Large model-based operating system construction system and method, electronic equipment and medium

Abstract

The invention discloses an operating system construction system and method based on a large model, electronic equipment and a medium, and belongs to the technical field of computers. The system comprises a front-end interface and a rear-end server, the front-end interface provides a display page, and the front-end interface is used for acquiring configuration information and demand description input by a user on the display page; in response to an operating system generation instruction triggered by a user, sending the configuration information and the demand description to a back-end server; and the back-end server is used for calling a pre-trained large model, inputting the configuration information and the demand description into the large model, obtaining an operating system output by the large model according to the configuration information and the demand description, and returning the operating system to the front-end interface. The large model is called to generate the operating system, compiling of items uploaded by a user and testing of the operating system are supported, a set of service from operating system development to testing is provided, the development period of the operating system is shortened, and the development efficiency of the operating system is improved.

Description

Technical Field

[0001] The present application belongs to the field of computer technology, and in particular, relates to a system, method, electronic device and medium for building an operating system based on a large model. Background Art

[0002] AI (Artificial Intelligence) large models usually refer to deep learning models with a large number of parameters. They are trained on massive data to learn complex features and patterns to handle various complex tasks.

[0003] Text generation models developed based on the Transformers architecture, such as GPT-3, Qwen, GLM, Tongyi Qianwen, Gemma, etc., have achieved remarkable achievements in dialogue systems, text summarization, content creation, etc. These models use the Transformers' self-attention mechanism and layered architecture to generate coherent and logical text content.

[0004] However, when large AI models are required to help developers generate a complete project, conduct project testing, and then generate result reports, the models can often only provide general suggestions or operational procedures rather than specific, directly executable solutions. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a system, method, electronic device and medium for building an operating system based on a large model to reduce the development cycle of the operating system and improve the development efficiency of the operating system.

[0006] In a first aspect, the present application provides an operating system construction system based on a large model, including a front-end interface and a back-end server;

[0007] The front-end interface is provided with a display page, and the front-end interface is used to obtain the configuration information and the requirement description input by the user on the display page; in response to the operating system generation instruction triggered by the user, the configuration information and the requirement description are sent to the back-end server;

[0008] The back-end server is used to call a pre-trained large model, input the configuration information and the requirement description into the large model, obtain the operating system output by the large model according to the configuration information and the requirement description, and return the operating system to the front-end interface.

[0009] According to the big model-based operating system construction system of the present application, through the front-end interface, users can easily input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs and calling the pre-trained big model to generate a customized operating system. It utilizes the powerful data processing and pattern recognition capabilities of the big model, and can quickly generate an operating system according to the user's personalized needs, greatly reducing the operating system development cycle and improving the operating system development efficiency.

[0010] According to one embodiment of the present application, the configuration information includes at least one of system architecture, device configuration, kernel configuration, operating system type, operating system version, and security settings.

[0011] According to an embodiment of the present application, returning the operating system to the front-end interface includes:

[0012] Returning the source code of the operating system to the front-end interface;

[0013] Alternatively, the source code is compiled by calling a compilation tool through the large model to obtain a compilation result, and the compilation result is returned to the front-end interface.

[0014] In this embodiment, by providing users with a flexible way to obtain the operating system, whether in source code or compiled binary form, the needs of different users can be met. Through the front-end interface, users can easily receive the source code of the operating system, which provides developers and advanced users with the possibility of customization and further development; for users who need to deploy immediately or do not have a compilation environment, the system provides a compilation result return function, which enables users to directly obtain an executable operating system, thereby simplifying the deployment process and reducing the development cycle.

[0015] According to an embodiment of the present application, the display page includes a configuration selection module and a requirement input module, and the obtaining of the configuration information and requirement description input by the user on the display page includes:

[0016] The configuration information is acquired according to the configuration selected by the user in the configuration selection module, and the requirement description is acquired according to the text input by the user in the requirement input module.

[0017] In this embodiment, by providing an intuitive and user-friendly interface, the configuration selection module allows the user to select options through a graphical user interface to obtain specific configuration information; the requirement input module enables the user to describe the special requirements for the operating system in detail in text form, which not only simplifies the interaction process between the user and the system, but also enables the back-end server to more accurately parse and respond to the user's personalized needs, thereby generating an operating system that better meets the user's expectations through a large model.

[0018] According to one embodiment of the present application, the display page includes an upload module;

[0019] The front-end interface is also used to obtain the source code of the operating system uploaded by the user in the upload module, and send the source code of the operating system uploaded by the user to the back-end server;

[0020] The back-end server is also used to call the compilation tool through the large model to compile the source code of the operating system uploaded by the user, obtain the compilation result, and return the compilation result to the front-end interface.

[0021] In this embodiment, an upload module is provided on the display page so that the user can upload the source code of the operating system through the upload module, allowing the user to directly participate in the construction process of the operating system, rather than just through configuration selection and requirement input. The back-end server compiles the uploaded source code by calling the compilation tool through the large model, so that the user can obtain a compilation result that can be directly used, greatly reducing the development cycle of the operating system and improving the development efficiency of the operating system.

[0022] According to one embodiment of the present application, the display page includes a test module;

[0023] The front-end interface is also used to obtain the test requirements input by the user in the test module and send the test requirements to the back-end server;

[0024] The back-end server is also used to call the test tool through the large model to test the operating system according to the test requirements, obtain a test report, and return the test report to the front-end interface.

[0025] In this embodiment, by providing a test module, an integrated and automated platform is provided for the construction and testing of the operating system. Not only can the operating system be built according to user needs, but the corresponding test tools can also be called using a large model to test the operating system, generate a test report, and return it to the front-end interface for users to review, providing users with a complete set of operating system development to testing services, greatly shortening the operating system development and testing cycle.

[0026] According to one embodiment of the present application, the backend server includes a model training module;

[0027] The backend server is further used to obtain model parameters configured by the user; wherein the model parameters include at least one of a model storage path, a CPU power consumption mode, an inference device configuration, a model output length range, an automatic derivation setting, a text sampling setting, and a model vocabulary range setting;

[0028] The model training module is used to train the large model according to the model parameters.

[0029] In this embodiment, through the model training module in the back-end server, users can configure model parameters according to their own needs, such as model storage path, CPU power consumption mode, inference device configuration, etc. These are key factors affecting model training efficiency and performance. The model training module uses these parameters to train the large model so that the model can better adapt to specific application scenarios and hardware environments. This not only improves the efficiency of model training, but also enables users to optimize the performance of the model according to actual needs, thereby obtaining better results in different business scenarios.

[0030] In a second aspect, the present application provides a method for constructing an operating system based on a large model, which is applied to a front-end interface, wherein the front-end interface provides a display page, and the method includes:

[0031] Obtaining configuration information and requirement description entered by the user on the display page;

[0032] In response to an operating system generation instruction triggered by a user, the configuration information and the requirement description are sent to a back-end server so that the back-end server can call a pre-trained large model, input the configuration information and the requirement description into the large model, obtain the operating system output by the large model based on the configuration information and the requirement description, and return the operating system to the front-end interface.

