Data processing method, device, equipment, medium and program product of cloud computer

Abstract

Embodiments of the present application provide a cloud computer data processing method, device, equipment, medium and program product, which are used for providing personalized cloud computer instances for objects, thereby improving the efficiency of the objects in obtaining cloud computer data. It can be applied to the fields of cloud technology, computers, etc. Including: obtaining a cloud computer instance request of a first object, the cloud computer instance request including portrait data of the first object; determining base image data and differential image data based on the portrait data, the base image data and the first differential image data being generated based on historical portrait data, the base image data including base system data of a cloud computer, and the differential image data including differential data generated based on the historical portrait data; encapsulating and generating a cloud computer instance of the first object based on the base image data and the differential image data; and sending the cloud computer instance to a terminal device of the first object, so that the terminal device of the first object runs the cloud computer instance.

Description

Technical Field

[0001] This application relates to the field of computers, and more particularly to a data processing method, apparatus, device, medium, and program product for a cloud computer. Background Technology

[0002] Cloud computing utilizes virtualization technology to virtualize various hardware and software resources in the cloud and transmits the virtual desktop over the network to various types of terminals for display, such as Windows devices, Android devices, iOS devices, PCs, and thin clients. Users can connect to the cloud computer through various terminals by connecting to the network and access the cloud computer, providing a similar experience to using a local computer.

[0003] In actual use, a cloud PC is no different from a regular computer. A user who buys a cloud PC does the same thing as a user who buys a physical computer; they need to download their data step-by-step from the factory settings, including various games, videos, and commonly used software. In other words, the platform cannot effectively recommend the cloud PC data that users urgently need, making the data acquisition process cumbersome.

[0004] Therefore, how can we provide a solution for quickly obtaining the corresponding cloud computer data? Summary of the Invention

[0005] This application provides a cloud computer data processing method, apparatus, device, medium, and program product, which are used to provide personalized cloud computer instances to objects, thereby improving the efficiency of objects in obtaining cloud computer data.

[0006] In view of this, this application provides a data processing method for a cloud computer, comprising: obtaining a first cloud computer instance request for a first object, the first cloud computer instance request including first profile data of the first object; determining first base image data and first differential image data based on the first profile data, the first base image data and the first differential image data being generated based on historical profile data, the first base image data including basic system data of the cloud computer, and the first differential image data including differential data generated based on the historical profile data; encapsulating and generating a first cloud computer instance for the first object based on the first base image data and the first differential image data; and sending the first cloud computer instance to the terminal device of the first object, so that the terminal device of the first object runs the first cloud computer instance.

[0007] Another aspect of this application provides a data processing apparatus, including: a transceiver module, configured to acquire a first cloud computer instance request of a first object, the first cloud computer instance request including first profile data of the first object;

[0008] The processing module is used to determine first basic image data and first differential image data based on the first profile data. The first basic image data and the first differential image data are generated based on historical profile data. The first basic image data includes basic system data of the cloud computer, and the first differential image data includes differential data generated based on the historical profile data. The module is used to encapsulate and generate a first cloud computer instance of the first object based on the first basic image data and the first differential image data.

[0009] The transceiver module is used to send the first cloud computer instance to the terminal device of the first object, so that the terminal device of the first object can run the first cloud computer instance.

[0010] In one possible design, in another implementation of another aspect of the embodiments of this application, the processing module is used to obtain a base image data set and a difference image data set, wherein each base image data in the base image data set is generated based on the historical image data, and each difference image data in the difference image data set is generated based on the historical image data.

[0011] Based on the portrait data, the first base image data is determined from the base image data set, and based on the portrait data, the first difference image data is determined from the difference image data set.

[0012] In one possible design, in another implementation of another aspect of the embodiments of this application, the processing module is used to acquire the historical portrait data;

[0013] Based on this historical profile data, a first generation strategy for basic mirror data and differentiated mirror data is determined;

[0014] Based on this generation strategy, multiple base image data and multiple differential image data are generated. The multiple base image data constitute the base image data set, and the multiple differential image data constitute the differential image data set.

[0015] In one possible design, in another implementation of another aspect of the embodiments of this application, the device further includes a storage module, which is used to synchronously store the base image data set to each device in the cloud backend, and to perform differentiated storage of each differential image data in the differential image data set.

[0016] In one possible design, in another implementation of another aspect of the embodiments of this application, the transceiver module is used to obtain the operation data of the first object based on the first cloud computer instance;

[0017] The processing module is used to update the first portrait data of the first object to the second portrait data based on the operation data; update the first generation strategy to the second generation strategy based on the second portrait data and the historical portrait data; and update the base mirror data set and the difference mirror data set based on the second generation strategy.

[0018] In one possible design, in another implementation of another aspect of the embodiments of this application, the transceiver module is used to obtain a second cloud computer instance request of the second object, the second cloud computer instance request including the third profile data of the second object;

[0019] The processing module is used to determine second basic image data and second differential image data based on the third profile data. The second basic image data and the second differential image data are generated based on the second profile data and the historical profile data. The second basic image data includes the basic system data of the cloud computer, and the second differential image data includes differential data generated based on the historical profile data.

[0020] A second cloud computer instance is generated by encapsulating the second object based on the second base image data and the second differential image data;

[0021] The transceiver module is used to send the second cloud computer instance to the terminal device of the second object, so that the terminal device of the second object can run the second cloud computer instance.

[0022] In one possible design, in another implementation of another aspect of the embodiments of this application, the processing module is used to update the first difference image data and the first base image data based on the second generation strategy.

[0023] This application also provides a computer device, including: a memory, a processor, and a bus system;

[0024] The memory is used to store programs;

[0025] The processor is used to execute programs in memory, and the processor is used to execute the methods mentioned above according to the instructions in the program code;

[0026] Bus systems are used to connect memory and processor to enable communication between them.

