of a preferred embodiment of the device application skinning method based on a large model in the present invention;

[0040] Figure 2 It is a flow chart of a specific large model-based device application skinning method disclosed in the present invention;

[0041] Figure 3 It is a flow chart of a specific large model-based device application skinning method disclosed in the present invention;

[0042] Figure 4 It is a flow chart of a specific large model-based device application skinning method disclosed in the present invention;

[0043] Figure 5 It is a functional principle block diagram of a preferred embodiment of a device application skinning device based on a large model in the present invention;

[0044] Figure 6 It is a schematic diagram of a large model-based device application skinning architecture disclosed in the present invention;

[0045] Figure 7 It is a schematic diagram of a skin replacement process disclosed in the present invention;

[0046] Figure 8 It is a schematic diagram of a large-model speech or image recognition process disclosed in the present invention;

[0047] Fig. 9 It is a functional principle block diagram of a preferred embodiment of the terminal in the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] See also Figure 1 , Figure 1 Flowchart of the device application skinning method based on the large model in the present invention. Figure 1 As shown, the device application skinning method based on a large model according to an embodiment of the present invention includes:

[0050] Step S11, receiving a skin-changing instruction issued by a device user, and identifying the skin-changing instruction through a pre-trained large model to extract corresponding skin-changing requirement information.

[0051] In this embodiment, a skin changing instruction issued by a device user is received, the skin changing instruction is passed into a pre-trained large model, and then the skin changing instruction is recognized by the pre-trained large model to extract corresponding skin changing requirement information, such as the location where the skin needs to be changed and the target color of the skin change.

[0052] It should be pointed out that when inputting skin-changing voice commands, the user's description may not be an accurate color or color value, but an abstract expression, such as "festive color" or "cold color", which may not be accurately understood by the general large model. Therefore, when training the large model, it is necessary to input relevant training data to establish the correspondence between color descriptions and specific colors. The relevant concepts of graphic design color matching principles are processed into multimodal training data that can be understood by the large model. The large model trained in this way can understand user needs and provide appropriate and beautiful colors. That is, the large model has been pre-trained and has a strong recognition ability for abstract text descriptions of colors and color patterns in pictures.

[0053] Step S12: construct the custom skin resource of the to-be-skinned application in the skinning requirement information through the large model according to the skinning requirement information, and return the custom skin resource to the to-be-skinned application so that the to-be-skinned application saves the custom skin resource to the local database.

[0054] In this embodiment, after the skin change instruction is identified by the pre-trained large model to extract the corresponding skin change requirement information, the large model is also required to construct the custom skin resource to be changed in the skin change requirement information according to the skin change requirement information. It can be understood that the required custom skin resource is generated by the large model, and the internal resources include not only color resources but also image resources. In this embodiment, no specific skin package is used, and the internal resources, except for the default resources, are basically generated by the large model.

[0055] In this embodiment, after the required custom skin resources are constructed through the large model, the custom skin resources are returned to the application to be skinned so that the application to be skinned can save the custom skin resources to the local database, and the application to be skinned saves the custom skin resources of the skin to be changed to the local database by replacing the old skin resources of the skin to be changed with the custom skin resources. That is to say, if the local database already has skin resources corresponding to the skin to be changed, the custom skin resources are used to replace the skin resources corresponding to the skin to be changed in the local database, thereby saving the custom skin resources to the local database, so that there are skin resources uniquely corresponding to the skin to be changed in the local database.

[0056] Step S13: performing resource matching in the local database according to the identification information corresponding to the skin to be replaced to match a target skin resource corresponding to the identification information, and reading the target skin resource from the local database.

[0057] In this embodiment, the user's skin-changing instruction is identified through a large model to obtain the user's skin-changing requirement information, that is, it is clear which skin of the application interface the user wants to change, that is, the skin to be changed can be known from the skin-changing requirement information, and each part of the skin on the application interface has been uniquely identified and classified in advance, so in the process of changing the skin to be changed, according to the identification information corresponding to the skin to be changed, resource matching can be performed in the local database to match the target skin resource corresponding to the identification information, and then the target skin resource is read from the local database. For example, according to the identification number and / or identification name corresponding to the skin to be changed, resource matching can be performed in the local database to match the target skin resource corresponding to the identification number and / or the identification name.