[0033] According to the big model-based operating system construction method of the present application, through the front-end interface, users can easily input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs and calling the pre-trained big model to generate a customized operating system. It utilizes the powerful data processing and pattern recognition capabilities of the big model, and can quickly generate an operating system according to the user's personalized needs, greatly reducing the operating system development cycle and improving the operating system development efficiency.

[0034] According to an embodiment of the present application, the display page includes a configuration selection module and a requirement input module, and the obtaining of the configuration information and the requirement description input by the user on the display page includes: obtaining the configuration information according to the configuration selected by the user in the configuration selection module, and obtaining the requirement description according to the text input by the user in the requirement input module;

[0035] The display page includes an upload module; the method further includes: obtaining the source code of the operating system uploaded by the user in the upload module; sending the source code of the operating system uploaded by the user to the back-end server, so that the back-end server can compile the source code of the operating system uploaded by the user by calling the compilation tool through the big model, obtain the compilation result, and return the compilation result to the front-end interface;

[0036] The display page includes a test module; the method also includes: obtaining the test requirements input by the user in the test module; sending the test requirements to the back-end server, so that the back-end server calls the test tool through the large model to test the operating system according to the test requirements, obtains a test report, and returns the test report to the front-end interface.

[0037] In a third aspect, the present application provides a method for constructing an operating system based on a large model, which is applied to a backend server, and the method includes:

[0038] Calling a pre-trained big model, inputting configuration information and requirement description into the big model, and obtaining an operating system output by the big model according to the configuration information and the requirement description; wherein the configuration information and the requirement description are sent by the front-end interface in response to an operating system generation instruction triggered by a user, and the configuration information and the requirement description are input by the user on a display page of the front-end interface;

[0039] Return the operating system to the front-end interface.

[0040] According to the big model-based operating system construction method of the present application, through the front-end interface, users can easily input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs and calling the pre-trained big model to generate a customized operating system. It utilizes the powerful data processing and pattern recognition capabilities of the big model, and can quickly generate an operating system according to the user's personalized needs, greatly reducing the operating system development cycle and improving the operating system development efficiency.

[0041] In a fourth aspect, the present application provides an operating system construction device based on a large model, which is applied to a front-end interface, wherein the front-end interface provides a display page, and the device includes:

[0042] An acquisition module, used to acquire configuration information and requirement description input by the user on the display page;

[0043] A sending module is used to respond to an operating system generation instruction triggered by a user, and send the configuration information and the requirement description to a back-end server, so that the back-end server can call a pre-trained large model, input the configuration information and the requirement description into the large model, obtain the operating system output by the large model according to the configuration information and the requirement description, and return the operating system to the front-end interface.

[0044] According to the big model-based operating system construction device of the present application, through the front-end interface, the user can conveniently input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs, calling the pre-trained big model to generate a customized operating system, and utilizing the powerful data processing and pattern recognition capabilities of the big model. It can quickly generate an operating system according to the user's personalized needs, greatly reducing the development cycle of the operating system and improving the development efficiency of the operating system.

[0045] In a fifth aspect, the present application provides an operating system construction device based on a large model, which is applied to a backend server, and the device includes:

[0046] A calling module is used to call a pre-trained large model, input configuration information and a requirement description into the large model, and obtain an operating system output by the large model according to the configuration information and the requirement description; wherein the configuration information and the requirement description are sent by the front-end interface in response to an operating system generation instruction triggered by a user, and the configuration information and the requirement description are input by the user on a display page of the front-end interface;

[0047] A return module is used to return the operating system to the front-end interface.

[0048] According to the big model-based operating system construction device of the present application, through the front-end interface, the user can conveniently input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs, calling the pre-trained big model to generate a customized operating system, and utilizing the powerful data processing and pattern recognition capabilities of the big model. It can quickly generate an operating system according to the user's personalized needs, greatly reducing the development cycle of the operating system and improving the development efficiency of the operating system.

[0049] In a sixth aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for constructing an operating system based on a large model as described in the second or third aspect above is implemented.

[0050] In a seventh aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the large model-based operating system construction method as described in the second or third aspect above.

[0051] In an eighth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the large model-based operating system construction method as described in the second or third aspect above.

[0052] In a ninth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the large model-based operating system construction method as described in the second or third aspect above.

[0053] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0054] According to the big model-based operating system construction system of the present application, through the front-end interface, users can easily input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs and calling the pre-trained big model to generate a customized operating system. It utilizes the powerful data processing and pattern recognition capabilities of the big model, and can quickly generate an operating system according to the user's personalized needs, greatly reducing the operating system development cycle and improving the operating system development efficiency.

[0055] Furthermore, in some embodiments, by providing users with flexible ways to obtain the operating system, whether in source code or compiled binary form, the needs of different users can be met. Through the front-end interface, users can easily receive the source code of the operating system, which provides developers and advanced users with the possibility of customization and further development; for users who need to deploy immediately or do not have a compilation environment, the system provides a compilation result return function, which enables users to directly obtain an executable operating system, thereby simplifying the deployment process and reducing the development cycle.

[0056] Furthermore, in some embodiments, by providing an intuitive and user-friendly interface, the configuration selection module allows the user to select options through a graphical user interface to obtain specific configuration information; the requirement input module enables the user to describe in detail the special requirements for the operating system in text form, which not only simplifies the interaction process between the user and the system, but also enables the back-end server to more accurately parse and respond to the user's personalized needs, thereby generating an operating system that better meets the user's expectations through a large model.

[0057] Furthermore, in some embodiments, by providing an upload module on the display page, the user can upload the source code of the operating system through the upload module, allowing the user to directly participate in the construction process of the operating system, rather than just through configuration selection and requirement input. The back-end server compiles the uploaded source code by calling the compilation tool through the large model, so that the user can obtain a compilation result that can be used directly, greatly reducing the development cycle of the operating system and improving the development efficiency of the operating system.

[0058] Furthermore, in some embodiments, by providing a test module, an integrated and automated platform is provided for the construction and testing of the operating system, which can not only build the operating system according to user needs, but also use the big model to call the corresponding test tools to test the operating system, generate a test report, and return it to the front-end interface for users to review, providing users with a complete set of operating system development to testing services, greatly shortening the operating system development and testing cycle.

[0059] Furthermore, in some embodiments, through the model training module in the back-end server, users can configure model parameters according to their own needs, such as model storage path, CPU power consumption mode, inference device configuration, etc. These are key factors affecting model training efficiency and performance. The model training module uses these parameters to train the large model so that the model can better adapt to specific application scenarios and hardware environments. This not only improves the efficiency of model training, but also enables users to optimize the performance of the model according to actual needs, thereby obtaining better results in different business scenarios.