[0027] Another aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the methods described above.

[0028] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the above aspects.

[0029] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: Based on user profile data, the generation of basic image data and differential image data, as well as the rapid deployment of personalized cloud PC instances, can improve the efficiency of users obtaining cloud PC data and enhance the intuitiveness and convenience of the user experience, thereby increasing the user retention rate of cloud PCs. Simultaneously, dividing image data into basic image data and differential image data can significantly improve image management efficiency and cloud PC instance generation speed, optimizing the utilization rate of cloud platform resources. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of a cloud computer system architecture in an embodiment of this application;

[0031] Figure 2 This is a schematic diagram of a cloud computer system in an embodiment of this application;

[0032] Figure 3 This is a schematic diagram of one embodiment of the data processing method for cloud computers in this application.

[0033] Figure 4 This is a schematic diagram of an interface of the cloud computer client in an embodiment of this application;

[0034] Figure 5 This is a schematic diagram of an interface of a cloud computer example in this application embodiment;

[0035] Figure 6 This is a schematic diagram of one embodiment of the data processing device for a cloud computer in this application.

[0036] Figure 7 This is a schematic diagram of another embodiment of the data processing device for the cloud computer in this application;

[0037] Figure 8 This is a schematic diagram of one embodiment of the server in this application;

[0038] Figure 9 This is a schematic diagram of one embodiment of the terminal in this application. Detailed Implementation

[0039] This application provides a cloud computer data processing method, apparatus, device, medium, and program product, which are used to provide personalized cloud computer instances to objects, thereby improving the efficiency of objects in obtaining cloud computer data.

[0040] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0041] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0042] Cloud computing utilizes virtualization technology to virtualize various hardware and software resources in the cloud and transmits the virtual desktop to various types of terminals for display, such as Windows devices, Android devices, iOS devices, PCs, and thin clients. Users can connect to the cloud computer through various terminals by connecting to the network and access it, experiencing a similar experience to using a local computer. In actual use, a cloud computer is no different from a regular computer. A user who buys a cloud computer does so just like a user who buys a physical computer; they need to download their data step by step from the factory configuration, such as various games, videos, and commonly used software. This makes it difficult for the platform to effectively recommend the cloud computer data that users urgently need, making the data acquisition process cumbersome. Therefore, how to quickly provide cloud computer data is a problem that urgently needs to be solved.

[0043] To address the aforementioned technical issues, this application provides the following technical solution: Obtaining a first cloud computer instance request from a first object, the first cloud computer instance request including first profile data of the first object; determining first basic image data and first differential image data based on the first profile data, the first basic image data and the first differential image data being generated based on historical profile data, the first basic image data including basic system data of the cloud computer, and the first differential image data including differentiated data generated based on the historical profile data; encapsulating and generating a first cloud computer instance of the first object based on the first basic image data and the first differential image data; and sending the first cloud computer instance to the terminal device of the first object, so that the terminal device of the first object runs the first cloud computer instance. Defining the generation of basic image data and differential image data based on user profile data, and enabling rapid deployment of personalized cloud computer instances, can improve the efficiency of users obtaining cloud computer data, enhance the intuitiveness and convenience of the user experience, and thus improve the user retention rate of cloud computers. Simultaneously, dividing image data into basic image data and differential image data can significantly improve image management efficiency and cloud computer instance generation speed, optimizing the utilization rate of cloud platform resources.

[0044] The cloud technology involved in this application refers to a hosting technology that unifies hardware, software, network, and other resources within a wide area network (WAN) or local area network (LAN) to achieve data computation, storage, processing, and sharing. Cloud technology is a general term encompassing network technology, information technology, integration technology, management platform technology, and application technology based on the cloud computing business model. It can form resource pools, be used on demand, and is flexible and convenient. Cloud computing technology will become a crucial support. The backend services of technical network systems require substantial computing and storage resources, such as video websites, image websites, and many portal websites. With the rapid development and application of the internet industry, every item may have its own identification mark in the future, requiring transmission to a backend system for logical processing. Data at different levels will be processed separately, and various industry data will require robust system support, which can only be achieved through cloud computing.

[0045] Cloud computing is a computing model that distributes computing tasks across a resource pool composed of a large number of computers, enabling various application systems to obtain computing power, storage space, and information services as needed. The network providing these resources is called the "cloud." From the user's perspective, the resources in the "cloud" are infinitely scalable and can be accessed, used on demand, scaled at any time, and paid for based on usage. As the foundational providers of cloud computing capabilities, they establish cloud resource pools (referred to as cloud platforms), generally known as Infrastructure as a Service (IaaS) platforms. Various types of virtual resources are deployed within these cloud resource pools for external customers to choose from. The cloud resource pool mainly includes: computing devices (virtualized machines containing operating systems), storage devices, and network devices. Logically, a Platform as a Service (PaaS) layer can be deployed on top of the IaaS layer, and a Software as a Service (SaaS) layer can be deployed on top of the PaaS layer. Alternatively, SaaS can be directly deployed on top of IaaS. PaaS is the platform on which software (such as databases, web containers, etc.) runs. SaaS refers to various types of business software (such as web portals, bulk SMS senders, etc.). Generally speaking, SaaS and PaaS are upper-layered compared to IaaS.

[0046] Cloud security refers to the general term for security software, hardware, users, organizations, and security cloud platforms based on cloud computing business models. Cloud security integrates emerging technologies and concepts such as parallel processing, grid computing, and unknown virus behavior judgment. It monitors abnormal software behavior in the network through a large number of clients in a network, obtains the latest information on Trojans and malicious programs on the Internet, and sends it to the server for automatic analysis and processing. Then, it distributes virus and Trojan solutions to each client. The main research directions of cloud security include: (1) Cloud computing security, which mainly studies how to ensure the security of the cloud itself and various applications on the cloud, including cloud computer system security, secure storage and isolation of user data, user access authentication, information transmission security, network attack protection, compliance auditing, etc.; (2) Cloudification of security infrastructure, which mainly studies how to use cloud computing to build and integrate security infrastructure resources and optimize security protection mechanisms, including building a large-scale security event, information collection and processing platform through cloud computing technology to realize the collection and correlation analysis of massive information and improve the ability to control security events and risks across the entire network; (3) Cloud security services, which mainly studies various security services provided to users based on cloud computing platforms, such as anti-virus services.