[0058] Step S14: Use the target skin resource to replace the corresponding skin resource in the skin package currently used in the application to be skinned, so as to complete the corresponding skinning operation when the application to be skinned is reloaded.

[0059] In this embodiment, after matching and reading the target skin resource to be replaced from the local database, the target skin resource can be used to replace the corresponding skin resource in the skin package currently used in the application to be replaced, so as to complete the corresponding skin replacement operation when the application to be replaced is reloaded, thereby realizing autonomous skin replacement.

[0060] It can be seen that in the embodiment of the present invention, the complex skin changing instructions issued by the device user are recognized through the large model, and the user's complex skin changing instructions can be accurately understood, and then the corresponding custom skin resources are constructed, so that the user can deeply customize the UI, thereby realizing deep customization of autonomous skin changing and enhancing the user experience.

[0061] See also Figure 2 As shown, the embodiment of the present invention discloses a specific device application skinning method based on a large model. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution.

[0062] Step S21, switching the current skin changing mode to a target skin changing mode selected by the device user; wherein the target skin changing mode is a voice skin changing mode or an image skin changing mode.

[0063] In this embodiment, in skin changing, the device user can change the skin by inputting voice or by inputting images, and each skin changing mode has its corresponding skin changing operation.

[0064] Step S22: In the voice skin-changing mode, a skin-changing voice instruction issued by a device user is received through a pre-built voice receiving interface.

[0065] In this embodiment, the user selects the voice skin-changing mode and inputs the skin-changing instruction through voice. For example, the user sends "change the skin color of the button to blue". That is, in the voice skin-changing mode, the skin-changing voice instruction sent by the device user is received through the pre-built voice receiving interface.

[0066] Step S23: Recognize the skin-changing voice instruction through a pre-trained large model to determine the first semantic information corresponding to the skin-changing voice instruction, and determine whether the skin-changing voice instruction is an instruction related to skin-changing according to the first semantic information.

[0067] In this embodiment, the skin-changing voice command input by the user is recognized through a pre-trained large model to determine the first semantic information corresponding to the skin-changing voice command, and then it is determined whether the skin-changing voice command is an instruction related to skin changing based on the first semantic information. When it is determined that the skin-changing voice command is not an instruction related to skin changing, it is directly prohibited to respond to the voice command.

[0068] Step S24: When the skin-changing voice instruction is an instruction related to skin-changing, the position information and color information of the skin to be changed are extracted from the first semantic information.

[0069] In this embodiment, when it is determined that the skin-changing voice instruction is an instruction related to skin-changing, the position information and color information of the skin to be changed are extracted from the first semantic information. For example, the position information is a button, and the color information is blue.

[0070] Step S25: constructing a custom skin resource of the skin to be replaced according to the position information and the color information of the skin to be replaced by using the large model.

[0071] In this embodiment, after the large model recognizes the skin-changing voice command issued by the user and extracts the location information and color information of the skin to be changed, the large model constructs the custom skin resource of the skin to be changed according to the location information and color information of the skin to be changed, such as constructing the blue skin resource corresponding to the button.

[0072] Step S26: Return the custom skin resource to the application to be skinned so that the application to be skinned can save the custom skin resource to a local database.

[0073] Step S27: performing resource matching in the local database according to the identification information corresponding to the skin to be replaced to match the target skin resource corresponding to the identification information, and reading the target skin resource from the local database.

[0074] Step S28: Use the target skin resource to replace the corresponding skin resource in the skin package currently used in the application to be skinned, so as to complete the corresponding skinning operation when the application to be skinned is reloaded.

[0075] For the specific contents of the above steps S26 to S28, reference may be made to the corresponding contents disclosed in the above embodiments, which will not be repeated here.

[0076] It can be seen that in the embodiment of the present invention, the complex skin-changing voice command issued by the device user is recognized through the large model, and the complex skin-changing voice command of the user can be accurately understood, and then the corresponding custom skin resources are constructed, so that the user can deeply customize the UI, thereby realizing the deep customization of self-skinning and improving the user experience. That is, through voice skinning, the user can customize the desired skin resources in a simple and fast way, and can further modify the original skin package, so that the user can obtain his own unique UI interface.