[0060] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 It is a schematic diagram of the architecture of an operating system construction system based on a large model provided in an embodiment of the present application;

[0063] Figure 2 It is a schematic diagram of a display page of a front-end interface provided in an embodiment of the present application;

[0064] Figure 3This is a schematic diagram of a download prompt provided on the front-end interface in an embodiment of the present application;

[0065] Figure 4 It is a parameter configuration diagram of the back-end server provided in the embodiment of the present application;

[0066] Figure 5 This is a scenario example of the development process of the operating system construction system based on the big model provided in the embodiment of the present application;

[0067] Figure 6 It is a schematic diagram of the process architecture of the operating system construction system based on the big model provided in the embodiment of the present application;

[0068] Figure 7 This is a schematic diagram of a login page provided in an embodiment of the present application;

[0069] Figure 8 This is a schematic diagram of a registration page provided in an embodiment of the present application;

[0070] Fig. 9 This is a schematic diagram of the upload page provided in the embodiment of the present application;

[0071] Fig.10 It is a schematic diagram of the test results provided in the embodiment of the present application;

[0072] Fig.11 It is a schematic diagram of the architecture of the Web front end provided in the embodiment of the present application;

[0073] Fig.12 This is a schematic diagram of the architecture of the Server backend provided in the embodiment of the present application;

[0074] Fig.13 This is a schematic diagram of the model training architecture of the Server backend provided in an embodiment of the present application;

[0075] Fig.14 This is one of the flow charts of the method for constructing an operating system based on a large model provided in an embodiment of the present application;

[0076] Fig.15 This is the second flow chart of the method for constructing an operating system based on a large model provided in an embodiment of the present application;

[0077] Fig.16 It is one of the flow diagrams of the operating system construction device based on the large model provided in the embodiment of the present application;

[0078] Fig.17 This is the second structural diagram of the operating system construction device based on the large model provided in the embodiment of the present application;

[0079] Fig.18It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0081] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0082] In conjunction with the accompanying drawings, the big model-based operating system construction system, method, electronic device and medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0083] like Figure 1 As shown, the big model-based operating system construction system 100 includes: a front-end interface 110 and a back-end server 120 .

[0084] The front-end interface 110 provides a display page, and the front-end interface 110 is used to obtain the configuration information and requirement description input by the user on the display page; in response to the operating system generation instruction triggered by the user, the configuration information and requirement description are sent to the back-end server;

[0085] The back-end server 120 is used to call a pre-trained large model, input configuration information and requirement description into the large model, obtain an operating system output by the large model according to the configuration information and requirement description, and return the operating system to the front-end interface 110.

[0086] In an embodiment of the present application, the front-end interface 110 is an interface for the user to interact with the large model-based operating system construction system 100. The front-end interface 110 provides a display page, which can be a graphical user interface. The user can input configuration information and requirement descriptions through the display page.

[0087] In some embodiments, the configuration information may be the user's selection of specific technical parameters and settings for the operating system. The configuration information may help the system 100 understand the user's specific technical requirements for the operating system. For example, the configuration information may include the system architecture (such as x86, aarch64), device configuration (network port device, camera device), kernel configuration, and may also include a specified operating system type, such as DeltaOS, Intewell, and may also include the operating system version, security settings, etc. The requirement description is the user's description of the operating system function. The requirement description may include a description of the purpose, functional requirements, customized features, etc. For example, an example of a requirement description may be: generate an operating system with a kernel version of 5.11.0+, including a USB driver module and a network port driver module, and an ext4 file system.

[0088] In an embodiment of the present application, the operations performed by the user on the display page of the front-end interface, such as selecting the type, version, interface preferences and other configuration information of the operating system, and the specific requirement description of the operating system function, will be collected by the display page. After the user completes the input of the configuration information and the requirement information, the operating system generation instruction can be triggered through the display page of the front-end interface. This instruction will tell the system that the user has completed the input. The front-end interface 110 will package the collected configuration information and requirement description and send it to the back-end server 120 for further processing. For example, a "Generate Operating System" button is set on the display page. If it is detected that the user clicks the "Generate Operating System" button, it means that the user has triggered the operating system generation instruction. In this case, the subsequent process is started to generate the operating system.

[0089] In an embodiment of the present application, the back-end server 120 can be a server or a server cluster composed of multiple servers. After receiving the configuration information and requirement description sent from the front-end interface, the back-end server 120 will call the pre-trained large model and pass the configuration information and requirement description as input to the large model. The large model processes and analyzes the received configuration information and requirement description, and then outputs a customized operating system. This process may include a series of complex reasoning tasks such as compilation, optimization, and encoding. The generated operating system will be returned to the front-end interface 110 by the back-end server 120, and the user can obtain the operating system from the front-end interface 110 for download, installation, or other operations. For example, the front-end interface 110 can create .c, .h, .hpp, .cpp, CMakeLists.txt and other files according to the code type of the operating system for users to download.

[0090] Large Language Model (LLM) refers to machine learning models with large-scale parameters and complex computational structures. These models are usually built with deep neural networks, have billions or even hundreds of billions of parameters, and can process massive amounts of data and complete various complex tasks.

[0091] In an embodiment of the present application, a pre-trained large model can be selected, and then the selected pre-trained large model can be fine-tuned on the task of generating an operating system so that the large model can better adapt to the application scenario of the production operating system. The pre-trained large model can be a large model such as BERT, GPT, T5, etc. These large models have been pre-trained on large-scale text data sets and can capture the complex patterns and structures of the language, without investing a lot of time and resources to train a model that can generate an operating system from scratch.

[0092] Specifically, you can first choose a pre-trained large model as the basic model, such as GPT, and then collect text data related to the operating system from various channels, pre-process the text data, build question-answer pairs, annotate, and so on to build a training data set, use the constructed training data set to fine-tune the pre-trained large model, and test the trained large model. After the test, if the verification accuracy of the large model reaches the expected value (for example, 0.85 and above), it means that the large model is available, and then start to build a back-end server to write API (Application Programming Interface) and model deployment, and then test whether the relevant API is available, whether concurrent access control is effective, etc., and then build the front-end interface to complete the corresponding function development, and debug the back-end server API to see whether the function is normal. After the entire project test is completed, it can be packaged and deployed.

[0093] In this embodiment, an upload module is provided on the display page so that the user can upload the source code of the operating system through the upload module, allowing the user to directly participate in the construction process of the operating system, rather than just through configuration selection and requirement input. The back-end server compiles the uploaded source code by calling the compilation tool through the large model, so that the user can obtain a compilation result that can be directly used, greatly reducing the development cycle of the operating system and improving the development efficiency of the operating system.

[0094] In some embodiments, returning the operating system to the front-end interface includes:

[0095] Return the source code of the operating system to the front-end interface;

[0096] Alternatively, the compilation tool is called through the large model to compile the source code, obtain the compilation result, and return the compilation result to the front-end interface.