[0047] This application relates to cloud computer instance generation in the field of cloud computing technology. A cloud computer is a comprehensive service solution including cloud resources, transmission protocols, and cloud terminals. Using open cloud terminals and transmission protocols, resources such as desktops, applications, and hardware are provided to users in an on-demand, elastically allocated service model. Users can achieve single-machine multi-user operation without needing to consider building complex information technology (IT) infrastructure. The IT industry has experienced rapid development over the past few decades, but this has also brought a series of negative impacts, including high costs, slow response times, and a lack of integrated management infrastructure. Cloud computing is a new type of IT service, also known as cloud computing service.

[0048] The technical solutions of this application and their effects are described below through several exemplary embodiments. It should be noted that the following embodiments can be referenced, borrowed from, or combined with each other. Identical terms, similar features, and similar implementation steps in different embodiments will not be repeated.

[0049] The cloud computer data processing method provided in this application embodiment can be applied to, for example, Figure 1 The system shown includes a terminal 100, a server 200, a database 300, and a network 400. The terminal 100, server 200, and database 300 are connected via the network 400. The database 300 is used to provide base image data and differential image data for the system. Figure 1 The number of terminals, servers, and databases in the system shown is merely an example. For instance, there may be multiple terminals, servers, and databases. This application does not limit the number of terminals, servers, and databases.

[0050] The aforementioned system is used in the technical field of providing virtual services for cloud computers, etc., and the embodiments of this application do not limit this to that. In cloud services, server 200 can process received cloud computer instance requests, and allocate corresponding base image data and differential image data to the object according to the profile data carried in the request. Then, it encapsulates the base image data and the differential image data to generate the corresponding cloud computer instance; finally, it feeds back to the client device of the cloud computer corresponding to the object. It should be noted that the base image data, differential image data, and profile data are authorized by the user or fully authorized by all parties.

[0051] In this configuration, terminal 100 communicates with server 200 via a network. Database 300 can be integrated onto server 200 or located in the cloud or on another server. During the configuration of the cloud PC instance, interaction can occur between terminal 100 and server 200. For example, a user generates a cloud PC instance request through terminal 100, which then sends the request to server 200. Server 200 generates the corresponding cloud PC instance based on the request and sends it back to terminal 100.

[0052] Terminal 100 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, smart voice interaction device, smart home appliance, or in-vehicle terminal, but is not limited to these. Server 200 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery network (CDN), big data, and artificial intelligence platforms.

[0053] In short, a database can be viewed as an electronic filing cabinet—a place to store electronic files, where users can perform operations such as adding, querying, updating, and deleting data. A "database" is a collection of data stored together in a certain way, shared by multiple users, with minimal redundancy, and independent of application programs. A Database Management System (DBMS) is a computer software system designed to manage databases, generally possessing basic functions such as storage, retrieval, security, and backup. DBMSs can be classified according to the database model they support, such as relational or Extensible Markup Language (XML); or according to the type of computer they support, such as server clusters or mobile phones; or according to the query language used, such as Structured Query Language (SQL) or XQuery; or according to performance priorities, such as maximum scale or maximum operating speed; or other classification methods. Regardless of the classification method used, some DBMSs can cross categories, for example, supporting multiple query languages ​​simultaneously.

[0054] It is understood that in the specific implementation of this application, data related to the basic image data, differential image data, and portrait data are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0055] Based on the above scheme, the following is an example. Figure 2 The cloud computer system architecture shown illustrates the solution of this application. Figure 2 The system shown includes a cloud computer client, a cloud computer backend, a cloud computer cluster scheduling server, and a cloud computer remote server.

[0056] Among them, the cloud PC client is usually Figure 1 The client application or browser on the terminal 100 (PC, mobile phone, tablet, etc.) shown is used to access the cloud computer service. Its main functions are to provide user login and authentication functions; and to submit requests to create a cloud computer and receive the desktop screen of the cloud computer (via video stream) and interact with it.

[0057] This cloud PC backend serves as the management center of the cloud PC system, responsible for user authentication, image management, resource allocation, and policy execution. Its main functions include processing user requests; selecting appropriate images (base image data and differential image data) based on user profile data; and invoking the cloud PC cluster scheduling server to create cloud PC instances.

[0058] This cloud PC cluster scheduling server is used for cluster management and scheduling modules, and is responsible for managing multiple remote cloud PC servers. Its main functions are to receive scheduling requests from the cloud PC backend; allocate resources and start virtual machine instances; and manage the loading and merging of base image data and differential image data.

[0059] This remote cloud PC server is the physical server that actually runs the cloud PC instance. It primarily provides computing, storage, and network resources. Its main functions are to host the user's virtual machine instance; configure and run the user's cloud PC operating environment based on the image; and transmit the user's operation results to the cloud PC terminal in the form of a video stream.

[0060] Based on the above system, the process for creating a user's cloud PC instance can be as follows:

[0061] 1. The terminal associated with the cloud PC client initiates a cloud PC instance request. This means the user logs into the client (such as a mobile app or PC software) through the cloud PC terminal. The user then selects to create a cloud PC, and the terminal sends a request to the cloud PC backend. This request may include the following information: user identity information (such as account, password, or OAuth credentials), user device information (such as screen resolution and network conditions), and user profile data, such as user preferences or needs (e.g., intended use: gaming, office work, development, etc.).