[0077] See also Figure 3 As shown, the embodiment of the present invention discloses a specific device application skinning method based on a large model. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution.

[0078] Step S31, switching the current skin changing mode to a target skin changing mode selected by the device user; wherein the target skin changing mode is a voice skin changing mode or an image skin changing mode.

[0079] Step S32: In the voice skin-changing mode, a skin-changing voice instruction issued by a device user is received through a pre-built voice receiving interface.

[0080] Step S33: Recognize the skin-changing voice instruction through a pre-trained large model to determine the first semantic information corresponding to the skin-changing voice instruction, and determine whether the skin-changing voice instruction is an instruction related to skin-changing according to the first semantic information.

[0081] Step S34: when the skin-changing voice instruction is an instruction related to skin-changing, determine whether the skin-changing information required for skin-changing is missing in the first semantic information.

[0082] In the present embodiment, when it is determined that the skin-changing voice instruction is an instruction related to skin-changing, it can be further determined whether the skin-changing information required for skin-changing is missing in the first semantic information. When the skin-changing information required for skin-changing is not missing in the semantic information, the skin-changing requirement information of the device user, that is, the location information and color information of the skin to be changed, can be directly extracted from the first semantic information. Then, the step of constructing the custom skin resource of the skin to be changed according to the location information and the color information of the skin to be changed through the large model and its subsequent steps are executed.

[0083] Step S35: When the skin-changing information required for skin-changing is missing in the first semantic information, corresponding prompt information is generated according to the missing skin-changing information.

[0084] In the present embodiment, when it is determined that the skin-changing voice instruction is an instruction related to skin-changing, it is further determined whether the skin-changing information required for skin-changing is missing in the first semantic information. When the skin-changing information required for skin-changing is missing in the first semantic information, corresponding prompt information is generated according to the missing skin-changing information, so that the device user can continue to issue new skin-changing voice instructions according to the prompt information, thereby supplementing the missing skin-changing information.

[0085] Step S36: receiving a new skin-changing voice instruction that the user of the receiving device continues to send according to the prompt information, and continuing to recognize the new skin-changing voice instruction through the large model to determine the second semantic information corresponding to the new skin-changing voice instruction.

[0086] In this embodiment, the device user continues to issue a new skin-changing voice instruction according to the prompt information, receives the new skin-changing voice instruction, and then continues to recognize the new skin-changing voice instruction through the large model to determine the second semantic information corresponding to the new skin-changing voice instruction.

[0087] Step S37: extracting the position information and color information of the skin to be replaced from the first semantic information and the second semantic information.

[0088] In this embodiment, after the user completes the missing skin-changing information, the position information and color information of the skin to be changed can be extracted from the first semantic information and the second semantic information.

[0089] Step S38: constructing a custom skin resource of the skin to be replaced according to the position information and the color information of the skin to be replaced by using the large model.

[0090] Step S39: Return the custom skin resource to the application to be skinned so that the application to be skinned can save the custom skin resource to a local database.

[0091] Step S310: performing resource matching in the local database according to the identification information corresponding to the skin to be replaced to match a target skin resource corresponding to the identification information, and reading the target skin resource from the local database.

[0092] Step S311: Use the target skin resource to replace the corresponding skin resource in the skin package currently used in the application to be skinned, so as to complete the corresponding skinning operation when the application to be skinned is reloaded.

[0093] For the specific contents of the above steps S31 to S34 and steps S38 to S311, reference may be made to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0094] It can be seen that in the embodiment of the present invention, the complex skin-changing voice command issued by the device user is recognized through the large model, and the complex skin-changing voice command of the user can be accurately understood, and then the corresponding custom skin resources are constructed, so that the user can deeply customize the UI, thereby realizing the deep customization of self-skinning and improving the user experience. That is, through voice skinning, the user can customize the desired skin resources in a simple and fast way, and can further modify the original skin package, so that the user can obtain his own unique UI interface.

[0095] See also Figure 4 As shown, the embodiment of the present invention discloses a specific device application skinning method based on a large model. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution.

[0096] Step S41, switching the current skin changing mode to a target skin changing mode selected by the device user; wherein the target skin changing mode is a voice skin changing mode or an image skin changing mode.