[0097] In this embodiment, after the user triggers the operating system generation instruction, the back-end server 120 will package the generated operating system source code and display it to the user through the front-end interface. The user can directly download the source code to view, modify, or compile and deploy it in the local environment. This method is suitable for users who need to conduct in-depth analysis or customization of the operating system source code.

[0098] For users who need to use the operating system immediately, the back-end server 120 can also provide compilation services. The large model on the back-end server 120 can call the corresponding compilation tools, such as GCC or Clang, to compile the source code and generate an executable binary file. The compilation process can include steps such as code optimization, link library files, and processing dependencies. After the compilation is completed, the back-end server 120 will return the compilation result, that is, the executable file or installation package of the operating system, to the front-end interface for users to download and install. In actual operation, the back-end server 120 can pre-set the compilation environment and the corresponding tool chain to support the construction process of different operating systems. In order to improve efficiency, the back-end server 120 can use technologies such as distributed compilation to distribute compilation tasks to multiple nodes for parallel processing.

[0099] In this embodiment, by providing users with a flexible way to obtain the operating system, whether in source code or compiled binary form, the needs of different users can be met. Through the front-end interface, users can easily receive the source code of the operating system, which provides developers and advanced users with the possibility of customization and further development; for users who need to deploy immediately or do not have a compilation environment, the system provides a compilation result return function, which enables users to directly obtain an executable operating system, thereby simplifying the deployment process and reducing the development cycle.

[0100] In some embodiments, the display page includes a configuration selection module and a requirement input module, and obtaining the configuration information and requirement description input by the user on the display page includes:

[0101] Configuration information is obtained according to the configuration selected by the user in the configuration selection module, and the requirement description is obtained according to the text input by the user in the requirement input module.

[0102] like Figure 2 As shown, in this embodiment, the configuration selection module can be presented in the form of a form in the display page, including controls such as a drop-down menu, a check box, a radio button, a slider, etc., so that the user can select different configuration options of the operating system. These configuration options may include the version of the operating system, the system architecture (such as x86, aarch64), the system type, the system kernel, the device configuration, the hardware support, etc.

[0103] In this embodiment, the demand input module can provide a text box or text editor in the display page, and the user can enter a specific demand description for the operating system in the text box or text editor, such as specific functional requirements, performance requirements, compatibility descriptions, etc. In order to help users better express their needs, the demand input module can also include some preset templates or sample texts to guide users on how to describe their needs. The text entered by the user will be collected as a demand description and sent to the backend server 120 together with the configuration information.

[0104] When the user completes the configuration selection and requirement input, and triggers the operating system generation instruction, the front-end interface 110 will package the configuration information and requirement description into a data packet, ready to be sent to the back-end server 120. For example, the front-end interface 110 can send the user's configuration information and requirement description to a specific API endpoint of the back-end server 120 through an HTTP request (such as a POST request). After receiving this data, the back-end server 120 will process it, such as verifying the integrity and correctness of the data, and then use this information to guide the large model to generate a customized operating system.

[0105] In this embodiment, by providing an intuitive and user-friendly interface, the configuration selection module allows the user to select options through a graphical user interface to obtain specific configuration information; the requirement input module enables the user to describe the special requirements for the operating system in detail in text form, which not only simplifies the interaction process between the user and the system, but also enables the back-end server to more accurately parse and respond to the user's personalized needs, thereby generating an operating system that better meets the user's expectations through a large model.

[0106] In some embodiments, the display page includes an upload module;

[0107] The front-end interface is also used to obtain the source code of the operating system uploaded by the user in the upload module, and send the source code of the operating system uploaded by the user to the back-end server;

[0108] The back-end server is also used to compile the source code of the operating system uploaded by the user through the large model calling the compilation tool, obtain the compilation result, and return the compilation result to the front-end interface.

[0109] like Figure 2As shown, in this embodiment, in the display page, the upload module can provide a file selector or a drag-and-drop area, and the user can upload the locally stored operating system source code file through the file selector or the drag-and-drop area. For example, the user clicks the upload button or drags and drops the file to the specified area to select the source code file to be uploaded. The front-end interface 110 can perform preliminary verification on the uploaded file, such as checking whether the file type and size meet the requirements to ensure that the uploaded file is a valid source code. After the verification is passed, the front-end interface 110 can send the source code file to a specific API endpoint of the back-end server via an HTTP request. It should be noted that during the transmission process, the HTTPS protocol can be used to encrypt the transmitted data to improve the security of the data.

[0110] In this embodiment, the large model is pre-trained and can understand different programming languages ​​and build operating systems. The back-end server 120 can receive and store source code files from the front-end interface 110. The back-end server 120 can call compilation tools such as GCC, Clang, Make, etc. through the large model to compile the source code. If errors occur during the compilation process, the back-end server 120 can capture these errors and feed them back to the front-end interface to display them to the user. If the compilation is successful, the back-end server 120 can package the compiled binary file or executable file and prepare to send it to the front-end interface 110. After receiving the compilation result, the front-end interface 110 displays the compilation result to the user. If the compilation is successful, the user can download the compiled file; if it fails, the user can see the error message and make corresponding corrections according to the prompts. Of course, in order to enable the user to understand the progress and status of the compilation, the front-end interface 110 can provide a progress bar or log output during the compilation process.

[0111] In this embodiment, an upload module is provided on the display page so that the user can upload the source code of the operating system through the upload module, allowing the user to directly participate in the construction process of the operating system, rather than just through configuration selection and requirement input. The back-end server compiles the uploaded source code by calling the compilation tool through the large model, so that the user can obtain a compilation result that can be directly used, greatly reducing the development cycle of the operating system and improving the development efficiency of the operating system.

[0112] In some embodiments, the display page includes a test module;

[0113] The front-end interface is also used to obtain the test requirements input by the user in the test module and send the test requirements to the back-end server;

[0114] The back-end server is also used to call the test tool through the large model to test the operating system according to the test requirements, obtain the test report, and return the test report to the front-end interface.

[0115] like Figure 2 As shown in the figure, users can enter the test requirements they want to perform on the operating system in the test module of the front-end interface, including performance testing, security testing, compatibility testing, stress testing, etc. For example, you can click the "Test Case" button and then select the test case corresponding to the test requirement to test the operating system. The test requirement can also be which modules of the operating system need to be tested, such as testing kernel features (such as scheduling, memory management, process communication), testing file systems (ext4, xfs, btrfs), testing network stacks (BSD, FTP / IP), etc. Users can add, delete, and other operations in the various modules of the operating system displayed in the display page to select the modules that need to be tested.

[0116] The front-end interface 110 can package the user's test requirements and send them to the back-end server 120 via HTTP requests. After receiving the test requirements, the back-end server 120 will use the large model and integrated test tools to perform the corresponding tests. For example, the back-end server can call test tools such as Selenium, JMeter, Postman, etc. through the large model to test the operating system according to the user's test requirements. After the test is completed, the back-end server 120 will collect the test results and generate a detailed test report using the large model. The test may include test coverage, defect statistics, performance indicators, security vulnerabilities, etc. The back-end server 120 can send the test report back to the front-end interface 110 via HTTP response, and the user can view the test results and test reports on the display page of the front-end interface. The test results and test reports can be displayed in PDF, HTML or other formats for easy reading and analysis by users.