[0062] 2. The cloud PC backend performs user authentication and profile matching. Specifically, after receiving a request from the terminal, the cloud PC backend completes the following operations:

[0063] First, user authentication is performed, which involves completing the authentication process based on the information provided by the user (such as account and password). After successful authentication, user profile data (based on user history, interests, location, etc.) is obtained.

[0064] Then, appropriate mirror data is selected based on the profile data. This involves profile matching to select suitable base mirror data and diff mirror data. For example, male users might choose mirror data containing popular games, while office users might choose mirror data containing the Office suite.

[0065] 3. The cloud PC backend initiates a creation request to the cloud PC cluster scheduling server. Based on the user's profile data and generation strategy, the cloud PC backend generates a request to create a cloud PC instance and submits it to the cloud PC cluster scheduling server. This request may include: user identity information (used for permission allocation); the storage paths of the selected Base image and Diff image; the user's required hardware configuration (such as CPU, GPU, memory, disk size, etc.); and the user's network conditions (such as bandwidth requirements).

[0066] 4. The cloud PC cluster scheduling server allocates resources. After receiving the request, the cloud PC cluster scheduling server will perform the following operations.

[0067] First, check the current load status of the remote cloud PC server; then select a remote server that meets the requirements (hardware and network conditions). Next, load the required Base image (containing the basic operating system and general software) from the repository; load the Diff image (containing user-specific content, such as pre-installed applications and configurations); combine the Base image and the Diff image to generate a complete cloud PC instance image. Finally, create a virtual machine instance on the selected remote server; mount the generated complete image and start the virtual machine.

[0068] 5. The remote cloud PC server starts the cloud PC instance. After receiving instructions from the cluster scheduling server, the remote cloud PC server starts the virtual machine instance based on the synthesized image file; it automatically loads the user-pre-installed content (such as games, applications, shortcuts, configurations, etc.). Then, it adapts and optimizes the cloud PC instance according to the user's terminal device information (such as screen resolution, input method). Finally, it encodes the virtual machine screen into a video stream and transmits it to the user's terminal device over the network.

[0069] 6. The terminal associated with the cloud PC client connects to the remote cloud PC server. The cloud PC terminal receives connection information from the cloud PC backend (such as the remote server address and video stream port). The terminal begins receiving the video stream from the remote cloud PC server and transmits user actions (such as mouse clicks and keyboard input) back to the remote cloud PC server in real time. The cloud PC instance perceived by the user on the terminal is completely decoupled from the actual physical computing resources, providing an experience similar to a local computer.

[0070] Given that this application involves some technical terms, these terms will be introduced below.

[0071] Mirroring: Mirroring is a file storage format and a type of redundancy. Data on one disk is copied exactly to another disk. Many files can be created into a single image file, placed on the same disk as programs like Ghost, and then opened by these programs to restore the original files. RAID 1 and RAID 10 utilize mirroring. Common image file formats include ISO, BIN, IMG, TAO, DAO, CIF, and FCD.

[0072] System disk: A storage device used to install the operating system and system files. It typically contains the operating system's installation files, system boot files, and other necessary system files. The system disk is essential for the normal operation of a computer or server; it stores the core components and configuration information of the operating system.

[0073] Data disk: A storage device for storing user data, applications, files, and other non-system-related data. Data disks can be used to store user-created files, databases, application configuration files, log files, etc. Data disks are typically used for persistent storage of user data and can be expanded or backed up as needed.

[0074] Based on the above introduction, the following section describes the data processing method of the cloud computer in this application, taking the cloud computer backend server as the execution entity. Please refer to [link / reference needed]. Figure 3 One embodiment of the data processing method for cloud computers in this application includes:

[0075] 301. Obtain the first cloud computer instance request of the first object, wherein the first cloud computer instance request includes the first portrait data of the first object.

[0076] In this embodiment, when the first object (i.e., the user) initiates a cloud computer instance creation request through a cloud computer client deployed on the terminal, the request content may include object identity information (such as account, password, or authentication credentials), user device information (such as screen resolution, network conditions), and profile data. The profile data can be understood as the user's preferences or needs (such as target usage: gaming, office work, development, etc.) and the user's attribute characteristics. For example, the user's age, gender, and location. It should be understood that if the first object is a user creating a cloud computer for the first time, the profile data may only include the above information; if the first object is a user who has previously created a cloud computer, the profile data may also include the first object's historical operation data.

[0077] The terminal then sends the cloud computer instance request to the cloud computer backend server. The cloud computer backend server will parse the cloud computer instance request to obtain the first profile data of the first object and the user authentication information corresponding to the first object.

[0078] like Figure 4 As shown in the cloud PC client interface, the first object can determine its preferences and needs through the cloud PC client (for example, if the need is to play games, its corresponding CPU and graphics card requirements can also be reported). At the same time, when the first object registers an account on the cloud platform, it can also create corresponding user authentication information such as account and password.

[0079] 302. Based on the first profile data, determine the first basic image data and the first differential image data. The first basic image data and the first differential image data are generated based on historical profile data. The first basic image data includes the basic system data of the cloud computer, and the first differential image data includes the differential data generated based on the historical profile data.

[0080] After receiving the request for the first cloud computer instance, the cloud computer backend server parses the request to obtain user authentication information and the first profile data of the first object; then it authenticates the first object based on the user authentication information; and after successful authentication, it performs profile matching based on the first profile data to obtain the first base image data and the first differential image data.

[0081] It should be understood that the specific process of the cloud PC backend server in performing profile matching can be as follows: The cloud PC backend server obtains the stored base image data set and the difference image data set. When storing the base image data set and the difference image data set, they can be associated with the profile data. Then, the cloud PC backend server determines the corresponding first base image data in the base image data set based on the first profile data, and determines the corresponding first difference image data from the difference image data set. In one possible implementation, the cloud PC backend server performs profile matching based on the first profile data to obtain the storage paths of the first base image data and the first difference image data.