[0097] Step S42: In the image skinning mode, the image input by the device user and the pre-specified position information of the skin to be changed are received through the pre-built image receiving interface.

[0098] In this embodiment, the user selects the image skinning mode, inputs a picture, and specifies the location information of the skin to be replaced, that is, the picture input by the device user and the pre-specified location information of the skin to be replaced are received through the pre-built image receiving interface.

[0099] Step S43: Recognize the image using a pre-trained large model to extract corresponding image information.

[0100] In this embodiment, the image input by the user is directly recognized by a pre-trained large model, so as to extract the corresponding image information.

[0101] Step S44: constructing a custom skin resource of the skin to be replaced according to the image information and the position information of the skin to be replaced by using the large model.

[0102] In this embodiment, after the image is recognized and the corresponding image information is extracted through the large model, the custom skin resource of the skin to be replaced can be constructed through the large model according to the image information and the location information of the skin to be replaced specified by the user.

[0103] Step S45: Return the custom skin resource to the application to be skinned so that the application to be skinned can save the custom skin resource to a local database.

[0104] Step S46: performing resource matching in the local database according to the identification information corresponding to the skin to be replaced to match the target skin resource corresponding to the identification information, and reading the target skin resource from the local database.

[0105] Step S47: Use the target skin resource to replace the corresponding skin resource in the skin package currently used in the application to be skinned, so as to complete the corresponding skinning operation when the application to be skinned is reloaded.

[0106] For the specific contents of the above-mentioned step S41 and steps S45 to S47, reference may be made to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0107] It can be seen that in the embodiment of the present invention, the picture input by the device user is recognized through a large model, and the image information of the picture can be accurately analyzed, and then the corresponding custom skin resource is constructed according to the location information of the skin to be changed specified by the user and the image information, so that the user can deeply customize the UI, thereby realizing deep customization of autonomous skin changing and improving the user experience. In addition, by uploading pictures to change skins, it can make up for the situation where voice skin changing cannot be used when the user finds it difficult to express the skin changing needs in words, so that the application can efficiently understand the user's complex skin changing needs, thereby increasing the fun and playability of the application.

[0108] In one embodiment, if Figure 5 As shown, based on the above-mentioned device application skinning method based on a large model, the present invention also provides a device application skinning device based on a large model, including:

[0109] The receiving module 11 is used to receive a skin-changing instruction sent by a device user;

[0110] A large model recognition module 12 is used to recognize the skin change instruction through a pre-trained large model to extract corresponding skin change requirement information;

[0111] The skin resource construction module 13 is used to construct the custom skin resource of the skin to be changed in the skin change demand information according to the skin change demand information through the large model, and return the custom skin resource to the application to be skinned so that the application to be skinned can save the custom skin resource to the local database;

[0112] A resource matching module 14, configured to perform resource matching in the local database according to the identification information corresponding to the skin to be replaced, so as to match a target skin resource corresponding to the identification information, and read the target skin resource from the local database;

[0113] The resource replacement module 15 is used to replace the corresponding skin resource in the skin package currently used in the application to be skinned with the target skin resource, so as to complete the corresponding skinning operation when the application to be skinned is reloaded.

[0114] It should be pointed out that any Android application can import aar files, and through the technical solution of this application, deep customization of self-skinning can be achieved on Android devices equipped with voice input modules or cameras. Among them, aar files are a file package containing code and other resources, which can help developers quickly add new functions or interface elements to their applications.

[0115] For example, see Figure 6 As shown in the figure, the smart skinning aar contains two modules: the skinning UI and resource module and the large model recognition module. The skinning UI and resource module provides the basic functions of skinning, which can read the skin package and provide color resources for the application to use. The application can also use the skinning control in it to directly obtain the skinning effect.