[0117] In this embodiment, by providing a test module, an integrated and automated platform is provided for the construction and testing of the operating system. Not only can the operating system be built according to user needs, but the corresponding test tools can also be called using a large model to test the operating system, generate a test report, and return it to the front-end interface for users to review, providing users with a complete set of operating system development to testing services, greatly shortening the operating system development and testing cycle.

[0118] In some embodiments, the display page includes a download module;

[0119] The front-end interface is also used to store the test report locally in response to a download instruction triggered by the user in the download module.

[0120] like Figure 2As shown, in this embodiment, the download module may include one or more buttons in the display page. For example, the download module may include a "save test results" button, and the user may trigger the download operation by clicking the button.

[0121] After viewing the test report, if the user needs to save it locally, he can click the download button in the download module. The click action will trigger a front-end event, such as an event handler in JavaScript. The event handler will send a download request to the back-end server 120. The download request may include an identifier of the test report or other necessary information so that the back-end server 120 can identify and locate the specific test report file. After receiving the download request, the back-end server 120 will search for the corresponding test report file and send the test report file to the front-end interface 110 via an HTTP response. After receiving the file, the front-end interface 110 will prompt the user to save the file according to the download management function of the browser, such as Figure 3 shown.

[0122] In this embodiment, through the download module of the front-end interface, the user can trigger the download instruction to store the test report generated by the back-end server locally, so that the user can quickly obtain and view the test results, further improving the test efficiency.

[0123] In some embodiments, the backend server includes a model training module;

[0124] The backend server is also used to obtain model parameters configured by the user; wherein the model parameters include at least one of a model storage path, a CPU power consumption mode, an inference device configuration, a model output length range, an automatic derivation setting, a text sampling setting, and a model vocabulary range setting;

[0125] The model training module is used to train the large model according to the model parameters.

[0126] In this embodiment, the model training module of the backend server 120 is a key part for training the large model. The user can configure the model parameters on the backend server 120, and the model parameters may include the model storage path, CPU (Central Processing Unit) power consumption mode, inference device configuration, model evaluation mode switching, model output length range, automatic derivation settings, text sampling settings, model vocabulary range settings, etc. Among them, the model path indicates the storage path of the model file trained based on the Transformers framework so that the backend server 120 can load and use the model; the CPU power consumption mode indicates setting the CPU performance mode, such as energy-saving mode or high-performance mode, to balance computing resources and energy consumption; the inference device configuration indicates specifying the hardware device used for model inference, such as CPU, GPU (Graphics Processing Unit) or TPU (Tensor Processing Unit); the model evaluation mode switching indicates allowing switching between the evaluation mode and training mode of the model for performance testing or actual application; the model maximum output length setting indicates setting the maximum output length of the model when generating text to control the level of detail of the generated content; the automatic differentiation setting indicates configuring whether the automatic differentiation function is enabled; the text sampling setting indicates setting the sampling strategy when generating text, such as random sampling or deterministic sampling, which affects the diversity of the text; the model vocabulary range setting indicates limiting the vocabulary range used by the model, which may be used to filter inappropriate vocabulary or focus on vocabulary in a specific field. An example of a model parameter configuration is as follows: Figure 4 shown.

[0127] In this embodiment, through the model training module in the back-end server, users can configure model parameters according to their own needs, such as model storage path, CPU power consumption mode, inference device configuration, etc. These are key factors affecting model training efficiency and performance. The model training module uses these parameters to train the large model so that the model can better adapt to specific application scenarios and hardware environments. This not only improves the efficiency of model training, but also enables users to optimize the performance of the model according to actual needs, thereby obtaining better results in different business scenarios.

[0128] In some embodiments, the backend server includes a service provision module;

[0129] The back-end server is also used to obtain service parameters configured by the user in the service provision module; the service parameters include at least one of the setting of the number of working processes, the setting of the maximum number of requests for a single process, and the setting of the hot deployment mode.

[0130] In this embodiment, the service provision module of the backend server is a key component that allows users to configure and optimize the operating parameters of the backend service. The service parameters may include IP settings, port settings, developer mode settings, source access settings, number of working processes settings, maximum number of requests for a single process settings, and hot deployment mode settings. An example of a service parameter configuration is as follows: Figure 4 shown.

[0131] In this embodiment, through the service provision module, users can configure key service parameters according to actual business needs. The setting of the number of working processes enables the server to dynamically adjust resource allocation according to the load and optimize the overall processing capacity of the system. The setting of the maximum number of requests for a single process helps to reduce system overload and improve the stability and response speed of the service. The setting of the hot deployment mode allows the service to be updated and maintained without interruption, improving the availability of the service and user experience. The configurability of these service parameters enables the back-end server to better adapt to different workloads and business scenarios, thereby providing users with more efficient and reliable services.

[0132] The following is an example scenario to illustrate the development process of the large model-based operating system construction system in an embodiment of the present application.

[0133] like Figure 5 As shown, firstly, the model is trained. The pre-trained large model is fine-tuned using the constructed training data set. The trained large model is tested. After the test, if the verification accuracy of the large model reaches the expected value (for example, 0.85 and above), it means that the large model is available. Then, the back-end server is built to write API (Application Programming Interface) and model deployment. Then, the relevant API is tested to see whether it is available and whether concurrent access control is effective. Then, the front-end interface is built to complete the corresponding function development, and the back-end server API is debugged to see whether the function is normal. After the entire project is tested, it is packaged and deployed.

[0134] In this scenario example, the process architecture of the operating system construction system based on the large model is as follows Figure 6 As shown. The Web front end corresponds to the front end interface 110, and the Server back end corresponds to the back end server 120. The Web front end may include functions such as Login, Register, Generate OS, Upload OS, Compile OS, Gen Test Report, and Download, which are implemented by calling the corresponding APIs in the Server back end. Users can interact with the operating system construction system based on the large model through the Web front end.

[0135] Specifically, Figure 7 As shown, users can enter the Login Page to log in or register an account; Figure 8 As shown in the figure, Register Page can enter the registered user information into the database DataBase; Generate OS can generate the corresponding operating system according to the configuration information and requirement description entered by the user through the display page; through Upload OS, the user can upload the source code of the developed operating system to generate the corresponding compilation result, as shown in the figure. Fig. 9 As shown in the figure; Compile OS can compile the source code of the operating system generated by the large model or the source code of the operating system uploaded by the user; Gen Test Report can test the operating system, for example, click "Test Case", select the module of the operating system to be tested, etc., and display the test information in the display page; Download can provide users with the test report download function, for example, users click the "Save Test Results" button to download the test report, as shown in the figure. Figure 3 As shown, the saved test report can be content.txt, which can include a summary of test results such as startup information, hardware testing and initialization, driver module loading, file system mounting and operation, device plug-in and hot-plug testing, network testing, performance testing, error and exception information processing, etc. Fig.10 shown.