[0082] In one exemplary scheme, the base image data set and the differential image data set are generated based on historical profile data. The specific process can be as follows: The cloud PC backend server obtains historical profile data. It should be understood that this historical profile data can be understood as the historical profile data of all users on the cloud PC platform. Then, the cloud PC backend server calls the cloud PC cluster scheduling server to determine the generation strategy for the base image data based on the historical profile data; then, based on the generation strategy, it generates multiple base image data sets and multiple differential image data sets; and generates the base image data set based on the multiple base image data sets; and generates the differential image data set based on the multiple differential image data sets.

[0083] It should be understood that after generating the base image data set and the differential image data set, in order to reduce the management cost of the image data, the base image data set can be synchronously stored across all devices in the cloud PC system, while the individual differential image data within the differential image data set can be stored differentially. That is, when initializing a cloud PC instance, the instance's files can be split into base image data and differential image data; then, the base image data is distributed to all physical machines in all data centers, while the differential image data is stored on a storage server within the data center. However, each physical machine can access this storage server to load the differential image data.

[0084] 303. Based on the first base image data and the first differential image data, a first cloud computer instance of the first object is encapsulated and generated.

[0085] The cloud PC backend server sends a cloud PC instance creation request to the cloud PC cluster scheduling server. This request may include: user identity information (for assigning permissions); the storage paths of the selected Base image and Diff image; the user's required hardware configuration (such as CPU, GPU, memory, disk size, etc.); and the user's network conditions (such as bandwidth requirements). Upon receiving the request, the cloud PC cluster scheduling server checks the current load status of the remote cloud PC server; then, it selects a remote server that meets the requirements (hardware and network conditions). Next, it loads the required base image data (containing the basic operating system and general software) and differential image data (containing personalized content for the user, such as pre-installed applications and configurations) from the repository; it combines the Base image and Diff image to generate a complete cloud PC instance image. Finally, it creates the cloud PC instance on the selected remote server; mounts the generated complete cloud PC instance image; and starts the virtual machine.

[0086] 304. Send the first cloud computer instance to the terminal device of the first object so that the terminal device of the first object can run the first cloud computer instance.

[0087] After a cloud PC instance is created on the remote server, the remote server receives instructions from the cloud PC cluster scheduling server and starts the virtual machine instance based on the synthesized image file. It automatically loads pre-installed user content (such as games, applications, shortcuts, configurations, etc.). Then, it adapts and optimizes the cloud PC instance according to the user's terminal device information (such as screen resolution and input method). Finally, it encodes the virtual machine screen into a video stream and transmits it to the user's terminal device over the network. The terminal associated with the cloud PC client connects to the remote cloud PC server. The cloud PC terminal receives connection information from the cloud PC backend (such as the remote server address and video stream port). The terminal begins receiving and displaying the video stream from the remote cloud PC server.

[0088] like Figure 5 As shown, assuming the differential image data in the cloud computer instance of the first object includes game A, the remote server of the cloud computer can display the startup interface of game A on the terminal interface of the first object. If the first object needs to run game A, it can directly start game A; if the first object does not want to run game A, it can close the startup interface of game A and then perform the corresponding operation.

[0089] It should be understood that, in order to dynamically adjust the base image data and differential image data, the first object, while running the first cloud PC instance, can also feed its operation data back to the cloud PC backend server. This allows the cloud PC service backend server to collect user operation data, use big data analysis to update the profile data of all users or analyze and update the profile data of a single user; then, it dynamically adjusts the base image data and differential image data based on the updated profile data; finally, it recommends the updated cloud PC instance to the user again, thereby optimizing the cloud PC instance. Specifically, deploying updated cloud PC instances can be understood as follows: each time a user sends a cloud PC instance request, a cloud PC instance is matched based on their profile data, and a new cloud PC instance is created; or it can be understood as creating relatively stable cloud PC instances for the user within a certain period, and then creating a new cloud PC instance for the user when there are significant updates to existing cloud PC instances. The specifics are not limited here.

[0090] To further achieve personalized recommendations based on the mirrored data, this embodiment can also provide personalized content recommendations based on the similarity of user groups. That is, the cloud computing system not only generates mirrored data corresponding to each individual user's behavior, but also refers to the operational data of other users similar to that user to recommend content that may be of interest. The specific implementation process can be as follows: Cluster analysis of user behavior data is performed using collaborative filtering algorithms (such as user-based collaborative filtering or item-based collaborative filtering). Recommended content from similar users is added when generating mirrored data, and the recommendation effect is verified through A / B testing. The weight of the recommended content is dynamically adjusted based on the user profile data and operational data.

[0091] In order to better adjust the base image data and the differential image data, this embodiment can also provide the following solution.

[0092] In one exemplary solution, real-time behavior prediction technology is introduced to dynamically adjust the differential mirror data as the user interacts with it. For instance, when a user is using a cloud PC, the cloud PC system can predict the user's next action in real time (such as a tendency to open a certain type of application or game) and immediately load the relevant content into the differential mirror data in the cloud PC background, without waiting for the action to complete before updating. The process can be as follows: The user's fine-grained behavior is analyzed using a deep learning model to predict user needs in real time; then, the relevant content is pre-loaded into the differential mirror data in the cloud PC background, so that it can be immediately displayed when the user interacts with it, without waiting for loading.