[0116] Among them, the skinning UI contains multiple skinning controls for developers to use. The skinning control is a ViewGroup with an internally integrated skinning interface. The application can introduce the intelligent skinning function without affecting the original design. ViewGroup is a container class in Android, which can accommodate multiple subviews (View). These subviews can be various UI components such as buttons, text boxes, and pictures. ViewGroup is responsible for managing the position and layout of these subviews, making the interface more flexible and diversified. The skinning control uses the skin id as the index and retrieves resources through the resource module to display them on the screen. In the development process, developers can classify specific UI components, such as buttons, lists, etc. The same type of UI components use the same and fixed skin id, so that the large model can link the user-specified location of the skin to be changed with the corresponding resources. The resource module provides a basic skin id docking process. In different skin resources, the same skin id and name are used to match the corresponding value of the same skin id in different skin packages, and then the corresponding value of this skin in different skin packages is provided to each application that needs to replace the skin, so as to achieve the effect of replacing the skin. For skin resources that users choose to replace, they need to use the skin resources in the currently used skin package to replace the corresponding skin color or image in the skin package, and record the skin name and the corresponding skin resource and save them in the local database. When the user chooses to use the skin resource corresponding to the skin name next time, the corresponding skin resource will be found in the local database according to the skin name. If it cannot be found, the resources of the skin package itself will be used. For example, in the process of skin replacement, see Figure 7 As shown, after the application receives the update notification of the local database, all Android controls determine whether the skin resources corresponding to the skin to be changed are in the local database according to the ID of the skin to be changed and the name and type corresponding to the skin ID through the skin change aar. If the skin resources corresponding to the skin to be changed exist in the local database, the color resources or image resources corresponding to the skin ID are read from the local database and set to the control to realize autonomous skin changing. If there are no skin resources corresponding to the skin to be changed in the local database, the color resources or image resources corresponding to the skin ID are read from the original resources of the skin package and set to the control, thereby maintaining the original skin state and failing to realize skin update.

[0117] Moreover, the principle of the resource module is to rewrite the relevant methods of the system's AssetManager through reflection to achieve skinning, which can support multiple skins and support dynamic seamless switching. Among them, AssetManager is a class in Android that is used to manage resource files in applications. These resource files can usually include but are not limited to images, audio files, text files, etc. Specifically, AssetManager provides a series of methods to access and load these resource files. It should be pointed out that the skin package resources that the resource module needs to use need to create a project through Android studio, put the skin resources into the project, write a compilation script (gradle script), and generate different skinning files by channel and package them.

[0118] The large model recognition module includes three sub-modules, namely, speech recognition sub-module, semantic understanding sub-module, and image recognition sub-module; among them, the speech recognition sub-module is used to recognize the voice input by the user to determine the user's command information, and then pass it to the semantic understanding sub-module. The semantic understanding sub-module understands the user's command information and extracts the information therein, such as the location where the skin needs to be changed, the target color of the skin change, etc.; the image recognition sub-module recognizes the picture input by the user and extracts the corresponding skin change resources.

[0119] For example, see Figure 8 As shown, when the user needs to customize the skin, and the user selects the voice input method, that is, the user inputs the relevant skin-changing instructions through voice, the voice recognition submodule in the large model converts the voice into text information, and transmits it to the semantic understanding submodule in the large model. The semantic understanding submodule parses the text information, extracts the "position where the skin needs to be changed" and the "target color of the skin change" from it, and finally builds a custom skin resource based on the extracted "position where the skin needs to be changed" and "target color of the skin change", and saves the custom skin resource to the local database of the application; when the user selects the image input method, that is, the user specifies the skin UI that needs to be customized (the position of the skin to be changed), and inputs the image, and records the id corresponding to the skin UI, the image recognition submodule in the large model recognizes the picture, and extracts it as a picture suitable for the skin-changing background, and finally builds a custom skin resource based on the extracted picture information and the skin UI that the user specifies needs to be customized, and saves the custom skin resource to the local database of the application.