[0136] In this scenario example, the architecture of the Web front end is as follows Fig.11 shown.

[0137] Framework (framework) chooses Vue framework. Vue framework was chosen as the basis for front-end development for the following reasons:

[0138] Progressive architecture: Vue's design allows developers to start with simple, small-scale applications and gradually introduce more complex features and components. Two-way data binding: Vue's responsive system makes synchronization between data and views simple and intuitive, improving development efficiency. Componentization: Vue's componentization model supports the creation of reusable and independent UI (User Interface) components, which helps organize and reuse code. Virtual DOM: Vue uses virtual DOM to improve performance and rendering efficiency, only updating necessary parts, reducing the overhead of direct DOM operations.

[0139] The reasons why UI chooses ElementUI, NProgress and Vue-particles are:

[0140] ElementUI: Provides a series of pre-made components that support custom and responsive layouts to ensure good display effects on different devices. NProgress: Displays loading progress during page jumps to improve user experience. Vue-particles: Used to add particle effects to the interface to enhance visual effects.

[0141] COM (communication) uses axios as the HTTP client for the following reasons:

[0142] Promise-based: supports chain calls, making asynchronous request processing more convenient. Automatic JSON conversion: automatically handles the serialization and deserialization of JSON data, simplifying the data exchange process. Interceptors: support request and response interceptors, convenient for centralized processing of request header addition, response error handling, etc. Cancel request: provides the ability to cancel ongoing requests. Concurrent requests: supports sending multiple requests at the same time. Custom configuration: allows custom request headers, URL (uniform resource locator), timeout and other parameters. Cross-platform: can run in multiple environments, including browsers and node.js.

[0143] FUNC (Function description):

[0144] Login Page: User login interface. Register Page: User registration interface. Generate OSProject: Users can configure the architecture, device, kernel and other parameters of the operating system, and request the big model to automatically build the operating system. Upload OS Project Dictionary: Allows users to upload custom operating system projects. Compile OSProject: The big model uses the server-side compilation tool to automatically compile the operating system project and returns the compilation results. Generate OS Test Report: The big model automatically generates a test report for the operating system, including functional testing, performance testing, compatibility testing, security testing, stability testing, usability testing, network testing, and multi-task concurrent testing.

[0145] In this scenario example, the architecture of the Server backend is as follows Fig.12 shown.

[0146] AIFramework (artificial intelligence framework) chooses the Transformers combined with PyTorch framework for the following reasons:

[0147] Transformers combined with the PyTorch framework can quickly deploy large models, allowing developers to adjust and optimize the model architecture according to the needs of specific tasks. PyTorch provides support for GPU accelerated computing, while Transformers optimizes the use of computing resources. The combination of the two can provide efficient model training and reasoning capabilities. It supports the deployment of models on multiple platforms, including local servers, cloud platforms, and edge computing devices, increasing the flexibility of model deployment. Transformers and PyTorch frameworks are highly compatible, allowing developers to seamlessly switch and integrate between the two.

[0148] ServerFrameWork (server framework) uses FastAPI and Uvicorn for the following reasons:

[0149] High performance and high throughput: FastAPI is a high-performance web framework for building APIs, and Uvicorn is a lightweight ASGI server. The combination of the two can provide high-concurrency request processing capabilities. Dependency injection: FastAPI supports dependency injection, making it easier and faster to configure and manage dependencies. Scalability: FastAPI can add middleware to implement authentication, logging, performance monitoring and other functions. Security: FastAPI has built-in security features such as OAuth2, JWT, etc., which enhance the security of the API.

[0150] SQL (database management) uses MySQL and dotenv for the following reasons:

[0151] Security: By saving the database link configuration in the .env file instead of writing it directly in the code, the risk of sensitive information leakage is reduced. Convenient management: Whether configuring a link pool or connecting directly to a MySQL database, you only need to modify the configuration parameters. Flexibility: The .env file can be easily switched between different environments, and dotenv also supports dynamic loading of environment variables to achieve hot linking of the database.

[0152] API Description:

[0153] Login API: Implement the front-end request interface for user login verification and load user data from the database. Register API: Implement the front-end request interface for new user registration and store user data in the database; GenerateOS API: Implement the front-end request interface to generate operating system projects based on user input; Upload OS API: Implement the front-end request interface to upload user projects; Compile OS API: Implement the front-end request interface to compile projects and return the compilation results to users; Generate Test Report API: Implement the front-end request interface to generate project test result reports and return them to users; Download File API: Implement the front-end request interface so that users can download the projects or test result reports generated by the system.

[0154] In this scenario example, the model training architecture of the Server backend is as follows Fig.13 shown.

[0155] AIFramework (artificial intelligence framework) chose Transformers and PyTorch for the following reasons:

[0156] The Transformers framework can handle a variety of NLP (Natural Language Processing) tasks, such as text classification, sequence labeling, question-answering systems, etc. It provides a wide range of pre-trained models, such as BERT, GPT, T5, etc. These models can be used as a starting point to accelerate the training of specific task models. PyTorch supports GPU acceleration, and both support distributed training, which can be used to train models on multiple GPUs or TPUs to improve computing efficiency and model performance.

[0157] DataHandle (data processing tool) chooses Pandas, Datasets, and Numpy for the following reasons:

[0158] Pandas: Provides DataFrame and Series data structures, suitable for processing one-dimensional and two-dimensional data, and can perform data cleaning, such as data missing, data filtering, one hot data conversion, etc., and can quickly perform IO operations, such as reading and writing data from csv, sql and other files. Datasets: Preset datasets are convenient for fast loading and use, and custom datasets such as csv, text, json, etc. use memory mapping to process large amounts of data and occupy a small amount of memory, and can also process data in parallel to improve data processing efficiency. Numpy: Provides efficient multi-dimensional array data calculations, low-level control and operations.

[0159] For Train Result, select Matplotlib and Tqdm for the following reasons:

[0160] Matplotlib: It can visualize data. According to predefined rules, it can visualize the indicators such as train_loss, train_acc, val_loss, val_acc, lr, etc. during the training process, and save the results according to epochs. Tqdm: It can visualize the changes of indicators such as epoch, train_acc, train_loss, val_loss, val_acc, lr, etc. during the training process.

[0161] Neural Network Description:

[0162] Embedding Layer: Converts word indexes into dense vector representations. Encoder Layer: Includes self-attention mechanism, feedforward neural network and normalization to capture global dependencies of input sequences. Decoder Layer: Includes masked self-attention, encoder-decoder attention and feedforward neural network to generate outputs related to the input sequence. Output Layer: Usually a Softmax layer, which is used to convert the output of the decoder into a probability distribution of each word on the vocabulary. Positional Encoding: Includes absolute position encoding and relative position encoding to preserve the order information of the sequence. Fine-tuning: Fine-tuning on data of specific tasks or fields to optimize the performance of the model on specific applications. Gen Policy: Includes greedy search, beam search, temperature sampling and kernel sampling to control the process of text generation. Optim: Includes loss function, regularization and learning rate scheduling to optimize the training process of the model. Compression: Includes knowledge distillation, quantization and pruning to improve the efficiency of the model and reduce the size of the model.