[0093] In another exemplary solution, a multi-dimensional dynamic weight model is introduced during the generation and updating of differential mirrored data to intelligently sort and prioritize the mirrored content. The dimensions of this weight model include, but are not limited to, the following: object behavior preference weight (e.g., clicks, usage time); content popularity weight (based on real-time trending topics across the entire network or platform); cloud platform operation strategy weight (e.g., promoting specific content or products); and user device performance weight (e.g., terminal device performance, network bandwidth conditions). The process can be as follows: during the generation of differential mirrored data, the priority of different content is calculated using a weighted algorithm (e.g., weighted linear regression or neural network model); then, the loading order and recommended display effect of the differential mirrored data are dynamically adjusted according to the priority. For example, if the user device performance is low, the cloud computer system will prioritize loading lightweight content; if network conditions are good, high-definition resources or complex applications can be prioritized.

[0094] In another exemplary approach, the differential mirror data not only records the content and configuration of the object but also the differentiated settings between devices, such as resolution, input method (touch / keyboard / mouse), and interface layout. This enables seamless migration of users across different terminal devices (such as mobile phones, PCs, and tablets), ensuring a consistent experience across different devices. The process can be as follows: when generating the differential mirror data, a device adaptation module is added to record the characteristics of the terminal device (such as screen resolution, input method, and performance metrics). When a user switches devices, the cloud computer system dynamically adjusts the content and configuration of the differential mirror data according to the characteristics of the new device. During the adaptation process, edge computing technology can be used to ensure real-time synchronization of user data and status.

[0095] In another exemplary solution, blockchain technology is integrated into the management of base image data and differential image data to achieve distributed storage, version traceability, and trusted management of image data. Specifically, information from each image update is recorded on the blockchain, ensuring the integrity and immutability of the image data while providing users with transparent version traceability. The process can be as follows: each time an image is generated or updated, the image's metadata (such as version number, update content, generation time, etc.) is written to the blockchain; the distributed storage characteristics of the blockchain ensure high reliability of the image data and avoid single points of failure. Users can view the historical update records of the image through the blockchain and choose to restore to any version.

[0096] The data processing apparatus in this application is described in detail below. Please refer to [link / reference]. Figure 6 , Figure 6 This is a schematic diagram of one embodiment of the data processing apparatus in this application. The data processing apparatus 20 includes:

[0097] The transceiver module 201 is used to obtain the first cloud computer instance request of the first object, the first cloud computer instance request including the first profile data of the first object;

[0098] Processing module 202 is used to determine first basic image data and first differential image data based on the first profile data. The first basic image data and the first differential image data are generated based on historical profile data. The first basic image data includes basic system data of the cloud computer, and the first differential image data includes differential data generated based on the historical profile data. Based on the first basic image data and the first differential image data, a first cloud computer instance of the first object is encapsulated and generated.

[0099] The transceiver module 201 is used to send the first cloud computer instance to the terminal device of the first object, so that the terminal device of the first object can run the first cloud computer instance.

[0100] This application provides a data processing apparatus. Using this apparatus, basic image data and differential image data are defined based on user profile data, enabling the rapid deployment of personalized cloud PC instances. This improves the efficiency of users obtaining cloud PC data, enhances the intuitiveness and convenience of the user experience, and ultimately increases user retention rates for cloud PCs. Furthermore, dividing image data into basic image data and differential image data significantly improves image management efficiency and cloud PC instance generation speed, optimizing the utilization of cloud platform resources.

[0101] Optionally, in the above Figure 6 Based on the corresponding embodiments, in another embodiment of the data processing apparatus 20 provided in this application,

[0102] The processing module 202 is used to obtain a base image data set and a difference image data set. Each base image data in the base image data set is generated based on the historical image data, and each difference image data in the difference image data set is generated based on the historical image data.

[0103] Based on the portrait data, the first base image data is determined from the base image data set, and based on the portrait data, the first difference image data is determined from the difference image data set.

[0104] This application provides a data processing apparatus. Using this apparatus, basic image data and differential image data are generated based on user profile data, and personalized cloud PC instances are rapidly deployed. This improves the efficiency of users obtaining cloud PC data, enhances the intuitiveness and convenience of the user experience, and ultimately increases the user retention rate of cloud PCs.

[0105] Optionally, in the above Figure 6 Based on the corresponding embodiments, in another embodiment of the data processing device 20 provided in this application, the processing module 202 is used to acquire the historical portrait data;

[0106] Based on this historical profile data, a first generation strategy for basic mirror data and differentiated mirror data is determined;

[0107] Based on this generation strategy, multiple base image data and multiple differential image data are generated. The multiple base image data constitute the base image data set, and the multiple differential image data constitute the differential image data set.

[0108] This application provides a data processing apparatus. Using this apparatus, basic image data and differential image data are generated based on user profile data, and personalized cloud PC instances are rapidly deployed. This improves the efficiency of users obtaining cloud PC data, enhances the intuitiveness and convenience of the user experience, and ultimately increases the user retention rate of cloud PCs.

[0109] Optionally, in the above Figure 6 Based on the corresponding embodiments, such as Figure 7 As shown, in another embodiment of the data processing apparatus 20 provided in this application,

[0110] The device 20 also includes a storage module 203, which is used to synchronously store the base image data set to each device in the cloud backend and to perform differentiated storage of each differential image data in the differential image data set.

[0111] This application provides a data processing apparatus. Using this apparatus, image data is divided into basic image data and differential image data. The basic image data is synchronously stored across the entire platform, while the differential image data is stored in a personalized manner. This significantly reduces the storage cost of image management. Furthermore, during the cloud PC instance generation process, only the differential image data can be loaded, thereby reducing the time cost of cloud PC instance generation. This significantly improves image management efficiency and cloud PC instance generation speed, optimizing the utilization of cloud platform resources.

[0112] Optionally, in the above Figure 7 Based on the corresponding embodiments, in another embodiment of the data processing apparatus 20 provided in this application,

[0113] The transceiver module 201 is used to obtain the operation data of the first object based on the first cloud computer instance;

[0114] The processing module 202 is used to update the first portrait data of the first object to the second portrait data based on the operation data; update the first generation strategy to the second generation strategy based on the second portrait data and the historical portrait data; and update the base mirror data set and the difference mirror data set based on the second generation strategy.