[0120] For example, an application developer imports a smart skin-changing aar and uses internal color resources and controls to draw the UI to obtain a skin-changing application. When the user enters the application, he selects the skin-changing method: voice skin-changing method. The user wants to change the skin of the application and sends a skin-changing voice command through voice input. The command is transmitted to the big model through the corresponding interface. The big model then recognizes the semantic information of the voice and analyzes the semantic information to determine whether the voice command issued by the user is a skin-changing command. If it is not a skin-changing command, there is no response. If it is a skin-changing command, the semantic information is further analyzed to see if it contains the skin-changing location information and color information. If the semantic information does not contain the skin-changing location information or color information, it means that the information is insufficient, and the user is prompted to complete the information. For example, the big model recognizes the voice command and analyzes the semantic information of the voice. It analyzes that the semantic information contains that the target color of the skin change is blue, but there is no location where the skin needs to be changed. Then it returns a prompt message "Please specify the location where the skin needs to be changed, such as a button or a list". The user sees the prompt message and then enters the voice command "Change the color of the button to blue". The semantic information contains the skin change information required for the skin change, and the big model can be used to extract the location information and color information of the skin to be changed. Then, the big model converts the "location where the skin needs to be changed" and the "target color of the skin change" into custom skin resources and returns them to the application to be skinned. The application to be skinned saves the custom skin resources to the local database by associating them with the id of the skin to be changed. If the skin to be changed corresponding to the id already has a corresponding skin resource in the local database, and If the skin resource is different from the custom skin resource, when the custom skin resource is saved to the local database, the custom skin resource will overwrite the skin resource previously existing in the local database for the skin to be replaced. The local database records the name of the skin package currently used by the application to be replaced, as well as the id of the skin to be replaced and its corresponding name. Then, in the process of skin replacement, the skin resource can be matched from the local database by the id and name of the skin. If the corresponding skin resource can be matched, it means that the skin to be replaced is a skin modified by the user. The color content that needs to replace the skin to be replaced is read from the local database and set to the skin replacement control. The skin replacement UI and resource module detects that the custom information has changed, refreshes the current UI, and redraws the UI, so that the user-defined part of the skin to be replaced can be updated to the latest color. If the skin resource corresponding to the skin to be replaced is not matched in the local database, the skin replacement aar will obtain the skin resource of the skin package currently used by the application to be replaced through the AssetManager, and read the default color content of the skin to be replaced from the skin package resource, that is, the skin to be replaced can only maintain the original skin color, and the skin update is not realized.

[0121] For another example, an application developer imports a smart skin-changing aar and uses internal color resources and controls to draw the UI to obtain a skin-changing application. When the user enters the application, he selects the skin-changing method: image skin-changing method. The user wants to skin the application and uploads a picture through the relevant interface and specifies the location where the skin needs to be changed. The large model then identifies the picture and analyzes the relevant picture information. The location and picture information that need to be skinned are converted into custom skin resources through the large model and returned to the application to be skinned. The application saves the custom skin resources to the local database by associating them with the skin ID to be changed. The local database records the name of the skin package currently used by the application to be skinned, as well as the skin ID to be changed and its corresponding name. Then, during the skin-changing process, the skin resource can be matched from the local database by the skin ID and name. If the corresponding skin resource can be matched, it means that the skin to be changed is the skin modified by the user. The picture content that needs to replace the skin to be changed is read from the local database and set to the skin-changing control. The skin-changing UI and resource module detects that the custom information has changed, refreshes the current UI, and redraws the UI, so that the user-defined part of the skin to be changed can be updated to the latest skin. If the skin resource corresponding to the skin to be changed is not matched in the local database, the skin-changing aar will obtain the skin resource of the skin package currently used by the skin-changing application through the AssetManager, and read the default image content of the skin to be changed from the skin package resource. That is, the skin to be changed can only maintain the original skin state, and the skin update is not realized.

[0122] Fig. 9 A schematic diagram of the structure of a terminal provided in an embodiment of the present application. The terminal may include:

[0123] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0124] When the processor 502 executes the program, the device application skinning method based on the large model provided in the above embodiment is implemented.

[0125] Furthermore, the terminal further includes:

[0126] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0127] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0128] The memory 501 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.

[0129] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0130] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0131] The processor 502 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.

[0132] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the device application skinning method based on a large model as described above is implemented.

[0133] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0134] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0135] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes additional implementations, in which the order shown or discussed may not be followed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0136] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can read instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary and then storing it in a computer memory.

[0137] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0138] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0139] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0140] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

[0141] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A device application skinning method based on a large model, characterized in that: The method comprises: Receive a skin-changing instruction issued by a device user, and identify the skin-changing instruction through a pre-trained large model to extract corresponding skin-changing requirement information; Constructing the custom skin resource of the skin to be changed in the skin change demand information according to the skin change demand information through the large model, and returning the custom skin resource to the application to be skinned so that the application to be skinned can save the custom skin resource to the local database; Perform resource matching in the local database according to the identification information corresponding to the skin to be replaced to match the target skin resource corresponding to the identification information, and read the target skin resource from the local database; The target skin resource is used to replace the corresponding skin resource in the skin package currently used in the application to be skinned, so as to complete the corresponding skinning operation when the application to be skinned is reloaded.