[0163] An embodiment of the present application also provides a method for building an operating system based on a big model. The execution subject of the method for building an operating system based on a big model can be a front-end interface. The front-end interface can be an electronic device or a functional module or functional entity in an electronic device that can implement the method for building an operating system based on a big model. The electronic devices mentioned in the embodiment of the present application include but are not limited to mobile phones, tablet computers, computers, cameras, and wearable devices. The method for building an operating system based on a big model provided in the embodiment of the present application is described below using an electronic device as an example of the execution subject.

[0164] like Fig.14 As shown, the large model-based operating system construction method includes: step 1410 and step 1420.

[0165] Step 1410: Obtain configuration information and requirement description input by the user on the display page;

[0166] Step 1420, in response to the operating system generation instruction triggered by the user, the configuration information and requirement description are sent to the back-end server, so that the back-end server can call the pre-trained large model, input the configuration information and requirement description into the large model, obtain the operating system output by the large model according to the configuration information and requirement description, and return the operating system to the front-end interface.

[0167] According to the big model-based operating system construction method of the present application, through the front-end interface, users can easily input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs and calling the pre-trained big model to generate a customized operating system. It utilizes the powerful data processing and pattern recognition capabilities of the big model, and can quickly generate an operating system according to the user's personalized needs, greatly reducing the operating system development cycle and improving the operating system development efficiency.

[0168] In some embodiments, the display page includes a configuration selection module and a requirement input module, and obtaining the configuration information and requirement description input by the user on the display page includes:

[0169] Configuration information is obtained according to the configuration selected by the user in the configuration selection module, and the requirement description is obtained according to the text input by the user in the requirement input module.

[0170] In this embodiment, by providing an intuitive and user-friendly interface, the configuration selection module allows the user to select options through a graphical user interface to obtain specific configuration information; the requirement input module enables the user to describe the special requirements for the operating system in detail in text form, which not only simplifies the interaction process between the user and the system, but also enables the back-end server to more accurately parse and respond to the user's personalized needs, thereby generating an operating system that better meets the user's expectations through a large model.

[0171] In some embodiments, the display page includes an upload module; the method further includes:

[0172] Get the source code of the operating system uploaded by the user in the upload module;

[0173] The source code of the operating system uploaded by the user is sent to the back-end server, so that the back-end server can compile the source code of the operating system uploaded by the user by calling the compilation tool through the large model, obtain the compilation result, and return the compilation result to the front-end interface.

[0174] In this embodiment, an upload module is provided on the display page so that the user can upload the source code of the operating system through the upload module, allowing the user to directly participate in the construction process of the operating system, rather than just through configuration selection and requirement input. The back-end server compiles the uploaded source code by calling the compilation tool through the large model, so that the user can obtain a compilation result that can be directly used, greatly reducing the development cycle of the operating system and improving the development efficiency of the operating system.

[0175] In some embodiments, the display page includes a test module; the method further includes:

[0176] Obtain the test requirements entered by the user in the test module;

[0177] Send the test requirements to the back-end server so that the back-end server can call the test tool through the large model to test the operating system according to the test requirements, obtain the test report, and return the test report to the front-end interface.

[0178] In this embodiment, by providing a test module, an integrated and automated platform is provided for the construction and testing of the operating system. Not only can the operating system be built according to user needs, but the corresponding test tools can also be called using a large model to test the operating system, generate a test report, and return it to the front-end interface for users to review, providing users with a complete set of operating system development to testing services, greatly shortening the operating system development and testing cycle.

[0179] In some embodiments, the display page includes a download module; the method further includes:

[0180] In response to a download instruction triggered by the user in the download module, the test report is stored locally.

[0181] In this embodiment, through the download module of the front-end interface, the user can trigger the download instruction to store the test report generated by the back-end server locally, so that the user can quickly obtain and view the test results, further improving the test efficiency.

[0182] The embodiment of the present application also provides a method for constructing an operating system based on a large model, and the execution subject of the method for constructing an operating system based on a large model can be a back-end server.

[0183] like Fig.15 As shown, the large model-based operating system construction method includes: step 1510 and step 1520.

[0184] Step 1510: call the pre-trained big model, input the configuration information and the requirement description into the big model, and obtain the operating system output by the big model according to the configuration information and the requirement description; wherein the configuration information and the requirement description are sent by the front-end interface in response to the operating system generation instruction triggered by the user, and the configuration information and the requirement description are input by the user on the display page of the front-end interface;

[0185] Step 1520: Return the operating system to the front-end interface.

[0186] According to the big model-based operating system construction method of the present application, through the front-end interface, users can easily input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs and calling the pre-trained big model to generate a customized operating system. It utilizes the powerful data processing and pattern recognition capabilities of the big model, and can quickly generate an operating system according to the user's personalized needs, greatly reducing the operating system development cycle and improving the operating system development efficiency.

[0187] The operating system construction method based on a large model provided in the embodiment of the present application can be executed by an operating system construction device based on a large model. In the embodiment of the present application, an operating system construction device based on a large model executing the operating system construction method based on a large model is taken as an example to illustrate the operating system construction device based on a large model provided in the embodiment of the present application.

[0188] The embodiment of the present application also provides an operating system construction device based on a large model, which is applied to a front-end interface.

[0189] like Fig.16 As shown, the operating system construction device based on the large model includes:

[0190] The acquisition module 1610 is used to acquire the configuration information and requirement description input by the user on the display page;

[0191] Sending module 1620 is used to respond to the operating system generation instruction triggered by the user, send the configuration information and requirement description to the back-end server, so that the back-end server can call the pre-trained big model, input the configuration information and requirement description into the big model, obtain the operating system output by the big model according to the configuration information and requirement description, and return the operating system to the front-end interface.

[0192] According to the big model-based operating system construction device of the present application, through the front-end interface, the user can conveniently input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs, calling the pre-trained big model to generate a customized operating system, and utilizing the powerful data processing and pattern recognition capabilities of the big model. It can quickly generate an operating system according to the user's personalized needs, greatly reducing the development cycle of the operating system and improving the development efficiency of the operating system.

[0193] In some embodiments, the display page includes a configuration selection module and a requirement input module, and the acquisition module 1610 is further used to:

[0194] Configuration information is obtained according to the configuration selected by the user in the configuration selection module, and the requirement description is obtained according to the text input by the user in the requirement input module.