[0115] This application provides a data processing apparatus. Using this apparatus, user operation data (such as application opening, acceptance / rejection of recommended content, etc.) is recorded in real time and fed back to the backend. Through big data analysis, new generation strategies are generated, thereby dynamically adjusting the configuration of the base image and differential images to achieve continuous optimization of image configuration.

[0116] Optionally, in the above Figure 7 Based on the corresponding embodiments, in another embodiment of the data processing device 20 provided in this application, the transceiver module 201 is used to obtain a second cloud computer instance request of the second object, the second cloud computer instance request including the third portrait data of the second object;

[0117] The processing module 202 is used to determine the second basic image data and the second differential image data based on the third profile data. The second basic image data and the second differential image data are generated based on the second profile data and the historical profile data. The second basic image data includes the basic system data of the cloud computer, and the second differential image data includes the differential data generated based on the historical profile data.

[0118] A second cloud computer instance is generated by encapsulating the second object based on the second base image data and the second differential image data;

[0119] The transceiver module 201 is used to send the second cloud computer instance to the terminal device of the second object, so that the terminal device of the second object can run the second cloud computer instance.

[0120] This application provides a data processing apparatus. Using this apparatus, basic image data and differential image data are defined based on user profile data, enabling the rapid deployment of personalized cloud PC instances. This improves the efficiency of users obtaining cloud PC data, enhances the intuitiveness and convenience of the user experience, and ultimately increases user retention rates for cloud PCs. Furthermore, dividing image data into basic image data and differential image data significantly improves image management efficiency and cloud PC instance generation speed, optimizing the utilization of cloud platform resources.

[0121] Optionally, in the above Figure 7Based on the corresponding embodiments, in another embodiment of the data processing apparatus 20 provided in this application, the processing module 202 is used to update the first difference image data and the first base image data based on the second generation strategy.

[0122] This application provides a data processing apparatus. Using this apparatus, user operation data (such as application opening, acceptance / rejection of recommended content, etc.) is recorded in real time and fed back to the backend. Through big data analysis, new generation strategies are generated, thereby dynamically adjusting the configuration of the base image and differential images to achieve continuous optimization of image configuration.

[0123] The data processing apparatus provided in this application can be used on a server; please refer to [link / reference]. Figure 8 , Figure 8 This is a schematic diagram of a server structure provided in an embodiment of this application. The server 300 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 322 (e.g., one or more processors) and memory 332, and one or more storage media 330 (e.g., one or more mass storage devices) for storing application programs 342 or data 344. The memory 332 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the server. Furthermore, the CPU 322 may be configured to communicate with the storage media 330 and execute the series of instruction operations stored in the storage media 330 on the server 300.

[0124] Server 300 may also include one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input / output interfaces 358, and / or one or more operating systems 341, such as Windows Server. TM Mac OS X TM Unix TM Linux TM FreeBSD TM etc.

[0125] The steps performed by the server in the above embodiments can be based on this Figure 8 The server structure shown.

[0126] The data processing apparatus provided in this application can be used in terminal devices; please refer to [link / reference]. Figure 9For ease of explanation, only the parts relevant to the embodiments of this application are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of this application. In the embodiments of this application, a smartphone is used as an example for illustration:

[0127] Figure 9 This is a block diagram illustrating a portion of the structure of a smartphone related to the terminal device provided in the embodiments of this application. (Reference) Figure 9 The smartphone includes components such as a radio frequency (RF) circuit 410, a memory 420, an input unit 430, a display unit 440, a sensor 450, an audio circuit 460, a wireless fidelity (WiFi) module 470, a processor 480, and a power supply 490. Those skilled in the art will understand that... Figure 9 The smartphone structure shown does not constitute a limitation on smartphones and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0128] The following is combined with Figure 9 A detailed introduction to the various components of a smartphone:

[0129] RF circuit 410 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink information from the base station and processes it with processor 480; additionally, it transmits uplink data to the base station. Typically, RF circuit 410 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier (LNA), and a duplexer. Furthermore, RF circuit 410 can also communicate wirelessly with networks and other devices. The aforementioned wireless communication can use any communication standard or protocol, including but not limited to Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, and Short Message Service (SMS).

[0130] The memory 420 can be used to store software programs and modules. The processor 480 executes various functions and data processing of the smartphone by running the software programs and modules stored in the memory 420. The memory 420 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the smartphone (such as audio data, phonebook, etc.). In addition, the memory 420 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0131] The input unit 430 can be used to receive input numerical or character information, and to generate key signal inputs related to user settings and function control of the smartphone. Specifically, the input unit 430 may include a touch panel 431 and other input devices 432. The touch panel 431, also known as a touch screen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel 431), and drive the corresponding connected devices according to a pre-set program. Optionally, the touch panel 431 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 480, and can also receive and execute commands sent by the processor 480. In addition, the touch panel 431 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 431, the input unit 430 may also include other input devices 432. Specifically, other input devices 432 may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0132] Display unit 440 can be used to display information input by the user or information provided to the user, as well as various menus of the smartphone. Display unit 440 may include display panel 441, optionally configured as a liquid crystal display (LCD), organic light-emitting diode (OLED), or similar form. Further, touch panel 431 may cover display panel 441. When touch panel 431 detects a touch operation on or near it, it transmits the information to processor 480 to determine the type of touch event. Subsequently, processor 480 provides corresponding visual output on display panel 441 based on the type of touch event. Although in Figure 9 In this embodiment, the touch panel 431 and the display panel 441 are two separate components to realize the input and output functions of the smartphone. However, in some embodiments, the touch panel 431 and the display panel 441 can be integrated to realize the input and output functions of the smartphone.