2. The device application skinning method based on a large model according to claim 1 is characterized in that: Before the receiving device user issues a skin-changing instruction, the method further includes: The current skin changing mode is switched to a target skin changing mode selected by a device user; wherein the target skin changing mode is a voice skin changing mode or an image skin changing mode.

3. The device application skinning method based on a large model according to claim 2 is characterized in that: The receiving device user sends a skin changing instruction, and identifies the skin changing instruction by using a pre-trained large model to extract corresponding skin changing requirement information, including: In the voice skinning mode, a skinning voice instruction issued by a device user is received through a pre-built voice receiving interface; Recognize the skin-changing voice command by using a pre-trained large model to determine first semantic information corresponding to the skin-changing voice command, and determine whether the skin-changing voice command is a skin-changing related command according to the first semantic information; When the skin-changing voice instruction is an instruction related to skin-changing, the position information and color information of the skin to be changed are extracted from the first semantic information.

4. The device application skinning method based on a large model according to claim 3 is characterized in that: Before extracting the position information and color information of the skin to be replaced from the first semantic information, the method further includes: Determining whether the first semantic information lacks skinning information required for skinning; When the skin-changing information required for skin-changing is missing in the first semantic information, generating corresponding prompt information according to the missing skin-changing information; A new skin-changing voice instruction that continues to be sent by the receiving device user according to the prompt information, and the new skin-changing voice instruction is continuously recognized by the large model to determine the second semantic information corresponding to the new skin-changing voice instruction; The step of extracting the position information and color information of the skin to be replaced from the first semantic information includes: The position information and color information of the skin to be replaced are extracted from the first semantic information and the second semantic information.

5. The device application skinning method based on a large model according to claim 3 or 4, characterized in that: The step of constructing the custom skin resource to be replaced in the skin replacement requirement information by using the large model according to the skin replacement requirement information includes: The custom skin resource of the skin to be replaced is constructed through the large model according to the position information and the color information of the skin to be replaced.

6. The device application skinning method based on a large model according to claim 2, characterized in that: The receiving device user sends a skin changing instruction, and identifies the skin changing instruction by using a pre-trained large model to extract corresponding skin changing requirement information, including: In the image skinning mode, a picture input by a device user and pre-specified location information of the skin to be replaced are received through a pre-built image receiving interface; The image is recognized by a pre-trained large model to extract corresponding image information.

7. The device application skinning method based on a large model according to claim 6 is characterized in that: The step of constructing the custom skin resource to be replaced in the skin replacement requirement information by using the large model according to the skin replacement requirement information includes: The custom skin resource of the skin to be replaced is constructed through the large model according to the picture information and the position information of the skin to be replaced.

8. A device application skinning device based on a large model, characterized in that: The device comprises: A receiving module, used for receiving a skin-changing instruction issued by a device user; A large model recognition module, used to recognize the skin-changing instruction through a pre-trained large model to extract corresponding skin-changing requirement information; A skin resource construction module, used to construct the custom skin resource of the skin to be changed in the skin change demand information through the large model according to the skin change demand information, and return the custom skin resource to the application to be skinned so that the application to be skinned can save the custom skin resource to the local database; A resource matching module, configured to perform resource matching in the local database according to the identification information corresponding to the skin to be replaced, so as to match a target skin resource corresponding to the identification information, and read the target skin resource from the local database; The resource replacement module is used to replace the corresponding skin resource in the skin package currently used in the application to be skinned with the target skin resource, so as to complete the corresponding skinning operation when the application to be skinned is reloaded.

9. A terminal, characterized in that: include: A memory, a processor, and a large model-based device application skinning program stored in the memory and executable on the processor, wherein the large model-based device application skinning program, when executed by the processor, implements the steps of the large model-based device application skinning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the device application skinning method based on a large model as described in any one of claims 1 to 7.

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