[0195] In some embodiments, the display page includes an upload module, an acquisition module 1610, and is further used to:

[0196] Get the source code of the operating system uploaded by the user in the upload module;

[0197] The source code of the operating system uploaded by the user is sent to the back-end server, so that the back-end server can compile the source code of the operating system uploaded by the user by calling the compilation tool through the large model, obtain the compilation result, and return the compilation result to the front-end interface.

[0198] In some embodiments, the display page includes a test module, an acquisition module 1610, and is further used to:

[0199] Obtain the test requirements entered by the user in the test module;

[0200] Send the test requirements to the back-end server so that the back-end server can call the test tool through the large model to test the operating system according to the test requirements, obtain the test report, and return the test report to the front-end interface.

[0201] In some embodiments, the display page includes a download module, the acquisition module 1610, and is further used to:

[0202] In response to a download instruction triggered by the user in the download module, the test report is stored locally.

[0203] The embodiment of the present application also provides an operating system construction device based on a large model, which is applied to a back-end server.

[0204] like Fig.17 As shown, the operating system construction device based on the large model includes:

[0205] The calling module 1710 is used to call the pre-trained big model, input the configuration information and the requirement description into the big model, and obtain the operating system output by the big model according to the configuration information and the requirement description; wherein the configuration information and the requirement description are sent by the front-end interface in response to the operating system generation instruction triggered by the user, and the configuration information and the requirement description are input by the user on the display page of the front-end interface;

[0206] The return module 1720 is used to return the operating system to the front-end interface.

[0207] According to the big model-based operating system construction device of the present application, through the front-end interface, the user can conveniently input the configuration information and requirement description of the operating system, and the back-end server is responsible for processing these inputs, calling the pre-trained big model to generate a customized operating system, and utilizing the powerful data processing and pattern recognition capabilities of the big model. It can quickly generate an operating system according to the user's personalized needs, greatly reducing the development cycle of the operating system and improving the development efficiency of the operating system.

[0208] The operating system construction device based on the large model in the embodiment of the present application can be an electronic device, or a component in the electronic device, such as an integrated circuit or a chip. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., and the embodiment of the present application is not specifically limited.

[0209] The operating system construction device based on the large model in the embodiment of the present application can be a device with an operating system. The operating system can be a Microsoft (Windows) operating system, an Android (Android) operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0210] In some embodiments, Fig.18As shown, an embodiment of the present application also provides an electronic device 1800, including a processor 1801, a memory 1802, and a computer program stored in the memory 1802 and executable on the processor 1801. When the program is executed by the processor 1801, the various processes of the above-mentioned large-model-based operating system construction method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0211] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0212] An embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned large-model-based operating system construction method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0213] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0214] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above-mentioned large model-based operating system construction method when executed by a processor.

[0215] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0216] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned large-model-based operating system construction method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0217] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0218] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0219] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0220] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0221] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" 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 representation of the above terms does 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 more embodiments or examples in a suitable manner.

[0222] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A large model-based operating system construction system, characterized in that: Including front-end interface and back-end server; The front-end interface is provided with a display page, and the front-end interface is used to obtain the configuration information and the requirement description input by the user on the display page; in response to the operating system generation instruction triggered by the user, the configuration information and the requirement description are sent to the back-end server; The back-end server is used to call a pre-trained large model, input the configuration information and the requirement description into the large model, obtain the operating system output by the large model according to the configuration information and the requirement description, and return the operating system to the front-end interface.

2. The system according to claim 1, characterized in that The configuration information includes at least one of system architecture, device configuration, kernel configuration, operating system type, operating system version, and security settings.

3. The system according to claim 1, characterized in that The step of returning the operating system to the front-end interface includes: Returning the source code of the operating system to the front-end interface; Alternatively, the source code is compiled by calling a compilation tool through the large model to obtain a compilation result, and the compilation result is returned to the front-end interface.

4. The system according to claim 1, characterized in that The display page includes a configuration selection module and a requirement input module, and the step of obtaining the configuration information and requirement description input by the user on the display page includes: The configuration information is acquired according to the configuration selected by the user in the configuration selection module, and the requirement description is acquired according to the text input by the user in the requirement input module.

5. The system according to claim 1, characterized in that The display page includes an upload module; The front-end interface is also used to obtain the source code of the operating system uploaded by the user in the upload module, and send the source code of the operating system uploaded by the user to the back-end server; The back-end server is also used to call the compilation tool through the large model to compile the source code of the operating system uploaded by the user, obtain the compilation result, and return the compilation result to the front-end interface.

6. The system according to claim 1, characterized in that The display page includes a test module; The front-end interface is also used to obtain the test requirements input by the user in the test module and send the test requirements to the back-end server; The back-end server is also used to call the test tool through the large model to test the operating system according to the test requirements, obtain a test report, and return the test report to the front-end interface.

7. The system according to claim 1, characterized in that The backend server includes a model training module; The backend server is further used to obtain model parameters configured by the user; wherein the model parameters include at least one of a model storage path, a CPU power consumption mode, an inference device configuration, a model output length range, an automatic derivation setting, a text sampling setting, and a model vocabulary range setting; The model training module is used to train the large model according to the model parameters.

8. A method for constructing an operating system based on a large model, characterized in that: Applied to a front-end interface, the front-end interface provides a display page, the method includes: Obtaining configuration information and requirement description entered by the user on the display page; In response to an operating system generation instruction triggered by a user, the configuration information and the requirement description are sent to a back-end server so that the back-end server can call a pre-trained large model, input the configuration information and the requirement description into the large model, obtain the operating system output by the large model based on the configuration information and the requirement description, and return the operating system to the front-end interface.

9. The method according to claim 8, characterized in that The display page includes a configuration selection module and a requirement input module, and the obtaining of the configuration information and the requirement description input by the user on the display page includes: obtaining the configuration information according to the configuration selected by the user in the configuration selection module, and obtaining the requirement description according to the text input by the user in the requirement input module; The display page includes an upload module; the method further includes: obtaining the source code of the operating system uploaded by the user in the upload module; sending the source code of the operating system uploaded by the user to the back-end server, so that the back-end server can compile the source code of the operating system uploaded by the user by calling the compilation tool through the big model, obtain the compilation result, and return the compilation result to the front-end interface; The display page includes a test module; the method also includes: obtaining the test requirements input by the user in the test module; sending the test requirements to the back-end server, so that the back-end server calls the test tool through the large model to test the operating system according to the test requirements, obtains a test report, and returns the test report to the front-end interface.

10. A method for constructing an operating system based on a large model, characterized in that: Applied to the backend server, the methods include: Calling a pre-trained big model, inputting configuration information and requirement description into the big model, and obtaining an operating system output by the big model according to the configuration information and the requirement description; wherein the configuration information and the requirement description are sent by the front-end interface in response to an operating system generation instruction triggered by a user, and the configuration information and the requirement description are input by the user on a display page of the front-end interface; Return the operating system to the front-end interface.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 8 to 10 is implemented.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 8 to 10 is implemented.

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