[0133] The smartphone may also include at least one sensor 450, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 441 according to the ambient light level, and the proximity sensor can turn off the display panel 441 and / or the backlight when the smartphone is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that recognize the smartphone's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition-related functions (such as pedometer, tapping), etc. Other sensors that may be configured in the smartphone, such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, will not be described in detail here.

[0134] Audio circuit 460, speaker 461, and microphone 462 provide an audio interface between the user and the smartphone. Audio circuit 460 converts received audio data into electrical signals and transmits them to speaker 461, where speaker 461 converts them into sound signals for output. On the other hand, microphone 462 converts collected sound signals into electrical signals, which are received by audio circuit 460, converted into audio data, and then processed by processor 480 before being transmitted via RF circuit 410 to, for example, another smartphone, or the audio data can be output to memory 420 for further processing.

[0135] WiFi is a short-range wireless transmission technology. Smartphones, through their WiFi modules (470), can help users send and receive emails, browse web pages, and access streaming media, providing wireless broadband internet access. Although Figure 9 WiFi module 470 is shown, but it is understood that it is not an essential component of a smartphone and can be omitted as needed without changing the nature of the invention.

[0136] The processor 480 is the control center of the smartphone, connecting various parts of the smartphone through various interfaces and lines. It performs various functions and processes data by running or executing software programs and / or modules stored in the memory 420, and by calling data stored in the memory 420, thereby providing overall monitoring of the smartphone. Optionally, the processor 480 may include one or more processing units; optionally, the processor 480 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the aforementioned modem processor may also not be integrated into the processor 480.

[0137] The smartphone also includes a power supply 490 (such as a battery) that powers various components. Optionally, the power supply can be logically connected to the processor 480 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.

[0138] Although not shown, smartphones may also include a camera, Bluetooth module, etc., which will not be described in detail here.

[0139] The steps performed by the terminal device in the above embodiments can be based on this Figure 9 The terminal device structure is shown.

[0140] This application also provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the methods described in the foregoing embodiments.

[0141] This application also provides a computer program product including a program, which, when run on a computer, causes the computer to perform the methods described in the foregoing embodiments.

[0142] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0146] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0147] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A data processing method for a cloud computer, characterized in that, include: A first cloud computer instance request for a first object is obtained, the first cloud computer instance request including first profile data of the first object, the first profile data being used to indicate the object characteristics of the first object; Based on the first profile data, a first basic image data and a first differential image data are determined. The first basic image data and the first differential image data are generated based on historical profile data. The first basic image data includes the basic system data of the cloud computer, and the first differential image data includes differential data generated based on the historical profile data. A first cloud computer instance of the first object is generated by encapsulating the first base image data and the first differential image data. The first cloud computer instance is sent to the terminal device of the first object so that the terminal device of the first object can run the first cloud computer instance.

2. The method according to claim 1, characterized in that, The step of determining the first base image data and the first difference image data based on the portrait data includes: Obtain a base image data set and a difference image data set, wherein each base image data in the base image data set is generated based on the historical profile data, and each difference image data in the difference image data set is generated based on the historical profile data; The first base image data is determined from the base image data set based on the image data, and the first difference image data is determined from the difference image data set based on the image data.

3. The method according to claim 2, characterized in that, The process of obtaining the base image data set and the difference image data set includes: Obtain the historical portrait data; Based on the historical portrait data, a first generation strategy for basic mirror data and differentiated mirror data is determined; Based on the first generation strategy, multiple base image data and multiple differential image data are generated. The multiple base image data constitute the base image data set, and the multiple differential image data constitute the differential image data set.

4. The method according to claim 3, characterized in that, The method further includes: The basic image data set is synchronously stored on each device in the cloud backend, and each differential image data in the differential image data set is stored differentially.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Obtain the operation data of the first object based on the first cloud computer instance; Based on the operation data, update the first portrait data of the first object to the second portrait data; The first generation strategy is updated to the second generation strategy based on the second portrait data and the historical portrait data; The base image data set and the differential image data set are updated based on the second generation strategy.

6. The method according to claim 5, characterized in that, The method further includes: The second cloud computer instance request for the second object is obtained, and the second cloud computer instance request includes the third profile data of the second object. Based on the third profile data, second basic image data and second differential image data are determined. The second basic image data and the second differential image data are generated based on the second profile data and the historical profile data. The second basic image data includes the basic system data of the cloud computer, and the second differential image data includes differential data generated based on the historical profile data. A second cloud computer instance is generated by encapsulating the second object based on the second base image data and the second differential image data; The second cloud computer instance is sent to the terminal device of the second object so that the terminal device of the second object runs the second cloud computer instance.

7. The method according to claim 5, characterized in that, The method further includes: The first differential image data and the first base image data are updated based on the second generation strategy.

8. A data processing device for a cloud computer, characterized in that, include: The transceiver module is used to obtain the first cloud computer instance request of the first object, wherein the first cloud computer instance request includes the first profile data of the first object; The processing module is configured to determine first basic image data and first differential image data based on the first profile data. The first basic image data and the first differential image data are generated based on historical profile data. The first basic image data includes basic system data of the cloud computer, and the first differential image data includes differential data generated based on the historical profile data. The module is also configured to encapsulate and generate a first cloud computer instance of the first object based on the first basic image data and the first differential image data. The transceiver module is used to send the first cloud computer instance to the terminal device of the first object, so that the terminal device of the first object can run the first cloud computer instance.

9. A computer device, characterized in that, include: Memory, processor, and bus system; The memory is used to store programs; The processor is configured to execute a program in the memory, and the processor is configured to execute the method of any one of claims 1 to 7 according to instructions in the program code; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

10. A computer-readable storage medium comprising instructions, when executed on a computer, causing the computer to perform the method as claimed in any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, The computer program is executed by a processor using the method as described in any one of claims 1 to 7.

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